Paper Info
Title
Sequence Mining for Business Analytics: Building Project Taxonomies for Resource Demand Forecasting
Abstract
We develop techniques for mining labor records from a large number of historical IT consulting projects in order to discover clusters of projects exhibiting similar resource usage over the project life-cycle. The clustering results, together with domain expertise, are used to build a meaningful project taxonomy that can be linked to project resource requirements. Such a linkage is essential for project-based workforce demand forecasting, a key input for more advanced workforce management decision support. We formulate the problem as a sequence clustering problem where each sequence represents a project and each observation in the sequence represents the weekly distribution of project labor hours across job role categories. To solve the problem, we use a model-based clustering algorithm based on explicit state duration left-right hidden semi-Markov models (HsMM) capable of handling high-dimensional, sparse, and noisy Dirichlet-distributed observations and sequences of widely varying lengths. We then present an approach for using the underlying cluster models to estimate future staffing needs. The approach is applied to a set of 250 IT consulting projects and the results discussed.
Year
Venue
Keywords
2007
DMBiz@PAKDD
Sequence Mining,project-based workforce demand forecasting,historical IT consulting project,mining labor record,project life-cycle,IT consulting project,Resource Demand Forecasting,Business Analytics,advanced workforce management decision,clustering result,project labor hour,model-based clustering algorithm,Building Project Taxonomies,meaningful project taxonomy
Field
DocType
Citations 
Data science,Business analytics,Demand forecasting,Knowledge management,Analytics,Sequential Pattern Mining,Business
Conference
4
PageRank 
References 
Authors
0.49
6
3
Name
Order
Citations
PageRank
Ritendra Datta12526104.69
Jianying Hu247835.52
Bonnie Ray3464.17