Self-Service Business Intelligence (SSBI) increases decision-making reactivity of companies by facilitating the data use by non-IT experts. An important SSBI dimension is data querying where businesspeople create their own queries by reducing the technical complexity of formal languages like SQL. However, existing solutions ignore two other key challenges of data querying identified in the literature: the databases technical jargon and the data overload. In this paper, we propose, following the Design Science Research methodology, a framework (i.e. DatAssistant) to complement existing querying solutions with two new theoretical artifacts. The first bridges the semantic gap between technical databases and businesspeople via a business-aware ontology of the Data Warehouse mapped to the business Data Catalog. The second artifact filters data overload by mobilizing a hybrid recommender engine combining semantic systems and business rules. This paper then demonstrates the validity and applicability of the framework through its technical implementation in a real-world environment.

Data are ubiquitous and generate unprecedented opportunities to help making decisions within organizations.This has led so-called data-driven Decision Support Systems (DSS) to become critical, if not vital, systems for most companies.The design of such DSS raises important methodological challenges, since data-driven DSS should expose only useful information to decision makers, but data available in a company's database are numerous and not equally supportive.Failing to provide the right data to the right decision-maker may reduce the usefulness of a DSS, and can lead to lower quality decision outputs.This is particularly striking in the case of Self-Service Business Intelligence (SSBI) where users build DSS outputs themselves.In this paper, we elaborate on this idea of data profusion and propose a data selection criterion, namely the decision-making data value.To do this, we discuss the concept of value and its application to data and decision making, we review existing literature and propose a taxonomy of the dimensions of data value in the context of decision making.We also validate this taxonomy with semi-direct interviews and discuss the future research we plan to conduct as a way to apply this approach for the specification of high-value data-driven DSS.

The EVOQ tool offers researchers in social sciences a set of text analysis tools relying on the post-structuralist approach. This analysis approach relies on the identification of association and opposition relations between terms (words or expressions). The so-defined graph is presented in EVOQ by a node-link diagram. The Shock Wave is a placement algorithm specifically designed to be combined with a classical force-directed algorithm to produce a graph layout which meets the interpretability needs of the text analysts while preserving efficiency on large numbers of nodes. It structures the nodes on a circular placement with transversal opposition relations to highlight oppositions within the text concepts. Beyond our use case, the interest of Shock Wave lies in the fact that it is a novel method to present graphs of text with a strong emphasis on underlying semantic fields.

Structural analysis is a text analysis technique that helps uncovering the association and opposition relationships between the terms of a text. It is used in particular in the field of humanities and social sciences. This technique is usually applied by hand with pen and paper as support. However, as any combination of words in the raw text may be considered as an association or opposition relationship, applying the technique by hand in a readable way can quickly prove overwhelming for the analyst. In this paper, we propose Evoq, an application that provides support to structural analysts in their work. Furthermore, we present interactive visualizations representing the relationships between terms. These visualizations help create alternative representations of text, as advocated by structural analysts. We conducted two usability evaluations that showed great potential for Evoq as a structural analysis support tool and for the use of alternative representations of texts in the analysis.