Abstract: Workers often forget to enter clockings indicating when they have arrived or left work or transferred between work activities. Various embodiments provide a system that predicts the risk of potential missed worker clockings and delivers alerts to the workers and/or their managers when that risk is high. The system analyzes historical data to find previous instances of missed clockings at an organization, extracts feature information regarding the context of the missed clockings, and calibrates a missing clocking risk model. The model is then used in either an online or batch mode to predict the risk of workers missing upcoming scheduled clockings. Based on these risk scores and system configurations, alerts are generated for employees and managers reminding them about the upcoming clockings and lowering the amount of missed clockings and subsequent remedies.

Abstract: Workers often forget to enter clockings indicating when they have arrived or left work or transferred between work activities. Various embodiments provide a system that predicts the risk of potential missed worker clockings and delivers alerts to the workers and/or their managers when that risk is high. The system analyzes historical data to find previous instances of missed clockings at an organization, extracts feature information regarding the context of the missed clockings, and calibrates a missing clocking risk model. The model is then used in either an online or batch mode to predict the risk of workers missing upcoming scheduled clockings. Based on these risk scores and system configurations, alerts are generated for employees and managers reminding them about the upcoming clockings and lowering the amount of missed clockings and subsequent remedies.

Abstract: Workers often forget to enter clockings indicating when they have arrived or left work or transferred between work activities. Various embodiments provide a system that predicts the risk of potential missed worker clockings and delivers alerts to the workers and/or their managers when that risk is high. The system analyzes historical data to find previous instances of missed clockings at an organization, extracts feature information regarding the context of the missed clockings, and calibrates a missing clocking risk model. The model is then used in either an online or batch mode to predict the risk of workers missing upcoming scheduled clockings. Based on these risk scores and system configurations, alerts are generated for employees and managers reminding them about the upcoming clockings and lowering the amount of missed clockings and subsequent remedies.