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  • 1.
    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)
  • 2.
    Mahmood, Khalid
    et al.
    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.
    Risch, Tore
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    NoSQL approach to large scale analysis of persisted streams2015In: Data Science, Springer, 2015, p. 152-156Conference paper (Refereed)
  • 3.
    Truong, Thanh
    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.
    Main-Memory Query Processing Utilizing External Indexes2016Doctoral 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.

    List of papers
    1. Transparent inclusion, utilization, and validation of main memory domain indexes
    Open this publication in new window or tab >>Transparent inclusion, utilization, and validation of main memory domain indexes
    2015 (English)In: Proc. 27th International Conference on Scientific and Statistical Database Management, New York: ACM Press, 2015Conference paper, Published paper (Refereed)
    Abstract [en]

    Main-memory database systems (MMDBs) are viable solutions for many scientific applications. Scientific and engineering data often require special indexing methods, and there is a large number of domain specific main memory indexing implementations developed. However, adding an index structure into a database system can be challenging. Mexima (Main memory External Index Manager) provides an MMDB where new main-memory index structures can be plugged-in without modifying the index implementations. This has allowed to plug into Mexima complex and highly optimized index structures implemented in C/C++ without code changes. To utilize new user defined indexes in queries transparently, Mexima automatically transforms query fragments into index operations based on index properly tables containing index meta-data. For scalable processing of complex numerical query expressions, Mexima includes an algebraic query transformation mechanism that reasons on numerical expressions to expose potential utilization of indexes. The index property tables furthermore enable validating the correctness of an index implementation by executing automatically generated test queries based on index meta-data. Experiments show that the performance penalty of using an index plugged into Mexima is low compared to using the corresponding stand-alone C/C++ implementation. Substantial performance gains are shown by the index exposing rewrite mechanisms.

    Place, publisher, year, edition, pages
    New York: ACM Press, 2015
    Keywords
    Domain Indexing; Extensible Databases; Query Processing; Automatic Testing
    National Category
    Computer Sciences
    Research subject
    Computer Science with specialization in Database Technology
    Identifiers
    urn:nbn:se:uu:diva-280368 (URN)10.1145/2791347.2791375 (DOI)978-1-4503-3709-0 (ISBN)
    Conference
    SSDBM 2015, June 29–July 1, San Diego, CA
    Available from: 2015-06-29 Created: 2016-03-09 Last updated: 2018-01-10Bibliographically approved
    2. Scalable Numerical Queries by Algebraic Inequality Transformations
    Open this publication in new window or tab >>Scalable Numerical Queries by Algebraic Inequality Transformations
    2014 (English)In: Database Systems for Advanced Applications, Dasfaa 2014, PT I, 2014, p. 95-109Conference paper, Published 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.

    Series
    Lecture Notes in Computer Science, ISSN 0302-9743 ; 8421
    National Category
    Computer and Information Sciences
    Identifiers
    urn:nbn:se:uu:diva-236268 (URN)000342909200007 ()978-3-319-05810-8; 978-3-319-05809-2 (ISBN)
    Conference
    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: 2018-01-11
    3. Scalable Numerical SPARQL Queries over Relational Databases
    Open this publication in new window or tab >>Scalable Numerical SPARQL Queries over Relational Databases
    2014 (English)Conference paper, Published paper (Refereed)
    Abstract [en]

    We present an approach for scalable processing of SPARQL queries to RDF views of numerical data stored in relational databases (RDBs). Such queries include numerical expressions, inequalities, comparisons, etc. inside FILTERs. We call such FILTERs numerical expressions and the queries - numerical SPARQL queries. For scalable execution of numerical SPARQL queries over RDBs, numerical operators should be pushed into SQL rather than executing the filters as post-processing outside the RDB; otherwise the query execution is slowed down, since a lot of data is transported from the RDB server and furthermore indexes on the server are not utilized. The NUMTranslator algorithm converts numerical expressions in numerical SPARQL queries into corresponding SQL expressions. We show that NUMTranslator improves substantially the scalability of SPARQL queries based on a benchmark that analyses numerical logs stored in an RDB. We compared the performance of our approach with the performance of other systems processing SPARQL queries to RDF views of RDBs and show that NUMTranslator improves substantially the scalability of numerical queries compared to the other systems’ approaches.

    National Category
    Computer and Information Sciences
    Identifiers
    urn:nbn:se:uu:diva-275027 (URN)
    Conference
    4th International workshop on linked web data management (LWDM 2014) in conjunction with the EDBT/ICDT 2014 Joint Conference, Ath-ens, Greece, March 28, 2014
    Available from: 2016-01-28 Created: 2016-01-28 Last updated: 2018-01-10
    4. 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
    5. NoSQL approach to large scale analysis of persisted streams
    Open this publication in new window or tab >>NoSQL approach to large scale analysis of persisted streams
    2015 (English)In: Data Science, Springer, 2015, p. 152-156Conference paper, Published paper (Refereed)
    Place, publisher, year, edition, pages
    Springer, 2015
    Series
    Lecture Notes in Computer Science, ISSN 0302-9743 ; 9147
    National Category
    Computer Sciences
    Identifiers
    urn:nbn:se:uu:diva-274783 (URN)10.1007/978-3-319-20424-6_15 (DOI)000364104600015 ()978-3-319-20423-9 (ISBN)
    Conference
    BICOD 2015, July 6–8, Edinburgh, UK
    Available from: 2015-06-11 Created: 2016-01-26 Last updated: 2018-01-10Bibliographically approved
  • 4.
    Truong, Thanh
    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 Numerical Queries by Algebraic Inequality Transformations2014In: Database Systems for Advanced Applications, Dasfaa 2014, PT I, 2014, p. 95-109Conference 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.

  • 5.
    Truong, Thanh
    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.
    Transparent inclusion, utilization, and validation of main memory domain indexes2015In: Proc. 27th International Conference on Scientific and Statistical Database Management, New York: ACM Press, 2015Conference paper (Refereed)
    Abstract [en]

    Main-memory database systems (MMDBs) are viable solutions for many scientific applications. Scientific and engineering data often require special indexing methods, and there is a large number of domain specific main memory indexing implementations developed. However, adding an index structure into a database system can be challenging. Mexima (Main memory External Index Manager) provides an MMDB where new main-memory index structures can be plugged-in without modifying the index implementations. This has allowed to plug into Mexima complex and highly optimized index structures implemented in C/C++ without code changes. To utilize new user defined indexes in queries transparently, Mexima automatically transforms query fragments into index operations based on index properly tables containing index meta-data. For scalable processing of complex numerical query expressions, Mexima includes an algebraic query transformation mechanism that reasons on numerical expressions to expose potential utilization of indexes. The index property tables furthermore enable validating the correctness of an index implementation by executing automatically generated test queries based on index meta-data. Experiments show that the performance penalty of using an index plugged into Mexima is low compared to using the corresponding stand-alone C/C++ implementation. Substantial performance gains are shown by the index exposing rewrite mechanisms.

  • 6. Zhu, Minpeng
    et al.
    Stefanova, Silvia
    Truong, Thanh
    Risch, Tore
    Scalable Numerical SPARQL Queries over Relational Databases2014Conference paper (Refereed)
    Abstract [en]

    We present an approach for scalable processing of SPARQL queries to RDF views of numerical data stored in relational databases (RDBs). Such queries include numerical expressions, inequalities, comparisons, etc. inside FILTERs. We call such FILTERs numerical expressions and the queries - numerical SPARQL queries. For scalable execution of numerical SPARQL queries over RDBs, numerical operators should be pushed into SQL rather than executing the filters as post-processing outside the RDB; otherwise the query execution is slowed down, since a lot of data is transported from the RDB server and furthermore indexes on the server are not utilized. The NUMTranslator algorithm converts numerical expressions in numerical SPARQL queries into corresponding SQL expressions. We show that NUMTranslator improves substantially the scalability of SPARQL queries based on a benchmark that analyses numerical logs stored in an RDB. We compared the performance of our approach with the performance of other systems processing SPARQL queries to RDF views of RDBs and show that NUMTranslator improves substantially the scalability of numerical queries compared to the other systems’ approaches.

1 - 6 of 6
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  • fi-FI
  • nn-NO
  • nn-NB
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More languages
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