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GEOINFORMATICA 2004年第4期8卷 311-346页

作者： Mamoulis, N Papadias, D Arkoumanis, D Univ Hong Kong Dept Comp Sci & Informat Syst Hong Kong Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci Hong Kong Hong Kong Peoples R China Natl Tech Univ Athens Dept Elect & Comp Engn Zografos 15773 Greece

The user of a Geographical Information System is not limited to conventional spatial selections and joins, but may also pose more complicated and descriptive queries. In this paper, we focus on the efficient processing and optimization of complex spatial queries that involve combinations of spatial selections and joins. Our contribution is manifold;we first provide formulae that accurately estimate the selectivity of such queries. These formulae, paired with cost models for selections and joins can be used to combine spatial operators in an optimal way. Second, we propose algorithms that process spatial joins and selections simultaneously and are typically more efficient than combinations os simple operators. Finally we study the problem of optimizing complex spatial queries using these operators, by providing (i) cost models, and (ii) rules that reduce the optimization space significantly. The accuracy of the selectivity models and the efficiency of the proposed algorithms are evaluated through experimentation.

关键词： spatial query processing spatial joins query optimization

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GISQAF: MapReduce guided spatial query processing and analytics system

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SOFTWARE-PRACTICE & EXPERIENCE 2016年第10期46卷 1329-1349页

作者： Al-Naami, Khaled Mohammed Seker, Sadi Evren Khan, Latifur Univ Texas Dallas Dept Comp Sci Dallas TX USA Istanbul Medeniyet Univ Dept Business Istanbul Turkey

The Global Database of Event, Language, and Tone (GDELT) is the only global political georeferenced event dataset with more than 250 million observations covering all countries in the world since January 1, 1979. TABARI and CAMEO are the tools that are used to collect and code events from all international news coverage. To query such big geospatial data, traditional RDBMS can no longer be used, and the need for parallel distributed solutions has become a necessity. MapReduce paradigm has proven to be a scalable platform to process and analyze Big Data in the cloud. Hadoop, as an implementation of MapReduce, is an open-source application that has been widely used and accepted in academia and industry. However, when dealing with spatial Data, Hadoop is not equipped well and does not perform efficiently. spatialHadoop is an extension of Hadoop with the support of spatial data. In this paper, we present Geographic Information System query and Analytics Framework (GISQAF), which has been built on top of spatialHadoop. GISQAF focuses on two parts: query processing and data analytics. For the query processing part, we show how this solution outperforms Hadoop query processing by orders of magnitude when applying queries on the GDELT dataset with a size of 60 GB. We show the results for various types of queries. For the data analytics part, we present an approach for finding spatial co-occurring events. We show how GISQAF is suitable and efficient to handle data analytics techniques. Copyright (c) 2015 John Wiley & Sons, Ltd.

关键词： big data MapReduce Hadoop spatial query processing data analytics spatial co-occurring events

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AT-GIS: Highly Parallel spatial query processing with Associative Transducers 16

AT-GIS: Highly Parallel Spatial Query Processing with Associ...

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ACM SIGMOD International Conference on Management of Data

作者： Ogden, Peter Thomas, David Pietzuch, Peter Imperial Coll London London England

ISBN: (纸本)9781450335317

Users in many domains, including urban planning, transportation, and environmental science want to execute analytical queries over continuously updated spatial datasets. Current solutions for large-scale spatial query processing either rely on extensions to RDBMS, which entails expensive loading and indexing phases when the data changes, or distributed map/reduce frameworks, running on resource-hungry compute clusters. Both solutions struggle with the sequential bottleneck of parsing complex, hierarchical spatial data formats, which frequently dominates query execution time. Our goal is to fully exploit the parallelism offered by modern multi core CPUs for parsing and query execution, thus providing the performance of a cluster with the resources of a single machine. We describe AT-GIS, a highly-parallel spatial query processing system that scales linearly to a large number of CPU cores. AT-GIS integrates the parsing and querying of spatial data using a new computational abstraction called associative transducers (ATs). ATs can form a single data-parallel pipeline for computation without requiring the spatial input data to be split into logically independent blocks. Using ATs, AT-GIS can execute, in parallel, spatial query operators on the raw input data in multiple formats, without any pre-processing. On a single 64-core machine, AT-GIS provides 3 x the performance of an 8-node Hadoop cluster with 192 cores for containment queries, and 10x for aggregation queries.

关键词： JSON XML spatial query processing NODB parallel automata multi-core CPUs

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Optimizing the spatial query processing with A Proxy-Based Approach 6

Optimizing the Spatial Query Processing with A Proxy-Based A...

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6th International Conference on Advanced Computing (ICoAC)

作者： Geetha, K. Kannan, A. Anna Univ Dept Informat Sci & Technol Coll Engn Madras 600025 Tamil Nadu India

ISBN: (纸本)9781479981595

In recent years, processing spatial queries fastly is a major challenge in mobile environments. Introduction of a proxy for spatial query processing reduces the number of queries submitted by the mobile clients to provide the relevant results. Hence, proxy based approach is prefered to server based approach. This is done to the reduction in waiting time of the query retrieval by proxy. Morover, it reduces the server load in mobile environments. Continuous Nearest Neighbor query processing increases the server load done to the number of consecutive queries submitted by the mobile client who tries to get the desired results. The mutual support between the results of the nearest neighbor queries and window queries are used to enhance the performance. Caching a number of queries in the cache leads to performance degradation. This is overcome in this work by using a cache replacement technique, and hence this system improves the performance of the spatial query processing.

关键词： Mobile environments spatial query processing spatial database

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Route Extraction, Road Name Disambiguation and Efficient spatial query processing under Location Constraints

Route Extraction, Road Name Disambiguation and Efficient Spa...

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作者： Zhang, Xiao PennState University Libraries

学位级别：Doctor of Philosophy

Geospatial information has drawn more and more attention from both academia and industry nowadays. As web and mobile technology thrives, publishing, sharing and retrieving of geospatial information have become much easier and more common than before. People generate large amount of text contents containing geospatial information every day, such as travel blogs, reviews of interesting places to visit in a new city, human-generated route directions, etc. Such documents are rich in mentions of geospatial objects and accounts of movements. Meanwhile, the demand for efficient geo-spatial object retrieval under location-based constraints is growing fast. Digital maps, GPS on mobile devices are products under such in- creasing demand. However, even if geospatial information sources are plentiful and available, fulfilling the information need remains difficult because: (1) it remains a hard problem to identify and extract different types of geospatial information from text, and (2) building an accurate mapping between the textual mentions of landmarks, places to their unique latitudes and longitudes and recovering them on maps are very challenging due to the ambiguity in text. The absence of good solutions to the above-mentioned problems makes such geospatial information in text hard to use. In addition, there lacks efficient algorithms for geospatial infor- mation retrieval given various location-based constraints. Such retrieval tasks are even more challenging on mobile devices since they suffer from limited storage, battery, computational power and update issues. A system which automatically extracts, disambiguates and geo-tags geospatial information in text and provides efficient solutions to location-based services will greatly satisfy people’s demand of sharing and retrieving of geospatial information. In this dissertation, we intro- duce our efforts towards this goal. We developed: (1) a solution for automatic route components and destination name extraction from tex

关键词： human generated route descriptions route information extraction road name disambiguation spatial query processing

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G-PICS: A Framework for GPU-Based spatial Indexing and query processing

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第3期34卷 1243-1257页

作者： Lewis, Zhila-Nouri Tu, Yi-Cheng Univ S Florida Comp Sci & Engn Tampa FL 33620 USA

Support for efficient spatial data storage and retrieval has become a vital component in almost all spatial database systems. While GPUs have become a mainstream platform for high-throughput data processing in recent years, exploiting the massively parallel processing power of GPUs is non-trivial. Current approaches that parallelize one query at a time have low work efficiency and cannot make good use of GPU resources. On the other hand, many spatial database systems could receive a large number of queries simultaneously. In this paper, we present a comprehensive framework named G-PICS for parallel processing of concurrent spatial queries on GPUs. G-PICS encapsulates efficient parallel algorithms for constructing a variety of spatial trees with different space partitioning methods. G-PICS also provides highly optimized programs for processing major spatial query types, and such programs can be accessed via an API that could be further extended to implement user-defined algorithms. While support for dynamic data inputs is missing in existing work, G-PICS implements efficient parallel algorithms for bulk updates of data. Furthermore, G-PICS is designed to work in a Multi-GPU environment to support datasets beyond the size of a single GPU's global memory. Empirical evaluation of G-PICS shows significant performance improvement over the state-of-the-art GPU and parallel CPU-based spatial query processing systems. In particular, G-PICS achieves double-digit speedup over such systems in tree construction (up to 53X) and query processing (up to 80X).

关键词： query processing Graphics processing units spatial databases Indexing Parallel processing Computational modeling spatial indexes spatial query processing massively parallel algorithms GPU computing

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Object-based directional query processing in spatial databases

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2003年第2期15卷 295-304页

作者： Liu, X Shekhar, S Chawla, S IBM Corp TJ Watson Res Ctr Hawthorne NY 10532 USA Univ Minnesota Dept Comp Sci Minneapolis MN 55455 USA Univ Sydney Sch Informat Technol Sydney NSW 2006 Australia

Direction-based spatial relationships are critical in many domains, including geographic information systems (GIS) and image interpretation. They are also frequently used as selection conditions in spatial queries. In this paper, we explore the processing of object-based direction queries and propose a new open shape-based strategy (OSS). OSS models the direction region as an open shape and converts the processing of the direction predicates into the processing of topological operations between open shapes and closed geometry objects. The proposed strategy OSS makes it unnecessary to know the boundary of the embedding world and also eliminates the computation related to the world boundary. OSS reduces both I/O and CPU costs by greatly improving the filtering effectiveness. Our experimental evaluation shows that OSS consistently outperforms classical range query strategies (RQS) while the degree of performance improvement varies by several parameters. Experimental results also demonstrate that OSS is more scalable than RQS for large data sets.

关键词： open shape object-based direction query spatial query processing range query

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A knowledge-based approach for retrieving images by content

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 1996年第4期8卷 522-532页

作者： Hsu, CC Chu, WW Taira, RK UNIV CALIF LOS ANGELES DEPT RADIOL SCILOS ANGELESCA 90095

A knowledge-based approach is introduced for retrieving images by content. It supports the answering of conceptual image queries involving similar-to predicates, spatial semantic operators, and references to conceptual terms. interested objects in the images are represented by contours segmented from images. Image content such as shapes and spatial relationships are derived from object contours according to domain-specific image knowledge. A three-layered model is proposed for integrating image representations, extracted image features, and image semantics. With such a model, images can be retrieved based on the features and content specified in the queries. The knowledge-based query processing is based on a query relaxation technique. The image features are classified by an automatic clustering algorithm and represented by Type Abstraction Hierarchies (TAHs) for knowledge-based query processing. Since the features selected for TAH generation are based on context and user profile, and the TAHs can be generated automatically by a clustering algorithm from the feature database, our proposed image retrieval approach is scalable and context-sensitive. The performance of the proposed knowledge-based query processing is also discussed.

关键词： knowledge-based query processing medical image database retrieve image by feature and content spatial query processing knowledge-based spatial image model cooperative query processing shape model spatial relationship model

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Interactive Multiple-User Location-Based Keyword Queries on Road Networks

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IEEE ACCESS 2018年 6卷 51401-51418页

作者： Wang, Yong Duan, Xiaoran Yang, Xiaodong Zhang, Yiquan Zhang, Xiaosong Univ Elect Sci & Technol China Sch Comp Sci & Engn Ctr CyberSecur Chengdu 611731 Sichuan Peoples R China

As the use of smart mobile devices increases, users can connect and coordinate with local people or events that match their interests through geosocial networking applications. Such applications require techniques that support multiple-user location-based keyword (MULK) query processing, which takes a set of geo-locations and a set of keywords as arguments and returns a list of relevant POIs that match the arguments. In geosocial networking, processing this query involves two issues: mining users' individual preferences and indexing road networks. In this paper, we investigate how to mine users' individual preferences by enhancing MULK query processing with interactions, resulting in interactive multiple-user location-based keyword query. Furthermore, we propose a hierarchical road network index structure to support efficient query processing. Finally, we conduct a set of experiments that demonstrate the effectiveness and efficiency of our solutions under various parameter settings.

关键词： Geosocial networking information processing interactive query keyword search location-based keyword query query processing spatial databases spatial query processing

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The DR-tree: A main memory data structure for complex multi-dimensional objects

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GEOINFORMATICA 2001年第2期5卷 181-207页

作者： Lee, YJ Chung, CW Sangju Natl Univ Dept Comp Engn Sanju Si 741711 South Korea Korea Adv Inst Sci & Technol Dept Comp Sci Yusong Gu Taejon 305701 South Korea

An efficient index structure for complex multi-dimensional object, is one of the most challenging requirements in non-traditional applications such as geographic information systems, computer-aided design, and multimedia databases. In this paper we first propose a main memory data structure for complex multi-dimensional objects. Then, we present an extension of the existing multi-dimensional index structure. Among existing multidimensional index structures, the popular R*-tree is selected. The R*-tree is coupled with the main memory data structure to improve the performance of spatial query processing. An analytical model is developed for our index structure. Experimental results show that the analytical model is accurate, the relative error being below 1.5%. The performance of our index structure is compared with that of a state-of-the-art index, structure by experimental measurements. Our index structure outperforms the state-of-the-art index structure due to its ability to reduce a large amount of storage.

关键词： multi-dimensional index structure complex multi-dimensional objects spatial query processing analytical model

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