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Scalable Numerical Queries by Algebraic Inequality Transformations
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science. (UDBL)
2014 (English)In: Database Systems for Advanced Applications, Dasfaa 2014, PT I, 2014, 95-109 p.Conference paper (Refereed)
Abstract [en]

To enable historical analyses of logged data streams by SQL queries, the Stream Log Analysis System (SLAS) bulk loads data streams derived from sensor readings into a relational database system. SQL queries over such log data often involve numerical conditions containing inequalities, e. g. to find suspected deviations from normal behavior based on some function over measured sensor values. However, such queries are often slow to execute, because the query optimizer is unable to utilize ordered indexed attributes inside numerical conditions. In order to speed up the queries they need to be reformulated to utilize available indexes. In SLAS the query transformation algorithm AQIT (Algebraic Query Inequality Transformation) automatically transforms SQL queries involving a class of algebraic inequalities into more scalable SQL queries utilizing ordered indexes. The experimental results show that the queries execute substantially faster by a commercial DBMS when AQIT has been applied to preprocess them.

Place, publisher, year, edition, pages
2014. 95-109 p.
, Lecture Notes in Computer Science, ISSN 0302-9743 ; 8421
National Category
Computer and Information Science
URN: urn:nbn:se:uu:diva-236268ISI: 000342909200007ISBN: 978-3-319-05810-8; 978-3-319-05809-2OAI: oai:DiVA.org:uu-236268DiVA: diva2:764162
19th International Conference on Database Systems for Advanced Applications (DASFAA), APR 21-24, 2014, Bali, INDONESIA
Available from: 2014-11-18 Created: 2014-11-17 Last updated: 2016-04-15
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.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1352
Database indexing, query processing, index structures, main-memory, index validation
National Category
Computer Science
Research subject
Computer Science with specialization in Database Technology
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)
Available from: 2016-04-13 Created: 2016-03-09 Last updated: 2016-04-21

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