Collaborative Exchange of Systematic Literature Review Results: The Case of Empirical Software Engineering

ABSTRACT Complementary to managing bibliographic information as done by digital libraries, the management of concrete research objects (e.g., experimental workflows, design patterns) is a pre-requisite to foster collaboration and re-use of research results. In this paper we describe the case of the Empirical Software Engineering domain, where researchers use systematic literature reviews (SLRs) to conduct and report on literature studies. Given their structured nature, the outputs of such SLR processes are a special and complex type of research object. Since performing SLRs is a time consuming process, it is highly desirable to enable sharing and reuse of the complex knowledge structures produced through SLRs. This would enable, for example, conducting new studies that build on the findings of previous studies. To support collaborative features necessary for multiple research groups to share and re-use each other's work, we hereby propose a solution approach that is inspired by software engineering best-practices and is implemented using Semantic Web technologies

Ontology-Based Data Integration in Multi-Disciplinary Engineering Environments: A Review

Today's industrial production plants are complex mechatronic systems. In the course of the production plant lifecycle, engineers from a variety of disciplines (e.g., mechanics, electronics, automation) need to collaborate in multi-disciplinary settings that are characterized by heterogeneity in terminology, methods, and tools. This collaboration yields a variety of engineering artifacts that need to be linked and integrated, which on the technical level is reflected in the need to integrate heterogeneous data. Semantic Web technologies, in particular ontologybased data integration (OBDI), are promising to tackle this challenge that has attracted strong interest from the engineering research community. This interest has resulted in a growing body of literature that is dispersed across the Semantic Web and Automation System Engineering research communities and has not been systematically reviewed so far. We address this gap with a survey reflecting on OBDI applications in the context of Multi-Disciplinary Engineering Environment (MDEE). To this end, we analyze and compare 23 OBDI applications from both the Semantic Web and the Automation System Engineering research communities. Based on this analysis, we (i) categorize OBDI variants used in MDEE, (ii) identify key problem context characteristics, (iii) compare strengths and limitations of OBDI variants as a function of problem context, and (iv) provide recommendation guidelines for the selection of OBDI variants and technologies for OBDI in MDEE