Data warehouses: Next challenges

9Citations
Citations of this article
43Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Data Warehouses are a fundamental component of today's Business Intelligence infrastructure. They allow to consolidate heterogeneous data from distributed data stores and transform it into strategic indicators for decision making. In this tutorial we give an overview of current state of the art and point out to next challenges in the area. In particular, this includes to cope with more complex data, both in structure and semantics, and keeping up with the demands of new application domains such as Web, financial, manufacturing, genomic, biological, life science, multimedia, spatial, and spatiotemporal applications. We review consolidated resaerch in spatio-temporal databases, and open research fields, like real-time Business Intelligence and Semantic Web Data Warehousing and OLAP. © 2012 Springer-Verlag.

Cite

CITATION STYLE

APA

Vaisman, A., & Zimányi, E. (2012). Data warehouses: Next challenges. In Lecture Notes in Business Information Processing (Vol. 96 LNBIP, pp. 1–26). Springer Verlag. https://doi.org/10.1007/978-3-642-27358-2_1

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free