On the relevance of reports—Integrating an automated archiving component into a business intelligence system

• We present a framework for integrating an archiving component into a BI system.
• We review common report types and deduce corresponding types of relevance patterns.
• We discuss how the future relevance of reports can be estimated automatically.
• The framework is evaluated in a simulation study.
• We find that archiving of reports can significantly reduce search costs.

In the last years, the scope of business intelligence (BI) systems has been extended from strategic to operational decision support (operational BI). This has led to an increase in the number of information needs and, at the same time, to a decrease in the “efficiency” of reports in terms of how many information needs they address. As a consequence, the number of reports has exploded. This slows down knowledge workers’ manual or automated search for information, resulting in high search costs to companies. However, it can be observed that in many cases only a small subset of all reports is (still) relevant to knowledge workers. The remainder is an unnecessary burden that could be sorted out without obstructing the access to information that still is needed. In this paper, we develop a framework to identify such reports and archive them automatically. The relevance of reports is concluded from users’ information retrieval behavior as recorded in the log files of the BI system, particularly of its search component. We evaluate the proposed framework through a simulation study. The results indicate that the integration of an automated archiving component into a BI system can significantly reduce search effort and, hence, search costs.