Data Warehouse (DW) and Online Analytical Processing (OLAP) technologies are the core of current Decision Support Systems. The widespread deployment of both DWs and OLAP technologies is due to the intuitive representation of data and simple primitives provided to data analysts or managers in support of management decisions. Research in data warehousing and OLAP has produced important technologies for the design, management, and use of information systems for decision support.

Business Intelligence (BI) of the future will be significantly different than what the current state-of-the-practice supports. The trend is to move from the current decision support systems that are "data presenting" to more dynamic systems that allow the semi- automation of the decision making process. This means that the systems partially guide their users towards data discovery, intuition, and system-aided decision making via intelligent techniques and visualization. In the back stage, the thrust of the big data era, with the requires that new methods, models, techniques, or architecture are needed to cope with the increasing demand in capacity, data type diversity, and responsiveness. And of course, this does not necessarily mean to re-invent the wheel, but rather, as recommended by Gartner to companies regarding BD adoption, "Build on existing BI programs - don't abandon or segregate them". We envision DOLAP 2018 as a venue where novel ideas around these new landscapes of business intelligence and big data are fostered and nurtured and new exciting results are produced, in an attempt to build a strong, vibrant community around these areas.

Research topics include, but are not limited to:

Design and Languages

    Data warehousing fundamentals: architectures, design, ETL/ELT, multidimensional modeling, query processing, DW maintenance, evolution, security, personalization and privacy in data warehouses
    Warehousing and Variety: unstructured data (e.g., text), semi-structured data (e.g., XML), multimedia, spatial, temporal, and spatio-temporal data warehouses, stream and sensor data, semantic Web & deep Web in data warehouses, data lakes, data quality, graph data management

Optimization

    Coping with Volume: physical organization of data warehouses, performance optimization and tuning, scalability of DW, MapReduce in data warehouses, performance optimization of ETL/ELT
    Coping with Velocity: DW deployment on parallel machine, database clusters, cloud infrastructures for DW, smart grid, active/real-time analytics & data warehouses, real-time queries

Analytical Processing and applications

    Analytics and Value: OLAP exploration through visualization, recommendation, reformulation, approximate query-answering, personalization, result presentation, data storytelling, graph analytics, process mining, advanced visualization for business contexts
    Analytics and Veracity: heterogeneous data integration for analtyics, quality aspects of OLAP analysis, exploration outcome and end-user experience, fairness of data analysis, analytics and data driven decision making for the data enthusiasts
    Integration of analytics with machine learning, data mining, information retrieval, search engines, predictive and prescriptive analytics
    Big Data applications: smart city, smart health, smart energy, etc.

Submission types:

Authors can submit:

    short papers (of maximum 5 pages) including vision, PhD, demos, and preliminary works to be discussed with the community
    long papers (of maximum 10 pages) including novel research contributions, an architecture of a commercial system or solution, results of case studies and experience report, survey papers, or on-going work on a challenging and emerging area

Publication:

Similarly as in previous years, the proceedings of DOLAP 2019 shall be submitted to CEUR-WS.org for online publication (open access) and will be indexed by Citeseer, DBLP, and Google Scholar.