In the era of monumental development in all spheres of technology, the repository of huge amounts of raw data is but a necessity. Also with increasing availability of storage space, this factor is not a big problem. Hence instead of tackling issues related to reducing the amount of data to be stored, there is bigger need to implement the creation of a knowledge base and hence create semantic data visualization. The work discussed presents an approach to integrate expert knowledge all along the data mining process in a coherent and uniform manner. A collective intelligence system plays a central role in this approach. The work primarily aims at converting the presence of large amounts of raw source data into a knowledge base and collective framework. Heterogeneous Web sources have been taken into consideration, which act as containers for the raw data source. Data visualization is the second major functionality. Taking into consideration the patterns mined, the onus is on the developer to present the mined patterns in a format that is most appealing to the end-user. Creation of such data visualizations requires various dimensions of the pattern to be understood. The estimated end product creates visualization from the knowledge base created from heterogeneous Web sources.
['M. R. Sumalatha', 'A. Ravi', 'M.L. Aravind', 'N.K. Prasanna']
Collective Intelligence in Distributed Systems and Semantic Data Visualization