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2014 IEEE 30th International Conference on Data Engineering

March 31 2014-April 4 2014

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Displaying Results 1 - 25 of 147
  • [USB label]

    Publication Year: 2014, Page(s): 1
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  • [USB welcome]

    Publication Year: 2014, Page(s): 1
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  • Hub page

    Publication Year: 2014, Page(s): 1
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  • ICDE conference 2014 session list

    Publication Year: 2014, Page(s): 1
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  • ICDE conference 2014 table of contents

    Publication Year: 2014, Page(s):1 - 16
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  • ICDE conference 2014 brief author index

    Publication Year: 2014, Page(s):1 - 12
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  • ICDE conference 2014 detailed author index

    Publication Year: 2014, Page(s):1 - 56
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  • The end of indexes

    Publication Year: 2014, Page(s): 1
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  • AboutCP

    Publication Year: 2014, Page(s): 1
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  • Frequently asked questions

    Publication Year: 2014, Page(s):1 - 6
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  • Message from the ICDE 2014 program committee and general chairs

    Publication Year: 2014, Page(s):i - ii
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  • ICDE 2014 committees

    Publication Year: 2014, Page(s):iii - xii
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  • Running with scissors: Fast queries on just-in-time databases

    Publication Year: 2014, Page(s): 1
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (34 KB)

    The amount of data collected in the last two years is higher than the amount of data collected since the dawn of time. Businesses are drowning in data, and need several months of ETL processing to barely prepare them for querying. Domain scientists collect data much faster than they can be transformed into valuable information and are often forced into hasty decisions on which parts to discard, po... View full abstract»

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  • Transforming Big Data into Smart Data: Deriving value via harnessing Volume, Variety, and Velocity using semantic techniques and technologies

    Publication Year: 2014, Page(s): 2
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (80 KB)

    Big Data has captured a lot of interest in industry, with anticipation of better decisions, efficient organizations, and many new jobs. Much of the emphasis is on the challenges of the four V's of Big Data: Volume, Variety, Velocity, and Veracity, and technologies that handle volume, including storage and computational techniques to support analysis (Hadoop, NoSQL, MapReduce, etc). However, the mo... View full abstract»

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  • Incremental cluster evolution tracking from highly dynamic network data

    Publication Year: 2014, Page(s):3 - 14
    Cited by:  Papers (11)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (446 KB) | HTML iconHTML

    Dynamic networks are commonly found in the current web age. In scenarios like social networks and social media, dynamic networks are noisy, are of large-scale and evolve quickly. In this paper, we focus on the cluster evolution tracking problem on highly dynamic networks, with clear application to event evolution tracking. There are several previous works on data stream clustering using a node-by-... View full abstract»

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  • Finding common ground among experts' opinions on data clustering: With applications in malware analysis

    Publication Year: 2014, Page(s):15 - 27
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1500 KB) | HTML iconHTML

    Data clustering is a basic technique for knowledge discovery and data mining. As the volume of data grows significantly, data clustering becomes computationally prohibitive and resource demanding, and sometimes it is necessary to outsource these tasks to third party experts who specialize in data clustering. The goal of this work is to develop techniques that find common ground among experts' opin... View full abstract»

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  • Towards effective and efficient mining of arbitrary shaped clusters

    Publication Year: 2014, Page(s):28 - 39
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3680 KB) | HTML iconHTML

    Mining arbitrary shaped clusters in large data sets is an open challenge in data mining. Various approaches to this problem have been proposed with high time complexity. To save computational cost, some algorithms try to shrink a data set size to a smaller amount of representative data examples. However, their user-defined shrinking ratios may significantly affect the clustering performance. In th... View full abstract»

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  • R-Store: A scalable distributed system for supporting real-time analytics

    Publication Year: 2014, Page(s):40 - 51
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (427 KB) | HTML iconHTML

    It is widely recognized that OLTP and OLAP queries have different data access patterns, processing needs and requirements. Hence, the OLTP queries and OLAP queries are typically handled by two different systems, and the data are periodically extracted from the OLTP system, transformed and loaded into the OLAP system for data analysis. With the awareness of the ability of big data in providing ente... View full abstract»

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  • Blazes: Coordination analysis for distributed programs

    Publication Year: 2014, Page(s):52 - 63
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (489 KB) | HTML iconHTML

    Distributed consistency is perhaps the most discussed topic in distributed systems today. Coordination protocols can ensure consistency, but in practice they cause undesirable performance unless used judiciously. Scalable distributed architectures avoid coordination whenever possible, but undercoordinated systems can exhibit behavioral anomalies under fault, which are often extremely difficult to ... View full abstract»

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  • Query optimization of distributed pattern matching

    Publication Year: 2014, Page(s):64 - 75
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (478 KB) | HTML iconHTML

    Greedy algorithms for subgraph pattern matching operations are often sufficient when the graph data set can be held in memory on a single machine. However, as graph data sets increasingly expand and require external storage and partitioning across a cluster of machines, more sophisticated query optimization techniques become critical to avoid explosions in query latency. In this paper, we introduc... View full abstract»

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  • Scalable distance-based outlier detection over high-volume data streams

    Publication Year: 2014, Page(s):76 - 87
    Cited by:  Papers (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2152 KB) | HTML iconHTML

    The discovery of distance-based outliers from huge volumes of streaming data is critical for modern applications ranging from credit card fraud detection to moving object monitoring. In this work, we propose the first general framework to handle the three major classes of distance-based outliers in streaming environments, including the traditional distance-threshold based and the nearest-neighbor-... View full abstract»

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  • Discriminative features for identifying and interpreting outliers

    Publication Year: 2014, Page(s):88 - 99
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (556 KB) | HTML iconHTML

    We consider the problem of outlier detection and interpretation. While most existing studies focus on the first problem, we simultaneously address the equally important challenge of outlier interpretation. We propose an algorithm that uncovers outliers in subspaces of reduced dimensionality in which they are well discriminated from regular objects while at the same time retaining the natural local... View full abstract»

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  • Memory-efficient centroid decomposition for long time series

    Publication Year: 2014, Page(s):100 - 111
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (620 KB) | HTML iconHTML

    Real world applications that deal with time series data often rely on matrix decomposition techniques, such as the Singular Value Decomposition (SVD). The Centroid Decomposition (CD) approximates the Singular Value Decomposition, but does not scale to long time series because of the quadratic space complexity of the sign vector computation. In this paper, we propose a greedy algorithm, termed Scal... View full abstract»

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  • Incremental discovery of prominent situational facts

    Publication Year: 2014, Page(s):112 - 123
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (419 KB) | HTML iconHTML

    We study the novel problem of finding new, prominent situational facts, which are emerging statements about objects that stand out within certain contexts. Many such facts are newsworthy-e.g., an athlete's outstanding performance in a game, or a viral video's impressive popularity. Effective and efficient identification of these facts assists journalists in reporting, one of the main goals of comp... View full abstract»

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  • Continuous fragmented skylines over distributed streams

    Publication Year: 2014, Page(s):124 - 135
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (322 KB) | HTML iconHTML

    Distributed skyline computation is important for a wide range of application domains, from distributed and web-based systems to ISP-network monitoring and distributed databases. The problem is particularly challenging in dynamic distributed settings, where the goal is to efficiently monitor a continuous skyline query over a collection of distributed streams. All existing work relies on the assumpt... View full abstract»

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