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Database Technology for Processing Temporal Data (Invited Paper)

Authors Michael H. Böhlen , Anton Dignös , Johann Gamper , Christian S. Jensen

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Author Details

Michael H. Böhlen
  • University of Zurich, Switzerland
Anton Dignös
  • Free University of Bozen-Bolzano, Italy
Johann Gamper
  • Free University of Bozen-Bolzano, Italy
Christian S. Jensen
  • Aalborg University, Denmark

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Michael H. Böhlen, Anton Dignös, Johann Gamper, and Christian S. Jensen. Database Technology for Processing Temporal Data (Invited Paper). In 25th International Symposium on Temporal Representation and Reasoning (TIME 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 120, pp. 2:1-2:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018) https://doi.org/10.4230/LIPIcs.TIME.2018.2

Abstract

Despite the ubiquity of temporal data and considerable research on processing such data, database systems largely remain designed for processing the current state of some modeled reality. More recently, we have seen an increasing interest in processing historical or temporal data. The SQL:2011 standard introduced some temporal features, and commercial database management systems have started to offer temporal functionalities in a step-by-step manner. There has also been a proposal for a more fundamental and comprehensive solution for sequenced temporal queries, which allows a tight integration into relational database systems, thereby taking advantage of existing query optimization and evaluation technologies. New challenges for processing temporal data arise with multiple dimensions of time and the increasing amounts of data, including time series data that represent a special kind of temporal data.

Subject Classification

ACM Subject Classification
  • Information systems → Data management systems
  • Information systems → Temporal data
Keywords
  • Temporal databases
  • temporal query processing
  • sequenced semantics
  • SQL

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References

  1. Mikkel Agesen, Michael H. Böhlen, Lasse Poulsen, and Kristian Torp. A split operator for now-relative bitemporal databases. In Proceedings of the 17th International Conference on Data Engineering, ICDE 2001, pages 41-50, 2001. URL: http://dx.doi.org/10.1109/ICDE.2001.914812.
  2. Mohammed Al-Kateb, Ahmad Ghazal, Alain Crolotte, Ramesh Bhashyam, Jaiprakash Chimanchode, and Sai Pavan Pakala. Temporal query processing in teradata. In Proceedings of the 16th International Conference on Extending Database Technology, EDBT 2013, pages 573-578, 2013. URL: http://dx.doi.org/10.1145/2452376.2452443.
  3. Michael H. Böhlen, Anton Dignös, Johann Gamper, and Christian S. Jensen. Temporal data management - an overview. In Esteban Zimányi, editor, Business Intelligence and Big Data - 7th European Summer School, eBISS 2017, Bruxelles, Belgium, July 2-7, 2017, Tutorial Lectures, volume 324 of Lecture Notes in Business Information Processing, pages 51-83. Springer, 2017. URL: http://dx.doi.org/10.1007/978-3-319-96655-7_3.
  4. Michael H. Böhlen, Johann Gamper, and Christian S. Jensen. Multi-dimensional aggregation for temporal data. In Proceedings of the 10th International Conference on Extending Database Technology, EDBT 2006, volume 3896 of Lecture Notes in Computer Science, pages 257-275. Springer, 2006. URL: http://dx.doi.org/10.1007/11687238_18.
  5. Michael H. Böhlen and Christian S. Jensen. Temporal data model and query language concepts. In Encyclopedia of Information Systems, pages 437-453. Elsevier, 2003. URL: http://dx.doi.org/10.1016/B0-12-227240-4/00184-2.
  6. Michael H. Böhlen, Christian S. Jensen, and Richard T. Snodgrass. Temporal statement modifiers. ACM Trans. Database Syst., 25(4):407-456, 2000. URL: http://portal.acm.org/citation.cfm?id=377674.377665.
  7. Panagiotis Bouros and Nikos Mamoulis. A forward scan based plane sweep algorithm for parallel interval joins. PVLDB, 10(11):1346-1357, 2017. URL: http://www.vldb.org/pvldb/vol10/p1346-bouros.pdf, URL: http://dx.doi.org/10.14778/3137628.3137644.
  8. Francesco Cafagna and Michael H. Böhlen. Disjoint interval partitioning. The VLDB J., 26(3):447-466, 2017. URL: http://dx.doi.org/10.1007/s00778-017-0456-7.
  9. Anton Dignös, Michael H. Böhlen, and Johann Gamper. Temporal alignment. In Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2012, pages 433-444, 2012. URL: http://dx.doi.org/10.1145/2213836.2213886.
  10. Anton Dignös, Michael H. Böhlen, and Johann Gamper. Overlap interval partition join. In Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2014, pages 1459-1470, 2014. URL: http://dx.doi.org/10.1145/2588555.2612175.
  11. Anton Dignös, Michael H. Böhlen, Johann Gamper, and Christian S. Jensen. Extending the kernel of a relational DBMS with comprehensive support for sequenced temporal queries. ACM Trans. Database Syst., 41(4):26:1-26:46, 2016. URL: http://dx.doi.org/10.1145/2967608.
  12. Mohamed Y. Eltabakh, Ramy Eltarras, and Walid G. Aref. Space-partitioning trees in postgresql: Realization and performance. In Ling Liu, Andreas Reuter, Kyu-Young Whang, and Jianjun Zhang, editors, Proceedings of the 22nd International Conference on Data Engineering, ICDE 2006, 3-8 April 2006, Atlanta, GA, USA, page 100. IEEE Computer Society, 2006. URL: http://dx.doi.org/10.1109/ICDE.2006.146.
  13. Dengfeng Gao, Christian S. Jensen, Richard T. Snodgrass, and Michael D. Soo. Join operations in temporal databases. The VLDB J., 14(1):2-29, 2005. URL: http://dx.doi.org/10.1007/s00778-003-0111-3.
  14. Joseph M. Hellerstein. Generalized search tree. In Ling Liu and M. Tamer Özsu, editors, Encyclopedia of Database Systems, pages 1222-1224. Springer US, 2009. URL: http://dx.doi.org/10.1007/978-0-387-39940-9_743.
  15. Christian S. Jensen, Curtis E. Dyreson, Michael H. Böhlen, James Clifford, Ramez Elmasri, Shashi K. Gadia, Fabio Grandi, Patrick J. Hayes, Sushil Jajodia, Wolfgang Käfer, Nick Kline, Nikos A. Lorentzos, Yannis G. Mitsopoulos, Angelo Montanari, Daniel A. Nonen, Elisa Peressi, Barbara Pernici, John F. Roddick, Nandlal L. Sarda, Maria Rita Scalas, Arie Segev, Richard T. Snodgrass, Michael D. Soo, Abdullah Uz Tansel, Paolo Tiberio, and Gio Wiederhold. The consensus glossary of temporal database concepts. In Temporal Databases, Dagstuhl, pages 367-405, 1997. URL: http://dx.doi.org/10.1007/BFb0053710.
  16. Christian S. Jensen, Richard T. Snodgrass, and Michael D. Soo. Extending existing dependency theory to temporal databases. IEEE Trans. Knowl. Data Eng., 8(4):563-582, 1996. URL: http://dx.doi.org/10.1109/69.536250.
  17. Martin Kaufmann, Amin Amiri Manjili, Panagiotis Vagenas, Peter M. Fischer, Donald Kossmann, Franz Färber, and Norman May. Timeline index: a unified data structure for processing queries on temporal data in SAP HANA. In Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2013, pages 1173-1184, 2013. URL: http://dx.doi.org/10.1145/2463676.2465293.
  18. Martin Kaufmann, Panagiotis Vagenas, Peter M. Fischer, Donald Kossmann, and Franz Färber. Comprehensive and interactive temporal query processing with SAP HANA. PVLDB, 6(12):1210-1213, 2013. URL: http://www.vldb.org/pvldb/vol6/p1210-kaufmann.pdf, URL: http://dx.doi.org/10.14778/2536274.2536278.
  19. Nick Kline and Richard T. Snodgrass. Computing temporal aggregates. In Proceedings of the 11th International Conference on Data Engineering, ICDE 1995, pages 222-231, 1995. URL: http://dx.doi.org/10.1109/ICDE.1995.380389.
  20. Krishna G. Kulkarni and Jan-Eike Michels. Temporal features in SQL: 2011. SIGMOD Record, 41(3):34-43, 2012. URL: http://dx.doi.org/10.1145/2380776.2380786.
  21. Inés Fernando Vega López, Richard T. Snodgrass, and Bongki Moon. Spatiotemporal aggregate computation: a survey. IEEE Trans. Knowl. Data Eng., 17(2):271-286, 2005. URL: http://dx.doi.org/10.1109/TKDE.2005.34.
  22. Nikos A. Lorentzos and Yannis G. Mitsopoulos. SQL extension for interval data. IEEE Trans. Knowl. Data Eng., 9(3):480-499, 1997. URL: http://dx.doi.org/10.1109/69.599935.
  23. Microsoft. SQL Server 2016 - temporal tables. https://docs.microsoft.com/en-us/sql/relational-databases/tables/temporal-tables, 2016.
  24. Bongki Moon, Inés Fernando Vega López, and Vijaykumar Immanuel. Efficient algorithms for large-scale temporal aggregation. IEEE Trans. Knowl. Data Eng., 15(3):744-759, 2003. URL: http://dx.doi.org/10.1109/TKDE.2003.1198403.
  25. Oracle. Database development guide - temporal validity support. https://docs.oracle.com/database/121/ADFNS/adfns_design.htm#ADFNS967, 2016.
  26. Dusan Petkovic. Temporal data in relational database systems: A comparison. In New Advances in Information Systems and Technologies - Volume 1, volume 444 of Advances in Intelligent Systems and Computing, pages 13-23. Springer, 2016. URL: http://dx.doi.org/10.1007/978-3-319-31232-3_2.
  27. Danila Piatov, Sven Helmer, and Anton Dignös. An interval join optimized for modern hardware. In Proceedings of the 32nd International Conference on Data Engineering, ICDE 2016, pages 1098-1109, 2016. URL: http://dx.doi.org/10.1109/ICDE.2016.7498316.
  28. PostgreSQL Global Development Group. Documentation manual PostgreSQL - range types. http://www.postgresql.org/docs/9.2/static/rangetypes.html, 2012.
  29. Cynthia Saracco, Matthias Nicola, and Lenisha Gandhi. A matter of time: Temporal data management in DB2 10. http://www.ibm.com/developerworks/data/library/techarticle/dm-1204db2temporaldata/dm-1204db2temporaldata-pdf.pdf, 2012.
  30. Richard T. Snodgrass, editor. The TSQL2 Temporal Query Language. Kluwer, 1995. Google Scholar
  31. David Toman. Point vs. interval-based query languages for temporal databases. In Proceedings of the 15th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, PODS 1996, pages 58-67, 1996. URL: http://dx.doi.org/10.1145/237661.237676.
  32. Jun Yang and Jennifer Widom. Incremental computation and maintenance of temporal aggregates. The VLDB J., 12(3):262-283, 2003. URL: http://dx.doi.org/10.1007/s00778-003-0107-z.
  33. Fred Zemke. Whats new in SQL: 2011. SIGMOD Record, 41(1):67-73, 2012. URL: http://dx.doi.org/10.1145/2206869.2206883.
  34. Donghui Zhang, Alexander Markowetz, Vassilis J. Tsotras, Dimitrios Gunopulos, and Bernhard Seeger. Efficient computation of temporal aggregates with range predicates. In Proceedings of the 20th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, PODS 2001, 2001. URL: http://dx.doi.org/10.1145/375551.375600.
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