On-line data stream mining has attracted much research interest, but systems that can be used as a workbench for online mining have not been researched, since they pose many difficult research challenges. The proposed system addresses these challenges by an architecture based on three main technical advances, (i) introduction of new constructs and synoptic data structures whereby complex KDD queries can be easily expressed and efficiently supported, (ii) an integrated library of mining algorithms that are fast & light enough to be effective on data streams, and (iii) support for Mining Model Definition Language (MMDL) that allows users to define new mining algorithms as a set of tasks and flows. Thus, the proposed system provides an extensible workbench for online mining, which is beyond the existing proposals for even static mining.
['Hetal Thakkar', 'Barzan Mozafari', 'Carlo Zaniolo']
A Data Stream Mining System