Spatiotemporal Data Engineering

1. Introduction

Curiosity to examine and understand the nature of data has been increasing significantly in all sectors of business because benefits that are brought by having useful information in hand for decision making are recognized. In addition to this, enormous historical data owned and managed by various organizations with the help of good quality software played a significant role in raising this curiosity too. Availability of location-based services and mobile computing enabled the collection of spatial and temporal data. This data managed in business information systems is a key and dependable resource for decision makers. Spatiotemporal data has many applications in domains such as transportation, ecology, medicine, geophysics, forestry, biology, agriculture, and meteorology. The complexity of spatiotemporal data due to spatial characteristics such as point, line, polygon and non-spatial properties in various formats raised many challenges for its efficient processing by the applications. It has created a significant demand for modeling, indexing, analysis and mining of spatiotemporal data.

Continuous arrival of spatiotemporal data from different devices in various formats creates voluminous data. It poses great challenges to the capability of human beings to understand the data and to extract potentially useful knowledge from it. Both industry and academia are showing an interest to take up these challenges involved in storing, processing and analysis of this data. The research community in spatiotemporal data engineering is investigating new techniques and systems for addressing these issues and challenges.

This chapter is organized into spatiotemporal database concepts, Issues in spatiotemporal data engineering, Spatiotemporal Data Models, Spatiotemporal Data Indexing, Spatiotemporal Data Analysis and Mining, and Spatiotemporal Big data management systems to gain insight into the state of the art in all relevant areas of the research work. The Spatiotemporal Database concepts describes spatial data concepts, temporal data concepts and spatiotemporal data concepts and applications. Section on Spatiotemporal Data Models discusses the existing spatiotemporal data models. Spatiotemporal data indexing section briefs the indexing techniques for different kinds of spatiotemporal data. The section on Spatiotemporal Data Analysis and Mining does survey on techniques and methods in the analysis and the mining. Distributed storage and processing systems such as Hadoop, Spark to handle spatiotemporal big data are discussed in last section.