The representation and understanding of the movement semantics of moving objects is a key issue for developing more accurate and efficient applications for Location Based
Services, fleet control and so on. This field became very important for researching in the area of Database and Artificial Intelligence. There are many proposals related with algorithms and techniques for this vein, most of them have been implemented on tools, but they are not in sight of researching community and not available for widely usage. In this paper we present a survey on tools for representing moving objects and reasoning on their movement semantics, analyzing proposals from the database context to recent artificial intelligence ones. Our main goal is to clarify the existence and importance of those unknown tools with high impact on representing moving objects considering the semantics of the movement for spatio-temporal analysis.
Table of Contents
I. INTRODUCTION
II. FOUNDATIONS AND MOTIVATION
III. EVALUATING EXISTING TOOLS
A. Reference framework for evaluation
B. MADS tools
C. SECONDO
D. HERMES
E. MoveMine
F. DAMSEL (DAta Mining and Semantic Enrichment query Language)
G. WEKA-STPM
H. Comparative summary
IV. DISCUSSION
V. CONCLUSIONS AND FUTURE WORK
Objectives and Topics
The primary objective of this review is to categorize and evaluate existing software tools designed for representing moving objects and reasoning about their movement semantics. It investigates how various database and artificial intelligence approaches address the challenges of spatio-temporal analysis, aiming to clarify the availability and usability of these tools for the research community.
- Review of current algorithms and techniques for spatio-temporal data mining.
- Evaluation of conceptual and data models for trajectory representation.
- Assessment of existing tools based on a defined framework of semantic richness and availability.
- Identification of gaps in semantic scalability and accessibility within current research tools.
- Exploration of future directions for integrating data mining and ontological approaches.
Excerpt from the book
II. FOUNDATIONS AND MOTIVATION
The analysis of movement semantics has become in an important issue for the correct representation and subsequent reasoning about moving objects in the world (trajectory analysis, movement patterns recognition, movement prediction). Researchers have developed algorithms and techniques needed to face challenges like: finding movement patterns for future movement or change prediction by the analysis of the history of the movement of people or migratory animals, or water level on river margins or inundations; dealing with uncertainty associated with unpredicted movement of birds, wild animals or fish shoals and detecting outlier behaviours using large amounts of data about trajectories stored in a DBMS.
Most of the high developed areas are:
• Conceptual and data models
• Location management
• Trajectory analysis
• Movement patterns recognition
• Outliers detection
• Uncertainty treatment
Examples of conceptual and data models are: [2] called the Moving Objects Spatio-Temporal (MOST) data model; [3], where Guting et al. present a foundational approach for moving objects algebra (data types and operators) including a limited consideration of lines for trajectory analysis; [4], where one proposal of abstract data types for moving points and moving regions is presented; [5] called the MADS conceptual model, among others.
Summary of Chapters
I. INTRODUCTION: This chapter introduces the growing importance of moving object analysis in computer science and outlines the structure of the review.
II. FOUNDATIONS AND MOTIVATION: This section details the theoretical basis of movement semantics, including key areas like location management, trajectory analysis, and uncertainty treatment.
III. EVALUATING EXISTING TOOLS: This chapter presents a systematic evaluation of specific software tools using a proposed reference framework based on usage, semantic richness, and availability.
IV. DISCUSSION: This chapter synthesizes the findings, noting that while various tools exist, many lack the semantic richness and accessibility required for broad research adoption.
V. CONCLUSIONS AND FUTURE WORK: This final chapter summarizes the current state of the field and suggests future research directions, particularly the integration of ontologies and data mining.
Keywords
moving objects, tools, spatio-temporal analysis, data mining, data models, movement semantics, trajectory analysis, ontology, DBMS, movement patterns, outlier detection, uncertainty, research survey, software evaluation, geographic information systems
Frequently Asked Questions
What is the core focus of this research paper?
The paper provides a comprehensive review of existing tools and algorithms used for the representation and semantic reasoning of moving objects within databases and artificial intelligence.
What are the central thematic fields addressed?
The work covers conceptual data models, location management, trajectory analysis, movement pattern recognition, outlier detection, and the handling of movement uncertainty.
What is the primary objective of this review?
The main goal is to clarify the existence and importance of specialized tools for moving object representation and to evaluate their usability and semantic richness for the scientific community.
Which scientific methods are analyzed?
The paper examines various methodologies including data mining, spatio-temporal query languages, algebraic models for moving objects, and recent ontology-driven semantic enrichment approaches.
What does the main body of the work cover?
The main body systematically catalogs and assesses specific tools like MADS, SECONDO, HERMES, MoveMine, DAMSEL, and WEKA-STPM based on a specific reference framework.
Which keywords best characterize this work?
Key terms include moving objects, spatio-temporal analysis, data models, movement semantics, and trajectory analysis.
What distinguishes the MADS tool suite from others mentioned?
The MADS suite focuses on integrated conceptual modeling and schema generation, providing separate editors for schema definition and query formulation.
How does the author define the "Semantic Richness" of a tool?
Semantic richness describes a tool's capacity to include, retrieve, and process semantic information about moving objects and their movement behaviors.
What is the main limitation identified by the author regarding existing tools?
A major limitation is that many tools remain "ad-hoc" and are not widely accessible for industry or academic experimentation, hindering their broader impact.
Why is the DAMSEL tool highlighted as innovative?
DAMSEL is considered highly innovative because it effectively integrates data mining query languages with ontological approaches to enhance semantic reasoning capacity.
- Arbeit zitieren
- Proff. Yuniel Proenza (Autor:in), 2014, Tools for Representing Moving Objects and Reasoning on their Semantics: A review, München, GRIN Verlag, https://www.hausarbeiten.de/document/288460
