About

Twenty three 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 2022 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 2022 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: Responsible Data Science. Data Science promises to bring significant improvements in people’s lives, accelerating knowledge discovery and innovation. However, lately, there has been an increasing concern regarding the lack of diversity (leading to exclusion), fairness (leading to discrimination), and transparency (leading to opacity) when making critical decisions. This motivates the need for methods, tools, and systems to ensure that data are used responsibly, especially in applications such as healthcare, education, and public policy. To promote novel solutions to this urgent problem, DOLAP 2022 will devote a special session to Responsible Data Science. Relevant topics include, but are not limited to:

• Explainable and interpretable analytics

• Bias in big data and how to mitigate it

• Data quality and data cleaning

• FAIRness (Findability, Accessibility, Interoperability and Reusability) in OLAP