%0 Journal Article
%T Time-Series Data Mining for E-Service Application Analysis
%A Arnis Kirshners
%A Yuri Kornienko
%J Scientific Journal of Riga Technical University. Computer Sciences
%D 2009
%I 
%R 10.2478/v10143-010-0013-y
%X This paper provides application analysis of e-services available on the joint state and municipal e-service portal www.latvija.lv. The research is performed using a combination of time series analysis and data mining techniques. Time series analysis has enabled the determination of the count of clusters that represent services classification by application frequency. Meta-information is processed using data pre-processing methods and the values obtained are then discretised. The methods combinations examined in the paper are tested experimentally on the limited data amount available. The data describe the existing e-service requests by months. The clusters obtained are then added to the initial meta-information available when planning and developing services. E-service membership in the formed data set is determined using inductive classification trees. These algorithms represent knowledge in the form of classification trees through analysing feature values and cyclically split training instances into classes. As a result, based on the analysis conducted, recommendations for e-service developers and implementers are elaborated and basic parameters for successful introduction and application of e-services are determined.
%K E-service analysis
%K time series
%K data mining
%K classification
%K decision
%U http://versita.metapress.com/content/7j1862887jx01p06/?p=6e4f7f6adfd743dfb8a3ceec9c6941bc&pi=4