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NoSQL approach to large scale analysis of persisted streams
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science. (UDBL)
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science. (UDBL)
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science. (UDBL)
2015 (English)In: Data Science, Springer, 2015, 152-156 p.Conference paper, Published paper (Refereed)
Resource type
Text
Place, publisher, year, edition, pages
Springer, 2015. 152-156 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9147
National Category
Computer Science
Identifiers
URN: urn:nbn:se:uu:diva-274783DOI: 10.1007/978-3-319-20424-6_15ISI: 000364104600015ISBN: 978-3-319-20423-9 (print)OAI: oai:DiVA.org:uu-274783DiVA: diva2:897635
Conference
BICOD 2015, July 6–8, Edinburgh, UK
Available from: 2015-06-11 Created: 2016-01-26 Last updated: 2016-04-15Bibliographically approved
In thesis
1. Main-Memory Query Processing Utilizing External Indexes
Open this publication in new window or tab >>Main-Memory Query Processing Utilizing External Indexes
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Many applications require storage and indexing of new kinds of data in main-memory, e.g. color histograms, textures, shape features, gene sequences, sensor readings, or financial time series. Even though, many domain index structures were developed, very a few of them are implemented in any database management system (DBMS), usually only B-trees and hash indexes. A major reason is that the manual effort to include a new index implementation in a regular DBMS is very costly and time-consuming because it requires integration with all components of the DBMS kernel. To alleviate this, there are some extensible indexing frameworks. However, they all require re-engineering the index implementations, which is a problem when the index has third-party ownership, when only binary code is available, or simply when the index implementation is complex to re-engineer. Therefore, the DBMS should allow including new index implementations without code changes and performance degradation. Furthermore, for high performance the query processor needs knowledge of how to process queries to utilize plugged-in index. Moreover, it is important that all functionalities of a plugged-in index implementation are correct.

The extensible main memory database system (MMDB) Mexima (Main-memory External Index Manager) addresses these challenges. It enables transparent plugging in main-memory index implementations without code changes. Index specific rewrite rules transform complex queries to utilize the indexes. Automatic test procedures validate the correctness of them based on user provided index meta-data. Moreover, the same optimization framework can also optimize complex queries sent to a back-end DBMS by exposing hidden indexes for its query optimizer.

Altogether, Mexima is a complete and extensible platform for transparently index integration, utilization, and evaluation.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2016. 45 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1352
Keyword
Database indexing, query processing, index structures, main-memory, index validation
National Category
Computer Science
Research subject
Computer Science with specialization in Database Technology
Identifiers
urn:nbn:se:uu:diva-280374 (URN)978-91-554-9509-1 (ISBN)
Public defence
2016-05-04, 2446, ITC, Lägerhyddsvägen 2, Uppsala, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2016-04-13 Created: 2016-03-09 Last updated: 2016-04-21

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Mahmood, KhalidTruong, ThanhRisch, Tore

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