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Author: Mohamed Medhat Gaber
Publisher: Springer Science & Business Media
ISBN: 3642125182
Category : Computers
Languages : en
Pages : 235
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Book Description
This book contains thoroughly refereed extended papers from the Second International Workshop on Knowledge Discovery from Sensor Data, Sensor-KDD 2008, held in Las Vegas, NV, USA, in August 2008. The 12 revised papers presented together with an invited paper were carefully reviewed and selected from numerous submissions. The papers feature important aspects of knowledge discovery from sensor data, e.g., data mining for diagnostic debugging; incremental histogram distribution for change detection; situation-aware adaptive visualization; WiFi mining; mobile sensor data mining; incremental anomaly detection; and spatiotemporal neighborhood discovery for sensor data.
Author: Mohamed Medhat Gaber
Publisher: Springer Science & Business Media
ISBN: 3642125182
Category : Computers
Languages : en
Pages : 235
Get Book Here
Book Description
This book contains thoroughly refereed extended papers from the Second International Workshop on Knowledge Discovery from Sensor Data, Sensor-KDD 2008, held in Las Vegas, NV, USA, in August 2008. The 12 revised papers presented together with an invited paper were carefully reviewed and selected from numerous submissions. The papers feature important aspects of knowledge discovery from sensor data, e.g., data mining for diagnostic debugging; incremental histogram distribution for change detection; situation-aware adaptive visualization; WiFi mining; mobile sensor data mining; incremental anomaly detection; and spatiotemporal neighborhood discovery for sensor data.
Author: Vincent Lemaire
Publisher: Springer Nature
ISBN: 3030390985
Category : Computers
Languages : en
Pages : 236
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Book Description
This book constitutes the refereed proceedings of the 4th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2019, held in Würzburg, Germany, in September 2019. The 7 full papers presented together with 9 poster papers were carefully reviewed and selected from 31 submissions. The papers cover topics such as temporal data clustering; classification of univariate and multivariate time series; early classification of temporal data; deep learning and learning representations for temporal data; modeling temporal dependencies; advanced forecasting and prediction models; space-temporal statistical analysis; functional data analysis methods; temporal data streams; interpretable time-series analysis methods; dimensionality reduction, sparsity, algorithmic complexity and big data challenge; and bio-informatics, medical, energy consumption, on temporal data.
Author: Manish Gupta
Publisher: Springer
ISBN: 9783031007774
Category : Computers
Languages : en
Pages : 110
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Book Description
Outlier (or anomaly) detection is a very broad field which has been studied in the context of a large number of research areas like statistics, data mining, sensor networks, environmental science, distributed systems, spatio-temporal mining, etc. Initial research in outlier detection focused on time series-based outliers (in statistics). Since then, outlier detection has been studied on a large variety of data types including high-dimensional data, uncertain data, stream data, network data, time series data, spatial data, and spatio-temporal data. While there have been many tutorials and surveys for general outlier detection, we focus on outlier detection for temporal data in this book. A large number of applications generate temporal datasets. For example, in our everyday life, various kinds of records like credit, personnel, financial, judicial, medical, etc., are all temporal. This stresses the need for an organized and detailed study of outliers with respect to such temporal data. In the past decade, there has been a lot of research on various forms of temporal data including consecutive data snapshots, series of data snapshots and data streams. Besides the initial work on time series, researchers have focused on rich forms of data including multiple data streams, spatio-temporal data, network data, community distribution data, etc. Compared to general outlier detection, techniques for temporal outlier detection are very different. In this book, we will present an organized picture of both recent and past research in temporal outlier detection. We start with the basics and then ramp up the reader to the main ideas in state-of-the-art outlier detection techniques. We motivate the importance of temporal outlier detection and brief the challenges beyond usual outlier detection. Then, we list down a taxonomy of proposed techniques for temporal outlier detection. Such techniques broadly include statistical techniques (like AR models, Markov models, histograms, neural networks), distance- and density-based approaches, grouping-based approaches (clustering, community detection), network-based approaches, and spatio-temporal outlier detection approaches. We summarize by presenting a wide collection of applications where temporal outlier detection techniques have been applied to discover interesting outliers. Table of Contents: Preface / Acknowledgments / Figure Credits / Introduction and Challenges / Outlier Detection for Time Series and Data Sequences / Outlier Detection for Data Streams / Outlier Detection for Distributed Data Streams / Outlier Detection for Spatio-Temporal Data / Outlier Detection for Temporal Network Data / Applications of Outlier Detection for Temporal Data / Conclusions and Research Directions / Bibliography / Authors' Biographies
Author: Hideyuki Nakashima
Publisher: Springer Science & Business Media
ISBN: 0387938087
Category : Computers
Languages : en
Pages : 1290
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Book Description
Our homes anticipate when we want to wake up. Our computers predict what music we want to buy. Our cars adapt to the way we drive. In today’s world, even washing machines, rice cookers and toys have the capability of autonomous decision-making. As we grow accustomed to computing power embedded in our surroundings, it becomes clear that these ‘smart environments’, with a number of devices controlled by a coordinating system capable of ‘ambient intelligence’, will play an ever larger role in our lives. This handbook provides readers with comprehensive, up-to-date coverage in what is a key technological field. . Systematically dealing with each aspect of ambient intelligence and smart environments, the text covers everything, from visual information capture and human/computer interaction to multi-agent systems, network use of sensor data, and building more rationality into artificial systems. The book also details a wide range of applications, examines case studies of recent major projects from around the world, and analyzes both the likely impact of the technology on our lives, and its ethical implications. With a wide variety of separate disciplines all conducting research relevant to this field, this handbook encourages collaboration between disparate researchers by setting out the fundamental concepts from each area that are relevant to ambient intelligence and smart environments, providing a fertile soil in which ground-breaking new work candevelop.
Author: Theophano Mitsa
Publisher: CRC Press
ISBN: 1420089773
Category : Business & Economics
Languages : en
Pages : 398
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Book Description
From basic data mining concepts to state-of-the-art advances, this book covers the theory of the subject as well as its application in a variety of fields. It discusses the incorporation of temporality in databases as well as temporal data representation, similarity computation, data classification, clustering, pattern discovery, and prediction. The book also explores the use of temporal data mining in medicine and biomedical informatics, business and industrial applications, web usage mining, and spatiotemporal data mining. Along with various state-of-the-art algorithms, each chapter includes detailed references and short descriptions of relevant algorithms and techniques described in other references.
Author: Ebenezer A. Sholarin
Publisher: Springer
ISBN: 3319276514
Category : Science
Languages : en
Pages : 413
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Book Description
This book offers a new framework that facilitates the development of more intelligent systems and methods for data analysis and international information sharing, such as the use of satellite imaging and geospatial data to predict changes in weather conditions and shifts in water levels, and to assess the extent of the forest cover remaining on Earth that is visible from space. It brings together the many aspects of science and technology, as well as formula and analytical approaches required for more informed decision-making. It also highlights the vital importance of understanding the technological, economic and social dimensions of environmental projects that have short-term results and long-term impacts. It is unique in that it clearly distinguishes between environmental project management (EnvPM) and green project management (GreenPM), and presents an amalgamation of environmental management and project management concepts, using geospatial methods to form an EnvPM concept. The book sets a benchmark for the professionalism with which environmental projects should be planned, executed, monitored, assessed and delivered. While primarily intended for professionals responsible for the management of environmental projects or interested in improving the overall efficiency of such projects, it is also a useful handbook for managers in the private, public and non-for-profit sectors. It is a valuable resource for students at both undergraduate and master’s levels and an indispensable guide for anyone wanting to develop their skills in modern project management, environmental management and geospatial techniques. ``We are the first generation to feel the impact of climate change, and the last generation that can do something about it.'' US President Obama's address to the United Nations on Climate Change and Global warming (2015) hison: This book provides an in-depth, well-researched and science-based approach to applying key project-management and spatial tools and practices in environmental projects. An important read for leaders considering projects that balance social-economic growth against minimising its ill-effects on Planet Earth. - Todd Hutchison, Global Chairman of Peopleistic group.
Author: Shashi Shekhar
Publisher: Springer Science & Business Media
ISBN: 038730858X
Category : Computers
Languages : en
Pages : 1392
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Book Description
The Encyclopedia of GIS provides a comprehensive and authoritative guide, contributed by experts and peer-reviewed for accuracy, and alphabetically arranged for convenient access. The entries explain key software and processes used by geographers and computational scientists. Major overviews are provided for nearly 200 topics: Geoinformatics, Spatial Cognition, and Location-Based Services and more. Shorter entries define specific terms and concepts. The reference will be published as a print volume with abundant black and white art, and simultaneously as an XML online reference with hyperlinked citations, cross-references, four-color art, links to web-based maps, and other interactive features.
Author: Robert J. Abrahart
Publisher: CRC Press
ISBN: 0203305809
Category : Technology & Engineering
Languages : en
Pages : 443
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Book Description
Geocomputation is essentially the follow-on revolution from Geographic Information Science and is expected to gather speed and momentum in the first decade of the 21st century. It comes into use once a GIS database has been set up, with a digital data library, and expanded and linked to a global geographical two or three dimensional co-ordinate system. It exploits developments in IT and new data gathering and earth observing technologies, and takes the notion of GIS beyond data and towards its analysis, modelling, and use in problem solving. This book provides pointers on how to harness these technologies in tandem and in the context of multiple different subjects and problem areas. It seeks to establish the principles and set the foundations for subsequent growth. L
Author: Manish Gupta
Publisher: Springer Nature
ISBN: 3031019059
Category : Computers
Languages : en
Pages : 110
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Book Description
Outlier (or anomaly) detection is a very broad field which has been studied in the context of a large number of research areas like statistics, data mining, sensor networks, environmental science, distributed systems, spatio-temporal mining, etc. Initial research in outlier detection focused on time series-based outliers (in statistics). Since then, outlier detection has been studied on a large variety of data types including high-dimensional data, uncertain data, stream data, network data, time series data, spatial data, and spatio-temporal data. While there have been many tutorials and surveys for general outlier detection, we focus on outlier detection for temporal data in this book. A large number of applications generate temporal datasets. For example, in our everyday life, various kinds of records like credit, personnel, financial, judicial, medical, etc., are all temporal. This stresses the need for an organized and detailed study of outliers with respect to such temporal data. In the past decade, there has been a lot of research on various forms of temporal data including consecutive data snapshots, series of data snapshots and data streams. Besides the initial work on time series, researchers have focused on rich forms of data including multiple data streams, spatio-temporal data, network data, community distribution data, etc. Compared to general outlier detection, techniques for temporal outlier detection are very different. In this book, we will present an organized picture of both recent and past research in temporal outlier detection. We start with the basics and then ramp up the reader to the main ideas in state-of-the-art outlier detection techniques. We motivate the importance of temporal outlier detection and brief the challenges beyond usual outlier detection. Then, we list down a taxonomy of proposed techniques for temporal outlier detection. Such techniques broadly include statistical techniques (like AR models, Markov models, histograms, neural networks), distance- and density-based approaches, grouping-based approaches (clustering, community detection), network-based approaches, and spatio-temporal outlier detection approaches. We summarize by presenting a wide collection of applications where temporal outlier detection techniques have been applied to discover interesting outliers. Table of Contents: Preface / Acknowledgments / Figure Credits / Introduction and Challenges / Outlier Detection for Time Series and Data Sequences / Outlier Detection for Data Streams / Outlier Detection for Distributed Data Streams / Outlier Detection for Spatio-Temporal Data / Outlier Detection for Temporal Network Data / Applications of Outlier Detection for Temporal Data / Conclusions and Research Directions / Bibliography / Authors' Biographies
Author: Daniel Barbará
Publisher: Springer Science & Business Media
ISBN: 9781402070549
Category : Business & Economics
Languages : en
Pages : 286
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Book Description
Data mining is becoming a pervasive technology in activities as diverse as using historical data to predict the success of a marketing campaign, looking for patterns in financial transactions to discover illegal activities or analyzing genome sequences. From this perspective, it was just a matter of time for the discipline to reach the important area of computer security. Applications Of Data Mining In Computer Security presents a collection of research efforts on the use of data mining in computer security. Applications Of Data Mining In Computer Security concentrates heavily on the use of data mining in the area of intrusion detection. The reason for this is twofold. First, the volume of data dealing with both network and host activity is so large that it makes it an ideal candidate for using data mining techniques. Second, intrusion detection is an extremely critical activity. This book also addresses the application of data mining to computer forensics. This is a crucial area that seeks to address the needs of law enforcement in analyzing the digital evidence.
