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Distributed multi-query optimization of continuous clustering queries
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science. (UDBL)
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.

Place, publisher, year, edition, pages
2014.
National Category
Computer Science
Research subject
Computer Science with specialization in Database Technology
Identifiers
URN: urn:nbn:se:uu:diva-302790OAI: oai:DiVA.org:uu-302790DiVA: diva2:967635
Conference
VLDB 2014
Available from: 2016-09-09 Created: 2016-09-09 Last updated: 2016-09-09Bibliographically approved
In thesis
1. Real-time data stream clustering over sliding windows
Open this publication in new window or tab >>Real-time data stream clustering over sliding windows
2016 (English)Doctoral 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.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2016. 33 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1431
Keyword
Data streaming; Sliding windows; Clustering;
National Category
Computer Systems
Research subject
Computer Science with specialization in Database Technology
Identifiers
urn:nbn:se:uu:diva-302799 (URN)978-91-554-9698-2 (ISBN)
Public defence
2016-11-23, ITC 2446, Lägerhyddsvägen 2, Uppsala, 10:00 (English)
Opponent
Supervisors
Available from: 2016-11-02 Created: 2016-09-09 Last updated: 2016-11-16

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CiteExportLink to record
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Citation style
  • apa
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Language
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Output format
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