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 ...

Nowadays, data warehousing and online analytical processing (OLAP) are core technologies in business intelligence and therefore have drawn much interest by researchers in the last decade. However, these technologies have been mainly developed for relational database systems in centralized environments. In other words, these technologies have not been designed to be applied in scalable systems s...

Data Warehouse (DW) and OLAP systems are Business Intelligence (BI) technologies intended to support decision-making process by means of on-line and multidimensional analysis of large volume of alphanumeric data (Kimball, 1996). DW models represent data according to the multidimensional model, which organizes warehoused data in dimensions (analysis axes) and facts, described by measures (analys...

In database management systems (DBMSs), query workloads can be classified as online transactional processing (OLTP) or analytical (OLAP). These often run within separate DBMSs. hybrid and (HTAP), both may execute the same DBMS. This article shows that it is possible to OLTP OLAP DBMSs, still support timely business decisions from queries running off fresh data. Several setups manage are analyse...

In this paper the authors describe a method to integrate explanatory business analytics in OLAP information systems. This method supports the discovery of exceptional values in OLAP data and the explanation of such values by giving their underlying causes. OLAP applications offer a support tool for business analysts and accountants in analyzing financial data because of the availability of diff...