A new framework for join product skew

Kyritsis, V; Lekeas, PV; Souliou, D; Afrati, F

dc.contributor.author	Kyritsis, V	en
dc.contributor.author	Lekeas, PV	en
dc.contributor.author	Souliou, D	en
dc.contributor.author	Afrati, F	en
dc.date.accessioned	2014-03-01T02:53:32Z
dc.date.available	2014-03-01T02:53:32Z
dc.date.issued	2012	en
dc.identifier.issn	03029743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/36398
dc.subject	data distribution	en
dc.subject	data skew	en
dc.subject	join operation	en
dc.subject	load balance	en
dc.subject	Parallel DBMS	en
dc.subject	shared nothing architecture	en
dc.subject.other	Data distribution	en
dc.subject.other	Data skew	en
dc.subject.other	Join operation	en
dc.subject.other	Load balance	en
dc.subject.other	Parallel dbms	en
dc.subject.other	Shared nothing	en
dc.subject.other	Artificial intelligence	en
dc.title	A new framework for join product skew	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/978-3-642-27392-6_1	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-642-27392-6_1	en
heal.publicationDate	2012	en
heal.abstract	Different types of data skew can result in load imbalance in the context of parallel joins under the shared nothing architecture. We study one important type of skew, join product skew (JPS). A static approach based on frequency classes is proposed which takes for granted the data distribution of join attribute values. It comes from the observation that the join selectivity can be expressed as a sum of products of frequencies of the join attribute values. As a consequence, an appropriate assignment of join sub-tasks that takes into consideration the magnitude of the frequency products can alleviate the join product skew. Motivated by the aforementioned remark, we propose an algorithm, called Handling Join Product Skew (HJPS), to handle join product skew. © 2012 Springer-Verlag.	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
dc.identifier.doi	10.1007/978-3-642-27392-6_1	en
dc.identifier.volume	6799 LNCS	en
dc.identifier.spage	1	en
dc.identifier.epage	10	en