DOLAP: Data management fOr anaLytics and Ai Processing

DOLAP is a premier colloquium of the international scientific community with the mission to promote research and engineering in the field of data management for analytics and AI processing. It promotes the publishing of research results in the scope of data management techniques, languages, algorithms, systems, and architectures that support data analytics, data-driven decision making, and intelligent data processing at scale. DOLAP 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, methodologies, and practical experiences.

Every year, the best papers published at DOLAP are invited to submit extended version to a special issue on a highly reputed international journal (like Information Systems or Data & Knowledge Engineering). Moreover, the high-quality program of DOLAP includes invited keynotes from reputed speakers, a best paper award since 2020, and uses open proceedings since 2017. A test-of-time award was given in 2023 to celebrate the 25th edition of DOLAP.

Research focus

Research in data warehousing and OLAP has produced important technologies for the design, management, and use of information systems for analytics and decision support. Nowadays, due to the evolution of large-scale and heterogeneous data ecosystems, Decision Support Systems (DSS) encompass a wider range of platforms, where modern solutions combine advanced data management, scalable data processing, and intelligent data analytics, (semi-)automating the data lifecycle from ingestion and integration to exploration, visualization, and AI-assisted decision making. Yet, modern analytics systems continue to acknowledge the relevance of managing data efficiently, by means of appropriate data models, architectures, and optimized processing techniques, to support innovative forms of data analysis bringing added value to organizations.

Data platforms of the future will consequently be significantly different from what the current state-of-the-practice supports. The trend is to move from systems that are mainly “data presenting” toward adaptive and intelligent environments that partially automate and guide users in data discovery, preparation, and system-aided decision making by exploiting artificial intelligence, advanced analytics, and visualization techniques beyond traditional OLAP. In the backstage, the increasing scale, heterogeneity, variability, and dynamic nature of modern data and workloads require the development of novel methods, models, languages, architectures, and optimization techniques to cope with the growing demand for scalability, flexibility, responsiveness, and integration of analytics and AI capabilities. And of course, this does not necessarily mean re-inventing the wheel, but rather complementing the wealth of research in data management and analytics with approaches originating from related research areas.

We envision DOLAP as a forum to discuss, foster and nurture novel ideas around these new landscapes of decision support systems and data platforms in the era of big data in order to produce new exciting results, within a strong, vibrant community around these areas.

After 25 years of workshops, we have revamped the original logo to represent the main themes that are driving the scope of DOLAP. While the cube is an obvious reference to the multidimensional data, here is the breakdown of the symbols on its sides.

OLAP

The star and snowflake schemas laid the foundation to multidimensional data modeling and implementation of data warehouses, i.e., the building block of any business intelligence and OLAP application, identifying the roots of DOLAP's scope

Big Data

The big data revolution imposed disruptive changes at the levels of the 4 Vs (volume, velocity, variety, veracity), pushing researchers and practitioners into rethinking practices of data (and metadata) management and analytical processing

ML & AI

If machine learning techniques have always been paired with OLAP analyses to support the decision-making process, the ground-breaking advancements in the field of artificial intelligence are further pushing the boundaries of what analytical applications can achieve

Data-driven decision-making is a research topic in continuous evolution, always encountering new challenges and opening to tremendous opportunities, inspiring research work that takes data analysis to an ever-higher level. DOLAP is the perfect venue for researchers to share their latest work in this area and join a strong and proactive data-enthusiast community.