It is essential to constantly collect data with various mobile applications from diverse sources, such as smartphones and ubiquitous sensors. However, how do you conduct the analysis on such a mass of mobile data or mobile web data aiming to solve issues in different areas of applications, including human behavior recognition, medication, recommendation and transportation? Nowadays, research in mobile and social computing environments is now turning to novel concepts to address the challenge of data processing and analyzing. The special issue Mobile web data analytics addresses issues of data management in mobile and social computing environments with a special focus on data processing and applications. The goal of the special issue is to build a forum for researchers from academy and industry to investigate challenging and innovative research issues on the subject, which combines data analytics within mobile and social environment and to explore creative concepts, theories, innovative technologies and intelligent solutions. We intend this special issue to act as an initial place where people from different areas can find a forum to discuss issues of data management and processing in new and emerging mobile computing environments.
We accepted 11 papers that provide deep research results to report the advance of mobile web data analytics and applications. These papers are grouped into two special issues. This first special issue contains 5 papers.
In this special issue, the first contribution is “Hot time periods discovery for facility proportioning in urban commercial districts using POIs and mobile phone data” by Xie et al., which presented an approach to mining hot time periods in urban commercial districts using POI information and mobile phone data, which is valuable for the analysis of activity regularity of population in urban cities. The second paper “A point of interest recommendation method using user similarity” by Zeng et al. proposed an efficient POI recommendation method that finds out users’ similarities using diverse data including their locations data to form an ordered POI recommendation list. The third paper “Content tracking by leveraging hashtag and time information in Twitter social media” by Xu et al. developed two methods to detect and track the evolution of content in Twitter social media by integrating hashtag and time information. The advantage of the proposed methods is that it captures the hashtags distribution over topics and topic changes over time simultaneously. In the fourth paper “Finger gesture recognition using a smartwatch with integrated motion sensors”, Li et al. utilized data collected from the sensors of accelerometer and gyroscope embedded in smartwatch to recognize finger gestures. Their results show the viability of recognizing finger or hand gestures using motion sensors. The fifth paper “uGait: a platform for automated quantitative gait analysis and its application to Parkinson’s disease” by Wu et al. presented their gait sensing platform that can discriminate Parkinson patients from healthy subjects by recognizing the patterns of their movements using motion sensors. Their experimental results show the capability of recognizing a variety of gait parameters improving the classification of different movements of Parkinson patients and healthy persons.
In summary, the goal of this special issue is to crystallize the emerging mobile web data technologies and trends into positive efforts to focus on the most promising solutions in applications. The papers show that mobile data technologies play a more and more important role in various applications. This special issue intends to act as an initial place for people from different areas to discuss issues in emerging mobile computing environments.