Enabling Context-Aware Search using Extracted Insights from IoT Data Streams

The rapid growth in collecting and sharing sensory observation form the urban environments provides opportunities to plan and manage the services in the cities better and allows citizens to also observe and understand the changes in their surrounding in a better way. The new urban sensory data also creates opportunities for further application and service development by creative industries and start-ups. However, as the size and diversity of this data increase, finding and accessing the right set of data in a timely manner is becoming more challenging. This paper describes a search engine designed for indexing, searching and accessing urban sensory data. We present the key feature and architecture of the system and demonstrate some of the functionalities that are provided by searching for raw sensory observations and also pattern search functions that are enabled by a pattern analysis algorithm, supported by monitoring of data streams for changes in quality of information and remediation.