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Scalable Numerical SPARQL Queries over Relational Databases
(UDBL)
(UDBL)
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.

Place, publisher, year, edition, pages
2014. 257-262 p.
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:uu:diva-275027OAI: oai:DiVA.org:uu-275027DiVA: diva2:898478
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: 2016-04-15
In thesis
1. Scalable Queries over Log Database Collections
Open this publication in new window or tab >>Scalable Queries over Log Database Collections
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In industrial settings, machines such as trucks, hydraulic pumps, etc. are widely distributed at different geographic locations where sensors on machines produce large volumes of data. The data produced is stored locally in autonomous databases called log databases. The collection of log databases is dynamically changing when new sites are dynamically added or removed from the federation.

In this application context, an efficient way to search and analyze passed behavior of products in use is desired. To enable scalable queries over collections of distributed and autonomous log databases we developed the FLOQ (Fused LOg database Query processor) system, which provides a global view of the working status of all machines on the sites through a meta-database integrating the dynamic log database collection. A particular challenge in this scenario is a scalable way to process numerical queries that identify anomalies by joining data from the meta-database with data selected from the collection of distributed and autonomous log databases. The Thesis describes the architecture of FLOQ. In particular different strategies to execute numerical queries over log database collections are investigated. FLOQ allows both the meta-database and the log databases to be stored in multiple formats using different kinds of data managers. FLOQ provides general and extensible mechanisms for efficient processing of queries over different kinds of distributed data sources.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2016. 51 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1343
National Category
Computer Science
Research subject
Computer Science with specialization in Database Technology
Identifiers
urn:nbn:se:uu:diva-275044 (URN)978-91-554-9472-8 (ISBN)
Public defence
2016-03-30, 2446, Department of Information Technology, Polacksbacken (Lägerhyddsvägen 2), Uppsala, 13:00 (English)
Opponent
Supervisors
Available from: 2016-03-03 Created: 2016-01-28 Last updated: 2016-03-03Bibliographically approved
2. 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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Zhu, MinpengTruong, Thanh

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