Skip to main content
main-content

Über dieses Buch

This SpringerBrief provides an overview within data mining of spatiotemporal frequent pattern mining from evolving regions to the perspective of relationship modeling among the spatiotemporal objects, frequent pattern mining algorithms, and data access methodologies for mining algorithms. While the focus of this book is to provide readers insight into the mining algorithms from evolving regions, the authors also discuss data management for spatiotemporal trajectories, which has become increasingly important with the increasing volume of trajectories.

This brief describes state-of-the-art knowledge discovery techniques to computer science graduate students who are interested in spatiotemporal data mining, as well as researchers/professionals, who deal with advanced spatiotemporal data analysis in their fields. These fields include GIS-experts, meteorologists, epidemiologists, neurologists, and solar physicists.

Inhaltsverzeichnis

Frontmatter

Chapter 1. A Gentle Introduction to Spatiotemporal Data Mining

Abstract
Spatiotemporal data mining refers to the extraction of knowledge, regularly repeating relationships, and interesting patterns from data with spatial and temporal aspects. In recent years, many spatiotemporal frequent pattern mining algorithms were developed for spatiotemporal event instances represented by a series of region objects that evolves over time. These algorithms focus on the discovery of spatiotemporal co-occurrence patterns and event sequences by inspecting the spatiotemporal overlap and follow relationships. Before moving onto these relationships, we will demonstrate different types of spatiotemporal knowledge to place the relationships and methods in the greater context. This chapter provides a bird-eye view on the output of spatiotemporal data mining techniques in the literature, gives rationale for mining spatiotemporal patterns from evolving regions, and explains the challenges of mining patterns from evolving region data.
Berkay Aydin, Rafal A. Angryk

Chapter 2. Modeling Spatiotemporal Trajectories

Abstract
In this chapter, we will focus on the spatiotemporal object modeling and put special attention on the moving objects with extended geometric representations. Our spatiotemporal frequent pattern mining algorithms primarily make use of region trajectories whose polygon-based region representations continuously evolve over time. In the rest of this chapter, we will firstly introduce the conceptual modeling of spatiotemporal trajectories and moving objects. Then, we will present the evolving region trajectories and spatiotemporal event instances which are the base data types in our mining schema.
Berkay Aydin, Rafal A. Angryk

Chapter 3. Modeling Spatiotemporal Relationships Among Trajectories

Abstract
In this chapter, we will explore the spatiotemporal relationships occurring among the spatiotemporal objects. These relationships have their roots in topological spatial and temporal relationships presented over many data mining studies. In essence, these relationships are the building blocks of the spatiotemporal frequent pattern mining from evolving region trajectories. Using them, our aim is to find and count the number of instances that have these types of relationships. We will start our discussion with generic temporal and spatial relationships, and later on we will further discuss the spatiotemporal co-occurrences and sequences of evolving region trajectories.
Berkay Aydin, Rafal A. Angryk

Chapter 4. Significance Measurements for Spatiotemporal Co-occurrences

Abstract
An important aspect of data mining research is the determination of the interestingness of patterns. In classical frequent pattern mining tasks (e.g., shopping basket analysis), the main goal is to identify items (e.g., types of purchased goods) frequently appearing together in an itemset (e.g., shopping cart). Such analyses require an appropriate interestingness measure to assess the strength of relationships among different types of items and to eliminate the spurious itemsets. Measures, such as support, confidence, correlation, and entropy, have been extensively used in many frequent pattern mining algorithms. Spatial and spatiotemporal extensions of frequent pattern mining presents a similar challenge, where the choice of measures may lead to the discovery of inadvisable or uninteresting information depending on the context. Though, unlike traditional frequent pattern mining from binary features, in both spatial and spatiotemporal pattern mining tasks, the spatial or spatiotemporal relationships among items (or instances) are often not explicit. Therefore, it is considered necessary to initially transform the implicit spatial and temporal information to a transaction-like embodiment. In this chapter, we will explore the interestingness measures from the perspective of spatiotemporal co-occurrence relationships appearing among the evolving region trajectories.
Berkay Aydin, Rafal A. Angryk

Chapter 5. Spatiotemporal Co-occurrence Pattern (STCOP) Mining

Abstract
Given a dataset of event instances which are represented as trajectories of evolving region trajectories, spatiotemporal co-occurrence patterns (STCOPs) can be defined as subsets of event types, whose instances frequently co-occur in both space and time. STCOPs are the first type of spatiotemporal frequent patterns, we will derive from the evolving region trajectories. Our ultimate goal in discovering the prevalent STCOPs is first to determine which instances co-occur with each other, then to answer which combination of the event types are the most common among these co-occurring instances. Eventually, the discovered STCOPs are subsets of all event types in the given dataset. How we effectively and efficiently discover all the STCOPs from a given dataset is the main focus of this chapter. We will first formally define the terms for STCOP mining and later present the mining algorithms.
Berkay Aydin, Rafal A. Angryk

Chapter 6. Spatiotemporal Event Sequence (STES) Mining

Abstract
Spatiotemporal event sequences are the ordered series of event types. These event types represent the types of evolving region trajectory based instances that follow each other. The goal of spatiotemporal event sequence mining is finding frequently occurring sequences of event types from the follow relationships among all event instances. The key aspect of spatiotemporal event sequences is the spatiotemporal follow relationship appearing among the event instances. The relationship is characterized by temporal sequence relationship with spatial proximity constraints. In this chapter, we will touch upon the key concepts of spatiotemporal event sequence models, describe the spatiotemporal follow relationship thoroughly, and then present the state-of-the-art algorithms for discovering the event sequences.
Berkay Aydin, Rafal A. Angryk

Backmatter

Weitere Informationen

Premium Partner

    Bildnachweise