Applying OLAP Pre-Aggregation Techniques to ... - Jacobs University

More documents

Recommendations

Info

18 2. Background and Related Work Data and Query Model for Stream Geo-Raster Imagery Gertz et al. [67] introduced a data and query model for managing and querying streams of remote-sensing imagery. The data model considers the spatio-temporal and geo-referenced nature of satellite imagery. Three classes of operators allow the formulation of queries. A stream restriction operator acts as a filter that selects points from a stream that satisfy a given condition of the spatial, temporal, or spatio-temporal component of the image. The stream transform operator maps the point or value associated with a stream to a new point or value set. This class of operators is useful for processing on a point-by-point basis. The third class of operators is called stream compositions, which allows the combination of image data from different spectral bands. To this end, each stream is considered to represent a single spectral band. However, since the primary objective of the authors was to stream geo-raster image data, they put less emphasis on post-processing satellite images. Core operations such as Fourier transforms and edge detection are therefore not supported by their framework. Array Algebra Baumann [75] introduced a formal array model called Array Algebra that supports the description and manipulation of multidimensional array data types [76]. The simple algebra consists of three core operators: an array constructor, a general condenser for computing aggregations, and an index sorter. The expressive power of Array Algebra through these operators enables a wide range of signal processing, imaging, and statistical operations. Moreover, the termination of any well-formed query is guaranteed by limiting the expressiveness power to non-recursive operations. Array Algebra is described in more detail in Chapter 3. To date, Array Algebra is the most comprehensive and complete approach supporting a variety of applications including sensor, image and statistical data. Recently, a Geo-raster service standard based on Array Algebra concepts has been issued by the Open GeoSpatial Consortium (OGC) [78]. A commercial and open-source implementation of Array Algebra is currently available for the scientific community. 2.1.4 Storage Management At present, handling large image data stored in a database is usually carried out by adopting a tiling strategy [23]. An image is split into sub-images (tiles), as shown in Fig. 2.3. When a region of interest is requested in a given query operation, only the relevant tiles are accessed. This strategy results in significant I/O bandwidth savings. Tiles form the basic processing units for indexing and compression. Spatial indexing allows for the quick retrieval of the identifier and location of a required tile, while compression improves disk I/O bandwidth efficiency. The choice of tile size is crucial for efficiency. While large tiles return much redundant data in response to a range query, small tiles result in a bad compression ratio where tile size varies from 8 KB (very small) to 512 KB (very large) of data [23, 96]. A comprehensive approach
2.1 Array Databases 19 toward the storage of large amounts of data on tertiary storage media considering tiling techniques in multidimensional database management systems is presented in [23, 24, 25]. Figure 2.3. Image Tiling A key factor influencing the effectiveness of a tiling scheme is compression. Raster data compression algorithms are the same as algorithms for compression of other image data. However, remote-sensing images are usually of much higher resolution, are multi-spectral and have significant larger volumes than natural images. To effectively compress raster data in GIS environments, emphasis must be placed on the management of schemas to deal with large volumes of remote-sensing imagery, and on the integration of various types of datasets such as vector and multidimensional datasets [3, 87]. Dehmel [3] proposed a comprehensive framework for the compression of multidimensional arrays based on different model layers, including various kinds of predictors and a generic wavelet engine for lossy compression with arbitrary quality levels. In particular, the author introduces concepts such as channel separation to compress values for each channel separately, and predictors that calculate approximate values for some cells and express those cell values relative to the approximate values. Further, the proposed method applies wavelets to transform the channels individually into multi-resolution representations with coarse approximations and various levels of detail information. This led to a wavelet engine architecture consisting of three major components: transformation, quantization and compression that helps improve compression rates considerably in array databases. 2.1.5 2D Pre-Aggregation Aggregate operations on GIS and remote-sensing applications have been shown to be computationally expensive due to the size and complexity of the operations [8]. One such operation is zooming (scaling), which is carried out by interpolating the values of the original dataset to downsample it to a lower resolution. This is particularly necessary in web-based raster applications, where limitations such as bandwidth and other resources prevent the efficient processing of the original raster datasets. For smooth interactive panning, browsers load the image in tiles and quantities larger than actually displayed. Zooming far out results in large scale factors, meaning that large amounts of data must be moved to deliver minimal results.
Page 1: Applying OLAP Pre-Aggregation Techn
Page 5 and 6: Acknowledgments I would like to exp
Page 7 and 8: Abstract Large multidimensional arr
Page 9 and 10: Contents 1 Introduction and Problem
Page 11 and 12: List of Figures 2.1 3D Array . . .
Page 13 and 14: List of Tables 3.1 UNO and FAO Suit
Page 15 and 16: Chapter 1 Introduction and Problem
Page 17 and 18: Relevant and complementary question
Page 19 and 20: 1.2 Publications Related to this Th
Page 21 and 22: Chapter 2 Background and Related Wo
Page 23: 2.1 Array Databases 17 Figure 2.2 s
Page 27 and 28: 2.1 Array Databases 21 • Bilinear
Page 29 and 30: 2.1 Array Databases 23 given image
Page 31 and 32: 2.2 On-Line Analytical Processing (
Page 39 and 40: 2.3 Discussion 33 spatial-vector da
Page 41 and 42: 2.3 Discussion 35 • Both applicat
Page 43 and 44: Chapter 3 Fundamental Geo-Raster Op
Page 45 and 46: 3.2 Geo-Raster Operations 39 3.1.2
Page 47 and 48: 3.2 Geo-Raster Operations 41 multip
Page 49 and 50: 3.2 Geo-Raster Operations 43 Table
Page 51 and 52: 3.2 Geo-Raster Operations 45 turn i
Page 53 and 54: 3.2 Geo-Raster Operations 47 (a) Or
Page 55 and 56: 3.2 Geo-Raster Operations 49 Query
Page 57 and 58: 3.2 Geo-Raster Operations 51 contai
Page 59 and 60: 3.2 Geo-Raster Operations 53 is the
Page 61 and 62: 3.2 Geo-Raster Operations 55 3.2.4
Page 63 and 64: 3.2 Geo-Raster Operations 57 As in
Page 65 and 66: 3.2 Geo-Raster Operations 59 Local
Page 67 and 68: 3.3 Summary 61 Slicing The slicing
Page 69 and 70: Chapter 4 Answering Basic Aggregate
Page 71 and 72: 4.1 Framework 65 pre-aggregated res
Page 73 and 74: 4.2 Cost Model 67 By partitioning t
Page 75 and 76:
4.2 Cost Model 69 Cost of independe
Page 77 and 78:
4.3 Implementation 71 Algorithm 1 Q
Page 79 and 80:
4.4 Experimental Results 73 Query E
Page 81 and 82:
4.5 Summary 75 pre-aggregates: inde
Page 83 and 84:
Chapter 5 Pre-Aggregation Support B
Page 85 and 86:
5.2 Conceptual Framework 79 Figure
Page 87 and 88:
5.2 Conceptual Framework 81 Benefit
Page 89 and 90:
5.4 Answering Scaling Operations Us
Page 91 and 92:
5.5 Experimental Results 85 Algorit
Page 93 and 94:
5.5 Experimental Results 87 (a) Que
Page 95 and 96:
5.5 Experimental Results 89 (a) Sel
Page 97 and 98:
5.5 Experimental Results 91 vectors
Page 99 and 100:
5.5 Experimental Results 93 root no
Page 101 and 102:
5.5 Experimental Results 95 Figure
Page 103 and 104:
Page 105 and 106:
Page 107 and 108:
5.6 Summary 101 we considered non-u
Page 109 and 110:
Chapter 6 Conclusion One of the big
Page 111 and 112:
6.1 Future Work 105 more non-spatio
Page 113 and 114:
Bibliography [1] Blakeley J. A., La
Page 115 and 116:
BIBLIOGRAPHY 109 [22] Moon B., Vega
Page 117 and 118:
BIBLIOGRAPHY 111 [47] ESRI Inc. Arc
Page 119 and 120:
BIBLIOGRAPHY 113 [73] Stefanovic N.
Page 121:
BIBLIOGRAPHY 115 [97] Kotidis Y. an
show all

Applying OLAP Pre-Aggregation Techniques to ... - Jacobs University

Create successful ePaper yourself

Delete template?

Save as template?