Stage-oe-small.jpg

Inproceedings3328: Unterschied zwischen den Versionen

Aus Aifbportal
Wechseln zu:Navigation, Suche
(Die Seite wurde neu angelegt: „{{Publikation Erster Autor |ErsterAutorNachname=Wagner |ErsterAutorVorname=Andreas }} {{Publikation Author |Rank=2 |Author=Veli Bicer }} {{Publikation Author |Ran…“)
 
 
Zeile 21: Zeile 21:
 
{{Publikation Details
 
{{Publikation Details
 
|Abstract=Many databases today are text-rich, comprising not only structured, but also textual data. Querying such databases involves predicates matching structured data combined with string predicates featuring textual constraints. Based on selectivity estimates for these predicates, query processing as well as other tasks that can be solved through such queries can be optimized. Existing work on selectivity estimation focuses either on string or on structured query predicates alone. Further, probabilistic models proposed to incorporate dependencies between predicates are focused on the re- lational setting. In this work, we propose a template-based probabilistic model, which enables selectivity estimation for general graph-structured data. Our probabilistic model allows dependencies between structured data and its text-rich parts to be captured. With this general probabilistic solution, BN+, selectivity estimations can be obtained for queries over text-rich graph-structured data, which may contain structured and string predicates (hybrid queries). In our experiments on real-world data, we show that capturing dependencies between structured and textual data in this way greatly improves the accuracy of selectivity estimates without compromising the efficiency.
 
|Abstract=Many databases today are text-rich, comprising not only structured, but also textual data. Querying such databases involves predicates matching structured data combined with string predicates featuring textual constraints. Based on selectivity estimates for these predicates, query processing as well as other tasks that can be solved through such queries can be optimized. Existing work on selectivity estimation focuses either on string or on structured query predicates alone. Further, probabilistic models proposed to incorporate dependencies between predicates are focused on the re- lational setting. In this work, we propose a template-based probabilistic model, which enables selectivity estimation for general graph-structured data. Our probabilistic model allows dependencies between structured data and its text-rich parts to be captured. With this general probabilistic solution, BN+, selectivity estimations can be obtained for queries over text-rich graph-structured data, which may contain structured and string predicates (hybrid queries). In our experiments on real-world data, we show that capturing dependencies between structured and textual data in this way greatly improves the accuracy of selectivity estimates without compromising the efficiency.
 +
|Download=Edbt_2013_select_estimation.pdf‎
 
|Projekt=IZEUS
 
|Projekt=IZEUS
 
|Forschungsgruppe=Wissensmanagement
 
|Forschungsgruppe=Wissensmanagement

Aktuelle Version vom 25. Februar 2013, 10:45 Uhr


Selectivity Estimation for Hybrid Queries over Text-Rich Data Graphs


Selectivity Estimation for Hybrid Queries over Text-Rich Data Graphs



Published: 2013 März

Buchtitel: International Conference on Extending Database Technology
Verlag: ACM

Referierte Veröffentlichung

BibTeX

Kurzfassung
Many databases today are text-rich, comprising not only structured, but also textual data. Querying such databases involves predicates matching structured data combined with string predicates featuring textual constraints. Based on selectivity estimates for these predicates, query processing as well as other tasks that can be solved through such queries can be optimized. Existing work on selectivity estimation focuses either on string or on structured query predicates alone. Further, probabilistic models proposed to incorporate dependencies between predicates are focused on the re- lational setting. In this work, we propose a template-based probabilistic model, which enables selectivity estimation for general graph-structured data. Our probabilistic model allows dependencies between structured data and its text-rich parts to be captured. With this general probabilistic solution, BN+, selectivity estimations can be obtained for queries over text-rich graph-structured data, which may contain structured and string predicates (hybrid queries). In our experiments on real-world data, we show that capturing dependencies between structured and textual data in this way greatly improves the accuracy of selectivity estimates without compromising the efficiency.

Download: Media:Edbt_2013_select_estimation.pdf‎

Projekt

IZEUS



Forschungsgruppe

Wissensmanagement


Forschungsgebiet

Semantische Suche