Data warehouse and OLAP are core aspects of business intelligence environments, since the former store integrated and time-variant data, while the latter enables multidimensional queries, visualization and analysis. The bitmap join index has been recognized as an efficient mechanism to speed up queries over data warehouses. However, existing OLAP tools does not use strictly this index to improv...

Data warehousing and On-Line Analytical Processing (OLAP) are two of the most signiicant new technologies in the business data processing arena. A data warehouse can be deened as a \very large" repository of historical data pertaining to an organization. OLAP refers to the technique of performing complex analysis over the information stored in a data warehouse. The complexity of queries require...

Recently, the case has been made for operational or real-time Business Intelligence (BI). As the traditional separation into OLTP database and OLAP data warehouse obviously incurs severe latency disadvantages for operational BI, hybrid OLTP&OLAP database systems are being developed. The advent of the first generation of such hybrid OLTP&OLAP database systems requires means to characterize their...

SAS/OLAP Server® Software is a powerful, flexible and scalable OLAP solution. It’s hybrid OLAP (HOLAP) architecture provides the flexibility to store data in whatever format is most appropriate based on the characteristics of the data and how the data will be used. This also means, however, that choices must be made as to what is the most appropriate data model for a successful OLAP application...

It has been well recognized that online analytical processing (OLAP) can provide important insights into huge archives of data. While the conventional OLAP model is capable of analyzing relational business data, it often cannot fit many kinds of complex data in emerging applications, such as bio-medical data, time series and semi-structured data. In this paper, we propose GOLAP, a general OLAP ...

The healthcare industry collects huge amounts of data which, unfortunately, are not turned into useful information for effective decision making. Clinical Decision support systems (CDSS) can now use advanced technologies such as On-Line Analytical Processing (OLAP) and Data Mining to deliver advanced capabilities. This paper presents a prototype CDSS using OLAP with Data Mining (OLAP Mining) wh...

Top-k is the most important query type in OLAP, a common data warehouses model. Recently, OLAP was extended to represent data ambiguity, specifically imprecision and uncertainty. However, how to deal with top-k queries in uncertain OLAP still remains an unanswered question. This project introduces a solution for top-k query evaluation in uncertain OLAP including query semantic definition, query...

Top-k is the most important query type in OLAP, the most common data warehouses model. Recently, OLAP was extended to represent data ambiguity, specifically imprecision and uncertainty. However, how to deal with top-k queries in uncertain OLAP still remain an unanswered question. This project introduces a complete solution for top-k query in uncertain OLAP including query semantic definition, q...

This paper discusses the PROMISE (Predicting User Behavior in Multidimensional Information System Environments) approach, that deploys information about characteristic patterns in the user’s multidimensional data access in order to improve caching algorithms of OLAP systems. The paper motivates this approach by presenting results of an analysis of the user behavior in a real-world OLAP environm...