Prof. Dr. rer. nat. Peter Poensgen
- ehemaliger externer Doktorand -
Institut für Informationssysteme
Universität zu Lübeck
Ratzeburger Allee 160 ( Gebäude 64 - 2.OG )
D-23562 Lübeck
Dissertation unter der Betreuung von Herrn Prof. Ralf Möller
Thesis: Algorithmen und Indexstrukturen für Top-k-Anfragen mit quasi-konvexen oder quadratischen Bewertungsfunktionen
Curriculum Vitae
Akademisch:
- Juli 2020: Professor für IT-Management an der IUBH
- Januar 2018 - Juni 2019: externer Doktorand an der Universität zu Lübeck (Prüfung zum Dr. rer. nat. am 17.06.2019)
- November 2015: Diplom in Mathematik an der FernUniversität Hagen
Thesis: Hyperelliptische Kurven in der Kryptographie
Forschungsinteressen
- Anfrageverarbeitung und -optimierung
- Algorithmen und Indexstrukturen
- Datenanalyse
- Konvexe Optimierung
Publikationen
2020
- Peter Poensgen, Ralf Möller: Branch-and-Bound Ranked Search by Minimizing Parabolic Polynomials
in: Open J. Databases, 2020, Vol.7, (1), p.12-20
- Peter Poensgen, Ralf Möller: Quasi-Convex Scoring Functions in Branch-and-Bound Ranked Search
in: Open Journal of Databases (OJDB), 2020, Vol.7, (1), p.1-11
:@Article{OJDB_2020v7i1n01_Poensgen, author = {Peter Poensgen and Ralf M{\"o}ller}, title = {{Quasi-Convex Scoring Functions in Branch-and-Bound Ranked Search}}, journal = {Open Journal of Databases (OJDB)}, issn = {2199-3459}, year = {2020}, volume = {7}, number = {1}, pages = {1--11}, url = {https://www.ronpub.com/ojdb/OJDB_2020v7i1n01_Poensgen.html}, publisher = {RonPub}, abstract = {For answering top-k queries in which attributes are aggregated to a scalar value for defining a ranking, usually the well-known branch-and-bound principle can be used for efficient query answering. Standard algorithms (e.g., Branch-and-Bound Ranked Search, BRS for short) require scoring functions to be monotone, such that a top-k ranking can be computed in sublinear time in the average case. If monotonicity cannot be guaranteed, efficient query answering algorithms are not known. To make branch-and-bound effective with descending or ascending rankings (maximum top-k or minimum top-k queries, respectively), BRS must be able to identify bounds for exploring search partitions, and only for monotonic ranking functions this is trivial. In this paper, we investigate the class of quasi-convex functions used for scoring objects, and we examine how bounds for exploring data partitions can correctly and efficiently be computed for quasi-convex functions in BRS for maximum top-k queries. Given that quasi-convex scoring functions can usefully be employed for ranking objects in a variety of applications, the mathematical findings presented in this paper are indeed significant for practical top-k query answering.} }