New Data Types and Operations to Support Geo-streams

Huang, Yan; Zhang, Chengyang

doi:10.1007/978-3-540-87473-7_7

Cited by 12 publications

(4 citation statements)

References 18 publications

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“…-Views handling [33] -Spatio-temporal histogram [25] Place * QTP model [90] StreamSecondo [96] Secondo (offline) [18] Possible link with Parallel-Secondo [52] -Stream Algebra [43] -Windows implementation [44] -Stream types…”

Section: Challenges Involved In a Distributed Data Stream Management System For Moving Objectsmentioning

confidence: 99%

Design principles of a stream-based framework for mobility analysis

Salmon

Ray

2016

Geoinformatica

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Trajectory analysis is of crucial importance in several fields as social analysis, zoology, climatology or traffic monitoring. Over the last decade, the number of mobile systems and devices recording their positions has grown significantly generating a deluge of spatial and temporal data to analyze. This increasing volume of data raises numerous issues in terms of storage, processing and extraction of information. Previous works considering movement analysis have been mainly oriented towards either archived data processing and mining or continuous handling of incoming streams. The research developed in this paper introduces the design principles of a holistic approach combining real-time processing and archived data analysis to process mobility data "on the fly". This solution aims to provide better results comparing to both purely offline and online approaches. This research considers distributed data and processing to be more efficient. The design principles are applied to maritime traffic analysis and a few representative examples are introduced to demonstrate the relevance of our approach.

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Section: Challenges Involved In a Distributed Data Stream Management System For Moving Objectsmentioning

confidence: 99%

Design principles of a stream-based framework for mobility analysis

Salmon

Ray

2016

Geoinformatica

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“…Each query is in both English and a geo-stream SQL with data types developed by Zhang et al [11]. Table 5.1 gives the schema used for these queries.…”

Section: Schema Used For Queriesmentioning

confidence: 99%

The design of a benchmark for geo-stream management systems

Shen

Huang

Powell

2011

Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

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Shen, Chao. The design of a benchmark for geo-stream management systems.Master of Science (Computer Science), December 2011, 68 pp., 5 tables, 18 illustrations, references, 18 titles.The recent growth in sensor technology allows easier information gathering in real-time as sensors have grown smaller, more accurate, and less expensive. The resulting data is often in a geo-stream format continuously changing input with a spatial extent. Researchers developing geo-streaming management systems (GSMS) require a benchmark system for evaluation, which is currently lacking. This thesis presents GSMark, a benchmark for evaluating GSMSs. GSMark provides a data generator that creates a combination of synthetic and real geo-streaming data, a workload simulator to present the data to the GSMS as a data stream, and a set of benchmark queries that evaluate typical GSMS functionality and query performance. In particular, GSMark generates both moving points and evolving spatial regions, two fundamental data types for a broad range of geo-stream applications, and the geo-streaming queries on this data.

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“…flood). We may use the syntax proposed in our previous paper [14,29] The above example can be generalized to the following query that involves streaming regions (e.g. flood) and static data set (e.g., county and house).…”

Section: Motivation and Problem Formu-lationmentioning

confidence: 99%

“…In our previous papers [14,29], We have proposed a datatype-based approach [14] to uniformly represent geo-streams with and without extended spatial extents. We also proposed to generalize GROUP BY to CLUSTER BY in SQL to allow static points to be aggregated into static regions and then participate in spatial queries.…”

Section: Introductionmentioning

confidence: 99%

Querying streaming point clusters as regions

Zhang

Huang

2010

Proceedings of the ACM SIGSPATIAL International Workshop on GeoStreaming

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This paper focuses on one important type of geo-streaming data -point geo-streams. Many interesting applications require selected discrete points with similar observations to be clustered according to spatial proximity and further elevated into higher-level spatial regions. Querying streaming point clusters as regions directly in a geo-stream database has many benefits, but is also very challenging. We propose two query optimization strategies, namely semanticsbased optimization and incremental optimization for answering queries involving both point geo-streams and static data set. The experimental results on a streaming meteorological data set demonstrate the effectiveness and the efficiency of the query processing techniques. Compared with the baseline methods, our optimization methods can reduce the total execution time by more than an order of magnitude.

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New Data Types and Operations to Support Geo-streams

Cited by 12 publications

References 18 publications

Design principles of a stream-based framework for mobility analysis

Design principles of a stream-based framework for mobility analysis

The design of a benchmark for geo-stream management systems

Querying streaming point clusters as regions

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