The nature of data has changed from the past decades. A large volume of spatial-temporal data has been produced, collected, and shared to observe events in the physical world at different spatial scales and temporal frequencies. A multitude of data streams such as weather patterns, stock prices, social media, traffic information, and disease incidents can be used to recognize evolving real-world situations. These situations vary and affect multiple aspects of people’s lives - such as traffic, flash floods, economic recession, and epidemic diseases. Detecting situations in time to take appropriate actions can help in saving lives and resources. Building upon a situation recognition and social network concept, where people can easily connect to people online, the Social Life Network (SLN) concept is introduced. The goal is to connect people with the right resources efficiently, effectively, and promptly, depending on the evolving situations. To achieve this goal, a novel platform for situation recognition is required.
In this dissertation, we propose a generic framework for situation recognition from heterogeneous event streams. First, to ingest and store massive data streams, we implement the archival system using the pub/sub messaging system, Kafka, and big data management system, AsterixDB. Second, to handle data and events at different granularities, multi-resolution processing is introduced in every step including data ingestion, query processing, and data visualization. To aid users in finding the right resolution, statistical information about estimated error and response time for each situation model at different resolutions are provided. The system can automatically select an appropriate resolution or users can interactively select the most satisfactory resolution according to their needs. The idea that action drives design is a key component in creating a situation model. Finally, to fuse disparate data sources, a micro-reports concept is proposed. These micro-reports are compelling, universal, spontaneous, and objective. They can be used to solve problems of existing micro-blogs and replace them. The applicability of this framework is presented in a healthcare application for asthma risk management, disaster rescue for flood and hurricane mitigation, and smart city innovation for trash management in Downtown Washington D.C.