Using LDA to Uncover the Underlying Structures and Relations in Smart City Data Streams

Abstract

Recent advancements in sensing, networking technologies, and collecting real-world data on a large scale and from various environments have created an opportunity for new forms of real-world services and applications. This is known under the umbrella term of the Internet of Things (IoT). Physical sensor devices constantly produce very large amounts of data. Methods are needed which give the raw sensor measurements a meaningful interpretation for building automated decision support systems. To extract actionable information from real-world data, we propose a method that uncovers hidden structures and relations between multiple IoT data streams. Our novel solution uses latent Dirichlet allocation (LDA), a topic extraction method that is generally used in text analysis. We apply LDA on meaningful abstractions that describe the numerical data in human understandable terms. We use symbolic aggregate approximation to convert the raw data into string-based patterns and create higher level abstractions based on rules. We finally investigate how heterogeneous sensory data from multiple sources can be processed and analyzed to create near real-time intelligence and how our proposed method provides an efficient way to interpret patterns in the data streams. The proposed method uncovers the correlations and associations between different patterns in IoT data streams. The evaluation results show that the proposed solution is able to identify the correlation with high efficiency with an F -measure up to 90%.