International Conference and Exhibition on Computing for Geospatial Research & Application最新文献

Efficient Online Sharing of Geospatial Big Data Using NoSQL XML Databases 利用NoSQL XML数据库实现地理空间大数据的高效在线共享

International Conference and Exhibition on Computing for Geospatial Research & Application Pub Date : 2013-07-22 DOI: 10.1109/COMGEO.2013.34

P. Amirian, A. Bassiri, A. Winstanley

{"title":"Efficient Online Sharing of Geospatial Big Data Using NoSQL XML Databases","authors":"P. Amirian, A. Bassiri, A. Winstanley","doi":"10.1109/COMGEO.2013.34","DOIUrl":"https://doi.org/10.1109/COMGEO.2013.34","url":null,"abstract":"Summary form only given: Today a huge amount of geospatial data is being created, collected and used more than ever before. The ever increasing observations and measurements of geo-sensor networks, satellite imageries, point clouds from laser scanning, geospatial data of Location Based Services (LBS) and location-based social networks has become a serious challenge for data management and analysis systems. Traditionally, Relational Database Management Systems (RDBMS) were used to manage and to some extent analyze the geospatial data. Nowadays these systems can be used in many scenarios but there are some situations when using these systems may not provide the required efficiency and effectiveness. More specifically when the geospatial data has high volume, high frequency of change (in both data content and data structure) and variety of structures, the conventional data storage systems cannot provide needed efficiency in online systems in terms of performance and scalability. In these situations, NoSQL solutions can provide the efficiency necessary for applications using geospatial data. This paper provides an overview of the characteristics of geospatial big data, possible solutions for managing and processing them. Then the paper provides an overview of the major types of NoSQL solutions, their advantages and disadvantages and the challenges they present in managing geospatial big data. Then the paper elaborates on serving geospatial data using standard geospatial web services with a NoSQL XML database as a backend.","PeriodicalId":400763,"journal":{"name":"International Conference and Exhibition on Computing for Geospatial Research & Application","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117202583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 20

A practical approach to developing a web-based geospatial workflow composition and execution system 开发基于web的地理空间工作流组成与执行系统的实用方法

International Conference and Exhibition on Computing for Geospatial Research & Application Pub Date : 2012-07-01 DOI: 10.1145/2345316.2345341

Jianting Zhang

引用次数: 6

Temporally coherent real-time labeling of dynamic scenes 动态场景的时间相干实时标记

International Conference and Exhibition on Computing for Geospatial Research & Application Pub Date : 2012-07-01 DOI: 10.1145/2345316.2345337

M. Vaaraniemi, M. Treib, R. Westermann

{"title":"Temporally coherent real-time labeling of dynamic scenes","authors":"M. Vaaraniemi, M. Treib, R. Westermann","doi":"10.1145/2345316.2345337","DOIUrl":"https://doi.org/10.1145/2345316.2345337","url":null,"abstract":"The augmentation of objects by textual annotations provides a powerful means for visual data exploration. Especially in interactive scenarios, where the view on the objects and, thus, the preferred placement of annotations changes continually, efficient labeling procedures are required. As identified by a preliminary study for this paper, these procedures have to consider a number of requirements for achieving an optimal readability, e.g. cartographic principles, visual association and temporal coherence. In this paper, we present a force-based labeling algorithm for 2D and 3D scenes, which can compute the placements of annotations at very high speed and fulfills the identified requirements. The efficient labeling of several hundred annotations is achieved by computing their layout in parallel on the GPU. This allows for a real-time and collision-free arrangement of both dynamically changing and static information. We demonstrate that our method supports a large variety of applications, e.g. geographical information systems, automotive navigation systems, and scientific or information visualization systems. We conclude the paper with an expert study which confirms the enhancements brought by our algorithm with respect to visual association and readability.","PeriodicalId":400763,"journal":{"name":"International Conference and Exhibition on Computing for Geospatial Research & Application","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124682555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 25

Big data and advanced spatial analytics 大数据和高级空间分析

International Conference and Exhibition on Computing for Geospatial Research & Application Pub Date : 2012-07-01 DOI: 10.1145/2345316.2345322

Xavier Lopez

{"title":"Big data and advanced spatial analytics","authors":"Xavier Lopez","doi":"10.1145/2345316.2345322","DOIUrl":"https://doi.org/10.1145/2345316.2345322","url":null,"abstract":"Today's business and government organizations are challenged when trying to manage and analyze information from enterprise databases, streaming servers, social media and open source. This is compounded by the complexity of integrating diverse data types (relational, text, spatial, images, spreadsheets) and their representations (customers, products, suppliers, events, and locations) - all of which need to be understood and re-purposed in different contexts. Identifying meaningful patterns across these different information sources is non-trivial. Moreover, conventional IT tools, such as conventional data warehousing and business intelligence alone, are insufficient at handling the volumes, velocity and variety of content at hand. A new framework and associated tools are needed. Dr. Lopez outlines how data scientists and analysts are applying Spatial and Semantic Web concepts to make sense of this Big Data stream. He will describe new approaches oriented toward search, discovery, linking, and analyzing information on the Web, and throughout the enterprise. The role of Map Reduce is described, as is importance of engineered systems to simplify the creation and configuration of Big Data environments. The key take away is use of spatial and linked open data concepts to enhance content alignment, interoperability, discovery and analytics in the Big Data stream.","PeriodicalId":400763,"journal":{"name":"International Conference and Exhibition on Computing for Geospatial Research & Application","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121553891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Modeling spatial dependencies and semantic concepts in data mining 数据挖掘中的空间依赖关系和语义概念建模

International Conference and Exhibition on Computing for Geospatial Research & Application Pub Date : 2012-07-01 DOI: 10.1145/2345316.2345369

Ranga Raju Vatsavai

{"title":"Modeling spatial dependencies and semantic concepts in data mining","authors":"Ranga Raju Vatsavai","doi":"10.1145/2345316.2345369","DOIUrl":"https://doi.org/10.1145/2345316.2345369","url":null,"abstract":"Data mining is the process of discovering new patterns and relationships in large datasets. However, several studies have shown that general data mining techniques often fail to extract meaningful patterns and relationships from the spatial data owing to the violation of fundamental geospatial principles. In this tutorial, we introduce basic principles behind explicit modeling of spatial and semantic concepts in data mining. In particular, we focus on modeling these concepts in the widely used classification, clustering, and prediction algorithms. Classification is the process of learning a structure or model (from user given inputs) and applying the known model to the new data. Clustering is the process of discovering groups and structures in the data that are \"similar,\" without applying any known structures in the data. Prediction is the process of finding a function that models (explains) the data with least error. One common assumption among all these methods is that the data is independent and identically distributed. Such assumptions do not hold well in spatial data, where spatial dependency and spatial heterogeneity are a norm. In addition, spatial semantics are often ignored by the data mining algorithms. In this tutorial we cover recent advances in explicitly modeling of spatial dependencies and semantic concepts in data mining.","PeriodicalId":400763,"journal":{"name":"International Conference and Exhibition on Computing for Geospatial Research & Application","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114830412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Evidence theory for reputation-based trust in wireless sensor networks 无线传感器网络中基于声誉信任的证据理论

International Conference and Exhibition on Computing for Geospatial Research & Application Pub Date : 2012-07-01 DOI: 10.1145/2345316.2345360

A. Matheus, Björn Stelte

引用次数: 3

Image-based structural damage assessment with sensor fusion 基于图像的传感器融合结构损伤评估

International Conference and Exhibition on Computing for Geospatial Research & Application Pub Date : 2012-07-01 DOI: 10.1145/2345316.2345351

P. Chang, Duoduo Liao

引用次数: 3

Variation of flow pattern in waterways due to degradation and aggradation 水道因退化和淤积而引起的流型变化

International Conference and Exhibition on Computing for Geospatial Research & Application Pub Date : 2012-07-01 DOI: 10.1145/2345316.2345356

A. Ghaly

{"title":"Variation of flow pattern in waterways due to degradation and aggradation","authors":"A. Ghaly","doi":"10.1145/2345316.2345356","DOIUrl":"https://doi.org/10.1145/2345316.2345356","url":null,"abstract":"The cross section and profile of waterways are constantly subjected to alteration due to changes in flow volume and velocity. Banks and beds of waterways could experience excessive scouring especially at locations with sharp bents. At these locations, the soil constituting the cross section of the waterway could be subjected to considerable degradation, which could significantly alter the flow pattern at these locations. With severe degradation, the volume of sediment transport increases and may exceed the water carrying capacity resulting in the phenomenon known as aggradation. Contrary to degradation, aggradation results from the deposition of carried aggregate transport, which can hinder water flow in the waterway and obstruct its path. As degradation and aggradation take hold at some location along the waterway, their effect gets compounded over time which exacerbates the problem and make it difficult for the waterway to recover. Geographic Information Systems (GIS) is used to study the effect of degradation and aggradation along the Schoharie Creek, which is one of the major tributaries of the Mohawk River in upstate New York. The change in the selected locations will be examined over time to show the gradual alteration that a given section experiences and its effect on flow pattern and waterway profile. The analysis will also include a Digital Elevation Model (DEM) study of bank slopes based on the creation of a contour map. The August and September 2011 Tropical Storms Irene and Lee, respectively, left in their wake tremendous change to the waterway due to excessive degradation and aggradation. This effect was sensed due to the severe brunt it brought on the area landscape and its infrastructure. This study will identify the areas most in need for buffering and most susceptible to the impact of these natural phenomena. This will help implement proper protection methods, and in case of a damage, it will help plan for effective restoration systems.","PeriodicalId":400763,"journal":{"name":"International Conference and Exhibition on Computing for Geospatial Research & Application","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114813423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cloud computing & big data computing 云计算与大数据计算

International Conference and Exhibition on Computing for Geospatial Research & Application Pub Date : 2012-07-01 DOI: 10.1145/2345316.2345328

Zhiming Xue

引用次数: 1

Performance comparisons of spatial data processing techniques for a large scale mobile phone dataset 大型移动电话数据集空间数据处理技术性能比较

International Conference and Exhibition on Computing for Geospatial Research & Application Pub Date : 2012-07-01 DOI: 10.1145/2345316.2345346

Apichon Witayangkurn, T. Horanont, R. Shibasaki

{"title":"Performance comparisons of spatial data processing techniques for a large scale mobile phone dataset","authors":"Apichon Witayangkurn, T. Horanont, R. Shibasaki","doi":"10.1145/2345316.2345346","DOIUrl":"https://doi.org/10.1145/2345316.2345346","url":null,"abstract":"Mobile technology, especially mobile phone, is very popular nowadays. Increasing number of mobile users and availability of GPS-embedded mobile phones generate large amount of GPS trajectories that can be used in various research areas such as people mobility and transportation planning. However, how to handle such a large-scale dataset is a significant issue particularly in spatial analysis domain. In this paper, we aimed to explore a suitable way for extracting geo-location of GPS coordinate that achieve large-scale support, fast processing, and easily scalable both in storage and calculation speed. Geo-locations are cities, zones, or any interesting points. Our dataset is GPS trajectories of 1.5 million individual mobile phone users in Japan accumulated for one year. The total number was approximately 9.2 billion records. Therefore, we conducted performance comparisons of various methods for processing spatial data, particularly for a huge dataset. In this work, we first processed data on PostgreSQL with PostGIS that is a traditional way for spatial data processing. Second, we used java application with spatial library called Java Topology suite (JTS). Third, we tried on Hadoop Cloud Computing Platform focusing on using Hive on top of Hadoop to allow SQL-like support. However, Hadoop/Hive did not support spatial query at the moment. Hence, we proposed a solution to enable spatial support on Hive. As the results, Hadoop/hive with spatial support performed best result in large-scale processing among evaluated methods and in addition, we recommended techniques in Hadoop/Hive for processing different types of spatial data.","PeriodicalId":400763,"journal":{"name":"International Conference and Exhibition on Computing for Geospatial Research & Application","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114640358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13