Profiling customers’ behavior has become increasingly important for many applications such as fraud detection, targeted marketing and promotion. Customer behavior profiles are created from very large collections of transaction data. This has motivated us to develop a data-warehouse and OLAP based, scalable and flexible profiling engine. We define profiles by probability distributions, and compu...

eBISS 2013 Third European Business Intelligence Summer School Business model ontologies capture the complex interdependencies between business objects. The analysis of the hence formalized knowledge eludes traditional OLAP systems which operate on numeric measures. Many real-world facts, however, do not boil down to a single number but are more accurately represented by business model ontologie...

Version 8 of the SAS® System brings powerful new features for managing a Hybrid OLAP (HOLAP) or Distributed Multidimensional Data environment. The HOLAP component of the SAS/MDDB® Server software enables you to include SAS Multidimensional databases (MDDB), SAS files, and relational (RDBMS) databases into a single, powerful OLAP reporting environment. Support for HOLAP data groups is fully inte...

Dimension hierarchies represent a substantial part of the data warehouse model. Indeed they allow decision makers to examine data at different levels of detail with On-Line Analytical Processing (OLAP) operators such as drill-down and roll-up. The granularity levels which compose a dimension hierarchy are usually fixed during the design step of the data warehouse, according to the identified an...

Today's information systems mostly rely on relational databases for transactional data storage and manipulation. Throughout the years, massive amounts of data have been gathered. Those data, if properly analyzed, could serve as a basis for strategic decisions. For the purposes of analysis data is transformed from relational model (database) into dimensional model and stored in a data warehouse....

In this paper we discuss indexing methods for On-Line Analytical Processing (OLAP) databases. We start with a survey of existing indexing methods and discuss their advantages and shortcomings. We then propose extensions to conventional multidimensional indexing methods to make them more suitable for indexing OLAP data. We compare and contrast R-trees with bit-mapped indices which is the most po...

Due to unstructured and large amounts of data, relational databases are no longer suitable for data management. As a result, new known as NOSQL have been introduced. The issue is that such database difficult analyze. Online analytical processing (OLAP) the foundational technology analysis in business intelligence. Because these technologies were designed primarily systems, performing OLAP diffi...

OLAP and Datawarehouse (DW) systems are technologies intended to support the decision-making process, enabling the analysis of a substantial volume of data. Decision makers explore warehoused data using OLAP operators to discover new trends and/or confirm business hypotheses. In the era of Big Data, the size of warehoused data has increased substantially, and the data have become increasingly d...

AbstrAct OLAP queries are characterized by short answering times. Materialized cube views, a pre-aggregation and storage of group-by values, are one of the possible answers to that condition. However, if all possible views were computed and stored, the amount of necessary materializing time and storage space would be huge. BLOCKINSelecting BLOCKINthe BLOCKINmost BLOCKINbeneficial BLOCKINset, BL...

This paper provides an overview of Data warehousing, Data Mining, OLAP, OLTP technologies, exploring the features, new applications and the architecture of Data Warehousing and data mining. The data warehouse supports on-line analytical processing (OLAP), the functional and performance requirements of which are quite different from those of the online transaction processing (OLTP) applications ...