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ACM SIGMOD International Conference on Management of Data

作者： Duggan, Jennie Papaemmanouil, Olga Battle, Leilani Stonebraker, Michael Northwestern Univ Evanston IL 60208 USA Brandeis Univ Waltham MA 02254 USA MIT Cambridge MA 02139 USA

ISBN: (纸本)9781450327589

Science applications are accumulating an ever-increasing amount of multidimensional data. Although some of it can be processed in a relational database, much of it is better suited to array-based engines. As such, it is important to optimize the query processing of these systems. This paper focuses on efficient query processing of join operations within an array database. These engines invariably "chunk" their data into multidimensional tiles that they use to efficiently process spatial queries. As such, traditional relational algorithms need to be substantially modified to take advantage of array tiles. Moreover, most n-dimensional science data is unevenly distributed in array space because its underlying observations rarely follow a uniform pattern. It is crucial that the optimization of array joins be skew-aware. In addition, owing to the scale of science applications, their query processing usually spans multiple nodes. This further complicates the planning of array joins. In this paper, we introduce a join optimization framework that is skew-aware for distributed joins. This optimization consists of two phases. In the first, a logical planner selects the query's algorithm (e.g., merge join), the granularity of the its tiles, and the reorganization operations needed to align the data. The second phase implements this logical plan by assigning tiles to cluster nodes using an analytical cost model. Our experimental results, on both synthetic and real-world data, demonstrate that this optimization framework speeds up array joins by up to 2.5X in comparison to the baseline.

关键词： distributed computation skew array database join optimization

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Decentralized Human Trajectories Tracking Using Hodge Decomposition in Sensor Networks 15

Decentralized Human Trajectories Tracking Using Hodge Decomp...

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23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL GIS)

作者： Yin, Xiaotian Ni, Chien-Chun Ding, Jiaxin Han, Wei Zhou, Dengpan Gao, Jie Gu, Xianfeng David Harvard Univ Dept Math Cambridge MA 02138 USA SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA

ISBN: (纸本)9781450339674

With the recent development of localization and tracking systems for both indoor and outdoor settings, we consider the problem of analyzing and representing the huge amount of natural trajectories from human movements that we expect to gather in the near future. In this paper we argue the topological representation, which records how a target moves around the natural obstacles in the underlying environment, can be sufficiently descriptive for many applications and efficient enough for both storing, comparing and classifying these natural human trajectories. Technically, the representation uses the homotopy type of the trajectory. By using harmonic one-forms and Hodge decomposition, we pre-process the sensor network with a purely decentralized algorithm such that the homology class of a trajectory can be obtained by a simple integration along the trajectory. This supports real-time classification of trajectories up to the homology accuracy with minimum communication cost. We test the effectiveness of our approach by showing how to classify randomly generated trajectories in a multi-level arts museum layout as well as how to distinguish real world taxi trajectories in a large city.

关键词： Homology Hodge decomposition trajectory analysis distributed computation sensor networks

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Communication Complexity of Approximate Matching in distributed Graphs 32

Communication Complexity of Approximate Matching in Distribu...

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32nd International Symposium on Theoretical Aspects of Computer Science (STACS)

作者： Huang, Zengfeng Radunovic, Bozidar Vojnovic, Milan Zhang, Qin Aarhus Univ MADALGO Aarhus Denmark Microsoft Res Cambridge England Indiana Univ Bloomington IN USA

ISBN: (纸本)9783939897781

In this paper we consider the communication complexity of approximation algorithms for maximum matching in a graph in the message-passing model of distributed computation. The input graph consists of n vertices and edges partitioned over a set of k sites. The output is an alpha-approximate maximum matching in the input graph which has to be reported by one of the sites. We show a lower bound on the communication complexity of Omega(alpha(2)kn) and show that it is tight up to poly-logarithmic factors. This lower bound also applies to other combinatorial problems on graphs in the message-passing computation model, including max-flow and graph sparsification.

关键词： approximate maximum matching distributed computation communication complexity

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Latent space inference of internet-scale networks

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2016年第1期17卷

作者： Kevin Murphy Bernhard Schölkopf Qirong Ho Junming Yin Eric P. Xing Google MPI for Intelligent Systems Institute for Infocomm Research A*STAR Singapore Department of Management Information Systems Eller College of Management University of Arizona Tucson AZ School of Computer Science Carnegie Mellon University Pittsburgh PA

The rise of Internet-scale networks, such as web graphs and social media with hundreds of millions to billions of nodes, presents new scientific opportunities, such as overlapping community detection to discover the structure of the Internet, or to analyze trends in online social behavior. However, many existing probabilistic network models are difficult or impossible to deploy at these massive scales. We propose a scalable approach for modeling and inferring latent spaces in Internet-scale networks, with an eye towards overlapping community detection as a key application. By applying a succinct representation of networks as a bag of triangular motifs, developing a parsimonious statistical model, deriving an efficient stochastic variational inference algorithm, and implementing it as a distributed cluster program via the Petuum parameter server system, we demonstrate overlapping community detection on real networks with up to 100 million nodes and 1000 communities on 5 machines in under 40 hours. Compared to other state-of-the-art probabilistic network approaches, our method is several orders of magnitude faster, with competitive or improved accuracy at overlapping community detection.

关键词： big data distributed computation probabilistic network models stochastic variational inference triangular modeling

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Data Outsourcing based on Secure Association Rule Mining Processes

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INTERNATIONAL JOURNAL OF SECURITY AND ITS APPLICATIONS 2015年第3期9卷 41-48页

作者： Sujatha, V. Bhattacharyya, Debnath Chaitanya, P. Silpa Kim, Tai-hoon Vignans Nirula ITW Palakaluru Dept Comp Sci & Engn Guntur AP India Vignans Inst Informat Technol Dept Comp Sci & Engn Visakhapatnam Andhra Pradesh India Sungshin Womens Univ Dept Convergence Secur Seoul 136742 South Korea

Data mining is the process of extracting information from data warehousing applications. Data outsourcing is the major task in present days, for accessing services and other features of the database processing. But sometimes this process may achieve to split among various parties with recommended data items in analyzing of the data. Data security is one of the key processes in outsourcing data to various outside users. Traditionally Fast Distribution Mining algorithm was proposed for securing distributed data. This paper addresses a problem by secure association rules over partitioned data in both horizontal and vertical representation. A secure frequency developed algorithm is used for doing above process efficiently in partitioned data, which includes services of the data in outsourcing process. Frequent item sets are used to access services in outsourcing data in recent application development data mining. Our proposed work maintains efficient security over vertical and horizontal view of representation in secure mining applications. The result shows that algorithm timing is desirable for big size data for security considerations using association rule mining operations in real time application development.

关键词： Association Rules Cryptography distributed Database distributed computation Privacy Preserving Data Mining

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distributed Grobner bases computation with MPJ

Distributed Grobner bases computation with MPJ

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IEEE 27th International Conference on Advanced Information Networking and Applications Workshops (WAINA)

作者： Kredel, Heinz Univ Mannheim IT Ctr Mannheim Germany

ISBN: (纸本)9780769549521;9781467362399

Grobner bases, an important building block in computer algebra, are computed on distributed memory parallel computers with multi-CPU compute nodes with increasing success. Based on previous efforts we present and compare new programs using the MPI message passing standard implemented using MPJ. MPJ is a specification of MPI for Java with efficient implementations. Also the new versions use one control communication channel between the master node and the worker nodes. In the hybrid version the polynomials are shared between the threads on a node and in the pure distributed version the polynomials are replicated on every process. The polynomials are transported asynchronous to the control-flow of the algorithm in a separate distributed data structure. The implementation is generic and works for all implemented fields. We present first promising performance measurements.

关键词： computer algebra distributed computation Groebner bases MPI MPJ

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Location Characterization in Noisy Range Network Localization

Location Characterization in Noisy Range Network Localizatio...

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2015 Workshop 11

作者： Mingzhu Wei Ryad Chellali Yang Yi Ting Wang Wen Qin College of Electric Engineering and Control Science Nanjing Tech University

Network localization is a fundamental problem in wireless sensor networks,mainly in location dependent applications.A common family of solutions to this problem is the range‐based network *** resulting localization algorithms are noise sensitive and thus lacking in terms of *** contribution provides an algorithm which is robust to measurement *** propose an analytical tool to analyze the effect of range errors in the final location and use a distributed method to solve the noisy range localization problem.

关键词： Network Localization Noisy Range Measurement distributed computation

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Graph diameter, eigenvalues, and minimum-time consensus

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AUTOMATICA 2014年第2期50卷 635-640页

作者： Hendrickx, Julien M. Jungers, Raphael M. Olshevsky, Alexander Vankeerberghen, Guillaume Catholic Univ Louvain Dept Engn Math ICTEAM B-1348 Louvain Belgium Univ Illinois Dept Ind & Enterprise Syst Engn Urbana IL 61801 USA

We consider the problem of achieving average consensus in the minimum number of linear iterations on a fixed, undirected graph. We are motivated by the task of deriving lower bounds for consensus protocols and by the so-called "definitive consensus conjecture", which states that for an undirected connected graph g, with diameter D there exist D matrices whose nonzero-pattern complies with the edges in 9, and whose product equals the all-ones matrix. Our first result is a counterexample to the definitive consensus conjecture, which is the first improvement of the diameter lower bound for linear consensus protocols. We then provide some algebraic conditions under which this conjecture holds, which we use to establish that all distance-regular graphs satisfy the definitive consensus conjecture. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Consensus distributed computation Algebraic graph theory Graph eigenvalues Distance-regular graphs

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A Spatio-Stochastic Framework for Cross-Layer Design in Cognitive Radio Networks

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IEEE TRANSACTIONS ON PARALLEL AND distributed SYSTEMS 2014年第11期25卷 2762-2771页

作者： Anifantis, Evangelos Karyotis, Vasileios Papavassiliou, Symeon Natl Tech Univ Athens Sch Elect & Comp Engn Athens 15780 Zografou Greece

In this paper, we address the problem of distributed resource management for secondary users in Cognitive Radio Networks (CRNs), through a topology aware and frequency agile cross-layer approach. We exploit the theory of spatial processes and propose a Markov Random Field (MRF) based framework, which enables secondary CRN users to achieve efficient and viable mechanisms in the lower protocol stack layers by exchanging local only information. Specifically, through Gibbs sampling secondary users can optimize in a distributed and parallel manner their channel allocation, medium access and routing without resolving to otherwise computationally demanding optimization approaches. Through analysis and simulation we exhibit the efficacy of the proposed framework and show that a semi-parallel implementation can significantly reduce the required overhead cost compared to the sequential Gibbs sampling approach, while retaining a very close performance to the latter. We also study the emerging trade-offs by demonstrating the performance benefits in terms of channel assignment, medium access and data flow.

关键词： Markov random fields cross-layer optimization cognitive radio networks simulated annealing Gibbs sampling distributed computation localized decision making spectrum sharing

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On Network Functional Compression

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IEEE TRANSACTIONS ON INFORMATION THEORY 2014年第9期60卷 5387-5401页

作者： Feizi, Soheil Medard, Muriel MIT Elect Res Lab Cambridge MA 02139 USA

In this paper, we consider different aspects of the problem of compressing for function computation across a network, which we call network functional compression. In network functional compression, computation of a function (or, some functions) of sources located at certain nodes in a network is desired at receiver(s). The rate region of this problem has been considered in the literature under certain restrictive assumptions, particularly in terms of the network topology, the functions, and the characteristics of the sources. In this paper, we present results that significantly relax these assumptions. For a one-stage tree network, we characterize a rate region by introducing a necessary and sufficient condition for any achievable coloring-based coding scheme called coloring connectivity condition. We also propose a modularized coding scheme based on graph colorings to perform arbitrarily closely to rate lower bounds. For a general tree network, we provide a rate lower bound based on graph entropies and show that, this bound is tight in the case of having independent sources. In particular, we show that, in a general tree network case with independent sources, to achieve the rate lower bound, intermediate nodes should perform computations. However, for a family of functions and random variables, which we call chain-rule proper sets, it is sufficient to have no computations at intermediate nodes to perform arbitrarily closely to the rate lower bound. In addition, we consider practical issues of coloring-based coding schemes and propose an efficient algorithm to compute a minimum entropy coloring of a characteristic graph under some conditions on source distributions and/or the desired function. Finally, extensions of these results for cases of having feedback and lossy function computations are discussed.

关键词： Functional compression distributed computation Slepian-Wolf compression graph entropy graph coloring feedback distortion

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