Last edited by Arashibei
Tuesday, July 21, 2020 | History

1 edition of Mining Spatio-Temporal Information Systems found in the catalog.

Mining Spatio-Temporal Information Systems

by Roy Ladner

  • 324 Want to read
  • 13 Currently reading

Published by Springer US in Boston, MA .
Written in English

    Subjects:
  • Information storage and retrieval systems,
  • Computer science,
  • Data structures (Computer science),
  • Geographic information systems

  • About the Edition

    Mining Spatio-Temporal Information Systems, an edited volume is composed of chapters from leading experts in the field of Spatial-Temporal Information Systems and addresses the many issues in support of modeling, creation, querying, visualizing and mining. Mining Spatio-Temporal Information Systems is intended to bring together a coherent body of recent knowledge relating to STIS data modeling, design, implementation and STIS in knowledge discovery. In particular, the reader is exposed to the latest techniques for the practical design of STIS, essential for complex query processing. Mining Spatio-Temporal Information Systems is structured to meet the needs of practitioners and researchers in industry and graduate-level students in Computer Science.

    Edition Notes

    Statementedited by Roy Ladner, Kevin Shaw, Mahdi Abdelguerfi
    SeriesThe Springer International Series in Engineering and Computer Science -- 699, International series in engineering and computer science -- 699.
    ContributionsShaw, Kevin, Abdelguerfi, Mahdi
    Classifications
    LC ClassificationsQA76.9.D35
    The Physical Object
    Format[electronic resource] /
    Pagination1 online resource (x, 170 pages).
    Number of Pages170
    ID Numbers
    Open LibraryOL27075120M
    ISBN 101461354161, 1461511496
    ISBN 109781461354161, 9781461511496
    OCLC/WorldCa852790643

    12 Spatiotemporal Pattern Mining: Algorithms and Applications 0 50 0 50 Fig. Figure on the left shows the trajectory of a bald eagle over 3 years. Each yellow pin is a recorded GPS locations. Figure on the right shows the density map of all the locations in the Size: KB. Spatial data mining is the application of data mining to spatial models. In spatial data mining, analysts use geographical or spatial information to produce business intelligence or other results. This requires specific techniques and resources to get the geographical data into relevant and useful formats.

    Mining spatio-temporal data Gennady Andrienko & Donato Malerba & Michael May & Maguelonne Teisseire # Springer Science + Business Media, LLC Spatio-temporal data mining is an emerging research area dedicated to the development and application of novel computational techniques for the analysis of large spatio-temporal databases. 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).

      4 Introduction • Spatial data mining is the process of discovering interesting, useful, non-trivial patterns from large spatial datasets – E.g. Determining hotspots: unusual locations. • Spatial Data Mining Tasks – Characteristics rule. – Discriminate rule. E.g. Comparison of price ranges of different geographical area. IJSTMIS is an international peer-reviewed journal covering innovative research and practice in the rapidly advancing area of computer science related to new generations of information systems. Taking a multidisciplinary approach, IJSTMIS encourages both fundamental and applied research papers that report on activities related to new research and development at the interface of spatial.


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Mining Spatio-Temporal Information Systems by Roy Ladner Download PDF EPUB FB2

Mining Spatio-Temporal Information Systems, an edited volume is composed of chapters from leading experts in the field of Spatial-Temporal Information Systems and addresses the many issues in support of modeling, creation, querying, visualizing and Spatio-Temporal Information Systems is intended to bring together a coherent body of recent knowledge relating to STIS data modeling Manufacturer: Springer.

Mining Spatio-Temporal Information Systems, an edited volume is composed of chapters from leading experts in the field of Spatial-Temporal Information Systems and addresses the many issues in support of modeling, creation, querying, visualizing and mining.

Mining Spatio-Temporal Information Systems. Mining Spatio-Temporal Information Systems, an edited volume is composed of chapters from leading experts in the field of Spatial-Temporal Information Systems and addresses the many issues in support of modeling, creation, querying, visualizing and Spatio-Temporal Information Systems is intended to bring together a coherent body of recent knowledge relating to STIS data modeling.

"Mining Spatio-Temporal Information Systems, an edited volume is composed of chapters from leading experts in the field of Spatial-Temporal Information Systems and addresses the many issues in support of modeling, creation, querying, visualizing and mining. Buy Mining Spatio-Temporal Information Systems (The Springer International Series in Engineering and Computer Science) on FREE SHIPPING on qualified orders Mining Spatio-Temporal Information Systems (The Springer International Series in Engineering and Computer Science): Roy Ladner, Kevin Shaw, Mahdi Abdelguerfi: Mining Spatio-Temporal Information Systems (The Springer International Series in Engineering and Computer Science) Pdf.

Download Link. E-Book Review and Description: Mining Spatio-Temporal Information Systems, an edited quantity consists of chapters from main specialists in the sector of Spatial-Temporal Information Systems and addresses the.

Spatio-temporal subgroup discovery combines the subgroup mining approach, which searches for interesting subgroups of analysis objects, with GIS and spatial analyses. In this chapter, we present SubgroupMiner, which is an advanced subgroup mining system supporting multirelational hypotheses, efficient data base integration, discovery of causal Cited by: 3.

Social media contains a lot of geographic information and has been one of the more important data sources for hazard mitigation. Compared with the traditional means of disaster-related geographic information collection methods, social media has the characteristics of real-time information provision and low cost.

Due to the development of big data mining technologies, it is now easier to Cited by: 3. 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.

conversion of spatio-temporal data at different levels of detail. In Section 4 we in - troduce the spatio-temporal data mining system and show how we apply it to spa. Temporal and Spatio-Temporal Data Mining presents probable solutions when discovering the spatial sequence patterns by incorporating the spatial information into the sequence of patterns, and.

Researchers in the field of Temporal Geographical Information Systems (TGIS) have been developing methods of incorporating time into geographical information systems. Spatio-temporal analysis embodies spatial modelling, spatio-temporal modelling and spatial reasoning and data mining.

Mining Spatio-Temporal Information Systems, an edited volume is composed of chapters from leading experts in the field of Spatial-Temporal Information Systems and addresses the many issues in support of modeling, creation, querying, visualizing and mining.

Mining Spatio-Temporal Information Systems is intended to bring together a coherent body. Spatiotemporal data mining refers to the process of discovering patterns and knowledge from spatiotemporal data. Typical examples of spatiotemporal data mining include discovering the evolutionary history of cities and lands, uncovering weather patterns, predicting earthquakes and hurricanes, and determining global warming trends.

This book covers the major fundamentals of and the latest research on next-generation spatio-temporal recommendation systems in social media. It begins by describing the emerging characteristics of social media in the era of mobile internet, and explores the limitations to be found in.

Evaluation of Spatio-Temporal Microsimulation Systems: /ch The increasing expressiveness of spatio-temporal microsimulation systems makes them attractive for a wide range of real world applications. However, the broadCited by: 3. Information Systems (TGIS) have been developing methods of incorporating time into geographical information systems.

Spatio-temporal analysis embodies spatial modelling, spatio-temporal modelling and spatial reasoning and data mining. Advances in Spatio-Temporal Analysiscontributes to the field of spatio-temporal analysis, presenting.

Mining in Spatio-Temporal Databases: /ch Recent interest in spatio-temporal applications has been fueled by the need to discover and predict complex patterns that occur when we observe the behaviorAuthor: Junmei Wang, Wynne Hsu, Mong Li Lee. Big Data Sciences (Spatial and Spatio-Temporal Data Analysis and Mining, Extracting Knowledge from raw Data, Enviromental and Tracking Applications), Distributed Computing (Map-Reduce Algorithms for Data Analysis), Spatial Clustering Algorithms, Spatio-Temporal Predictions, Spatio-Temporal Indexing, NOSQL systems performance comparision.

Sebnem Duzgun Fred Banfield Distinguished Endowed Chair and Professor, Mining Engineering and interdisciplinary topics including geographic information systems, remote sensing, spatial and spatio-temporal data mining, landslide and earthquake risk assessment, critical infrastructure resilience.

data mining is a challenging task due to the reasons: (1) spatio-temporal datasets are usually much larger than spatial data sets, (2) many common spatial techniques are unable to deal with objects that change location, size or shape, and (3) complex and often.Mining relationships in spatio-temporal datasets.

Abstract. The generation of spatio-temporal datasets has seen a phenomenal growth in the past few years with the advances in remote sensing and location sensing devices. Data in many domains like climate, remote sensing, mobile computing, network monitoring, etc.

are characterized by.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).Cited by: