Business Intelligence for Small and Middle-Sized Entreprises
Oksana Grabova (ERIC), J\'er\^ome Darmont (ERIC), Jean-Hugues Chauchat, (ERIC), Iryna Zolotaryova

TL;DR
This paper reviews web-based and in-memory business intelligence approaches suitable for small and medium-sized enterprises, focusing on lightweight, cost-effective solutions for real-time data analysis.
Contribution
It provides a comprehensive review of existing web-based and in-memory BI tools tailored for small and middle-sized enterprises, highlighting their suitability and limitations.
Findings
Web-based BI tools are suitable for small enterprises.
In-memory processing offers real-time analysis with lower storage costs.
Most existing solutions are adapted from large-scale enterprise systems.
Abstract
Data warehouses are the core of decision support sys- tems, which nowadays are used by all kind of enter- prises in the entire world. Although many studies have been conducted on the need of decision support systems (DSSs) for small businesses, most of them adopt ex- isting solutions and approaches, which are appropriate for large-scaled enterprises, but are inadequate for small and middle-sized enterprises. Small enterprises require cheap, lightweight architec- tures and tools (hardware and software) providing on- line data analysis. In order to ensure these features, we review web-based business intelligence approaches. For real-time analysis, the traditional OLAP architecture is cumbersome and storage-costly; therefore, we also re- view in-memory processing. Consequently, this paper discusses the existing approa- ches and tools working in main memory and/or with web interfaces…
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Taxonomy
TopicsCloud Computing and Resource Management · Advanced Database Systems and Queries · Big Data and Business Intelligence
