Jorn Bakker

Stess@Work: from measuring stress to its understanding, prediction and handling with personalized coaching

The problem of job stress is generally recognized as one of the major factors leading to a spectrum of health problems. People with certain professions, like intensive care specialists or call-center operators, and people in certain phases of their lives, like working parents with young children, are at increased risk of getting overstressed. For instance, one third of the intensive care specialists in the Netherlands are reported to have (had) a burn-out. Stress management should start far before the stress starts causing illnesses. The current state of sensor technology allows to develop systems measuring physical symptoms reflecting the stress level. We propose to use data mining and predictive modeling for gaining insight in the stress effects of the events at work and for enabling better stress management by providing timely and personalized coaching. In this paper we present a general framework allowing to achieve this goal and discuss the lessons learnt from the conducted case study.

Online mass flow prediction in CFB boilers with explicit detection of sudden concept drift

Fuel feeding and inhomogeneity of fuel typically cause fluctuations in the circulating fluidized bed (CFB) process. If control systems fail to compensate the fluctuations, the whole plant will suffer from dynamics that is reinforced by the closed-loop controls. This phenomenon causes reducing efficiency and the lifetime of process components. In this paper we address the problem of online mass flow prediction, which is a part of control. Particularly, we consider the problem of learning an accurate predictor with explicit detection of abrupt concept drift and noise handling mechanisms. We emphasize the importance of having domain knowledge concerning the considered case and constructing the ground truth for facilitating the quantitative evaluation of different approaches. We demonstrate the performance of change detection methods and show their effect on the accuracy of the online mass flow prediction with real datasets collected from the experimental laboratory-scale CFB boiler.

Towards Context Aware Food Sales Prediction

Sales prediction is a complex task because of a large number of factors affecting the demand. We present a context aware sales prediction approach, which selects the base predictor depending on the structural properties of the historical sales. In the experimental part we show that there exist product subsets on which, using this strategy, it is possible to outperform naive methods. We also show the dependencies between product categorization accuracies and sales prediction accuracies. A case study of a food wholesaler indicates that moving average prediction can be outperformed by intelligent methods, if proper categorization is in place, which appears to be a difficult task.

Handling outliers and concept drift in online mass flow prediction in CFB boilers

In this paper we consider an application of data mining technology to the analysis of time series data from a pilot circulating fluidized bed (CFB) reactor. We focus on the problem of the online mass prediction in CFB boilers. We present a framework based on switching regression models depending on perceived changes in the data. We analyze three alternatives for change detection. Additionally, a noise canceling and a state determination and windowing mechanisms are used for improving the robustness of online prediction. We validate our ideas on real data collected from the pilot CFB boiler.

Online Mass Flow Prediction in CFB Boilers

Fuel feeding and inhomogeneity of fuel typically cause process fluctuations in the circulating fluidized bed (CFB) process. If control systems fail to compensate for the fluctuations, the whole plant will suffer from fluctuations that are reinforced by the closed-loop controls. This phenomenon causes a reduction of efficiency and lifetime of process components. Therefore, domain experts are interested in developing tools and techniques for getting better understanding of underlying processes and their mutual dependencies in CFB boilers. In this paper we consider an application of data mining technology to the analysis of time series data from a pilot CFB reactor. Namely, we present a rather simple and intuitive approach for online mass flow prediction in CFB boilers. This approach is based on learning and switching regression models. Additionally, noise canceling, and windowing mechanisms are used for improving the robustness of online prediction. We validate our approach with a set of simulation experiments with real data collected from the pilot CFB boiler.

Food Wholesales Prediction: What Is Your Baseline?

Sales prediction is an important problem for different companies involved in manufacturing, logistics, marketing, wholesaling and retailing. Different approaches have been suggested for food sales forecasting. Several researchers, including the authors of this paper, reported on the advantage of one type of technique over the others for a particular set of products. In this paper we demonstrate that besides an already recognized challenge of building accurate predictive models, the evaluation procedures themselves should be considered more carefully. We give illustrative examples to show that e.g. popular MAE and MSE estimates can be intuitive with one type of product and rather misleading with the others. Furthermore, averaging errors across differently behaving products can be also counter intuitive. We introduce new ways to evaluate the performance of wholesales prediction and discuss their biases with respect to different error types.

OMFP: An Approach for Online Mass Flow Prediction in CFB Boilers

Fuel feeding and inhomogeneity of fuel typically cause process fluctuations in the circulating fluidized bed (CFB) boilers. If control systems fail to compensate the fluctuations, the whole plant will suffer from fluctuations that are reinforced by the closed-loop controls. Accurate estimates of fuel consumption among other factors are needed for control systems operation. In this paper we address a problem of online mass flow prediction. Particularly, we consider the problems of (1) constructing the ground truth , (2) handling noise and abrupt concept drift, and (3) learning an accurate predictor. Last but not least we emphasize the importance of having the domain knowledge concerning the considered case. We demonstrate the performance of OMPF using real data sets collected from the experimental CFB boiler.

Finding Incident-Related Social Media Messages for Emergency Awareness

An information retrieval framework is proposed which searches for incident-related social media messages in an automated fashion. Using P2000 messages as an input for this framework and by extracting location information from text, using simple natural language processing techniques, a search for incident-related messages is conducted. A machine learned ranker is trained to create an ordering of the retrieved messages, based on their relevance. This provides an easy accessible interface for emergency response managers to aid them in their decision making process.