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  • Articles  (778)
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  • 1
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    Ganzheitliche Ingenieurausbildung (2015)
    Springer
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    Publication Date: 2015-08-13
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    Topics: Electrical Engineering, Measurement and Control Technology , Computer Science
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    HackerSpace22 – IMST-Projekt an der htl donaustadt (2015)
    Springer
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    Publication Date: 2015-08-13
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  • 3
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    Multirate filter design and implementation for mixed-signal ICs (2015)
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    Publication Date: 2015-08-13
    Description: This paper describes the design of multirate filters used for the sample rate reduction of digital signals and their implementation on mixed-signal integrated circuits. Reaching target specifications of power, speed and silicon area as well as realizing multi-mode operation presents challenges. Possible solutions are discussed particularly for those filter stages which operate at the high sample rates and are illustrated in examples.
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  • 4
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    Sehr geehrte Leserinnen und Leser! (2015)
    Springer
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    Publication Date: 2015-08-13
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    Topics: Electrical Engineering, Measurement and Control Technology , Computer Science
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  • 5
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    A netlist-level fault-injection tool for FPGAs (2015)
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    Publication Date: 2015-08-13
    Description: A fault-injection tool can be very interesting in context to safety-critical applications, e.g., to test fault-detection and avoidance mechanisms or simply to stress an application and analyze its behavior when faults occur. In this work, a fault-injection tool is presented which can be used to instrument an FPGA design with fault-injection logic on netlist level during the implementation phase and to inject faults during runtime afterwards. The proposed approach can be smoothly integrated into an industrial FPGA tool flow, supports devices from multiple FPGA vendors and is highly configurable in order to fit to the number of available FPGA logic resources. Differences to related approaches which are applied on either HDL- and netlist-level as well as on the FPGA configuration bitstream are described. Finally, some results are presented to prove the applicability of the proposed solution.
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  • 6
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    15 Jahre Fachschule für Informationstechnik – eine Vorschau mit Rückblick (2015)
    Springer
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    Publication Date: 2015-08-15
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  • 7
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    Design and development of a high throughput airborne line-of-sight communication link (2015)
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    Publication Date: 2015-08-15
    Description: Line-of-sight (LOS) communication links have an ever increasing demand for high throughput IP-based data transmission. Such LOS systems are of particular interest for bi-directional communications from an aircraft to a ground station. A key requirement for the operation in various countries is carrier frequency agility because of the regulatory issues. That is why the LOS link developed by JOANNEUM RESEARCH DIGITAL supports adjustable carrier frequencies in the range from 2 GHz to 6 GHz. The used TDD (Time Division Duplex) medium access has the advantage that only one frequency for up- and downlink is required. A further advantage is configurable allocation of the available carrier bandwidth between the downlink to the ground and uplink to the aircraft. The maximum throughput in the downlink is 8 Mbit/s. The system is based on a software defined radio (SDR) approach which leads to high flexibility because of fast development and testing of signal processing algorithms. This fact allows cost effective adaption of the system to different user requirements. The paper is organized in the following way. Section 1 provides a system overview. In Section 2 the modem design parameters are discussed in detail, followed by Section 3 and 4 which present the signal processing algorithms implemented and the RF front end. Finally, Section 5 shows the signal characteristic of a test flight with a DA42 aircraft.
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  • 8
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    Maximizing a Record’s Standing in a Relation (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-07
    Description: Given a database table with records that can be ranked, an interesting problem is to identify selection conditions for the table, which are qualified by an input record and render its ranking as high as possible among the qualifying tuples. In this paper, we study this standing maximization problem, which finds application in object promotion and characterization. After showing the hardness of the problem, we propose greedy methods, which are experimentally shown to achieve high accuracy compared to exhaustive enumeration, while scaling very well to the problem input size. Our contributions include a linear-time algorithm for determining the optimal selection range for an ordinal attribute and techniques for choosing and prioritizing the most promising selection predicates to apply. Experiments on real datasets confirm the effectiveness and efficiency of our techniques.
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  • 9
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    XACML Policy Evaluation with Dynamic Context Handling (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-07
    Description: Some fairly recent research has focused on providing XACML-based solutions for dynamic privacy policy management. In this regard, a number of works have provided enhancements to the performance of XACML policy enforcement point (PEP) component, but very few have focused on enhancing the accuracy of that component. This paper improves the accuracy of an XACML PEP by filling some gaps in the existing works. In particular, dynamically incorporating user access context into the privacy policy decision, and its enforcement. We provide an XACML-based implementation of a dynamic privacy policy management framework and an evaluation of the applicability of our system in comparison to some of the existing approaches.
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  • 10
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    Pattern-Aided Regression Modeling and Prediction Model Analysis (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-07
    Description: This paper first introduces pattern aided regression (PXR) models, a new type of regression models designed to represent accurate and interpretable prediction models. This was motivated by two observations: (1) Regression modeling applications often involve complex diverse predictor-response relationships , which occur when the optimal regression models (of given regression model type) fitting two or more distinct logical groups of data are highly different. (2) State-of-the-art regression methods are often unable to adequately model such relationships. This paper defines PXR models using several patterns and local regression models, which respectively serve as logical and behavioral characterizations of distinct predictor-response relationships. The paper also introduces a contrast pattern aided regression (CPXR) method, to build accurate PXR models. In experiments, the PXR models built by CPXR are very accurate in general, often outperforming state-of-the-art regression methods by big margins. Usually using (a) around seven simple patterns and (b) linear local regression models, those PXR models are easy to interpret; in fact, their complexity is just a bit higher than that of (piecewise) linear regression models and is significantly lower than that of traditional ensemble based regression models. CPXR is especially effective for high-dimensional data. The paper also discusses how to use CPXR methodology for analyzing prediction models and correcting their prediction errors.
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  • 11
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    Modeling the Probability of a Strikeout for a Batter/Pitcher Matchup (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-07
    Description: We analyze models for predicting the probability of a strikeout for a batter/pitcher matchup in baseball using player descriptors that can be estimated accurately from small samples. We start with the log5 model which has been used extensively for describing matchups in sports. Log5 is a special case of a logit model and we use constrained logistic regression over nearly one million matchup observations to assess the use of the log5 explanatory variables for this application. We also show that a batter/pitcher ground ball rate interaction variable is significant for the prediction of strikeout probability and we provide physical justification for the inclusion of this variable in the model. We quantify the differences among the models and show that batters control the majority of the variance in predicted strikeout rate.
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  • 12
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    Medientechnik-Ausbildung an der IT-HTL Ybbs (2015)
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    Publication Date: 2015-08-13
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  • 13
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    Digital Design bei ams (2015)
    Springer
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    Publication Date: 2015-08-13
    Description: Diese Arbeit gibt einen Einblick in die Entwurfsmethodik der Digitalteile für Mixed-Signal-Designs des österreichischen Halbleiterherstellers ams. Der Beitrag liefert zudem auch einen Überblick über die Entwicklung von Chips von ams für Audio- und Media-Player aus historischer Perspektive.
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  • 14
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    Building reliable systems-on-chip in nanoscale technologies (2015)
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    Publication Date: 2015-08-13
    Description: Modern application-specific integrated circuits (ASICs) contain complete systems on a single die, composed of many processing elements that communicate over a dedicated router-based on-chip network. As systems-on-chip comprise billions of transistors with feature sizes in the range of 10 nm, reliable operation cannot be established without carefully engineered support at all levels, from technology to the circuit- and the system-layer. This article surveys contributions of research groups at TU Wien to this field. At lower levels of abstraction, they range from the generation of fault models for simulation that closely match reality and are at the same time efficient to use, to circuit-level radiation-tolerance techniques. At the level of on-chip networks, novel fault-tolerant routing algorithms are being developed together with architectural techniques to isolate faulty parts while keeping the healthy parts connected and active. The article will briefly portray the associated research activities and summarize their most relevant results.
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  • 15
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    PruDent: A Pruned and Confident Stacking Approach for Multi-Label Classification (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-07
    Description: Over the past decade or so, several research groups have addressed the problem of multi-label classification where each example can belong to more than one class at the same time. A common approach, called  Binary Relevance (BR) , addresses this problem by inducing a separate classifier for each class. Research has shown that this framework can be improved if mutual class dependence is exploited: an example that belongs to class $X$ is likely to belong also to class $Y$ ; conversely, belonging to $X$ can make an example less likely to belong to $Z$ . Several works sought to model this information by using the vector of class labels as additional example attributes. To fill the unknown values of these attributes during prediction, existing methods resort to using outputs of other classifiers, and this makes them prone to errors. This is where our paper wants to contribute. We identified two potential ways to prune unnecessary dependencies and to reduce error-propagation in our new classifier-stacking technique, which is named PruDent . Experimental results indicate that the classification performance of PruDent compares favorably with that of other state-of-the-art approaches over a broad range of testbeds. Mor- over, its computational costs grow only linearly in the number of classes.
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  • 16
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    Effective Classification Using a Small Training Set Based on Discretization and Statistical Analysis (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-07
    Description: This work deals with the problem of producing a fast and accurate data classification, learning it from a possibly small set of records that are already classified. The proposed approach is based on the framework of the so-called Logical Analysis of Data (LAD), but enriched with information obtained from statistical considerations on the data. A number of discrete optimization problems are solved in the different steps of the procedure, but their computational demand can be controlled. The accuracy of the proposed approach is compared to that of the standard LAD algorithm, of support vector machines and of label propagation algorithm on publicly available datasets of the UCI repository. Encouraging results are obtained and discussed.
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  • 17
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    Graph Based Constrained Semi-Supervised Learning Framework via Label Propagation over Adaptive Neighborhood (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-07
    Description: A new graph based constrained semi-supervised learning (G-CSSL) framework is proposed. Pairwise constraints (PC) are used to specify the types (intra- or inter-class) of points with labels. Since the number of labeled data is typically small in SSL setting, the core idea of this framework is to create and enrich the PC sets using the propagated soft labels from both labeled and unlabeled data by special label propagation (SLP), and hence obtaining more supervised information for delivering enhanced performance. We also propose a Two-stage Sparse Coding, termed TSC, for achieving adaptive neighborhood for SLP. The first stage aims at correcting the possible corruptions in data and training an informative dictionary, and the second stage focuses on sparse coding. To deliver enhanced inter-class separation and intra-class compactness, we also present a mixed soft-similarity measure to evaluate the similarity/dissimilarity of constrained pairs using the sparse codes and outputted probabilistic values by SLP. Simulations on the synthetic and real datasets demonstrated the validity of our algorithms for data representation and image recognition, compared with other related state-of-the-art graph based semi-supervised techniques.
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  • 18
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    Improving Accuracy and Robustness of Self-Tuning Histograms by Subspace Clustering (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-07
    Description: In large databases, the amount and the complexity of the data calls for data summarization techniques. Such summaries are used to assist fast approximate query answering or query optimization. Histograms are a prominent class of model-free data summaries and are widely used in database systems. So-called self-tuning histograms look at query-execution results to refine themselves. An assumption with such histograms, which has not been questioned so far, is that they can learn the dataset from scratch, that is—starting with an empty bucket configuration. We show that this is not the case. Self-tuning methods are very sensitive to the initial configuration. Three major problems stem from this. Traditional self-tuning is unable to learn projections of multi-dimensional data, is sensitive to the order of queries, and reaches only local optima with high estimation errors. We show how to improve a self-tuning method significantly by starting with a carefully chosen initial configuration. We propose initialization by dense subspace clusters in projections of the data, which improves both accuracy and robustness of self-tuning. Our experiments on different datasets show that the error rate is typically halved compared to the uninitialized version.
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  • 19
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    Time-Series Classification with COTE: The Collective of Transformation-Based Ensembles (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-07
    Description: Recently, two ideas have been explored that lead to more accurate algorithms for time-series classification (TSC). First, it has been shown that the simplest way to gain improvement on TSC problems is to transform into an alternative data space where discriminatory features are more easily detected. Second, it was demonstrated that with a single data representation, improved accuracy can be achieved through simple ensemble schemes. We combine these two principles to test the hypothesis that forming a collective of ensembles of classifiers on different data transformations improves the accuracy of time-series classification. The collective contains classifiers constructed in the time, frequency, change, and shapelet transformation domains. For the time domain, we use a set of elastic distance measures. For the other domains, we use a range of standard classifiers. Through extensive experimentation on 72 datasets, including all of the 46 UCR datasets, we demonstrate that the simple collective formed by including all classifiers in one ensemble is significantly more accurate than any of its components and any other previously published TSC algorithm. We investigate alternative hierarchical collective structures and demonstrate the utility of the approach on a new problem involving classifying Caenorhabditis elegans mutant types.
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  • 20
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    Subgraph Matching with Set Similarity in a Large Graph Database (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-07
    Description: In real-world graphs such as social networks, Semantic Web and biological networks, each vertex usually contains rich information, which can be modeled by a set of tokens or elements. In this paper, we study a subgraph matching with set similarity (SMS $^2$ ) query over a large graph database, which retrieves subgraphs that are structurally isomorphic to the query graph, and meanwhile satisfy the condition of vertex pair matching with the (dynamic) weighted set similarity. To efficiently process the SMS $^2$ query, this paper designs a novel lattice-based index for data graph, and lightweight signatures for both query vertices and data vertices. Based on the index and signatures, we propose an efficient two-phase pruning strategy including set similarity pruning and structure-based pruning, which exploits the unique features of both (dynamic) weighted set similarity and graph topology. We also propose an efficient dominating-set-based subgraph matching algorithm guided by a dominating set selection algorithm to achieve better query performance. Extensive experiments on both real and synthetic datasets demonstrate that our method outperforms state-of-the-art methods by an order of magnitude.
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  • 21
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    TRIP: An Interactive Retrieving-Inferring Data Imputation Approach (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-07
    Description: Data imputation aims at filling in missing attribute values in databases. Most existing imputation methods to string attribute values are inferring-based approaches, which usually fail to reach a high imputation recall by just inferring missing values from the complete part of the data set. Recently, some retrieving-based methods are proposed to retrieve missing values from external resources such as the World Wide Web, which tend to reach a much higher imputation recall, but inevitably bring a large overhead by issuing a large number of search queries. In this paper, we investigate the interaction between the inferring-based methods and the retrieving-based methods. We show that retrieving a small number of selected missing values can greatly improve the imputation recall of the inferring-based methods. With this intuition, we propose an inTeractive Retrieving-Inferring data imPutation approach (TRIP), which performs retrieving and inferring alternately in filling in missing attribute values in a data set. To ensure the high recall at the minimum cost, TRIP faces a challenge of selecting the least number of missing values for retrieving to maximize the number of inferable values. Our proposed solution is able to identify an optimal retrieving-inferring scheduling scheme in deterministic data imputation, and the optimality of the generated scheme is theoretically analyzed with proofs. We also analyze with an example that the optimal scheme is not feasible to be achieved in $tau$ -constrained stochastic data imputation ( $tau$ -SDI), but still, our proposed solution identifies an expected-optimal scheme in $tau$ -SDI. Extensive experiments on four data collections show that TRIP retrieves on average 20 percent missing values and achieves the same high recall that was reached by the retrieving-based approach.
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  • 22
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    Visual Classification by $ell _1$ -Hypergraph Modeling (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-07
    Description: Visual classification has attracted considerable research interests in the past decades. In this paper, a novel $ell _1$ -hypergraph model for visual classification is proposed. Hypergraph learning, as a natural extension of graph model, has been widely used in many machine learning tasks. In previous work, hypergraph is usually constructed by attribute-based or neighborhood-based methods. That is, a hyperedge is generated by connecting a set of samples sharing a same feature attribute or in a neighborhood. However, these methods are unable to explore feature space globally or sensitive to noises. To address these problems, we propose a novel hypergraph construction approach that leverages sparse representation to generate hyperedges and learns the relationship among hyperedges and their vertices. First, for each sample, a hyperedge is generated by regarding it as the centroid and linking it as well as its nearest neighbors. Then, the sparse representation method is applied to represent the centroid vertex by other vertices within the same hyperedge. The vertices with zero coefficients are removed from the hyperedge. Finally, the representation coefficients are used to define the incidence relation between the hyperedge and the vertices. In our approach, we also optimize the hyperedge weights to modulate the effects of different hyperedges. We leverage the prior knowledge on the hyperedges so that the hyperedges sharing more vertices can have closer weights, where a graph Laplacian is used to regularize the optimization of the weights. Our approach is named $ell _1$ -hypergraph since the $ell _1$ sparse representation is employed in the hypergraph construction process. The method is evaluated on various visual classification tasks, and it demonstrates promising performance.
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  • 23
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    Non-Myopic Adaptive Route Planning in Uncertain Congestion Environments (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-07
    Description: We consider the problem of adaptively routing a fleet of cooperative vehicles within a road network in the presence of uncertain and dynamic congestion conditions. To tackle this problem, we first propose a Gaussian process dynamic congestion model that can effectively characterize both the dynamics and the uncertainty of congestion conditions. Our model is efficient and thus facilitates real-time adaptive routing in the face of uncertainty. Using this congestion model, we develop efficient algorithms for non-myopic adaptive routing to minimize the collective travel time of all vehicles in the system. A key property of our approach is the ability to efficiently reason about the long-term value of exploration, which enables collectively balancing the exploration/exploitation trade-off for entire fleets of vehicles. Our approach is validated by traffic data from two large Asian cities. Our congestion model is shown to be effective in modeling dynamic congestion conditions. Our routing algorithms also generate significantly faster routes compared to standard baselines, and achieve near-optimal performance compared to an omniscient routing algorithm. We also present the results from a preliminary field study, which showcases the efficacy of our approach.
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  • 24
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    Scalable Online Betweenness Centrality in Evolving Graphs (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-07
    Description: Betweenness centrality is a classic measure that quantifies the importance of a graph element (vertex or edge) according to the fraction of shortest paths passing through it. This measure is notoriously expensive to compute, and the best known algorithm runs in $mathcal {O}(nm)$ time. The problems of efficiency and scalability are exacerbated in a dynamic setting, where the input is an evolving graph seen edge by edge, and the goal is to keep the betweenness centrality up to date. In this paper, we propose the first truly scalable algorithm for online computation of betweenness centrality of both vertices and edges in an evolving graph where new edges are added and existing edges are removed. Our algorithm is carefully engineered with out-of-core techniques and tailored for modern parallel stream processing engines that run on clusters of shared-nothing commodity hardware. Hence, it is amenable to real-world deployment. We experiment on graphs that are two orders of magnitude larger than previous studies. Our method is able to keep the betweenness centrality measures up-to-date online, i.e., the time to update the measures is smaller than the inter-arrival time between two consecutive updates.
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  • 25
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    PB + -Tree: PCM-Aware B + -Tree (2015)
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    Publication Date: 2015-08-07
    Description: Phase change memory (PCM) is non-volatile memory that is byte-addressable. It is two to four times denser than DRAM, orders of magnitude better than NAND Flash memory in read latency, and 10 times better than NAND Flash memory in write endurance. However, it still limits the number of write operations to at most $10^6$ times per PCM cell. To extend its lifetime, it is necessary to evenly distribute write operations over all the memory cells. Up to now, the $mathrm{B^{+}}$ -Tree index structure has been used to quickly locate a search key in a relational database management system (RDBMS). All the record keys in each node are sorted and packed upon insertion in, and deletion from, the $mathrm{B^{+}}$ -Tree. In addition, a counter keeps track of the number of valid keys in the $mathrm{B^{+}}$ -Tree. Consequently, a $mathrm{B^{+}}$ -Tree algorithm results in a large number of write operations, which deteriorates the endurance of PCM. This restricts the usage of PCM on a database server and deteriorates performance of database servers. In this paper, we propose a novel PCM-aware $math- m{B^{+}}$ -Tree index structure, called $mathrm{PB^{+}}$ -Tree, to provide wear-leveling in PCM. According to our experiment results, $mathrm{PB^{+}}$ -Tree is much faster than the existing $mathrm{B^{+}}$ -Tree algorithms for PCM and NAND Flash memory with versatile workloads. More importantly, our scheme also greatly reduces the number of write operations compared to other $mathrm{B^{+}}$ -Tree algorithms. All of these results suggest that $mathrm{PB^{+}}$ -Tree is the $mathrm{B^{+}}$ -Tree algorithm best fitted to PCM.
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    Austrian International Networking Academy Conference – AINAC (2015)
    Springer
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    Publication Date: 2015-08-13
    Print ISSN: 0932-383X
    Electronic ISSN: 1613-7620
    Topics: Electrical Engineering, Measurement and Control Technology , Computer Science
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    Die aktuellen HTL-Lehrpläne am Beispiel der Höheren Lehranstalt für Informatik und der Höheren Lehranstalt für Informationstechnologie (2015)
    Springer
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    Publication Date: 2015-08-15
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    Topics: Electrical Engineering, Measurement and Control Technology , Computer Science
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    Verification challenges of complex system-on-chip devices (2015)
    Springer
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    Publication Date: 2015-08-15
    Description: In recent years rising complexity, shrinking silicon feature sizes, and reduced design cycles have posed new challenges on the verification of modern system-on-chip solutions. To tackle the issues caused by the rising complexity various design and verification languages, as well as methodologies and tools have been introduced. Likewise, new and better physical process models allow for improved simulation of both analog and digital designs. Finally, strict management plans are used to cope with the shrinking design and verification cycles. Despite all these efforts, however, many problems exist in industrial state-of-the-art processes and tools. This article gives some insights and presents some lessons learned from the design and verification of a recent automotive microcontroller, a complex system-on-chip solution. Based on these findings, a new verification flow is proposed that closes an identified gap between pre-silicon and post-silicon verification.
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    Topics: Electrical Engineering, Measurement and Control Technology , Computer Science
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    Beyond Millisecond Latency $k$ NN Search on Commodity Machine (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-09-11
    Description: The $k$ nearest neighbor ( $k$ NN) search on road networks is an important function in web mapping services. These services are now dealing with rapidly arriving queries, that are issued by a massive amount of users. While overlay graph-based indices can answer shortest path queries efficiently, there have been no studies on utilizing such indices to answer $k$ NN queries efficiently. In this paper, we fill this research gap and present two efficient $k$ NN search solutions on overlay graph-based indices. Experimental results show that our solutions offer very low query latency (0.1 ms) and require only small index sizes, even for 10-million-node networks.
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    A Large Probabilistic Semantic Network Based Approach to Compute Term Similarity (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-09-11
    Description: Measuring semantic similarity between two terms is essential for a variety of text analytics and understanding applications. Currently, there are two main approaches for this task, namely the knowledge based and the corpus based approaches. However, existing approaches are more suitable for semantic similarity between words rather than the more general multi-word expressions (MWEs), and they do not scale very well. Contrary to these existing techniques, we propose an efficient and effective approach for semantic similarity using a large scale semantic network. This semantic network is automatically acquired from billions of web documents. It consists of millions of concepts, which explicitly model the context of semantic relationships. In this paper, we first show how to map two terms into the concept space, and compare their similarity there. Then, we introduce a clustering approach to orthogonalize the concept space in order to improve the accuracy of the similarity measure. Finally, we conduct extensive studies to demonstrate that our approach can accurately compute the semantic similarity between terms of MWEs and with ambiguity, and significantly outperforms 12 competing methods under Pearson Correlation Coefficient. Meanwhile, our approach is much more efficient than all competing algorithms, and can be used to compute semantic similarity in a large scale.
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    A Critical-Time-Point Approach to All-Departure-Time Lagrangian Shortest Paths (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-09-11
    Description: Given a spatio-temporal network, a source, a destination, and a desired departure time interval, the All-departure-time Lagrangian Shortest Paths (ALSP) problem determines a set which includes the shortest path for every departure time in the given interval. ALSP is important for critical societal applications such as eco-routing. However, ALSP is computationally challenging due to the non-stationary ranking of the candidate paths across distinct departure-times. Current related work for reducing the redundant work, across consecutive departure-times sharing a common solution, exploits only partial information e.g., the earliest feasible arrival time of a path. In contrast, our approach uses all available information, e.g., the entire time series of arrival times for all departure-times. This allows elimination of all knowable redundant computation based on complete information available at hand. We operationalize this idea through the concept of critical-time-points (CTP), i.e., departure-times before which ranking among candidate paths cannot change. In our preliminary work, we proposed a CTP based forward search strategy. In this paper, we propose a CTP based temporal bi-directional search for the ALSP problem via a novel impromptu rendezvous termination condition. Theoretical and experimental analysis show that the proposed approach outperforms the related work approaches particularly when there are few critical-time-points.
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    DSP-CC-: I/O Efficient Parallel Computation of Connected Components in Billion-Scale Networks (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-09-11
    Description: Computing connected components is a core operation on graph data. Since billion-scale graphs cannot be resident in memory of a single server, several approaches based on distributed machines have recently been proposed. The representative methods are $mathsf{Hashhbox{-}Tohbox{-}Min}$ and $mathsf{PowerGraph}$ . $mathsf{Hashhbox{-}Tohbox{-}Min}$ is the state-of-the art disk-based distributed method which minimizes the number of MapReduce rounds. $mathsf{PowerGraph}$ is the-state-of-the-art in-memory distributed system, which is typically faster than the disk-based distributed one, however, requires a lot of machines for handling billion-scale graphs. In this paper, we propose an I/O efficient parallel algorithm for billion-scale graphs in a single PC. We first propose the Disk-based Sequential access-oriented Parallel processing  (DSP) model that exploits sequential disk access in terms of disk I/Os and parallel processing in terms of computation. We then propose an ultra-fast disk-based parallel algorithm for computing connected components, $mathsf{DSPhbox{-}CC}$ , which largely improves the performance through sequential disk scan and page-level cache-conscious parallel processing . Extensive experimental results show that $mathsf{DSPhbox{-}CC}$ 1) computes connected components in billion-scale graphs using the limited memory size whereas in-memory algorithms can only support medium-sized graphs with the same memory size, and 2) significantly outperforms all distributed competitors as well as a representative disk-based parallel method.
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    Efficient Answering of Why-Not Questions in Similar Graph Matching (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-09-11
    Description: Answering why-not questions in databases is promised to have wide application prospect in many areas and thereby, has attracted recent attention in the database research community. This paper addresses the problem of answering these so-called why-not questions in similar graph matching for graph databases. Given a set of answer graphs of an initial query graph $q$ and a set of missing ( why-not ) graphs, we aim to modify $q$ into a new query graph $q^*$ such that the missing graphs are included in the new answer set of $q^*$ . We present an approximate solution to address the above as the optimal solution is NP-hard to compute. In our approach, we first compute the bounded search space and the distance to be minimized for $q^*$ . Then, we present a two-phase algorithm to find the new query $q^*$ . In the first phase, we generate a set of candidate edges to be added/deleted into/from the initial query $q$ within the bounded search space and in the second phase, we select a subset of candidate edges generated in the first phase to minimize the distance for $q^*$ . We also demonstrate the effectiveness and efficiency of our approach by conducting extensive experiments on two real datasets.
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    Four Decades of Data Mining in Network and Systems Management (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-09-11
    Description: How has the interdisciplinary data mining field been practiced in Network and Systems Management (NSM)? In Science and Technology, there is a wide use of data mining in areas like bioinformatics, genetics, Web, and, more recently, astroinformatics. However, the application in NSM has been limited and inconsiderable. In this article, we provide an account of how data mining has been applied in managing networks and systems for the past four decades, presumably since its birth. We look into the field’s applications in the key NSM activities—discovery, monitoring, analysis, reporting, and domain knowledge acquisition. In the end, we discuss our perspective on the issues that are considered critical for the effective application of data mining in the modern systems which are characterized by heterogeneity and high dynamism.
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    Geo-Social K-Cover Group Queries for Collaborative Spatial Computing (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-09-11
    Description: With the rapid development of location-aware mobile devices, ubiquitous Internet access and social computing technologies, lots of users’ personal information, such as location data and social data, has been readily accessible from various mobile platforms and online social networks. The convergence of these two types of data, known as geo-social data , has enabled collaborative spatial computing that explicitly combines both location and social factors to answer useful geo-social queries for either business or social good. In this paper, we study a new type of Geo-Social K-Cover Group (GSKCG) queries that, given a set of query points and a social network, retrieves a minimum user group in which each user is socially related to at least $k$ other users and the users’ associated regions (e.g., familiar regions or service regions) can jointly cover all the query points. Albeit its practical usefulness, the GSKCG query problem is NP-complete. We consequently explore a set of effective pruning strategies to derive an efficient algorithm for finding the optimal solution. Moreover, we design a novel index structure tailored to our problem to further accelerate query processing. Extensive experiments demonstrate that our algorithm achieves desirable performance on real-life datasets.
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    CRoM and HuspExt: Improving Efficiency of High Utility Sequential Pattern Extraction (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-09-11
    Description: High utility sequential pattern mining has been considered as an important research problem and a number of relevant algorithms have been proposed for this topic. The main challenge of high utility sequential pattern mining is that, the search space is large and the efficiency of the solutions is directly affected by the degree at which they can eliminate the candidate patterns. Therefore, the efficiency of any high utility sequential pattern mining solution depends on its ability to reduce this big search space, and as a result, lower the computational complexity of calculating the utilities of the candidate patterns. In this paper, we propose efficient data structures and pruning technique which is based on Cumulated Rest of Match (CRoM) based upper bound. CRoM, by defining a tighter upper bound on the utility of the candidates, allows more conservative pruning before candidate pattern generation in comparison to the existing techniques. In addition, we have developed an efficient algorithm, High Utility Sequential Pattern Extraction (HuspExt), which calculates the utilities of the child patterns based on that of the parents’. Substantial experiments on both synthetic and real datasets from different domains show that, the proposed solution efficiently discovers high utility sequential patterns from large scale datasets with different data characteristics, under low utility thresholds.
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    Fusing Monotonic Decision Trees (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-09-11
    Description: Ordinal classification with a monotonicity constraint is a kind of classification tasks, in which the objects with better attribute values should not be assigned to a worse decision class. Several learning algorithms have been proposed to handle this kind of tasks in recent years. The rank entropy-based monotonic decision tree is very representative thanks to its better robustness and generalization. Ensemble learning is an effective strategy to significantly improve the generalization ability of machine learning systems. The objective of this work is to develop a method of fusing monotonic decision trees. In order to achieve this goal, we take two factors into account: attribute reduction and fusing principle. Through introducing variable dominance rough sets, we firstly propose an attribute reduction approach with rank-preservation for learning base classifiers, which can effectively avoid overfitting and improve classification performance. Then, we establish a fusing principe based on maximal probability through combining the base classifiers, which is used to further improve generalization ability of the learning system. The experimental analysis shows that the proposed fusing method can significantly improve classification performance of the learning system constructed by monotonic decision trees.
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    On the Upper Bounds of Spread for Greedy Algorithms in Social Network Influence Maximization (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-09-11
    Description: Influence maximization, defined as finding a small subset of nodes that maximizes spread of influence in social networks, is NP-hard under both Independent Cascade (IC) and Linear Threshold (LT) models, where many greedy-based algorithms have been proposed with the best approximation guarantee. However, existing greedy-based algorithms are inefficient on large networks, as it demands heavy Monte-Carlo simulations of the spread functions for each node at the initial step [7] . In this paper, we establish new upper bounds to significantly reduce the number of Monte-Carlo simulations in greedy-based algorithms, especially at the initial step. We theoretically prove that the bound is tight and convergent when the summation of weights towards (or from) each node is less than 1. Based on the bound, we propose a new Upper Bound based Lazy Forward algorithm ( UBLF in short) for discovering the top-k influential nodes in social networks. We test and compare UBLF with prior greedy algorithms, especially CELF [30] . Experimental results show that UBLF reduces more than 95 percent Monte-Carlo simulations of CELF and achieves about $2hbox{-}10$ times speedup when the seed set is small.
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    Feature Selection via Global Redundancy Minimization (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-09-11
    Description: Feature selection has been an important research topic in data mining, because the real data sets often have high-dimensional features, such as the bioinformatics and text mining applications. Many existing filter feature selection methods rank features by optimizing certain feature ranking criterions, such that correlated features often have similar rankings. These correlated features are redundant and don’t provide large mutual information to help data mining. Thus, when we select a limited number of features, we hope to select the top non-redundant features such that the useful mutual information can be maximized. In previous research, Ding et al. recognized this important issue and proposed the minimum Redundancy Maximum Relevance Feature Selection (mRMR) model to minimize the redundancy between sequentially selected features. However, this method used the greedy search, thus the global feature redundancy wasn’t considered and the results are not optimal. In this paper, we propose a new feature selection framework to globally minimize the feature redundancy with maximizing the given feature ranking scores, which can come from any supervised or unsupervised methods. Our new model has no parameter so that it is especially suitable for practical data mining application. Experimental results on benchmark data sets show that the proposed method consistently improves the feature selection results compared to the original methods. Meanwhile, we introduce a new unsupervised global and local discriminative feature selection method which can be unified with the global feature redundancy minimization framework and shows superior performance.
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    Joint Structural Learning to Rank with Deep Linear Feature Learning (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-09-11
    Description: Multimedia information retrieval usually involves two key modules including effective feature representation and ranking model construction. Most existing approaches are incapable of well modeling the inherent correlations and interactions between them, resulting in the loss of the latent consensus structure information. To alleviate this problem, we propose a learning to rank approach that simultaneously obtains a set of deep linear features and constructs structure-aware ranking models in a joint learning framework. Specifically, the deep linear feature learning corresponds to a series of matrix factorization tasks in a hierarchical manner, while the learning-to-rank part concentrates on building a ranking model that effectively encodes the intrinsic ranking information by structural SVM learning. Through a joint learning mechanism, the two parts are mutually reinforced in our approach, and meanwhile their underlying interaction relationships are implicitly reflected by solving an alternating optimization problem. Due to the intrinsic correlations among different queries (i.e., similar queries for similar ranking lists), we further formulate the learning-to-rank problem as a multi-task problem, which is associated with a set of mutually related query-specific learning-to-rank subproblems. For computational efficiency and scalability, we design a MapReduce-based parallelization approach to speed up the learning processes. Experimental results demonstrate the efficiency, effectiveness, and scalability of the proposed approach in multimedia information retrieval.
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    Reputation Aggregation in Peer-to-Peer Network Using Differential Gossip Algorithm (2015)
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    Publication Date: 2015-09-11
    Description: In a peer-to-peer system, a node should estimate reputation of other peers not only on the basis of its own interaction, but also on the basis of expression of other nodes. Reputation aggregation mechanism implements strategy for achieving this. Reputation aggregation in peer to peer networks is generally a very time and resource consuming process. Moreover, most of the methods consider that a node will have the same reputation after aggregation with all the nodes in the network, which is not true. This paper proposes a reputation aggregation algorithm that uses a variant of gossip algorithm called differential gossip. In this paper, estimate of reputation is considered to be having two parts, one common component which is same with every node, and the other one is the information received from immediate neighbours based on the neighbours’ direct interaction with the node. The differential gossip is fast and requires a lesser amount of resources. This mechanism allows computation of independent reputation value by every node, of every other node in the network. The differential gossip trust has been investigated for a power law network formed using preferential attachment (PA) Model. The reputation computed using differential gossip trust shows good amount of immunity to the collusion. We have verified the performance of the algorithm on the power law networks with sizes ranging from 100 nodes to 50,000 nodes.
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    Change-Point Detection in a Sequence of Bags-of-Data (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-09-11
    Description: In this paper, the limitation that is prominent in most existing works of change-point detection methods is addressed by proposing a nonparametric, computationally efficient method. The limitation is that most works assume that each data point observed at each time step is a single multi-dimensional vector. However, there are many situations where this does not hold. Therefore, a setting where each observation is a collection of random variables, which we call a bag of data, is considered. After estimating the underlying distribution behind each bag of data and embedding those distributions in a metric space, the change-point score is derived by evaluating how the sequence of distributions is fluctuating in the metric space using a distance-based information estimator. Also, a procedure that adaptively determines when to raise alerts is incorporated by calculating the confidence interval of the change-point score at each time step. This avoids raising false alarms in highly noisy situations and enables detecting changes of various magnitudes. A number of experimental studies and numerical examples are provided to demonstrate the generality and the effectiveness of our approach with both synthetic and real datasets.
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    Efficient Top-k Retrieval on Massive Data (2015)
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    Publication Date: 2015-09-11
    Description: In many applications, top- k query is an important operation to return a set of interesting points in a potentially huge data space. It is analyzed in this paper that the existing algorithms cannot process top- k query on massive data efficiently. This paper proposes a novel table-scan-based T2S algorithm to efficiently compute top- k results on massive data. T2S first constructs the presorted table, whose tuples are arranged in the order of the round-robin retrieval on the sorted lists. T2S maintains only fixed number of tuples to compute results. The early termination checking for T2S is presented in this paper, along with the analysis of scan depth. The selective retrieval is devised to skip the tuples in the presorted table which are not top- k results. The theoretical analysis proves that selective retrieval can reduce the number of the retrieved tuples significantly. The construction and incremental-update/batch-processing methods for the used structures are proposed in this paper. The extensive experimental results, conducted on synthetic and real-life data sets, show that T2S has a significant advantage over the existing algorithms.
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    Querying Knowledge Graphs by Example Entity Tuples (2015)
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    Publication Date: 2015-09-11
    Description: We witness an unprecedented proliferation of knowledge graphs that record millions of entities and their relationships. While knowledge graphs are structure-flexible and content-rich, they are difficult to use. The challenge lies in the gap between their overwhelming complexity and the limited database knowledge of non-professional users. If writing structured queries over “simple” tables is difficult, complex graphs are only harder to query. As an initial step toward improving the usability of knowledge graphs, we propose to query such data by example entity tuples, without requiring users to form complex graph queries. Our system, Graph Query By Example ( $mathsf {GQBE}$ ), automatically discovers a weighted hidden maximum query graph based on input query tuples, to capture a user’s query intent. It then efficiently finds and ranks the top approximate matching answer graphs and answer tuples. We conducted experiments and user studies on the large Freebase and DBpedia datasets and observed appealing accuracy and efficiency. Our system provides a complementary approach to the existing keyword-based methods, facilitating user-friendly graph querying. To the best of our knowledge, there was no such proposal in the past in the context of graphs.
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    Principled Graph Matching Algorithms for Integrating Multiple Data Sources (2015)
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    Publication Date: 2015-09-11
    Description: This paper explores combinatorial optimization for problems of max-weight graph matching on multi-partite graphs, which arise in integrating multiple data sources. In the most common two-source case, it is often desirable for the final matching to be one-to-one; the database and statistical record linkage communities accomplish this by weighted bipartite graph matching on similarity scores. Such matchings are intuitively appealing: they leverage a natural global property of many real-world entity stores—that of being nearly deduped—and are known to provide significant improvements to precision and recall. Unfortunately, unlike the bipartite case, exact max-weight matching on multi-partite graphs is known to be NP-hard. Our two-fold algorithmic contributions approximate multi-partite max-weight matching: our first algorithm borrows optimization techniques common to Bayesian probabilistic inference; our second is a greedy approximation algorithm. In addition to a theoretical guarantee on the latter, we present comparisons on a real-world entity resolution problem from Bing significantly larger than typically found in the literature, on publication data, and on a series of synthetic problems. Our results quantify significant improvements due to exploiting multiple sources, which are made possible by global one-to-one constraints linking otherwise independent matching sub-problems. We also discover that our algorithms are complementary: one being much more robust under noise, and the other being simple to implement and very fast to run.
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    Spatio-Temporal Analysis of Passive Consumption in Internet Media (2015)
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    Publication Date: 2015-09-11
    Description: Internet users can be classified as two types: (a) active users —actively contribute to blogs, publish opinions, write comments in Youtube, tweet messages, etc. and (b) passive consumers who only consume Internet information without contributing to it. While the majority of current social media research deals with active user analysis, there is very little work in understanding the dynamics of passive consumers and their influence. Our global scale Internet measurement of user access patterns of a diverse set of Internet media services indicates conclusively that majority of consumers are passive. In this paper, we develop a spatio-temporal mathematical model and the corresponding stochastic analysis to understand the passive consumer dynamics. Both discrete and continuous time analysis are presented. We also show how the analysis can be used to identify spatial points of influence, i.e., spatial locations that have maximum expertise or influence on a topic. The analysis takes into account, the initial level of consumption at each spatial location and the influence of different passive consumers at different geographic locations on each other. The effect of information noise is taken into account to derive fundamental limits of passive information consumption. Theoretical results are verified using real Internet measurement data. The large scale data have been made available for other researchers.
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    Electronic ISSN: 1558-2191
    Topics: Computer Science
    Published by Institute of Electrical and Electronics Engineers (IEEE) on behalf of The IEEE Computer Society.
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    Using Copulas in Data Mining Based on the Observational Calculus (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
    Details
    Publication Date: 2015-09-11
    Description: The objective of the paper is a contribution to data mining within the framework of the observational calculus, through introducing ǵeneralized quantifiers related to copulas. Fitting copulas to multidimensional data is an increasingly important method for analyzing dependencies, and the proposed quantifiers of observational calculus assess the results of estimating the structure of joint distributions of continuous variables by means of hierarchical Archimedean copulas. To this end, the existing theory of hierarchical Archimedean copulas has been slightly extended in the paper: It has been proven that sufficient conditions for the function defining a hierarchical Archimedean copula to be indeed a copula, which have so far been rigorously established only for the special case of fully nested Archimedean copulas, hold in general. These conditions allow us to define three new generalized quantifiers, which are then thoroughly validated on four benchmark data sets and one data set from a real-world application. The paper concludes by comparing the proposed quantifiers to a more traditional approach—maximum weight spanning trees.
    Print ISSN: 1041-4347
    Electronic ISSN: 1558-2191
    Topics: Computer Science
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    Safe Distribution and Parallel Execution of Data-Centric Workflows over the Publish/Subscribe Abstraction (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
    Details
    Publication Date: 2015-09-11
    Description: In this work, we develop an approach for the safe distribution and parallel execution of data-centric workflows over the publish/subscribe abstraction. In essence, we design a unique representation of data-centric workflows, specifically designed to exploit the loosely coupled and distributed nature of publish/subscribe systems. Furthermore, we argue for the practicality and expressiveness of our approach by mapping a standard and industry-strength data-centric workflow model, namely, IBM Business Artifacts with Guard-Stage-Milestone (GSM), into the publish/subscribe abstraction. In short, the contributions of this work are three-fold: (1) mapping of data-centric workflows into publish/subscribe to achieve distributed and parallel execution; (2) detailed theoretical analysis of the mapping; and (3) formulation of the complexity of the optimal workflow distribution over the publish/subscribe abstraction as an NP-hard problem.
    Print ISSN: 1041-4347
    Electronic ISSN: 1558-2191
    Topics: Computer Science
    Published by Institute of Electrical and Electronics Engineers (IEEE) on behalf of The IEEE Computer Society.
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    Schulzentrum HTL HAK Ungargasse (SZU): Kompetenzzentrum für Integration von körper- und sinnesbehinderten Jugendlichen (2015)
    Springer
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    Publication Date: 2015-09-16
    Print ISSN: 0932-383X
    Electronic ISSN: 1613-7620
    Topics: Electrical Engineering, Measurement and Control Technology , Computer Science
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    Hoch hinaus mit den österreichischen Staatsmeisterschaften im Bereich Netzwerk- und Systemadministration (2015)
    Springer
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    Publication Date: 2015-09-16
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    Topics: Electrical Engineering, Measurement and Control Technology , Computer Science
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    Mädchenförderung zur Technik (2015)
    Springer
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    Publication Date: 2015-09-16
    Print ISSN: 0932-383X
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    Topics: Electrical Engineering, Measurement and Control Technology , Computer Science
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    Diplomarbeiten und Kooperationsprojekte an der htl donaustadt (2015)
    Springer
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    Publication Date: 2015-09-19
    Print ISSN: 0932-383X
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    Topics: Electrical Engineering, Measurement and Control Technology , Computer Science
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    From research and living lab to implementation of C-ITS (2015)
    Springer
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    Publication Date: 2015-09-29
    Description: A new technology for a further improved future. Who is the acting subject? Key actors in this ECo-AT Living Lab do the preparation jointly together; this is unusual and the newest trend. No commercial structure, no traditional valley of death-type deployment pitfall. Asfinag, Kapsch, Siemens, Swarco and an entire ECo-AT consortium are setting the scene for Europe’s test of the latest C-ITS technology. And, because all this is embedded within an international corridor Rotterdam–Frankfurt/M.–Vienna, this Living Lab approach is evolving to a new instrument for preparing C-ITS deployment.
    Print ISSN: 0932-383X
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    Topics: Electrical Engineering, Measurement and Control Technology , Computer Science
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    Extremsport (2015)
    Springer
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    Publication Date: 2015-11-21
    Electronic ISSN: 1862-2607
    Topics: Computer Science , Law
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    Europäisches Recht — Kommt die Verordnung oder kommt sie nicht? (2015)
    Springer
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    Publication Date: 2015-11-21
    Electronic ISSN: 1862-2607
    Topics: Computer Science , Law
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    Die Safe-Harbor-Entscheidung (2015)
    Springer
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    Publication Date: 2015-11-21
    Description: Der Gerichtshof der Europäischen Union hat mit Urteil vom 6. Oktober 2015 die Entscheidung der Kommission vom 26.7.2000 (2000/520/EG) zur Angemessenheit des von den Grundsätzen des „sicheren Hafens“ und der diesbezüglichen „Häufig gestellten Fragen“ (FAQ) gewährleisteten Schutzes, vorgelegt vom Handelsministerium der USA, für ungültig erklärt. 1 Der folgende Beitrag erläutert die Hintergründe der Entscheidung, fasst deren wesentliche Punkte noch einmal zusammen und gibt einen ersten Ausblick, welche Konsequenzen aus der Entscheidung zu ziehen sind.
    Electronic ISSN: 1862-2607
    Topics: Computer Science , Law
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    EU-Datenschutzgrundverordnung (2015)
    Springer
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    Publication Date: 2015-11-21
    Electronic ISSN: 1862-2607
    Topics: Computer Science , Law
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    Der neue Datenschutz (2015)
    Springer
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    Publication Date: 2015-11-21
    Electronic ISSN: 1862-2607
    Topics: Computer Science , Law
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    Die Bußgeldbewehrtheit von Datenpannen (2015)
    Springer
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    Publication Date: 2015-11-21
    Description: Datenschutz und Datensicherheit bilden die Eckpfeiler im Umgang mit personenbezogenen Daten. Auch wenn viele Unternehmen hinsichtlich dieser Themen sensibilisiert sind, ist die unfreiwillige Preisgabe von personenbezogenen Daten an Dritte nicht völlig ausgeschlossen. Der folgende Beitrag befasst sich daher mit der Frage der Bußgeldbewehrtheit von Datenpannen und geht dabei insbesondere auf den Begriff der Verarbeitung i. S. v. § 3 Abs. 4 BDSG ein.
    Electronic ISSN: 1862-2607
    Topics: Computer Science , Law
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  • 60
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    Durchsetzung und Sanktionierung des Datenschutzrechts nach den Entwürfen der Datenschutz-Grundverordnung (2015)
    Springer
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    Publication Date: 2015-11-21
    Description: Nachdem die Kommission und das Parlament bereits ihre Entwürfe der Datenschutz-Grundverordnung veröffentlicht hatten, folgte im Juni 2015 der Entwurf des Rates der Europäischen Union, wobei sich immer mehr abzeichnet, wie die Reform des europäischen Datenschutzrechts am Ende aussehen wird. So muss insbesondere bei der Durchsetzung und Sanktionierung mit erheblichen praxisrelevanten Veränderungen gerechnet werden.
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    Das Zweckbindungsprinzip zwischen Innovationsoffenheit und Rechtssicherheit (2015)
    Springer
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    Publication Date: 2015-11-21
    Description: Der datenschutzrechtliche Zweckbindungsgrundsatz wurde vor einiger Zeit 1 in den Medien hitzig 2 diskutiert. Anlass gab die Veröffentlichung des aktuellen Ratsentwurfs der Datenschutzgrundverordnung durch European Digital Rights. 3 Unter anderen berichtete der Tagesspiegel, dass durch den aktuellen Entwurf das Zweckbindungsprinzip ausgehebelt werden würde. 4 Der folgende Beitrag möchte mit Blick auf die jeweils verfassungsrechtliche Bedeutung den gesetzgeberischen Spielraum bei der Umsetzung des Zweckbindungsprinzips beleuchten.
    Electronic ISSN: 1862-2607
    Topics: Computer Science , Law
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    Interview with the UN special rapporteur on the right to privacy (2015)
    Springer
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    Publication Date: 2015-11-21
    Electronic ISSN: 1862-2607
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    Entschließungen der 90. Konferenz der DSB (DSK) am 30.09. und 1.10.2015 in Darmstadt (2015)
    Springer
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    Publication Date: 2015-11-21
    Electronic ISSN: 1862-2607
    Topics: Computer Science , Law
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    Rezensionen (2015)
    Springer
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    Publication Date: 2015-11-21
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    Report (2015)
    Springer
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  • 66
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    DuD Recht (2015)
    Springer
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    Publication Date: 2015-11-21
    Electronic ISSN: 1862-2607
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  • 67
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    Digital gepflegt, Datenschutz gehegt? (2015)
    Springer
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    Publication Date: 2015-11-21
    Description: Im Zusammenhang mit einer vom Bundesministerium für Gesundheit propagierten „Entbürokratisierung in der Pflege“ mit dem Ziel des „Abbaus von Bürokratie in der Pflegedokumentation“ könnten die auch in Pflegeeinrichtungen nicht mehr wegzudenkenden IT-Systeme eine große Rolle spielen. Bei der Umstellung auf bzw. der Einführung von digitalisierter Pflegedokumentation bestehen aber gleichwohl datenschutzrechtliche Aspekte, die nicht unberücksichtigt bleiben dürfen. Dies gilt insbesondere vor dem Hintergrund, dass es sich bei den in der Pflegedokumentation enthaltenen Informationen vielfach um Gesundheitsdaten der Pflegebedürftigen handelt. 1
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    Pumpspeichertechnologien mit den Möglichkeiten für die Netzregelung und Netzstabilität in zentralen und dezentralen Netzen (2015)
    Springer
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    Publication Date: 2015-11-25
    Print ISSN: 0932-383X
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    Topics: Electrical Engineering, Measurement and Control Technology , Computer Science
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    IT-Sicherheit und Datenschutz im vernetzten Fahrzeug (2015)
    Springer
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    Publication Date: 2015-05-31
    Description: Größter Innovationstreiber moderner vernetzter Fahrzeuge ist die Informationstechnologie. Jedoch ergeben sich hierdurch auch neue Bedrohungen für IT-Sicherheit und Datenschutz. In diesem Artikel wird ein Überblick über vernetzte Fahrzeuge, mögliche Anwendungen, die benötigten Daten und die sich ergebenden Bedrohungen gegeben. Weiterhin werden Herausforderungen für die aktuelle Forschung identifiziert.
    Electronic ISSN: 1862-2607
    Topics: Computer Science , Law
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    Das Netz im Auto & das Auto im Netz (2015)
    Springer
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    Publication Date: 2015-05-31
    Description: Aus der Vernetzung des Autos ergeben sich vielfältige Datenschutzfragen. Inwieweit lassen sich die Erfahrungen mit Internet und Smartphones übertragen?
    Electronic ISSN: 1862-2607
    Topics: Computer Science , Law
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    Der alte Herr Oberförster (2015)
    Springer
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    Publication Date: 2015-05-31
    Electronic ISSN: 1862-2607
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  • 72
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    „Pay as you drive“ – neue und altbekannte Probleme (2015)
    Springer
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    Publication Date: 2015-05-31
    Description: Der „Pay-as-you-drive“-Tarif hat Einzug in den deutschen Versicherungsmarkt gehalten. Mit ihm kommt es zu einer neuen Dimension der Datenerhebung im Alltagsleben. Dadurch stellen sich zahlreiche Fragen rund um das Recht der Datenverarbeitung, die für die Zukunft reichlich Diskussionsstoff beinhalten.
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    89. DSK am 18. und 19. März 2015: Die Entschließungen (2015)
    Springer
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    Publication Date: 2015-05-31
    Electronic ISSN: 1862-2607
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    Message to Facebook (2015)
    Springer
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    Publication Date: 2015-05-31
    Description: Facebook hat wieder einmal seine Bedingungen und Richtlinien geändert–und ist offensichtlich mächtig stolz darauf. Grund genug, Facebook auf diesem Wege einmal eine kurze Rückmeldung zur neuen Datenrichtlinie zu geben.
    Electronic ISSN: 1862-2607
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    Rezensionen (2015)
    Springer
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    Publication Date: 2015-05-31
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    Privacy by Default und Privacy by Design (2015)
    Springer
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    Publication Date: 2015-05-31
    Electronic ISSN: 1862-2607
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    Das vernetzte Automobil – sichere und freie Mobilität (2015)
    Springer
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    Publication Date: 2015-05-31
    Electronic ISSN: 1862-2607
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    Entschließungen der 89. Konferenz der DSB (DSK) am 18. und 19. März 2015 in Wiesbaden (2015)
    Springer
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    Publication Date: 2015-05-31
    Electronic ISSN: 1862-2607
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    Gemeinsames Verständnis von Vertrauen (2015)
    Springer
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    Publication Date: 2015-05-01
    Electronic ISSN: 1862-2607
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    Glaubwürdigkeit und Vertrauen von Online-News (2015)
    Springer
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    Publication Date: 2015-05-01
    Description: Die Erforschung von Vertrauen und Glaubwürdigkeit spielt in den Kommunikationswissenschaften eine bedeutsame Rolle für die Beschreibung, Erklärung und Vorhersage von Mediennutzungsprozessen und -mustern und daraus resultierenden Wirkungen. Im Fokus hinsichtlich der Rezeption von Nachrichten stehen dabei bislang vor allem Nachrichten in Printmedien und Fernsehen, nicht aber elektronische Medien. Der Beitrag geht auf den Zusammenhang zwischen der Rezeption von Nachrichten und politischen Informationen aus dem Internet und der Bedeutung des Vertrauens in diese bzw. deren Glaubwürdigkeit ein.
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    Topics: Computer Science , Law
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    Das Äußerungsrecht der Datenschutzaufsichtsbehörden (Teil 1) (2015)
    Springer
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    Publication Date: 2015-05-01
    Description: Öffentliche Äußerungen sind ein wirksames, oft das wirksamste Instrument für Datenschutzbehörden zur Durchsetzung des Datenschutzrechts. Demgemäß gibt es eine differenzierte und umfassende Öffentlichkeitsarbeit und oft eine enge Kooperation zwischen Aufsichtsbehörden und Presse. Hierbei kann im Einzelfall in die Rechte Dritter eingegriffen werden. Darauf gibt es in den Gesetzen bisher keine adäquate, differenzierende Antwort. Dem gegenüber hat die Rechtsprechung hierzu Aussagen gemacht, wobei Untergerichte regelmäßig eine restriktive Linie verfolgten, ohne dabei den verfassungs- und europarechtlichen Rahmen der Öffentlichkeitsarbeit von Aufsichtsbehörden hinreichend zu berücksichtigen.
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    Prädiktoren sicherheitsrelevanten Verhaltens bei jugendlichen Computernutzern (2015)
    Springer
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    Publication Date: 2015-05-01
    Description: Vor dem Hintergrund der stetig zunehmenden Fallzahlen von kriminellen Handlungen im Internet wird der Selbstschutz von Usern immer wichtiger. Gerade Jugendliche gelten als gefährdet, da sie sich häufig besonders sorglos im Netz bewegen. Der Beitrag präsentiert die Ergebnisse einer Befragung, die den Einfluss von fünf unterschiedlichen Faktoren auf das sicherheitsrelevante Verhalten von Jugendlichen untersucht hat. Besonderes Augenmerk wird dabei auf die Rolle von generalisiertem Vertrauen im Verhältnis zu anderen Faktoren gelegt.
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    Web of Services Security (2015)
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    Publication Date: 2015-05-01
    Description: Die Bezeichnung „Web of Services“ bezieht sich nach einer Definition des W3C auf ein nachrichtenbasiertes Designprinzip, das häufig zum Entwurf von Internet-Anwendungen oder Unternehmenssoftware zum Einsatz kommt. Die beiden dominierenden Ansätze sind hier derzeit SOAP und REST. Für REST existiert jedoch keine der SOAP-Security entsprechende Sicherheitsarchitektur. Mit den zunehmenden Einsatzmöglichkeiten in verteilten Anwendungen wird eine solche „REST-Security“ jedoch immer dringender benötigt. Diese muss abstrakte Sicherheitsmethoden definieren, deren konkrete Umsetzung über die bei Webanwendungen gebräuchlichen Sicherheitsmechanismen hinausgeht. Der Beitrag gibt einen Überblick über den aktuellen Stand der Technik und formuliert offene Forschungs- und Entwicklungsaufgaben in Form von Anforderungen an REST-Security.
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    Der Antagonist (2015)
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    Rezensionen (2015)
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    DuD Recht (2015)
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    Vertrauen und Privatheit (2015)
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    Publication Date: 2015-05-01
    Description: In diesem Beitrag wird Privatheit entlang der Datenschutzprinzipien mithilfe des Referenzmodells über Vertrauen aufgeschlüsselt. Dazu wird das Referenzmodell über Vertrauen zunächst in Hinblick auf dessen Anwendbarkeit auf technische Lösungen angepasst, so dass die Elemente und Beziehungen des Modells für die Datenschutzprinzipien Transparenz, Einwilligung und Datensparsamkeit, Vertraulichkeit usw. für die elektronische Kommunikation anwendbar sind. Eine solche Modellierung erlaubt eine neue Sicht auf das IT-Risiko für den Missbrauch personenbezogener Daten. Anschließend erfolgt exemplarisch eine konkrete Abbildung der Modellkomponenten für das Datenschutzprinzip der Vertraulichkeit.
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  • 88
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    Auftraggeberhaftung für den „Mindestlohn“ aus Datenschutzsicht (2015)
    Springer
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  • 89
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    Vertrauen (2015)
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  • 90
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    Vertrauensförderung in E-Partizipation (2015)
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    Publication Date: 2015-05-01
    Description: Bei einer Online-Bürgerbeteiligung (E-Partizipation) geben Bürgerinnen und Bürger vielfach direkt oder indirekt ihre eigene Meinung, ihre Gesinnung, ihre Herkunft etc. an die Öffentlichkeit bzw. in einem „halb-öffentlichen“ Bereich preis. Damit setzen sie sich–bewusst oder unbewusst–dem Risiko des Missbrauchs dieser persönlichen, sensiblen Daten durch andere Akteure aus. Um Vertrauen in E-Partizipationsangebote zu fördern, müssen Maßnahmen zur IT-Sicherheit und zum Datenschutz ergriffen werden, die Vertraulichkeit, Transparenz, Verfügbarkeit und Integrität der Kommunikation zwischen öffentlicher Verwaltung, den politischen Akteuren und den Bürgerinnen und Bürgern gewährleisten. Der Beitrag zeigt Gefahrenpotenziale und schützenswerte Güter auf und identifiziert erforderliche Schutzmaßnahmen sowie Forschungsbedarf.
    Electronic ISSN: 1862-2607
    Topics: Computer Science , Law
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    Report (2015)
    Springer
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    Publication Date: 2015-05-01
    Electronic ISSN: 1862-2607
    Topics: Computer Science , Law
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  • 92
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    Neue Wahlkampfinstrumente als Mittel gegen die Vertrauenskrise der EU? (2015)
    Springer
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    Publication Date: 2015-05-01
    Description: Die EU hat in den vergangenen Jahren Vertrauen verloren. Verfehlte Politik, mangelnde Identifikation, negative Kommunikation, fehlender politischer Wettbewerb–das sind die vermuteten Ursachen. Im Europawahlkampf 2014 wurde erstmals eine TV-Debatte der Kandidaten für das Amt des EU-Kommissionpräsidenten durchgeführt. Ob es der Debatte gelungen ist, verlorenes Vertrauen zurückzugewinnen und welche Rolle dabei die Kommunikation über die Debatte in den sozialen Medien spielt, untersucht der vorliegende Beitrag.
    Electronic ISSN: 1862-2607
    Topics: Computer Science , Law
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  • 93
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    Vertrauen (2015)
    Springer
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    Publication Date: 2015-05-01
    Description: Vertrauen ist Forschungsgegenstand vieler wissenschaftlicher Disziplinen. Da die Forschungsfragen und Anwendungskontexte jedoch stark variieren, stellt sich die Frage, wie man Vertrauen konzeptuell so fassen kann, dass es ein gemeinsames Verständnis der verschiedenen Disziplinen ermöglicht und dabei gleichzeitig die Möglichkeit eröffnet, disziplinäre Besonderheiten zu berücksichtigen. Ausgehend von einem sozialwissenschaftlich begründete Modell von Mayer, Davis und Schoormann [1, 2] geht der Beitrag der Frage nach, welche Gemeinsamkeiten und welche Unterschiede Vertrauenskonzepte in Psychologie, Politik- und Kommunikationswissenschaft sowie Informatik aufweisen. Dabei steht die Bedeutung von Vertrauen im politischen Kommunikationsprozess, also Vertrauen in politische Repräsentanten und Institutionen sowie Vertrauen in die Kommunikationsmedien, über die Bürger politische Inhalte rezipieren, im Mittelpunkt.
    Electronic ISSN: 1862-2607
    Topics: Computer Science , Law
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    Domain Invariant Transfer Kernel Learning (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
    Details
    Publication Date: 2015-05-02
    Description: Domain transfer learning generalizes a learning model across training data and testing data with different distributions. A general principle to tackle this problem is reducing the distribution difference between training data and testing data such that the generalization error can be bounded. Current methods typically model the sample distributions in input feature space, which depends on nonlinear feature mapping to embody the distribution discrepancy. However, this nonlinear feature space may not be optimal for the kernel-based learning machines. To this end, we propose a transfer kernel learning (TKL) approach to learn a domain-invariant kernel by directly matching source and target distributions in the reproducing kernel Hilbert space (RKHS). Specifically, we design a family of spectral kernels by extrapolating target eigensystem on source samples with Mercer’s theorem. The spectral kernel minimizing the approximation error to the ground truth kernel is selected to construct domain-invariant kernel machines. Comprehensive experimental evidence on a large number of text categorization, image classification, and video event recognition datasets verifies the effectiveness and efficiency of the proposed TKL approach over several state-of-the-art methods.
    Print ISSN: 1041-4347
    Electronic ISSN: 1558-2191
    Topics: Computer Science
    Published by Institute of Electrical and Electronics Engineers (IEEE) on behalf of The IEEE Computer Society.
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    Evolutionary Bayesian Rose Trees (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-05-02
    Description: We present an evolutionary multi-branch tree clustering method to model hierarchical topics and their evolutionary patterns over time. The method builds evolutionary trees in a Bayesian online filtering framework. The tree construction is formulated as an online posterior estimation problem, which well balances both the fitness of the current tree and the smoothness between trees. The state-of-the-art multi-branch clustering method, Bayesian rose trees, is employed to generate a topic tree with a high fitness value. A constraint model is also introduced to preserve the smoothness between trees. A set of comprehensive experiments on real world news data demonstrates that the proposed method better incorporates historical tree information and is more efficient and effective than the traditional evolutionary hierarchical clustering algorithm. In contrast to our previous method [31] , we implement two additional baseline algorithms to compare them with our algorithm. We also evaluate the performance of the clustering algorithm based on multiple constraint trees. Furthermore, two case studies are conducted to demonstrate the effectiveness and usefulness of our algorithm in helping users understand the major hierarchical topic evolutionary patterns in text data.
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    Discovery of Path Nearby Clusters in Spatial Networks (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
    Details
    Publication Date: 2015-05-02
    Description: The discovery of regions of interest in large cities is an important challenge. We propose and investigate a novel query called the path nearby cluster (PNC) query that finds regions of potential interest (e.g., sightseeing places and commercial districts) with respect to a user-specified travel route. Given a set of spatial objects $O$ (e.g., POIs, geo-tagged photos, or geo-tagged tweets) and a query route $q$ , if a cluster $c$ has high spatial-object density and is spatially close to $q$ , it is returned by the query (a cluster is a circular region defined by a center and a radius). This query aims to bring important benefits to users in popular applications such as trip planning and location recommendation. Efficient computation of the PNC query faces two challenges: how to prune the search space during query processing, and how to identify clusters with high density effectively. To address these challenges, a novel collective search algorithm is developed. Conceptually, the search process is conducted in the spatial and density domains concurrently. In the spatial domain, network expansion is adopted, and a set of vertices are selected from the query route as expansion centers. In the density domain, clusters are sorted according to their density distributions and they are scan- ed from the maximum to the minimum. A pair of upper and lower bounds are defined to prune the search space in the two domains globally. The performance of the PNC query is studied in extensive experiments based on real and synthetic spatial data.
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    Focal-Test-Based Spatial Decision Tree Learning (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-05-02
    Description: Given learning samples from a raster data set, spatial decision tree learning aims to find a decision tree classifier that minimizes classification errors as well as salt-and-pepper noise. The problem has important societal applications such as land cover classification for natural resource management. However, the problem is challenging due to the fact that learning samples show spatial autocorrelation in class labels, instead of being independently identically distributed. Related work relies on local tests (i.e., testing feature information of a location) and cannot adequately model the spatial autocorrelation effect, resulting in salt-and-pepper noise. In contrast, we recently proposed a focal-test-based spatial decision tree (FTSDT), in which the tree traversal direction of a sample is based on both local and focal (neighborhood) information. Preliminary results showed that FTSDT reduces classification errors and salt-and-pepper noise. This paper extends our recent work by introducing a new focal test approach with adaptive neighborhoods that avoids over-smoothing in wedge-shaped areas. We also conduct computational refinement on the FTSDT training algorithm by reusing focal values across candidate thresholds. Theoretical analysis shows that the refined training algorithm is correct and more scalable. Experiment results on real world data sets show that new FTSDT with adaptive neighborhoods improves classification accuracy, and that our computational refinement significantly reduces training time.
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    Top-k Similarity Join in Heterogeneous Information Networks (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
    Details
    Publication Date: 2015-05-02
    Description: As a newly emerging network model, heterogeneous information networks (HINs) have received growing attention. Many data mining tasks have been explored in HINs, including clustering, classification, and similarity search. Similarity join is a fundamental operation required for many problems. It is attracting attention from various applications on network data, such as friend recommendation, link prediction, and online advertising. Although similarity join has been well studied in homogeneous networks, it has not yet been studied in heterogeneous networks. Especially, none of the existing research on similarity join takes different semantic meanings behind paths into consideration and almost all completely ignore the heterogeneity and diversity of the HINs. In this paper, we propose a path-based similarity join (PS-join) method to return the top $k$ similar pairs of objects based on any user specified join path in a heterogeneous information network. We study how to prune expensive similarity computation by introducing bucket pruning based locality sensitive hashing (BPLSH) indexing. Compared with existing Link-based Similarity join (LS-join) method, PS-join can derive various similarity semantics. Experimental results on real data sets show the efficiency and effectiveness of the proposed approach.
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    Scalable Classification of Repetitive Time Series Through Frequencies of Local Polynomials (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-05-02
    Description: Time-series classification has attracted considerable research attention due to the various domains where time-series data are observed, ranging from medicine to econometrics. Traditionally, the focus of time-series classification has been on short time-series data composed of a few patterns exhibiting variabilities, while recently there have been attempts to focus on longer series composed of multiple local patrepeating with an arbitrary irregularity. The primary contribution of this paper relies on presenting a method which can detect local patterns in repetitive time-series via fitting local polynomial functions of a specified degree. We capture the repetitiveness degrees of time-series datasets via a new measure. Furthermore, our method approximates local polynomials in linear time and ensures an overall linear running time complexity. The coefficients of the polynomial functions are converted to symbolic words via equi-area discretizations of the coefficients’ distributions. The symbolic polynomial words enable the detection of similar local patterns by assigning the same word to similar polynomials. Moreover, a histogram of the frequencies of the words is constructed from each time-series’ bag of words. Each row of the histogram enables a new representation for the series and symbolizes the occurrence of local patterns and their frequencies. In an experimental comparison against state-of-the-art baselines on repetitive datasets, our method demonstrates significant improvements in terms of prediction accuracy.
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    TASC:Topic-Adaptive Sentiment Classification on Dynamic Tweets (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
    Details
    Publication Date: 2015-05-02
    Description: Sentiment classification is a topic-sensitive task, i.e., a classifier trained from one topic will perform worse on another. This is especially a problem for the tweets sentiment analysis. Since the topics in Twitter are very diverse, it is impossible to train a universal classifier for all topics. Moreover, compared to product review, Twitter lacks data labeling and a rating mechanism to acquire sentiment labels. The extremely sparse text of tweets also brings down the performance of a sentiment classifier. In this paper, we propose a semi-supervised topic-adaptive sentiment classification (TASC) model, which starts with a classifier built on common features and mixed labeled data from various topics. It minimizes the hinge loss to adapt to unlabeled data and features including topic-related sentiment words, authors’ sentiments and sentiment connections derived from “@” mentions of tweets, named as topic-adaptive features. Text and non-text features are extracted and naturally split into two views for co-training. The TASC learning algorithm updates topic-adaptive features based on the collaborative selection of unlabeled data, which in turn helps to select more reliable tweets to boost the performance. We also design the adapting model along a timeline (TASC-t) for dynamic tweets. An experiment on 6 topics from published tweet corpuses demonstrates that TASC outperforms other well-known supervised and ensemble classifiers. It also beats those semi-supervised learning methods without feature adaption. Meanwhile, TASC-t can also achieve impressive accuracy and F-score. Finally, with timeline visualization of “river” graph, people can intuitively grasp the ups and downs of sentiments’ evolvement, and the intensity by color gradation.
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