Journal
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Volume 31, Issue 5, Pages 923-937Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2018.2848658
Keywords
Workforce analytics; generative model; graphical model; latent variable model
Categories
Funding
- CSC
- NSF [CNS 1115375, IIP 1230740, NSF 1547102, SaTC 1564097]
- National Natural Science Foundation of China [61272129]
- National Science and Technology Supporting Program of China [2015BAH18F02]
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As more business workflow systems are being deployed in modern enterprises and organizations, more employee-activity log data are being collected and analyzed. In this paper, we develop a latent ability model (LAM) as a generative probabilistic learning framework for workforce analytics over employee-activity logs. The LAM development is novel in three aspects. First, we introduce the concept of latent ability variables to model hidden relations between employees and activities in terms of job performance, such as the set of skills provided by an employee and the set of skills required by an activity, and how well they matchup in employee-activity assignment. Second, we construct the latent ability model by learning latent ability parameters from the employee-activity log data using expectation-maximization and gradient descent. Finally, we leverage LAM to build inference and prediction models for employee performance prediction, employee ability comparison, and employee-activity matchup quality estimation. We evaluate the accuracy and efficiency of our approach using real log datasets collected from a workflow system deployed in the government of the city of Hangzhou, China, which consists of 5,287,621 log records over two years involving 744 activities and 1,725 employees. We show that LAM approach outperforms existing representative methods in both accuracy and efficiency.
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