Data Warehouse (DW) and Online Analytical Processing (OLAP) technologies are the core of current Decision Support Systems. Traditionally, a data warehouse has been a historical (and relatively static) repository of data collected from a wide variety of heterogeneous data sources by means of Extraction-Transformation-Loading (ETL) processes. The widespread deployment of both DWs and OLAP technologies is due to the intuitive representation of data provided to data analysts or managers in support of management decisions. Recently, the trend is that DWs become more and more dynamic, with near-real-time updates, and include more complex types of data.

Research in data warehousing and OLAP has produced important technologies for the design, management and use of information systems for decision support. Much of the interest and success in this area can be attributed to the need for software and tools to improve data management and analysis given the large amounts of information that are being accumulated in corporate as well as scientific databases. Nevertheless, the high maturity of these technologies as well as new data and applications needs not only demand more capacity, but also new methods, models, techniques, or architectures to satisfy these new needs. Some of the hot topics in data warehouse research include distributed data warehouses, web warehouses, data streams, real-time data warehouses, GIS/location-based services, 2 biomedical data, integration of semi-structured and unstructured data, security/privacy and quality management.

DOLAP accepts submissions on data warehousing in a broad sense from both universities and industry. We are expanding topics to include work that bridges data warehousing and other large scale data processing platforms. This year, we encourage submissions from industry data warehouse technology developers describing technical details about their products as well as companies exploiting data warehousing technology describing case studies, experiences and technology limitations.