A Data and Query Model for Streaming Geospatial Image Data

Gertz, Michael; Hart, Quinn; Rueda, Carlos; Singhal, Shefali; Zhang, Jie

doi:10.1007/11896548_52

Cited by 9 publications

(7 citation statements)

References 10 publications

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“…Work in processing streaming images, such as [10], attempts to treat an image as a complex data type and introduces map/image algebra operations to allow users to manipulate streaming images. Supporting queries with extended spatio-temporal extent in real time were not addressed in the work.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

confidence: 99%

Querying streaming point clusters as regions

Zhang

Huang

2010

Proceedings of the ACM SIGSPATIAL International Workshop on GeoStreaming

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“…The explosion of both the data volumes and dimensionality of these geospatial field data makes the storage, management, query and processing a daunting challenge to existing solutions [3]. Multidimensional data are stored and accessed linearly in memory and on hard disk but should be randomly queried and updated from any dimension.…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Tensor-Based Approach to Compressing, Updating and Querying Geospatial Data

Yuan

Luo

et al. 2015

IEEE Trans. Knowl. Data Eng.

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With the rapid development of data observation and model simulation in geoscience, spatial-temporal data have become increasingly multidimensional, massive and are consistently being updated. As a result, the integrated maintenance of these data is becoming a challenge. This paper presents a blocked hierarchical tensor representation within the split-and-merge paradigm for the compressed storage, continuously updating and data querying of multidimensional geospatial field data. The original multidimensional geospatial field data are split into small blocks according to their spatial-temporal references. These blocks are represented and compressed hierarchically, and then combined into a single hierarchical tree as the representation of original data. With a buffered binary tree data structure and corresponding optimized operation algorithms, the original multidimensional geospatial field data can be continuously compressed, appended, and queried. Data from the 20th Century Reanalysis Monthly Mean Composites are used to evaluate the performance of this approach. Compared to traditional methods, the new approach is shown to retain the quality of the original data with much lower storage costs and faster computational performance. The result suggests that the blocked hierarchical tensor representation provides an effective structure for integrated storage, presentation and computation of multidimensional geospatial field data.

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“…To describe the data and the operations on remotely sensed image data, we use the algebra described by Gertz et al [2]. We define a raster image as a function from a raster point set X to a value set F, where X is a regularly-spaced lattice in R 2 or R 3 , and F is some algebraic system (a set together with a finite number of operations).…”

Section: Stream Operatorsmentioning

confidence: 99%

“…An appropriate semantics actually involves considerations related to spatio-temporal granularities, synchronization, and handling of missing data. See [2] for details.…”

Section: Stream Operatorsmentioning

confidence: 99%

“…. , a t−δ , i.e., the current image in D δ (α) is the image that was current δ clocks ago in α. Temporal restriction, which selects the images in a stream whose timestamps are contained in a given set of time intervals, as well as value transformation and spatial reprojection (conversion from some coordinate system to another) are also typical operations on streams [2]. Figure 2 illustrates some of the operators in terms of workflow actors.…”

Section: Stream Operatorsmentioning

confidence: 99%

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