Informatik - Forschung und Entwicklung

, Volume 20, Issue 3, pp 121–137 | Cite as

Efficient interval management using object-relational database servers

  • Christoph Brochhaus
  • Jost Enderle
  • Achim Schlosser
  • Thomas Seidl
  • Knut Stolze
Original Article

Zusammenfassung

Benutzerdefinierte Datentypen wie beispielsweise Intervalle setzen zur effizienten Realisierung von Suchanfragen spezialisierte Zugriffsmethoden voraus. Da nicht für jeden denkbaren Datentyp datenbankseitig auch die entsprechenden Indexstrukturen und passenden Zugriffs- und Anfragemethoden zur Verfügung gestellt werden können, bieten moderne objekt-relationale Datenbanksysteme erweiterbare Indexschnittstellen an, die den Entwicklern die Möglichkeit geben, die eingebauten Indexstrukturen um maßgeschneiderte Zugriffsmethoden zu erweitern. Obwohl diese Schnittstellen die nahtlose Integration von benutzerdefinierten Indexierungstechniken in die Anfragebearbeitung ermöglichen, erleichtern sie nicht die eigentliche Implementierung der tatsächlichen Zugriffsmethode. Um die Vorteile dieser Schnittstellen zu nutzen, verlassen sich Zugriffsmethoden wie beispielsweise der Relationale Intervallbaum (RI-Baum), eine Indexstruktur zur effizienten Bearbeitung von Intervallschnittanfragen, hauptsächlich auf die Funktionalität, Robustheit und Leistung von eingebauten Indexen, wodurch die Indeximplementierung wesentlich vereinfacht wird. Um das Verhalten und die Leistung des kürzlich veröffentlichten IBM DB2 Indexing Framework zu untersuchen, wurde der RI-Baum in den DB2-Datenbankserver mittels dieser Schnittstelle integriert. Die Standardimplementation des RI-Baums jedoch genügt nicht den restriktiven Anforderungen der DB2-Schnittstelle, welche nur die Verwendung eines einzelnen Indexes zulässt. Daher wird hier sowohl eine Adaption der ursprünglichen Zwei-Index-Technik gemäß der Einschränkung auf einen Index vorgestellt als auch eine approximierte Version, welche konzeptionell nur einen einzelnen Index benötigt. Experimentelle Ergebnisse zeigen, dass die auf diese Weise integrierten Zugriffsmethoden verglichen mit anderen Techniken exzellente Leistungswerte aufweisen.

Schlüsselwörter

Relationale Indexierung Relationale Datenbanken Intervallverwaltung Relationaler Intervallbaum (RI-Baum) 

Abstract

User-defined data types such as intervals require specialized access methods to be efficiently searched and queried. As database implementors cannot provide appropriate index structures and query processing methods for each conceivable data type, present-day object-relational database systems offer extensible indexing frameworks that enable developers to extend the set of built-in index structures by custom access methods. Although these frameworks permit a seamless integration of user-defined indexing techniques into query processing they do not facilitate the actual implementation of the access method itself. In order to leverage the applicability of indexing frameworks, relational access methods such as the Relational Interval Tree (RI-tree), an efficient index structure to process interval intersection queries, mainly rely on the functionality, robustness and performance of built-in indexes, thus simplifying the index implementation significantly. To investigate the behavior and performance of the recently released IBM DB2 indexing framework we use this interface to integrate the RI-tree into the DB2 server. The standard implementation of the RI-tree, however, does not fit to the narrow corset of the DB2 framework which is restricted to the use of a single index only. We therefore present our adaptation of the original two-tree technique to the single index constraint as well as an approximate adaptation which conceptually only needs a single index. As experimental results with interval intersection queries show, the plugged-in access methods deliver excellent performance compared to other techniques.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Literatur

  1. 1.
    http://www-i9.informatik.rwth-aachen.de/ritreeGoogle Scholar
  2. 2.
    IBM Corp (2002) IBM DB2 Universal Database Application Development Guide, Version 8Google Scholar
  3. 3.
    IBM Corp (2003) IBM Informix Virtual-Index Interface Programmer’s Guide, Version 9.4 Armonk, NYGoogle Scholar
  4. 4.
    Oracle Corp. (2004) Oracle Data Cartridge Developers Guide, 10g Release 1 (10.1.0.2.0), Redwood City, CAGoogle Scholar
  5. 5.
    Adler DW (2001) DB2Spatial Extender – spatial data within the RDBMS. In: Proceedings of 27th International Conference on Very Large Data Bases, pp 687–690Google Scholar
  6. 6.
    Ang C-H, Tan K-P (1995) The interval B-tree. Inf Process Lett 53(2):85–89Google Scholar
  7. 7.
    Arge L, Chatham A (2003) Efficient object-relational interval management and beyond. In: Proc Int Symp on Spatial and Temporal DatabasesGoogle Scholar
  8. 8.
    Bayer R (1997) The universal b-tree for multidimensional indexing: general concepts. In: Proc of WWCA ’97, Tsukuba, Japan, pp 198–209Google Scholar
  9. 9.
    Bayer R, McCreight EM (1972) Organization and maintenance of large ordered indices. Acta Inf 1:173–189Google Scholar
  10. 10.
    Bliujute R, Saltenis S, Slivinskas G, Jensen CS (1999) Developing a datablade for a new index. In: Proceedings of the 15th International Conference on Data Engineering, pp 314–323Google Scholar
  11. 11.
    Bozkaya T, Özsoyoglu Z (1998) Indexing valid time intervals. In: Proc Int Conf on Database and Expert Systems Applications, pp 541–550Google Scholar
  12. 12.
    Chen W, Chow J-H, Fuh Y-C, Grandbois J, Jou M, Mendonça Mattos N, Tran BT, Wang Y (1999) High level indexing of user-defined types. In: Proceedings of 25th International Conference on Very Large Data Bases. Morgan Kaufmann, pp 554–564Google Scholar
  13. 13.
    Edelsbrunner H (1980) Dynamic rectangle intersection searching. Inst for Information Processing Report 47, Technical University of Graz, AustriaGoogle Scholar
  14. 14.
    Edelsbrunner H (1983) A new approach to rectangle intersections. Int J Comput Math 13:209–229Google Scholar
  15. 15.
    Elmasri R, Wuu GTJ, Kim Y-J (1990) The Time Index: An access structure for temporal data. In: Proceedings of the International Conference on Very Large Data Bases, pp 1–12Google Scholar
  16. 16.
    Enderle J, Hampel M, Seidl T (2004) Joining interval data in relational databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, Paris, France, pp 683–694Google Scholar
  17. 17.
    Faloutsos C, Roseman S (1989) Fractals for secondary key retrieval. In: Proceedings of the Eighth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. ACM Press, pp 247–252Google Scholar
  18. 18.
    Fenk R, Markl V, Bayer R (2002) Interval processing with the ub-tree. In: Proc of IDEAS’02, Edmonton, Canada, pp 12–22Google Scholar
  19. 19.
    Goh CH, Lu H, Ooi BC, Tan K-L (1996) Indexing temporal data using existing B+-trees. Data Knowl Eng 18(2):147–165Google Scholar
  20. 20.
    Graefe G (2003) Partitioned B-trees – a user’s guide. In: Proc 10th GI-Conf. on Database Systems for Business, Technology, and the Web (BTW), pp 668–671Google Scholar
  21. 21.
    Graefe G (2004) Write-optimized b-trees. In: Proc of the International Conference on Very Large Data Bases, Toronto, Canada, pp 672–683Google Scholar
  22. 22.
    ISO/IEC (2003) 9075-2:2003. Information Technology – Database Languages – SQL – Part 2: Foundation (SQL/Foundation)Google Scholar
  23. 23.
    ISO/IEC (2003) 9075-3:2003. Information Technology – Database Languages – SQL Multimedia and Application Packages – Part 3: SpatialGoogle Scholar
  24. 24.
    Kornacker M (1999) High-performance extensible indexing. In: Proceedings of 25th International Conference on Very Large Data Bases, pp 699–708Google Scholar
  25. 25.
    Kriegel H-P, Pfeifle M, Pötke M, Seidl T (2002) A cost model for interval intersection queries on ri-trees. In: Proceedings of the 14th International Conference on Scientific and Statistical Database Management, 2002, Edinburgh, Scotland, UK, pp 131–141Google Scholar
  26. 26.
    Kriegel H-P, Pfeifle M, Pötke M, Seidl T (2003) The paradigm of relational indexing: a survey. In: Proc 10th GI-Conf on Database Systems for Business, Technology, and the Web (BTW), pp 285–304Google Scholar
  27. 27.
    Kriegel H-P, Pfeifle M, Pötke M, Seidl T, Enderle J (2004) Object-relational spatial indexing. In: Manolopoulos Y, Papadopoulos A, Vassilakopoulos M (eds) Spatial Databases: Technologies, Techniques and Trends. Idea Group Publishing, pp 49–80Google Scholar
  28. 28.
    Kriegel H-P, Pötke M, Seidl T (2000) Managing intervals efficiently in object-relational databases. In: Proceedings of 26th International Conference on Very Large Data Bases. Morgan Kaufmann, pp 407–418Google Scholar
  29. 29.
    Kriegel H-P, Pötke M, Seidl T (2001) Interval sequences: An object-relational approach to manage spatial data. In: Advances in Spatial and Temporal Databases, 7th International Symposium, SSTD 2001, Redondo Beach, CA, USA, July 12–15, 2001, Proceedings, pp 481–501Google Scholar
  30. 30.
    Kriegel H-P, Pötke M, Seidl T (2001) Object-relational indexing for general interval relationships. In: Advances in Spatial and Temporal Databases, 7th International Symposium, SSTD 2001, Redondo Beach, CA, USA, July 12–15, 2001, Proceedings, pp 522–542Google Scholar
  31. 31.
    Lomet DB (2004) Simple, robust and highly concurrent b-trees with node deletion. In: Proceedings of the 20th International Conference on Data Engineering, Boston, MA, USA, pp 18–28Google Scholar
  32. 32.
    McCreight EM (1980) Efficient algorithms for enumerating intersecting intervals and rectangles. XEROX Palo Alto Research CenterGoogle Scholar
  33. 33.
    Nascimento MA, Dunham MH (1999) Indexing valid time databases via B+-trees. IEEE Trans Knowl Data Eng 11(6):929–947Google Scholar
  34. 34.
    Ramaswamy S (1997) Efficient indexing for constraint and temporal databases. In: Database Theory – ICDT ’97, 6th International Conference, Delphi, Greece, January 8–10, 1997, Proceedings, Lecture Notes in Computer Science, vol 1186. Springer, pp 419–431Google Scholar
  35. 35.
    Ramsak F, Markl V, Fenk R, Zirkel M, Elhardt K, Bayer R (2000) Integrating the ub-tree into a database system kernel. In: Proceedings of 26th International Conference on Very Large Data Bases, pp 263–272Google Scholar
  36. 36.
    Shen H, Ooi BC, Lu H (1994) The TP-Index: A dynamic and efficient indexing mechanism for temporal databases. In: Proceedings of the Tenth International Conference on Data Engineering. IEEE Computer Society, pp 274–281Google Scholar
  37. 37.
    Snodgrass RT, Ahn I (1985) A taxonomy of time in databases. In: SIGMOD Conference, pp 236–246Google Scholar
  38. 38.
    Srinivasan J, Murthy R, Sundara S, Agarwal N, DeFazio S (2000) Extensible indexing: A framework for integrating domain-specific indexing schemes into Oracle8i. In: Proc 16th Int Conf on Data Engineering, pp 91–100Google Scholar
  39. 39.
    Steinbach T, Stolze K (2003) Index extensions by example and in detail. DB2 Developer DomainGoogle Scholar
  40. 40.
    Stolze K (2003) SQL/MM Spatial – the standard to manage spatial data in a relational database system. In: Proc 10th GI-Conf on Database Systems for Business, Technology, and the Web (BTW), pp 247–264Google Scholar
  41. 41.
    Stonebraker M (1986) Inclusion of new types in relational data base systems. In: Proceedings of the Second International Conference on Data Engineering. IEEE Computer Society, pp 262–269Google Scholar

Copyright information

© Springer-Verlag 2005

Authors and Affiliations

  • Christoph Brochhaus
    • 1
  • Jost Enderle
    • 1
  • Achim Schlosser
    • 1
  • Thomas Seidl
    • 1
  • Knut Stolze
    • 2
    • 3
  1. 1.Department of Computer Science IX (Data Management and Exploration Group)RWTH Aachen UniversityAachenGermany
  2. 2.Information Integration DevelopmentIBM GermanyGermany
  3. 3.Database and Information Systems GroupUniversity of JenaJenaGermany

Personalised recommendations