Abstract Even though much research has been devoted on real-time data warehousing, most of it ignores business concerns that underlie all uses of such data. The complete Business Intelligence (BI) problem begins with modeling and analysis of business objectives and specifications, followed by a systematic derivation of real-time BI queries on warehouse data. In this position paper, we motivate the need for the development of a complete Real Time BI stack able to continuously evaluate and reason about strategic objectives. We argue that an integrated system, able to receive formal specifications of the organization's strategic objectives and to transform them into a set of queries that are continuously evaluated against the warehouse, offers significant benefits. In this context, we propose the development of a set of real-time query answering mechanisms able to identify warehouse segments with temporal patterns of special interest, as well as novel techniques for mining warehouse regions that represent expected, or unexpected threats and opportunities. With such a vision in mind, we propose an architecture for such a framework, and discuss relevant challenges and research directions.

Strategic Management for Real-Time Business Intelligence

Zoumpatianos, Konstantinos;Palpanas, Themistoklis;Mylopoulos, Ioannis
2013-01-01

Abstract

Abstract Even though much research has been devoted on real-time data warehousing, most of it ignores business concerns that underlie all uses of such data. The complete Business Intelligence (BI) problem begins with modeling and analysis of business objectives and specifications, followed by a systematic derivation of real-time BI queries on warehouse data. In this position paper, we motivate the need for the development of a complete Real Time BI stack able to continuously evaluate and reason about strategic objectives. We argue that an integrated system, able to receive formal specifications of the organization's strategic objectives and to transform them into a set of queries that are continuously evaluated against the warehouse, offers significant benefits. In this context, we propose the development of a set of real-time query answering mechanisms able to identify warehouse segments with temporal patterns of special interest, as well as novel techniques for mining warehouse regions that represent expected, or unexpected threats and opportunities. With such a vision in mind, we propose an architecture for such a framework, and discuss relevant challenges and research directions.
2013
AA. VV.
BIRTE 2012
Berlin
Berlin: Springer-Verlag
9783642398711
9783642398728
Zoumpatianos, Konstantinos; Palpanas, Themistoklis; Mylopoulos, Ioannis
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/66716
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