Handling of Uncertainty and Temporal Indeterminacy for What-if Analysis View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2011

AUTHORS

Katrin Eisenreich , Gregor Hackenbroich , Volker Markl , Philipp Rösch , Robert Schulze

ABSTRACT

Enabling experts to not only analyze current and historic data but also to evaluate the impact of decisions on the future state of the business greatly increased the value of decision support. However, the highly relevant aspect of representing and processing uncertain and temporally indeterminate data is often ignored in this context. Although the management of uncertainty has been researched intensely in the last decade, its role in decision support has not attracted much attention. We hold that not considering such information restricts the analyses users can run and the insights they can get into their data. In this paper, we complement large-scale data analyses with support for what-if analyses over uncertain and temporally indeterminate data. We use a histogram-based model to represent arbitrary uncertainty and temporal indeterminacy and allow its processing in a flexible manner using operators for analyzing, deriving, and modifying uncertainty in decision support tasks. We describe a prototypical implementation and approaches for parallelization on a commercial column store and present an initial evaluation of our solution. More... »

PAGES

100-115

Book

TITLE

Enabling Real-Time Business Intelligence

ISBN

978-3-642-22969-5
978-3-642-22970-1

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-22970-1_8

DOI

http://dx.doi.org/10.1007/978-3-642-22970-1_8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1036749266


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