Making Data Meaningful: The Business Intelligence Model and Its Formal Semantics in Description Logics

Abstract

Business Intelligence (BI) offers great opportunities for strategic analysis of current and future business operations; however, existing BI tools typically provide data-oriented responses to queries, difficult to understand in terms of business objectives and strategies. To make BI data meaningful, we need a conceptual modeling language whose primitive concepts represent business objectives, processes, opportunities and threats. We have previously introduced such a language, the Business Intelligence Model (BIM). In this paper we consolidate and rationalize earlier work on BIM, giving a precise syntax, reducing the number of fundamental concepts by using meta-attributes, and introducing the novel notion of "pursuit". Significantly, we also provide a formal semantics of BIM using a subset of the OWL Description Logic (DL). Using this semantics as a translation, DL reasoners can be exploited to (1) propagate evidence and goal pursuit in support of "what if?" reasoning, (2) allow extensions to the BIM language, (3) detect inconsistencies in specific BIM models, and (4) automatically classify defined concepts relative to existing concepts, organizing the model.

TBIM: A Language for Modeling and Reasoning about Business Plans

“…The Business Intelligence Model (BIM) [12] is a modeling language for representing business strategies. BIM relies on primitives that decision makers are familiar with, such as goal, task/process, indicator, situation, and influence relations.…”

“…and "is it possible?" analyses [12]. Figure 1 briefly illustrates the syntax of BIM by modeling part of the Montreaux Jazz Festival (MJF) organization case study [19].…”

“…The language is a link between the strategic level and the tactical level in the sense that a business plan comprises a set of business goals, as well as the tactical plans for reaching those goals, defined in terms of value propositions, market segments, distribution channels, production and delivery activities, as well as partnerships. TBIM builds on and extends state-of-the-art modeling techniques: (i) the Business Intelligence Model [12], a strategic modeling language based on primitives such as goal, situation, indicator, and (ii) the Business Model Ontology [19], which offers a core set of concepts to conceive business models at a tactical level.…”

From Business Intelligence Insights to Actions: A Methodology for Closing the Sense-and-Respond Loop in the Adaptive Enterprise

“…It reviewed and extended BIM reasoning techniques with composite and incomplete indicators, summarized information requirements and connected them to the proposed methodology. Horkoff et al [15] used OWL Description Logic (DL) to provide a formal semantics for BIM language. The semantics serves as a connection to DL reasoners in order to assist various "what-if?"…”

A web-based system architecture for ontology-based data integration in the domain of IT benchmarking

“…The lack of a uniform description of any arbitrary parameter that is measured and the relationships between parameters, limit the comparability of different types of benchmarks. In general, a domain-specific ontology may be a solution to ensure that the collected data are meaningful and to overcome these limitations of data comparability (Horkoff et al 2012;Pfaff and Krcmar 2014).…”