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  • 1.
    Badiozamany, Sobhan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Distributed multi-query optimization of continuous clustering queries2014In: Proc. VLDB 2014 PhD Workshop, 2014Conference paper (Refereed)
    Abstract [en]

    This work addresses the problem of sharing execution plans for queries that continuously cluster streaming data to provide an evolving summary of the data stream. This is challenging since clustering is an expensive task, there might be many clustering queries running simultaneously, each continuous query has a long life time span, and the execution plans often overlap. Clustering is similar to conventional grouped aggregation but cluster formation is more expensive than group formation, which makes incremental maintenance more challenging. The goal of this work is to minimize response time of continuous clustering queries with limited resources through multi-query optimization. To that end, strategies for sharing execution plans between continuous clustering queries are investigated and the architecture of a system is outlined that optimizes the processing of multiple such queries. Since there are many clustering algorithms, the system should be extensible to easily incorporate user defined clustering algorithms.

  • 2.
    Badiozamany, Sobhan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Computing Science. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Real-time data stream clustering over sliding windows2016Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    In many applications, e.g. urban traffic monitoring, stock trading, and industrial sensor data monitoring, clustering algorithms are applied on data streams in real-time to find current patterns. Here, sliding windows are commonly used as they capture concept drift.

    Real-time clustering over sliding windows is early detection of continuously evolving clusters as soon as they occur in the stream, which requires efficient maintenance of cluster memberships that change as windows slide.

    Data stream management systems (DSMSs) provide high-level query languages for searching and analyzing streaming data. In this thesis we extend a DSMS with a real-time data stream clustering framework called Generic 2-phase Continuous Summarization framework (G2CS).  G2CS modularizes data stream clustering by taking as input clustering algorithms which are expressed in terms of a number of functions and indexing structures. G2CS supports real-time clustering by efficient window sliding mechanism and algorithm transparent indexing. A particular challenge for real-time detection of a high number of rapidly evolving clusters is efficiency of window slides for clustering algorithms where deletion of expired data is not supported, e.g. BIRCH. To that end, G2CS includes a novel window maintenance mechanism called Sliding Binary Merge (SBM). To further improve real-time sliding performance, G2CS uses generation-based multi-dimensional indexing where indexing structures suitable for the clustering algorithms can be plugged-in.

    List of papers
    1. Scalable ordered indexing of streaming data
    Open this publication in new window or tab >>Scalable ordered indexing of streaming data
    2012 (English)In: 3rd International Workshop on Accelerating Data Management Systems using Modern Processor and Storage Architectures, 2012, p. 11-Conference paper, Published paper (Refereed)
    National Category
    Computer Sciences
    Identifiers
    urn:nbn:se:uu:diva-185068 (URN)
    Conference
    ADMS 2012, Istanbul, Turkey
    Projects
    eSSENCE
    Available from: 2012-08-27 Created: 2012-11-19 Last updated: 2018-01-12Bibliographically approved
    2. Grand challenge: Implementation by frequently emitting parallel windows and user-defined aggregate functions
    Open this publication in new window or tab >>Grand challenge: Implementation by frequently emitting parallel windows and user-defined aggregate functions
    Show others...
    2013 (English)In: Proc. 7th ACM International Conference on Distributed Event-Based Systems, New York: ACM Press, 2013, p. 325-330Conference paper, Published paper (Refereed)
    Place, publisher, year, edition, pages
    New York: ACM Press, 2013
    National Category
    Computer Sciences
    Identifiers
    urn:nbn:se:uu:diva-211954 (URN)10.1145/2488222.2488284 (DOI)978-1-4503-1758-0 (ISBN)
    External cooperation:
    Conference
    DEBS 2013
    Available from: 2013-06-29 Created: 2013-12-03 Last updated: 2018-01-11Bibliographically approved
    3. Distributed multi-query optimization of continuous clustering queries
    Open this publication in new window or tab >>Distributed multi-query optimization of continuous clustering queries
    2014 (English)In: Proc. VLDB 2014 PhD Workshop, 2014Conference paper, Published paper (Refereed)
    Abstract [en]

    This work addresses the problem of sharing execution plans for queries that continuously cluster streaming data to provide an evolving summary of the data stream. This is challenging since clustering is an expensive task, there might be many clustering queries running simultaneously, each continuous query has a long life time span, and the execution plans often overlap. Clustering is similar to conventional grouped aggregation but cluster formation is more expensive than group formation, which makes incremental maintenance more challenging. The goal of this work is to minimize response time of continuous clustering queries with limited resources through multi-query optimization. To that end, strategies for sharing execution plans between continuous clustering queries are investigated and the architecture of a system is outlined that optimizes the processing of multiple such queries. Since there are many clustering algorithms, the system should be extensible to easily incorporate user defined clustering algorithms.

    National Category
    Computer Sciences
    Research subject
    Computer Science with specialization in Database Technology
    Identifiers
    urn:nbn:se:uu:diva-302790 (URN)
    External cooperation:
    Conference
    VLDB 2014
    Available from: 2016-09-09 Created: 2016-09-09 Last updated: 2018-01-10Bibliographically approved
    4. Framework for real-time clustering over sliding windows
    Open this publication in new window or tab >>Framework for real-time clustering over sliding windows
    2016 (English)In: Proc. 28th International Conference on Scientific and Statistical Database Management, New York: ACM Press, 2016, p. 1-13, article id 19Conference paper, Published paper (Refereed)
    Place, publisher, year, edition, pages
    New York: ACM Press, 2016
    National Category
    Computer Sciences
    Identifiers
    urn:nbn:se:uu:diva-302792 (URN)10.1145/2949689.2949696 (DOI)978-1-4503-4215-5 (ISBN)
    External cooperation:
    Conference
    SSDBM 2016
    Available from: 2016-07-18 Created: 2016-09-09 Last updated: 2018-01-10Bibliographically approved
  • 3.
    Badiozamany, Sobhan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Melander, Lars
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Truong, Thanh
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Xu, Cheng
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Risch, Tore
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Grand challenge: Implementation by frequently emitting parallel windows and user-defined aggregate functions2013In: Proc. 7th ACM International Conference on Distributed Event-Based Systems, New York: ACM Press, 2013, p. 325-330Conference paper (Refereed)
  • 4.
    Badiozamany, Sobhan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Orsborn, Kjell
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Risch, Tore
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Framework for real-time clustering over sliding windows2016In: Proc. 28th International Conference on Scientific and Statistical Database Management, New York: ACM Press, 2016, p. 1-13, article id 19Conference paper (Refereed)
  • 5.
    Badiozamany, Sobhan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Risch, Tore
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Scalable ordered indexing of streaming data2012In: 3rd International Workshop on Accelerating Data Management Systems using Modern Processor and Storage Architectures, 2012, p. 11-Conference paper (Refereed)
1 - 5 of 5
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  • en-GB
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  • nn-NO
  • nn-NB
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