Advertisement

Datenbank-Spektrum

, Volume 15, Issue 3, pp 223–228 | Cite as

Die Arbeitsgruppe Datenbanksysteme an der Philipps-Universität Marburg

  • Bernhard Seeger
DATENBANKGRUPPEN VORGESTELLT
  • 192 Downloads

Zusammenfassung

In diesem Beitrag wird die Arbeitsgruppe Datenbanksysteme an der Universität Marburg vorgestellt. Es wird ein kurzer historischer Rückblick gegeben und auf aktuelle Forschungsgebiete eingegangen. Zudem wird das Engagement der Arbeitsgruppe in der Lehre dargestellt und die Kooperation mit anderen Wissenschaftlern kurz skizziert.

Notes

Acknowledgement

Mein Dank geht an meine derzeitigen wissenschaftlichen Mitarbeiter im Team: Christian Authmann, Christian Beilschmidt, Johannes Drönner, Dr. Bastian Hoßbach, Michael Mattig und Marc Seidemann. Mein besonderer Dank geht an Frau Keßler, die uns tatkräftig bei allen organisatorischen Aufgaben und anderen Dingen stets bestens unterstützt.

Literatur

  1. 1.
    Achakeev D, Seeger B (2013) Efficient bulk updates on multiversion b-trees. PVLDB 6(14):1834–1845Google Scholar
  2. 2.
    Achakeev D, Seeger B, Widmayer P (2012) Sort-based query-adaptive loading of r-trees. In: 21st ACM International Conference on Information and Knowledge Management, CIKM'12, Maui, HI, USA, October 29 - November 02, 2012, S 2080–2084Google Scholar
  3. 3.
    Achakeev D, Seidemann M, Schmidt M, Seeger B (2012) Sort-based parallel loading of r-trees. In: Proceedings of the 1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, ACM, S 62–70Google Scholar
  4. 4.
    Alexandrov A, Bergmann R, Ewen S, Freytag JC, Hueske F, Heise A, Kao O, Leich M, Leser U, Markl V et al (2014) The stratosphere platform for big data analytics. VLDB J 23(6):939–964Google Scholar
  5. 5.
    Arge L (1995) The buffer tree: a new technique for optimal I/O-algorithms. LNCS 955, Springer, BerlinGoogle Scholar
  6. 6.
    Authmann C, Beilschmidt C, Drönner J, Mattig M, Seeger B (2015) Rethinking spatial processing in data-intensive science. BTW 2015, WorkshopGoogle Scholar
  7. 7.
    Authmann C, Beilschmidt C, Drönner J, Mattig M, Seeger B (2015) VAT: a system for visualizing, analyzing and transforming spatial data in science. Datenbank-Spektrum 15(3)Google Scholar
  8. 8.
    Babcock B, Babu S, Datar M, Motwani R, Widom J (2002) Models and issues in data stream systems. In: Proceedings of the Twenty-first ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems; June 3-5; Madison, Wisconsin, USA, S 1–16Google Scholar
  9. 9.
    Bach K, Schäfer D, Enke N et al (2012) A comparative evaluation of technical solutions for long-term data repositories in integrative biodiversity research. Ecol Inform 11:16–24Google Scholar
  10. 10.
    Barga RS, Goldstein J, Ali MH, Hong M (2007) Consistent streaming through time: a vision for event stream processing. In: CIDR 2007, Third Biennial Conference on Innovative Data Systems Research, Asilomar, CA, USA, January 7-10, 2007; Online Proceedings, S 363–374Google Scholar
  11. 11.
    Baumgärtner L, Strack C, Hoßbach B, Seidemann M, Seeger B, Freisleben B (2015) Complex event processing for reactive security monitoring in virtualized computer systems. In: Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems, DEBS '15, Oslo, Norway, June 29 - July 3, S 22–33Google Scholar
  12. 12.
    Becker B, Gschwind S, Ohler T, Seeger B, Widmayer P (1996) An asymptotically optimal multiversion b-tree. VLDB J 5(4):264–275Google Scholar
  13. 13.
    Bender MA, Farach-Colton M, Fineman JT, Fogel YR, Kuszmaul BC, Nelson J (2007) Cache-oblivious streaming b-trees. In: Proceedings of the nineteenth annual ACM symposium on Parallel algorithms and architectures. ACM, S 81–92Google Scholar
  14. 14.
    den Bercken JV, Blohsfeld B, Dittrich J, Krämer J, Schäfer T, Schneider M, Seeger B (2001) XXL – a library approach to supporting efficient implementations of advanced database queries. In: VLDB 2001, Proceedings of 27th International Conference on Very Large Data Bases, September 11-14; 2001, Roma, Italy, S 39–48Google Scholar
  15. 15.
    Brown PG (2010) Overview of sciDB: large scale array storage, processing and analysis. In: Proc. of the ACM SIGMOD Conference, S 963–968Google Scholar
  16. 16.
    Cammert M, Heinz C, Krämer J, Riemenschneider T, Schwarzkopf M, Seeger B, Zeiss A (2006) Stream processing in production-to-business software. In: Proceedings of the 22nd International Conference on Data Engineering, ICDE 2006, 3-8 April 2006, Atlanta, GA, USA, S 168Google Scholar
  17. 17.
    Cuevas-Vicentt&00ED#;n V, Dey S et al (2012) Scientific workflows and provenance: introduction and research opportunities. Datenbank-Spektrum 12(3):193–203Google Scholar
  18. 18.
    Diepenbroek M, Glöckner F, Grobe P et al (2014) Towards an integrated biodiversity and ecological research data management and archiving platform: the German Federation for the Curation of Biological Data (GFBio). GI: Informatik 2014 – Big Data Komplexität meisternGoogle Scholar
  19. 19.
    Enke N, Thessen A, Bach K, Bendix J, Seeger B, Gemeinholzer B (2012) The user’s view on biodiversity data sharing – investigating facts of acceptance and requirements to realize a sustainable use of research data –. Ecol Inform 11:25–33. (Data platforms in integrative biodiversity research)Google Scholar
  20. 20.
    Glombiewski N, Hoßbach B, Morgen A, Ritter F, Seeger B (2013) Event processing on your own database. In: Datenbanksysteme für Business, Technologie und Web (BTW), - Workshopband, 15. Fachtagung des GI-Fachbereichs, ‚Datenbanken und Informationssysteme‛' (DBIS), 11.–15.3.2013 in Magdeburg, Germany. Proceedings, S 33–42Google Scholar
  21. 21.
    Hoßbach B, Glombiewski N, Morgen A, Ritter F, Seeger B (2013) JEPC: the java event processing connectivity. Datenbank-Spektrum 13(3):167–178Google Scholar
  22. 22.
    Krämer J, Seeger B (2004) PIPES - a public infrastructure for processing and exploring streams. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, Paris, France, June 13-18, S 925–926Google Scholar
  23. 23.
    Krämer J, Seeger B (2005) A temporal foundation for continuous queries over data streams. In: Advances in Data Management 2005, Proceedings of the Eleventh International Conference on Management of Data, January 6, 7, and 8, 2005, Goa, India, S 70–82Google Scholar
  24. 24.
    Krämer J, Seeger B (2009) Semantics and implementation of continuous sliding window queries over data streams. ACM Trans Database Syst 34(1)4–49Google Scholar
  25. 25.
    O’Neil P, Cheng E, Gawlick D, O’Neil E (1996) The log-structured merge-tree (lsm-tree). Acta Inform 33(4):351–385Google Scholar
  26. 26.
    Pinnecke M, Hoßbach B (2015) Query optimization in heterogenous event processing federations. Datenbank-Spektrum 15(3):81–90Google Scholar
  27. 27.
    Reichman O, Jones MB, Schildhauer MP (2011) Challenges and opportunities of open data in ecology. Science 331(6018):703–705Google Scholar
  28. 28.
    Snodgrass RT (1987) The temporal query language tquel. ACM Trans Database Syst 12(2):247–298Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Fachbereich Mathematik und InformatikPhilipps-Universität MarburgMarburgDeutschland

Personalised recommendations