Optimizing Sample Design for Approximate Query Processing

Philipp Rösch; Wolfgang Lehner

AutorIn: Philipp Rösch; Wolfgang Lehner
Titel: Optimizing Sample Design for Approximate Query Processing
Zitierfähige Url:: https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-729302
Quellenangabe: International Journal of Knowledge-Based Organizations Erscheinungsort: Hershey
Verlag: IGI Global

Erscheinungsjahr: 2013

Jahrgang: 3
Heft: 4
Seiten: 1-21
E-ISSN: 2155-6407
Erstveröffentlichung: 2013
Abstract (EN): The rapid increase of data volumes makes sampling a crucial component of modern data management systems. Although there is a large body of work on database sampling, the problem of automatically determine the optimal sample for a given query remained (almost) unaddressed. To tackle this problem the authors propose a sample advisor based on a novel cost model. Primarily designed for advising samples of a few queries specified by an expert, the authors additionally propose two extensions of the sample advisor. The first extension enhances the applicability by utilizing recorded workload information and taking memory bounds into account. The second extension increases the effectiveness by merging samples in case of overlapping pieces of sample advice. For both extensions, the authors present exact and heuristic solutions. Within their evaluation, the authors analyze the properties of the cost model and demonstrate the effectiveness and the efficiency of the heuristic solutions with a variety of experiments.
Andere Ausgabe: Link zum Artikel der zuerst in der Zeitschrift 'International Journal of Knowledge-Based Organizations' erschienen ist
DOI: 10.4018/ijkbo.2013100101
Freie Schlagwörter (DE): Beraterwerkzeuge, Informatik, Informationssysteme, Zufallsstichproben, Stichprobenfehlertypen
Freie Schlagwörter (EN): Advisor Tools, Computer Science, Information Systems, Random Sampling, Sampling Error Types
Klassifikation (DDC): 650
Verlag: IGI Global, Hershey
Version / Begutachtungsstatus: publizierte Version / Verlagsversion
URN Qucosa: urn:nbn:de:bsz:14-qucosa2-729302
Veröffentlichungsdatum Qucosa: 30.11.2020
Dokumenttyp: Artikel
Sprache des Dokumentes: Englisch
Lizenz / Rechtehinweis: https://rightsstatements.org/files/buttons/InC.dark-white-interior.svg/

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