A Spatio-Temporal and Categorical Correlation Computing Method for Inductive and Deductive Data Analysis

Abstract

This paper proposes a spatio-temporal and categorical correlation computing method for induction and deduction analysis. This method is a data analytics method to reveal spatial, temporal, and categorical relationships between two heterogeneous sets in past events by correlation calculation, thereby finding insights to build new connections between the sets in the future. The most significant feature of this method is that it allows inductive and deductive data analysis by applying context vectors to compute the relationship between the sets whose elements are time, space, and category. Inductive analysis corresponds to data mining, which composes a context vector as a hypothesis to extract meaningful relationships from trends and patterns of past events. Deductive analysis searches past events similar to a context vector’s temporal, spatial, and categorical conditions. Spatio-temporal information about the events and information such as frequency, scale, and category are used as parameters for correlation computing. In this method, a multi-dimensional vector space that consists of time, space, and category dimensions is dynamically created, and the data of each set expressed as vectors is mapped onto the space. The similarity degree of the computing shows the strength of relationships between the two sets. This context vector is also mapped onto the space and is calculated distances between the context vector and other vectors of the sets. This paper shows the details of this method and implementation method and assumed applications in commerce activities.