Today, information for decision making processes is derived from stored data in information systems in business organizations. Accessing and mining business intelligence from stored data is crucial as this process should be fast and accurate to support on-time decision making processes. Provided that current information systems store more and more data (ranging from few GBs to several PBs), it is necessary to have efficient and effective access mechanisms. Thus, it is vital to develop new methods for faster information access, as current methods do not support efficient access to derive information. It is due to these methods access raw data to produce results. In this research, we focus on developing a new layer of access mechanism over data to support information retrieval without accessing raw data. The system stores models to represent the underlying data and user queries are answered by accessing the models rather than raw data directly. This mechanism provides faster access to information and also provides complete view of data at any point in time while hiding the incomplete and erroneous data. The model-based information access is expected to be scalable when data grows since models need low memory footprint and provide efficient querying on large data.

This topic examines different types of models for different types of data and propose a model designer that determines a good set of candidate models for a given data set and query workload. Users can access information through models by SQL like declarative queries that are transparently processed by the DBMS. It is easy to use same models to access the past data as well as predicted future data. Thus, it also focus on adjusting the models in order to do time travel over data, which is an essential technique for business intelligence.

Main Advisor at Technische Universität Dresden (TUD)
Co-advisor at Aalborg Universitet (AAU)