webSPADE: a parallel sequence mining algorithm to analyze web log data

Author

Demiriz, Ayhan

Author_Institution

Inf. Technol., Verizon Inc., Irving, TX, USA

fYear

2002

fDate

2002

Firstpage

755

Lastpage

758

Abstract

Enterprise-class web sites receive a large amount of traffic, from both registered and anonymous users. Data warehouses are built to store and help analyze the click streams within this traffic to provide companies with valuable insights into the behavior of their customers. This article proposes a parallel sequence mining algorithm, webSPADE, to analyze the click streams found in site web logs. In this process, raw web logs are first cleaned and inserted into a data warehouse. The click streams are then mined by webSPADE. An innovative web-based front-end is used to visualize and query the sequence mining results. The webSPADE algorithm is currently used by Verizon to analyze the daily traffic of the Verizon.com web site.

Keywords

Web sites; data mining; Web log data; data warehouses; enterprise-class web sites; parallel sequence mining algorithm; raw web logs; sequence mining; web-based front-end; webSPADE; Algorithm design and analysis; Appropriate technology; Companies; Data analysis; Data visualization; Data warehouses; Frequency; Information technology; Relational databases; Service oriented architecture;

fLanguage

English

Publisher

ieee

Conference_Titel

Data Mining, 2002. ICDM 2003. Proceedings. 2002 IEEE International Conference on

Print_ISBN

0-7695-1754-4

Type

conf

DOI

10.1109/ICDM.2002.1184046

Filename

1184046