The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Principles of data mining
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
A delivery framework for health data mining and analytics
ACSC '05 Proceedings of the Twenty-eighth Australasian conference on Computer Science - Volume 38
Adopting IT for effective management of social welfare programs
dg.o '04 Proceedings of the 2004 annual national conference on Digital government research
Successfully adopting IT for social welfare program management
dg.o '04 Proceedings of the 2004 annual national conference on Digital government research
Supporting self-evaluation in local government via KDD
dg.o '08 Proceedings of the 2008 international conference on Digital government research
Proceedings of the 12th Annual International Digital Government Research Conference: Digital Government Innovation in Challenging Times
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New technology in knowledge discovery and data mining (KDD) make it possible to extract valuable information from operational data. Private businesses already use the technology for better management, planning, and marketing. Social welfare government agencies have a wealth of information about the experiences of families and individuals that are the most needy in our society in their administrative databases. These data too can be mined and analyzed with proper application of KDD technology. Such social science research could be priceless for better welfare program administration, program evaluation, and policy analysis. In this paper, we discuss a successful case study involving research in computer science as well as social welfare. In a long standing collaboration between the North Carolina DHHS and the University of North Carolina, we have (1) successfully built a longitudinal information system that tracks the experiences of families and individuals on welfare in NC since 1995 (2) developed a dynamic website reporting on the various aspects of the welfare program at the county level in order to assist county staff in the administration of the welfare program and (3) developed a new method to analyze sequential data, which can detect common patterns of welfare services given over time.