Computing With Spatial Trajectories
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| Author | : Yu Zheng |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 328 |
| Release | : 2011-10-02 |
| Genre | : Computers |
| ISBN | : 1461416299 |
Spatial trajectories have been bringing the unprecedented wealth to a variety of research communities. A spatial trajectory records the paths of a variety of moving objects, such as people who log their travel routes with GPS trajectories. The field of moving objects related research has become extremely active within the last few years, especially with all major database and data mining conferences and journals. Computing with Spatial Trajectories introduces the algorithms, technologies, and systems used to process, manage and understand existing spatial trajectories for different applications. This book also presents an overview on both fundamentals and the state-of-the-art research inspired by spatial trajectory data, as well as a special focus on trajectory pattern mining, spatio-temporal data mining and location-based social networks. Each chapter provides readers with a tutorial-style introduction to one important aspect of location trajectory computing, case studies and many valuable references to other relevant research work. Computing with Spatial Trajectories is designed as a reference or secondary text book for advanced-level students and researchers mainly focused on computer science and geography. Professionals working on spatial trajectory computing will also find this book very useful.
| Author | : Yu Zheng |
| Publisher | : MIT Press |
| Total Pages | : 633 |
| Release | : 2019-02-05 |
| Genre | : Computers |
| ISBN | : 0262039087 |
An authoritative treatment of urban computing, offering an overview of the field, fundamental techniques, advanced models, and novel applications. Urban computing brings powerful computational techniques to bear on such urban challenges as pollution, energy consumption, and traffic congestion. Using today's large-scale computing infrastructure and data gathered from sensing technologies, urban computing combines computer science with urban planning, transportation, environmental science, sociology, and other areas of urban studies, tackling specific problems with concrete methodologies in a data-centric computing framework. This authoritative treatment of urban computing offers an overview of the field, fundamental techniques, advanced models, and novel applications. Each chapter acts as a tutorial that introduces readers to an important aspect of urban computing, with references to relevant research. The book outlines key concepts, sources of data, and typical applications; describes four paradigms of urban sensing in sensor-centric and human-centric categories; introduces data management for spatial and spatio-temporal data, from basic indexing and retrieval algorithms to cloud computing platforms; and covers beginning and advanced topics in mining knowledge from urban big data, beginning with fundamental data mining algorithms and progressing to advanced machine learning techniques. Urban Computing provides students, researchers, and application developers with an essential handbook to an evolving interdisciplinary field.
| Author | : Chiara Renso |
| Publisher | : Cambridge University Press |
| Total Pages | : 393 |
| Release | : 2013-10-14 |
| Genre | : Computers |
| ISBN | : 1107292360 |
Mobility of people and goods is essential in the global economy. The ability to track the routes and patterns associated with this mobility offers unprecedented opportunities for developing new, smarter applications in different domains. Much of the current research is devoted to developing concepts, models, and tools to comprehend mobility data and make it manageable for these applications. This book surveys the myriad facets of mobility data, from spatio-temporal data modeling, to data aggregation and warehousing, to data analysis, with a specific focus on monitoring people in motion (drivers, airplane passengers, crowds, and even animals in the wild). Written by a renowned group of worldwide experts, it presents a consistent framework that facilitates understanding of all these different facets, from basic definitions to state-of-the-art concepts and techniques, offering both researchers and professionals a thorough understanding of the applications and opportunities made possible by the development of mobility data.
| Author | : Ming-Syan Chen |
| Publisher | : Springer |
| Total Pages | : 427 |
| Release | : 2003-07-01 |
| Genre | : Computers |
| ISBN | : 3540363890 |
We are rapidly heading towards a world in which the computing infrastructure will contain billions of devices, which will interact with other computing/communications devices that are carried or worn by users as they go through their daily routines. Such devices will provide data access to mobile users as they move within buildings, cities, or across the globe. This new infrastructure presents tremendous challenges for data management technology, including: huge scale; variable and intermittent connectivity; location and context-aware applications; bandwidth, power, and devi- size limitations; and multimedia data delivery across hybrid networks and systems. Traditional data management technologies such as query processing, transaction management, workflow, business process management, and metadata management must all be reevaluated in this emerging environment. Furthermore, nontraditional issues such as the semantics of mobile data, location-dependent querying, broadcast and multicast delivery, and caching/prefetching techniques must all be addressed. The ability to track people as they move about their daily tasks raises serious issues of security and privacy. This conference is the fourth in the Mobile Data Management series, focusing on the challenges and opportunities for the management of data in mobile, pervasive, and wearable computing. MDM 2000 and 2001 were in Hong Kong and MDM 2002 was in Singapore. Eighty-seven papers were submitted to the conference from 23 countries and were subject to a rigorous review procedure. Every paper had three or four independent reviews. Twenty-one full papers and 15 short papers from both academia and industry were selected for publication in this volume of proceedings.
| Author | : Michael Gertz |
| Publisher | : Springer |
| Total Pages | : 454 |
| Release | : 2017-08-07 |
| Genre | : Computers |
| ISBN | : 3319643673 |
This book constitutes the refereed proceedings of the 15th International Symposium on Spatial and Temporal Databases, SSTD 2017, held in Arlington, VA, USA, in August 2017.The 19 full papers presented together with 8 demo papers and 5 vision papers were carefully reviewed and selected from 90 submissions. The papers are organized around the current research on concepts, tools, and techniques related to spatial and temporal databases.
| Author | : Matt Duckham |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 330 |
| Release | : 2012-07-27 |
| Genre | : Science |
| ISBN | : 3642308538 |
Computing increasingly happens somewhere, with that geographic location important to the computational process itself. Many new and evolving spatial technologies, such as geosensor networks and smartphones, embody this trend. Conventional approaches to spatial computing are centralized, and do not account for the inherently decentralized nature of "computing somewhere": the limited, local knowledge of individual system components, and the interaction between those components at different locations. On the other hand, despite being an established topic in distributed systems, decentralized computing is not concerned with geographical constraints to the generation and movement of information. In this context, of (centralized) spatial computing and decentralized (non-spatial) computing, the key question becomes: "What makes decentralized spatial computing special?" In Part I of the book the author covers the foundational concepts, structures, and design techniques for decentralized computing with spatial and spatiotemporal information. In Part II he applies those concepts and techniques to the development of algorithms for decentralized spatial computing, stepping through a suite of increasingly sophisticated algorithms: from algorithms with minimal spatial information about their neighborhoods; to algorithms with access to more detailed spatial information, such as direction, distance, or coordinate location; to truly spatiotemporal algorithms that monitor environments that are dynamic, even using networks that are mobile or volatile. Finally, in Part III the author shows how decentralized spatial and spatiotemporal algorithms designed using the techniques explored in Part II can be simulated and tested. In particular, he investigates empirically the important properties of a decentralized spatial algorithm: its computational efficiency and its robustness to unavoidable uncertainty. Part III concludes with a survey of the opportunities for connecting decentralized spatial computing to ongoing research and emerging hot topics in related fields, such as biologically inspired computing, geovisualization, and stream computing. The book is written for students and researchers of computer science and geographic information science. Throughout the book the author's style is characterized by a focus on the broader message, explaining the process of decentralized spatial algorithm design rather than the technical details. Each chapter ends with review questions designed to test the reader's understanding of the material and to point to further work or research. The book includes short appendices on discrete mathematics and SQL. Simulation models written in NetLogo and associated source code for all the algorithms presented in the book can be found on the author's accompanying website.
| Author | : Jianzhong Li |
| Publisher | : Springer |
| Total Pages | : 354 |
| Release | : 2018-02-23 |
| Genre | : Computers |
| ISBN | : 9811081239 |
This book constitutes the refereed proceedings of the 11th China Conference on Wireless Sensor Networks, CWSN 2017, held in Tianjin, China, in October 2017. The 28 revised full papers were carefully reviewed and selected from 213 submissions. The papers are organized in topical sections on wireless sensor networks; energy efficiency and harvesting; data fusion; mobile computing and social services.
| Author | : Hassan A. Karimi |
| Publisher | : CRC Press |
| Total Pages | : 410 |
| Release | : 2024-08-01 |
| Genre | : Computers |
| ISBN | : 1040090257 |
Over the past decade, since the publication of the first edition, there have been new advances in solving complex geoinformatics problems. Advancements in computing power, computing platforms, mathematical models, statistical models, geospatial algorithms, and the availability of data in various domains, among other things, have aided in the automation of complex real-world tasks and decision-making that inherently rely on geospatial data. Of the many fields benefiting from these latest advancements, machine learning, particularly deep learning, virtual reality, and game engine, have increasingly gained the interest of many researchers and practitioners. This revised new edition provides up-to-date knowledge on the latest developments related to these three fields for solving geoinformatics problems. FEATURES Contains a comprehensive collection of advanced big data approaches, techniques, and technologies for geoinformatics problems Provides seven new chapters on deep learning models, algorithms, and structures, including a new chapter on how spatial metaverse is used to build immersive realistic virtual experiences Presents information on how deep learning is used for solving real-world geoinformatics problems This book is intended for researchers, academics, professionals, and students in such fields as computing and information, civil and environmental engineering, environmental sciences, geosciences, geology, geography, and urban studies.
| Author | : Marcos R. Vieira |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 122 |
| Release | : 2013-10-15 |
| Genre | : Computers |
| ISBN | : 3319024086 |
This brief presents several new query processing techniques, called complex motion pattern queries, specifically designed for very large spatio-temporal databases of moving objects. The brief begins with the definition of flexible pattern queries, which are powerful because of the integration of variables and motion patterns. This is followed by a summary of the expressive power of patterns and flexibility of pattern queries. The brief then present the Spatio-Temporal Pattern System (STPS) and density-based pattern queries. STPS databases contain millions of records with information about mobile phone calls and are designed around cellular towers and places of interest. Density-based pattern queries capture the aggregate behavior of trajectories as groups. Several evaluation algorithms are presented for finding groups of trajectories that move together in space and time, i.e. within a predefined distance to each other. Finally, the brief describes a generic framework, called DivDB, for diversifying query results. Two new evaluation methods, as well as several existing ones, are described and tested in the proposed DivDB framework. The efficiency and effectiveness of all the proposed complex motion pattern queries are demonstrated through an extensive experimental evaluation using real and synthetic spatio-temporal databases. This clear evaluation of new query processing techniques makes Spatio-Temporal Database a valuable resource for professionals and researchers studying databases, data mining, and pattern recognition.
| Author | : Weiwei Ni |
| Publisher | : Springer Nature |
| Total Pages | : 725 |
| Release | : 2019-09-17 |
| Genre | : Computers |
| ISBN | : 3030309525 |
This book constitutes the proceedings of the 16th International Conference on Web Information Systems and Applications, WISA 2019, held in Qingdao, China, in September 2019. The 39 revised full papers and 33 short papers presented were carefully reviewed and selected from 154 submissions. The papers are grouped in topical sections on machine learning and data mining, cloud computing and big data, information retrieval, natural language processing, data privacy and security, knowledge graphs and social networks, blockchain, query processing, and recommendations.
