A Data Stream Mining System

Author

Thakkar, Hetal ; Mozafari, Barzan ; Zaniolo, Carlo

Author_Institution

Univ. of California at Los Angeles, Los Angeles, CA

fYear

2008

fDate

15-19 Dec. 2008

Firstpage

987

Lastpage

990

Abstract

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.

Keywords

data analysis; data mining; query processing; complex KDD queries; data stream mining system; integrated library; mining algorithm; mining model definition language; online data stream mining; online mining; synoptic data structures; Algorithm design and analysis; Association rules; Buildings; Conferences; Data mining; Data structures; Intrusion detection; Libraries; Milling machines; Proposals; data stream mining system; high level mining language; online mining system;

fLanguage

English

Publisher

ieee

Conference_Titel

Data Mining Workshops, 2008. ICDMW '08. IEEE International Conference on

Conference_Location

Pisa

Print_ISBN

978-0-7695-3503-6

Electronic_ISBN

978-0-7695-3503-6

Type

conf

DOI

10.1109/ICDMW.2008.133

Filename

4734034