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 be- gins with modeling and analysis of business objectives and specications, 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 rea- son about strategic objectives. We argue that an integrated system, able to receive formal specications of the organization's strategic objectives and to transform them into a set of queries that are continuously eval- uated against the warehouse, oers signicant benets. In this context, we propose the development of a set of real-time query answering mech- anisms 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
2012-01-01

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 be- gins with modeling and analysis of business objectives and specications, 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 rea- son about strategic objectives. We argue that an integrated system, able to receive formal specications of the organization's strategic objectives and to transform them into a set of queries that are continuously eval- uated against the warehouse, oers signicant benets. In this context, we propose the development of a set of real-time query answering mech- anisms 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.
2012
Proceedings of the 6th International Workshop on Business Intelligence for the Real Time Enterprise
AA. VV.
Berlin
Springer
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/11572/95228
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