Automated Concept Extraction in Internet-of-Things
Yuanyuan Bao, Wai Chen · 2018
With the emergence of Internet-of-Things (IoT), we are witnessing rapid increases in the amount of sensory data, which are streaming, volatile, often real-time, and heterogeneous in nature, when we enable interactions with the physical world by using sensors and intelligent objects. To help derive value-add insights into the IoT systems and their users, we need methods to automatically extract machine-readable concepts from raw sensory data. To date, only limited research effort has been devoted to automatically extracting (machine-readable) concepts from large sets of heterogeneous multivariate time-series sensory data. In this paper, we propose a framework for real-time automatic concept-extraction in the IoT environments. We enhance the symbolic aggregate approximation (SAX) algorithm into an optimized version (referred to as MultiSAX) for multivariate time-series sensory data - thereby transforming the multivariate time-series sensory data into symbolic representations. By redefining the distance function and density function of the symbolic representations, we extend clustering algorithm and automatically group symbolic representations into different concepts, which can be further operated on by utilizing a rule-based mechanism to make the concepts machine understandable. Our evaluation results show that our proposed method, operating on heterogeneous multivariate sensory data, can achieve automatic concept-extraction with low construction error and low communications overhead.