Estimating OLAP queries over uncertain and imprecise multidimensional data streams

Alfredo Cuzzocrea, Jiawei Han

Research output: Contribution to journalArticle

Abstract

In this paper, we propose a novel framework for estimating OLAP queries over uncertain and imprecise multidimensional data streams. The proposed framework introduces the following three relevant research contributions: (i) a probabilistic data stream model, which describes both precise and imprecise multidimensional data stream readings in terms of nice interval-confidence-based Probability Distribution Functions (PDF); (ii) a possible-worlds semantics for uncertain and imprecise multidimensional data streams, which is based on an innovative data-driven approach that exploits "natural" features of OLAP data, such as the presence of clusters and high correlations; (iii) an innovative approach for providing theoretically-founded estimates to OLAP queries over uncertain and imprecise multidimensional data streams that exploits the well-recognized probabilistic estimators theory. We complete our theoretical contributions throughout several case studies demonstrating the suitability of our proposed framework in the context of modern data stream applications and systems, which are more and more characterized by the presence of uncertainty and imprecision.

Original languageEnglish (US)
Pages (from-to)171-181
Number of pages11
JournalEngineering Intelligent Systems
Volume20
Issue number3
StatePublished - Sep 1 2012

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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