About

Twenty two 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 broad area of data decision support systems. DOLAP maintains a high quality of accepted papers, as attested by its ranking as "very good event" in the last edition of the GII-GRIN-SCIE Conference Ranking. It features a 2 rounds reviewing process, is associated with special issues of highly reputed international journals (like IS or DKE), invites keynotes from reputed speakers, attributes a best paper award since 2020, and favors open proceedings since 2017.

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 2021 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.

Like the previous DOLAP workshops, DOLAP 2021 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.

Special Theme: Data Exploration. To promote novel solutions to tackle data management for novel DSS, DOLAP 2021 will devote a session to Data Exploration and their impact on novel Big Data Management and Analytics approaches. Today, there is a need to develop novel paradigms for exploratory user-data interactions that emphasize user context and interactivity with the goal of facilitating exploration, interpretation, retrieval, analysis and assimilation of information. Various applications need an exploratory form of querying. Ranked retrieval techniques for relational databases, XML, RDF and graph databases, scientific and statistical databases, social networks and many others, is a first step in this direction. Several additional aspects for exploratory search, such as preferences, diversity, novelty, surprise and serendipity, are gaining increasing importance. Complementarily, recommenders anticipate user needs by automatically suggesting the information, which is most appropriate to the users and their current context. Also, a new line of research is fueled by the growth of online social interactions within social networks and Web communities. Many useful facts about entities and their relationships can be found in a multitude data sources. Therefore, novel discovery methods are required to provide expressive capabilities over big data.