A Review:Implementation Of OLAP Semantic Web Technologies For Business Analytic System Developement
Business Intelligence (BI) tools provide fundamental supporting analyzing large volumes of information. Data Warehouses (DW) and Online Analytical Processing (OLAP) tools are used to store and analyze data. Nowadays more and more information is available on the Web in the form of Resource Description Framework (RDF) and BI tools have huge potential of achieving better results by integrating real-time data from web sources into the analysis process. We describe the convergence of some of the most influential technologies in the last few years, namely data warehousing (DW), on-line analytical processing (OLAP), and the Semantic Web (SW). OLAP is used by enterprises to derive important business-critical knowledge from data inside the company. However, the most interesting OLAP queries can no longer be answered on internal data alone, external data must also be discovered (most often on the web), acquired, integrated, and (analytically)queried, resulting in a new type of OLAP, exploratory OLAP. When using external data, an important issue knows the precise semantics of the data. Here, SW technologies come to the rescue, as they allow semantics (ranging from very simple to very complex) to be specified for web-available resources. Next, we goes on to survey the use of SW Technologies for data modeling and data provisioning, including semantic data annotation and semanticaware extract, transform, and load (ETL) processes. Finally, all the findings are discussed and a number of directions for future research are outlined, including SW support for intelligent MD querying, using SW technologies for providing context to data warehouses, and scalability issues.