Intelligence metasynthesis in building business intelligence systems

  • Authors:

  • Longbing Cao;Chengqi Zhang;Dan Luo;Ruwei Dai

  • Affiliations:

  • Faculty of Information Technology, University of Technology, Sydney, Australia;Faculty of Information Technology, University of Technology, Sydney, Australia;Faculty of Information Technology, University of Technology, Sydney, Australia;Institute of Automation, Chinese Academy of Sciences, China

  • Venue:
  • WImBI'06 Proceedings of the 1st WICI international conference on Web intelligence meets brain informatics
  • Year:
  • 2006

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Abstract

In our previous work, we have analyzed the shortcomings of existing business intelligence (BI) theory and its actionable capability. One of the works we have presented is the ontology-based integration of business, data warehousing and data mining. This way may make existing BI systems as user and business-friendly as expected. However, it is challenging to tackle issues and construct actionable and business-friendly systems by simply improving existing BI framework. Therefore, in this paper, we further propose a new framework for constructing next-generation BI systems. That is intelligence metasynthesis, namely the next-generation BI systems should to some extent synthesize four types of intelligence, including data intelligence, domain intelligence, human intelligence and network/web intelligence. The theory for guiding the intelligence metasynthesis is metasynthetic engineering. To this end, an appropriate intelligence integration framework is substantially important. We first address the roles of each type of intelligence in developing next-generation BI systems. Further, implementation issues are addressed by discussing key components for synthesizing the intelligence. The proposed framework is based on our real-world experience and practice in designing and implementing BI systems. It also greatly benefits from multi-disciplinary knowledge dialog such as complex intelligent systems and cognitive sciences. The proposed theoretical framework has potential to deal with key challenges in existing BI framework and systems.