Research in data warehousing and OLAP has produced important technologies for the design, management, and use of information systems for decision support. Nowadays, due to the advent of Big Data, Decision Support Systems (DSS) embrace a wider range of systems, in which novel solutions combining advanced data management and data analytics, (semi-)automating the data lifecycle (from ingestion to visualization). Yet, the DSS principles remain the same: these systems acknowledge the relevance to manage data in an efficient way (by means of data modeling and optimized data processing) to serve innovative data analysis bringing added value to organizations.
DSS of the future will consequently be significantly different than what the current state-of-the-practice supports. The trend is to move from current systems that are "data presenting" to more dynamic systems that allow the semi-automation of the decision making process (including both data management and data analysis tasks). This means that systems partially guide their users towards data discovery, management and system-aided decision making via intelligent techniques (beyond OLAP) and visualization. In the back stage, the advent of the big data era, requires that new methods, models, techniques and architectures are developed to cope with the increasing demand in capacity, data type diversity, schema and data variability and responsiveness. And of course, this does not necessarily mean to re-invent the wheel, but rather, complement the wealth of research in DSS with other approaches. We envision DOLAP 2020 as a forum to discuss, foster and nurture novel ideas around these new landscapes of decision support systems in the era of big data in order to produce new exciting results, within a strong, vibrant community around these areas.
DOLAP 2020 strengths its commitment towards and open community attracting new ideas around decision support systems by introducing a special theme "Semantic Web Technologies meet Big Data Management and Analytics". Semantic Web Technologies allow the enrichment of tasks / processes by means of rich annotations. Such annotations have already been successfully exploited by previous works in the field of decision support system to (semi-)automate data management tasks such as data integration, provenance, evolution or even query optimization via past evidences traced, among others. Similarly, semantic web technologies had a big impact on data analytics. For example, using annotations to enable reinforced learning, or exploiting their underlying graph-based formalisms to unleash graph traversals, pattern matching, graph algorithms or graph mining that naturally fit graph-alike data.
DOLAP 2020 welcomes novel ideas on advanced data management and analytics for next generation decision support systems and will devote one session to the special theme. Since the main objective is to promote discussion, short papers presenting interesting ideas not fully developed are welcome and might also be considered for the DOLAP 2020 best papers journal special issue.
Twenty one DOLAP workshops have been held in the past with great success. During these years, DOLAP has been established as one of the reference places for researchers to publish their work in the area of data decision support systems and maintains a high quality of accepted papers. Like the previous DOLAP workshops, DOLAP 2020 aims at synergistically connecting the research community and industry practitioners and provides an international forum where both researchers and practitioners can share their findings in theoretical foundations, current methodologies, and practical experiences, and where industry technology developers can describe technical details about their products and companies exploiting BI and Big Data technology can discuss case studies and experiences.