Showing papers on "Online analytical processing published in 1970"

Data Mining: A Database Perspective.

Abstract: Data mining on large databases has been a major concern in research community, due to the difficulty of analyzing huge volumes of data using only traditional OLAP tools. This sort of process implies a lot of computational power, memory and disk I/O, which can only be provided by parallel computers. We present a discussion of how database technology can be integrated to data mining techniques. Finally, we also point out several advantages of addressing data consuming activities through a tight integration of a parallel database server and data mining techniques.

Knowledge Discovery With Retail BasketAnalysis

Abstract: Minute by minute the amount of data in the world databases is increasing inexorably. To support this growth of data the concept of data warehouse (DW) was created. DW when combined with On-Line Analytical Processing (OLAP) Codd[2] and Executive Information Systems (EIS) Buytendijk[l] tools, enable data access and visualization in a very flexible way. Features include very quick data exploration, vertical navigation (drill up/drill down), aggregation and graphical facilities. However, the amount and the complexity of data in data warehouses is so big that it becomes difficult to the business analysts to recognise trends and relations in data even with multidimensional decision support systems. A new generation of tools and techniques for automated intelligent database analysis is needed. These tools and techniques are the subject of the rapidly emerging field of Knowledge Discovery in Data Bases (KDD). In this paper, we propose an integrated system DECADIS DEscoberta de Conhecimento em Armazens de Dados de DIStribuigao (Knowledge Discovery in Retail Data Warehouses), designed for understanding customer behaviour and consumption patterns in a Portuguese company in the retail industry. Transactions on Information and Communications Technologies vol 19 © 1998 WIT Press, www.witpress.com, ISSN 1743-3517

Integration between location based service (LBS) and online analytical processing (OLAP): semantic approach

Abstract: Location Based Service (LBS) LBS are mobile service that has the capability to provide real time information basedon the user's location. Geographical Information System (GIS) has been the heart of LBS in order to provide all the functionalities in LBS. Although mostly transparent, GIS provides the basis for most functionality, from services like geocoding, routing, location search to map presentation in LBS. In the Knowledge Discovery realm, Spatial Online Analytical Processing (SOLAP) integrates conventional OLAP with GIS data sets .Integration of these two heterogeneous data sources deals with issues such as different data model structures, different schemas and query languages. In the implementation of SOLAP, two different data model must be considered. Geographical Information System (GIS) describes its data model in a hierarchical structure, use to represent spatial features. Online Analytical Processing (OLAP) however describes its data in a multi-dimensional structure, known for fast analytical processing. Although having such differences, it is now possible to distribute the data available from these three systems (LBS, GIS, OLAP) in the web. The internet has become the main transport for data and information exchange, and a proper integration framework should be use. This paper explains the process of data integration in LBS and SOLAP using Semantic Web Technology.