Karsten Riis

XML-extended OLAP querying

The rapidly changing data requirements of todays dynamic business environments are not handled well by current online analytical processing (OLAP) systems. Physically integrating data from new sources into OLAP systems is a long and time-consuming process, making logical integration the better choice in many situations. The increasing use of Extended Markup Language (XML) suggests that the required external data will most often be available in XML format. We present a theoretically well-founded approach to the logical federation of OLAP and XML data sources. The approach allows external XML data to be presented along with dimensional data in OLAP query results and enables the use of external XML data for selection and grouping. Special care is taken to ensure that semantic problems do not occur in the integration process. This opens up many new application areas for OLAP. A number of effective optimization techniques for OLAP-XML federations are presented. Performance results from the prototype implementation show that the approach is an attractive alternative to physical integration. The approach is exemplified using a real-world case study from the B2B domain.

A powerful and SQL-compatible data model and query language for OLAP

In this paper we present the SQLM OLAP data model, formal algebra, and query language that, unlike current OLAP data models and languages, are both powerful, meaning that they support irregular dimension hierarchies, automatic aggregation of data, and correct aggregation of data, and SQL-compatible, allowing seamless integration with relational technology. We also consider the requirements to the data model posed by integration of OLAP data with external XML data. The concepts are illustrated with a real-world case study from the Business-to-Business electronic commerce (B2B) domain.

Query optimization for OLAP-XML federations

The changing data requirements of todays dynamic business environments are not handled well by current OLAP systems. Physically integrating unexpected data into such systems is a long and time-consuming process making logical integration, i.e., federation, the better choice in many situations. The increasing use of Extended Markup Language (XML), e.g. in business-to-business (B2B) applications, suggests that the required data will often be available as XML data. This means that logical federations of OLAP and XML databases will be very attractive in many cases. However, for such OLAP-XML federations to be useful, effective optimization techniques for such systems are needed.In this paper we present novel techniques for query optimization in federations of OLAP and XML databases. The techniques include so-called inlining of XML data in OLAP predicates, caching, and pre-fetching. Experiments show that the proposed optimization techniques improve query execution times significantly. Furthermore, the performance of the optimized federation queries is comparable to the performance achieved with physical integration of the data, showing that our federated OLAP approach is indeed a feasible alternative to physical integration.

On-demand multidimensional data integration: toward a semantic foundation for cloud intelligence

Cloud intelligence is a collection of technologies emerging from the migration of business intelligence and analytics technologies to a cloud computing environment combined with exploiting the massive range of new intelligence opportunities opened up by cloud computing. Cloud computing introduces several trends which require traditional business intelligence techniques to be re-thought, including agility, the ability to assemble resources, e.g., data sources, on-demand, and virtualization, e.g., that data are provided as a service over the web rather than stored in local databases. This paper focuses on the combination of data source agility and data-as-a-service virtualization and its use for cloud intelligence. After presenting the novel vision of the Cloud Warehouse, the paper goes on to present a comprehensive semantic foundation for on-demand multidimensional data integration, including formal data models, a range of query operators and re-write rules for optimization. This semantic foundation provides a sound formal basis for on-demand multidimensional data integration, which is a cornerstone of cloud intelligence.

Cost Modeling and Estimation for OLAP-XML Federations

The ever-changing data requirements of todays dynamic businesses are not handled well by current OLAP systems. Physical integration of data into OLAP systems is a time-consuming process, making logical federations the better choice in many cases. The increasing use of XML suggests that the required data will often be available in XML format. Thus, federations of OLAP and XML databases will be very attractive in many situations. In an efficient implementation of OLAP-XML federations, cost-based optimization is a must, creating a need for an effective cost model for OLAP-XML federations.In this paper we present a cost model for OLAP-XML federations, and outline techniques for estimating the cost model parameters in a federated OLAP-XML environment. The paper also outlines the cost models for the OLAP and XML components in the federation on which the federation cost model is built. The cost model has been used as the basis for effective cost-based query optimization in OLAP-XML federations. Experiments show that the cost model is precise enough to make a substantial difference in the query optimization process.