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.

A stakeholder decides which information to share during requirements elicitation. These decisions are made under uncertainty: stakeholders may not have precise, concrete, and complete information about their own expectations from, or about the environment of the system-to-be, they may misunderstand information which a requirements engineer gives them, and they may not have perfect knowledge about the various opportunities that the current technology may offer. Stakeholders' decision-making is therefore likely to involve the use of heuristics. Understanding if stakeholders do indeed use heuristics during requirements elicitation, and if so, then which ones, should help understand requirements elicitation better, and design methods which can stimulate positive heuristics (those which help elicit relevant requirements) and mitigate negative ones. This paper defines what an elicitation heuristic may be, and proposes a set of research questions under the form of a matrix for the theoretical and empirical study of elicitation heuristics.

Blockchain technology is argued to have the potential to facilitate the development and improvement of cross-organizational business processes, on various aspects of the business process management discipline. In this paper, we review existing work on the use of blockchain technology to support business processes with a focus on cross-organizational settings. Based on this, we suggest to view blockchain as a software connector helping organizations to integrate the IT systems they use to support business processes. In addition, this paper suggests to use a model-driven engineering approach for the development of such connectors. By doing so, the need to develop a methodology helping to model and implement blockchain-based software connectors supporting cross-organizational business processes is highlighted. By adopting such an approach, organizations would be able to facilitate and speed up the development of blockchain-based solutions. This in turn would facilitate experimentations with this technology, which is considered as an important phase for organizations to derive value from it.

Requirements engineering is a creative process in which stakeholders and engineers work together to create ideas for new products, services and systems. Several techniques have proved to be effective for eliciting creative requirements. Yet, most of these techniques are heavy to implement and require long periods of time to be applied correctly. Few lightweight creativity techniques have been developed for use in requirements engineering. One such lightweight technique is the creativity trigger, which provides simple guidance to stakeholders and engineers to help produce creative requirements. While easy to apply, creativity triggers were derived informally from experience of practitioners and have not been validated in a systematic way. This paper reports design and preliminary validation research, that sought to provide empirical foundations for a more complete set of lightweight creativity triggers, to be used by stakeholders and engineers to quickly and simply generate new and useful requirements on products, services and systems.

Graphical Requirements Modeling (GRM) consists of representing requirements in diagrams: requirements (and other relevant information) are represented as nodes, and relationships between them as edges. Relationships can show, for example, that one requirement refines another, that some are in conflict with others, that they are more or less desirable, and so on. Various software tools have been proposed over the years as a support to doing GRM, some capable of performing computations over diagrams, such as searching for text strings, or determining if a requirement is satisfied (and how much). We present yet another tool, available at AnalyticGraph.com. The tool departs from much of prior work in the following ways. (i) The tool is a web application, is available on-demand, and requires no installation of specialized software. (ii) Each model made with the tool gets its own permanent and unique URL, so that models can be linked in research papers. (iii) If a model on AnalyticGraph is linked in a paper, then any reader can click on the link, open a free account, and edit and run a copy of the linked model. (iv) The tool supports the definition of various requirements modeling languages. (v) Models are stored in a graph database, and standard graph queries (such as find the shortest path between two nodes) are included by default. (vi) It is possible to combine models made with various languages, and do computations over the resulting mixed models.

Requirements elicitation is the activity in requirements engineering (RE) which focuses on the collection of information about requirements of the system-to-be and its environment. One important challenge is elicitation incompleteness; it occurs when information, which may have been relevant for requirements engineering, is not elicited. This may be due to various factors, such as that the requirements engineer asked no questions about it, and the stakeholders did not consider it important. To help requirements engineers reduce elicitation incompleteness, we propose the so-called Model of Elicitation Topic Relevance (METRe). METRe is a diagram that shows topics which can be discussed during requirements elicitation, and expresses the relative importance of each topic to stakeholders and engineers. The more likely it is that a stakeholder or engineer will discuss the topic spontaneously during elicitation, the more important it is for, respectively, stakeholders or engineers. METRe was made by combining our prior work on the importance of topics to stakeholders, and a new round of empirical research. The new round consisted of data collection using a survey, in which the various topics were presented to and evaluated by 50 IT-experts in Belgium. Subjects were asked to evaluate the relative importance of the topics, that is, how relevant they find these topics when eliciting information, and how pro-active they would be in collecting them.

Requirements elicitation consists in collecting information about the requirements and the environment of a systemto-be.It usually involves business analysts who are eliciting information, and stakeholders who are providing information.This paper investigates how the commitment of stakeholders to a RE project influences the results of elicitation.We suggest a way to measure the commitment of stakeholders during RE, and propose the so-called "commitment matrix", which shows what analysts can expect from stakeholders who are more, as opposed to those who are less committed.The matrix builds on a survey of 87 stakeholders.Our results suggest that commitment somehow affects the information provided by stakeholders, and that it is therefore a relevant criterion to account for when selecting stakeholders to be involved in elicitation.

Requirements Elicitation (RE) consists of collecting requirements for a future system. It involves engineers who are eliciting information, and stakeholders who are involved in the project to provide information. This research note aims to stimulate research about the impact of stakeholders’ commitment on RE. We define commitment, discuss how it can be measured during RE, and present a first exploratory study that we conducted to build a prototype “commitment matrix”. The matrix aims to clarify what engineers may expect during RE when involving stakeholders who are more or less committed.