Emiel Caron

Explanation of Exceptional Values in Multi-dimensional Business Databases

In this paper, we describe an extension of the OnLine Analytical Processing (OLAP) framework with causal explanation, offering the possibility to automatically generate explanations for exceptional cell values. This functionality can be built into conventional OLAP databases using a generic explanation formalism, which supports the work of managers in diagnostic processes. The central goal is the identification of specific knowledge structures and reasoning methods required to construct computerized explanations from multi-dimensional data and business models. The methodology was tested on a case study involving the comparison of financial figures of a firm’s business units. The findings suggest improved decision-making by managers because the current tedious and error-prone manual analysis process is enhanced by automated problem identification and explanation generation. It is also noted that this novel methodology has general utility for decision-support systems, for example, for automated diagnosis in the financial and accountancy domain.

A Survey of event extraction methods from text for decision support systems

Event extraction, a specialized stream of information extraction rooted back into the 1980s, has greatly gained in popularity due to the advent of big data and the developments in the related fields of text mining and natural language processing. However, up to this date, an overview of this particular field remains elusive. Therefore, we give a summarization of event extraction techniques for textual data, distinguishing between data-driven, knowledge-driven, and hybrid methods, and present a qualitative evaluation of these. Moreover, we discuss common decision support applications of event extraction from text corpora. Last, we elaborate on the evaluation of event extraction systems and identify current research issues. We identify data-driven, knowledge-driven, and hybrid event extraction approaches.A wide variety of decision support applications can benefit from event extraction.Pressing research issues to be addressed are scalability and domain dependencies.Evaluation with annotated data from standard benchmarks or crowdsourcing is advised.

BUSINESS ANALYSIS IN THE OLAP CONTEXT

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Automated business diagnosis in the OLAP context

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Sensitivity Analysis in OLAP Databases.

The theoretical underpinnings under which sensitivity analysis is valid in OLAP databases are dealt with in this paper. Sensitivity analysis is considered to be the reverse of explanation generation in diagnostic reasoning. Our exposition differentiates between sensitivity analysis in systems of purely drill-down equation and mixed systems of equations with also business model equations. It is proven that there is an unique additive drilldown measure defined on all cubes of the aggregation lattice. This proof is the basis for sensitivity analysis in OLAP databases, where a change in some base cell in the lattice is propagated to all descendants in its upset. For sensitivity analysis in mixed systems of equations a matrix notation is presented and the conditions for solvability are discussed. Due to the fact that such systems are typically overdetermined in OLAP databases, the implicit function theorem cannot be applied. Therefore, we proposed a method to reduce the number of equations in the system and apply the implicit function theorem on a subsystem of the original system. We conclude with an alternative method for what-if analysis in mixed systems of equations.

Identification of Organization Name Variants in Large Databases using Rule-based Scoring and Clustering

This research describes a general method to automatically clean organizational and business names variants within large databases, such as: patent databases, bibliographic databases, databases in business information systems, or any other database containing organisational name variants. The method clusters name variants of organizations based on similarities of their associated meta-data, like, for example, postal code and email domain data. The method is divided into a rule-based scoring system and a clustering system. The method is tested on the cleaning of research organisations in the Web of Science database for the purpose of bibliometric analysis and scientific performance evaluation. The results of the clustering are evaluated with metrics such as precision and recall analysis on a verified data set. The evaluation shows that our method performs well and is conservative, it values precision over recall, with on average 95% precision and 80% recall for clusters.

Explanatory analytics in OLAP

In this paper the authors describe a method to integrate explanatory business analytics in OLAP information systems. This method supports the discovery of exceptional values in OLAP data and the explanation of such values by giving their underlying causes. OLAP applications offer a support tool for business analysts and accountants in analyzing financial data because of the availability of different views and managerial reporting facilities. The purpose of the methods and algorithms presented here, is to extend OLAP applications with more powerful analysis and reporting functions. The authors describe how exceptional values at any level in the data, can be automatically detected by statistical models. Secondly, a generic model for diagnosis of atypical values is realized in the OLAP context. By applying it, a full explanation tree of causes at successive levels can be generated. If the tree is too large, the analyst can use appropriate filtering measures to prune the tree to a manageable size. This methodology has a wide range of applications such as interfirm comparison, analysis of sales data and the analysis of any other data that possess a multi-dimensional hierarchical structure. The method is demonstrated in a case study on financial data.