Storage Schemes for Spatio-Temporal Network Datasets

• Published in: Spatial Network Big Databases pp. 73 - 97
• Main Authors: Yang, Kwangsoo, Shekhar, Shashi
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2017; Springer International Publishing
• Subjects: Boundary Node; COMPUTER COMMUNICATIONS & NETWORKING; Cost Model; Data Page; Gain Function; Geographical information systems (GIS) & remote sensing; Infinite series; Page Size
• ISBN: 3319566563; 9783319566566
• DOI: 10.1007/978-3-319-56657-3_6

Emerging large-sized Spatial Network Big Data require novel data storage and access methods. Increasingly, these big data contain both spatial and temporal network data. Given a spatio-temporal network (STN) and a set of STN operations, the goal of the Storing Spatio-Temporal Networks (SSTN) problem is to produce an efficient method of storing STN data that minimizes disk I/O costs for given STN operations. The SSTN problem is important for many societal applications, such as surface and air transportation management systems. The problem is NP hard, and is challenging due to an inherently large data volume and novel semantics (e.g., Lagrangian reference frame). This chapter describes storage methods that minimize disk I/O costs for two STN operations (i.e., LGetOneSuccessor() and LGetAllSuccessors()).