| Social and community intelligence | |
 |
•Revealing the individual/group behaviors, social interactions as well as community dynamics by mining the digital traces left by people while interacting with cyber-physical spaces •Fusion data from Internet and Web applications, static infrastructure, and mobile and wearable |
Big Data Analytics |
|
 |
• Large-scale taxi data analytics • Large-scale mobile phone data analytics • Large-scale social media data analytics |
| Mobility Phone Data Analytics | |
 |
• Mobility model and prediction • Collective behavior-based mobility prediction • Energy-efficient mobile crowdsourcing |
| Taxi GPS Trajectory Mining | |
 |
•Social dynamics mining (Mobility pattern, Hotspot, etc.)
•Traffic dynamics mining (Traffic prediction, time estimation, etc.)
•Operation dynamics mining (Next passenger finding, shortest route discovery, etc.)
•Anomalous taxi trajectory detection
•Bus route planning
•Taxi service strategy understanding
|
| Location Based Social Media Data Analytics | |
 |
•Location based social community detection •Sentiment-enhanced location recommendation and search |
| Mobile Social Networking | |
 |
•Community Creation in Mobile Social Networks •Community detection using location based social network data •Social community profiling •Location search and recommendation •Intelligent social contact management •Combining spontaneous and online social network |
| Urban Computing | |
 |
•Bus route planning leveraging taxi GPS traces •Traffic prediction •Energy-efficient mobile crowdsourcing •Logical location recognition |
| Context model and middleware | |
 |
•Context acquisition/ representation
•On-the-fly context aggregation and dissemination
•Cross space context discovery and query
•Dynamic context aware service adaptation
•Separation of context acquisition, processing and usage |
Human activity recognition for pervasive elderly care |
|
 |
•Multi-modal sensor based human activity recognition •Combining logic-based and statistical learning based approach for activity recognition •Towards a general framework for multi-sensor data fusion |
Context-aware reminders for people with dementia |
|
 |
•Address cognitive reinforcement in four aspects: helping to remember, maintaining social contact, enhanced feeling of safety, and performing daily activities |