A large plethora of models to predict human mobility exists in the literature. The problem of how to select the most appropriate model to solve a specific mobility prediction task has however received only little attention. Yet, a wrong model choice may lead to severe performance losses. In this paper, we address the model selection problem in human mobility prediction and make the following contributions. We present SELECTOR, a generic framework to explore human mobility data and compute both population models and individual models to predict human mobility. The former are models that are adapted to the characteristics of an entire population of users and can be used to overcome the cold-start problem. The latter are prediction models optimized for individual users. We present and analyze the results obtainable using SELECTOR on the Nokia data set, which is one of the largest and richest, publicly available data sets of human mobility data. We show that for many users, generic population models can be used in place of individual models with negligible performance losses. Yet for about 25 percent of the users, individual models perform at least three percentage points better than population models. Thereby, we show that the use of phone context data does not lead to significantly better performance of human mobility predictors with respect to the case in which only temporal and spatial features are used. We further observe that the population models we derive are robust against the demographics of the users and that building different population models for different periods of the day leads to performance improvements. We make SELECTOR publicly available to allow other researchers and practitioners to explore further mobility data sets and to embed the code base of SELECTOR in their applications.

Advances in development and deployment of mobile business applications (MBAs) based on the hybrid Web approach (hybrid MBAs) create new possibilities for enterprises to mobilize their business processes (BPs). Despite the fact that variability has a natural appearance in complex environments among different enterprises, current development tools for hybrid MBAs are lacking systematic support for variable features. We analyse in this paper the formation of hybrid MBAs' variable workflows on the BP-level by conducting an exploratory study within the context of a real-world, widespread BP instance. We also discuss quality assurance of domain activity diagrams (DADs), which document variability in BPs' workflows. Novel verification techniques for a structural analysis of DADs are highlighted.

Several algorithms to predict the next place visited by a user have been proposed in the literature. The accuracy of these algorithms -- measured as the ratio of the number of correct predictions and the number of all computed predictions -- is typically very high. In this paper, we show that this good performance is due to the high predictability intrinsic in human mobility. We also show that most algorithms fail to correctly predict transitions, i.e. situations in which users move between different places. To this end, we analyze the performance of 18 prediction algorithms focusing on their ability to predict transitions. We run our analysis on a data set of mobility traces of 37 users collected over a period of 1.5 years. Our results show that even algorithms achieving an overall high accuracy are unable to reliably predict the next location of the user if this is different from the current one. Building upon our analysis we then present a novel next-place prediction algorithm that can both achieve high overall accuracy and reliably predict transitions. Our approach combines all the 18 algorithms considered in our analysis and achieves its good performance at the cost of a higher computational and memory overhead.

Predicting the arrival and residence time of individuals at their relevant places enables a plethora of novel applications. In this work we first analyze the theoretical predictability of arrival and residence times and then evaluate the performance of eight different residence time predictors. We show that these predictors tend to underestimate the time a user will spend at her relevant places.