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2017 | Buch

Introduction Kapitel lesen Erstes Kapitel lesen

Spatio-Temporal Graph Data Analytics

verfasst von: Venkata M. V. Gunturi, Dr. Shashi Shekhar

Verlag: Springer International Publishing

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

This book highlights some of the unique aspects of spatio-temporal graph data from the perspectives of modeling and developing scalable algorithms. The authors discuss in the first part of this book, the semantic aspects of spatio-temporal graph data in two application domains, viz., urban transportation and social networks. Then the authors present representational models and data structures, which can effectively capture these semantics, while ensuring support for computationally scalable algorithms.

In the first part of the book, the authors describe algorithmic development issues in spatio-temporal graph data. These algorithms internally use the semantically rich data structures developed in the earlier part of this book. Finally, the authors introduce some upcoming spatio-temporal graph datasets, such as engine measurement data, and discuss some open research problems in the area.

This book will be useful as a secondary text for advanced-level students entering into relevant fields of computer science, such as transportation and urban planning. It may also be useful for researchers and practitioners in the field of navigational algorithms.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction
Abstract
Spatio-temporal graphs can appear in a number of application domains such as transportation (e.g., for use in route-planning algorithms), social science (e.g., for modeling geospatial aspect of different social phenomena), urban planning and public safety (e.g., for studying road accidents), commerce (trades between countries), etc. Out of these domains, transportation is likely to be the biggest consumer of the techniques developed for spatio-temporal graphs.
Venkata M. V. Gunturi, Shashi Shekhar
Chapter 2. Fundamental Concepts for Spatio-Temporal Graphs
Abstract
Data generated on a spatio-temporal graph implicitly captures several concepts related to the domain from where the data is originating.
Venkata M. V. Gunturi, Shashi Shekhar
Chapter 3. Representational Models for Spatio-Temporal Graphs
Abstract
Designing suitable representational models for spatio-temporal graphs is a challenging task. On one end, the model should be expressive enough to be able to easily represent a wide variety of concepts (e.g., holistic properties and Lagrangian reference frame) captured in the underlying datasets on urban road networks and social networks. But on the other end, the model should also aid in developing computationally scalable algorithms.
Venkata M. V. Gunturi, Shashi Shekhar
Chapter 4. Fastest Path for a Single Departure-Time
Abstract
This chapter provides a gentle introduction to fastest path finding algorithms in spatio-temporal graphs. We first discuss a simple adaptation of the Dijkstra’s algorithm for finding the fastest path. Following this we cover more advanced concepts of A* and bi-directional search on temporal digraphs. The chapter also discusses adaptations of few of the centrality metrics for temporal digraphs.
Venkata M. V. Gunturi, Shashi Shekhar
Chapter 5. Advanced Concepts: Critical Time Point Based Approaches
Abstract
This chapter proposes the concept of critical time points, which are the time points at which the shortest path between a source-destination pair (in a spatio-temporal graph) changes. We formalize this concept through the problem of all-start-time Lagrangian shortest path (ALSP) problem. Using the idea of critical-time-points, we discuss an algorithm, called CTAS, for the ALSP problem. This chapter also establishes the correctness and completeness of CTAS.
Venkata M. V. Gunturi, Shashi Shekhar
Chapter 6. Advanced Concepts: Bi-Directional Search for Temporal Digraphs
Abstract
This chapter introduces the concept of bi-directional search on temporal digraphs. The concept is illustrated by adapting the critical time point based solver for the all-start-time Lagrangian shortest path (ALSP) problem discussed in the previous chapter. A bi-directional search is more efficient than that forward only search.
Venkata M. V. Gunturi, Shashi Shekhar
Chapter 7. Knowledge Discovery: Temporal Disaggregation in Social Interaction Data
Abstract
This chapter discusses the notion of aggregation over the temporal dimension of temporal digraphs. This issues is particularly important in knowledge discovery and centrality metrics in social networks. We first present an simple temporal adaptation of a random walk based technique (Markov Clustering Algorithm) for community detection and centrality. Later, we present experiments to show the importance of temporal disaggregation in community detection and centrality measures. The experiments show that social structures observed at smaller temporal granularities are in general different from the ones at seen at larger granularities.
Venkata M. V. Gunturi, Shashi Shekhar
Chapter 8. Trend Topics: Engine Data Analytics
Abstract
Rich instrumentation (e.g, GPS receivers and engine sensors) in modern fleet vehicles allow us to periodically measure vehicle sub-system properties
Venkata M. V. Gunturi, Shashi Shekhar
Backmatter
Metadaten
Titel
Spatio-Temporal Graph Data Analytics
verfasst von
Venkata M. V. Gunturi
Dr. Shashi Shekhar
Copyright-Jahr
2017
Electronic ISBN
978-3-319-67771-2
Print ISBN
978-3-319-67770-5
DOI
https://doi.org/10.1007/978-3-319-67771-2