Investigating the Effects of Spatial Data Redundancy in Query Performance over Geographical Data Warehouses
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1 Investigating the Effects of Spatial Data Redundancy in Query Performance over Geographical Data Warehouses Thiago Luís Lopes Siqueira Ricardo Rodrigues Ciferri Valéria Cesário Times Cristina Dutra de Aguiar Ciferri (UFSCar) (UFPE) (USP) Observatório da Educação
2 Outline Introduction Related Work Our Experimental Evaluation Indices Bitmap Index Our Index Structure Proposal: SB-index Experimental Evaluation applied to SB-index SB-index Enhancement for Redundant GDW Conclusions 2
3 Introduction Related Work Our Experimental Evaluation Indices Bitmap Index Our Index Structure Proposal: SB-index Experimental Evaluation applied to SB-index SB-index Enhancement for Redundant GDW Conclusions 3
4 Geographical Data Warehouse To integrate Geographical Information Systems (GIS) Geographical data + descriptive attributes Spatial analysis Data Warehouse (DW) Integrated Historical Multidimensional On-Line Analytical Processing (OLAP) Multidimensional analytical queries GIS, DW and OLAP are related to decision-making support 4
5 Data Warehouse Star schema Attribute hierarchies Redundancy! region nation city address 5
6 Data Warehouse Snowflake schema Avoids redundancy Possible, but not recommended region nation city address 6
7 Geographical Data Warehouse region nation city address HOW? 7
8 Geographical Data Warehouse Predefined spatial attributes hierarchies region nation city address 8
9 Geographical Data Warehouse Predefined spatial attributes hierarchies region nation city address 9
10 Geographical Data Warehouse Predefined spatial attributes hierarchies region nation city address 10
11 Geographical Data Warehouse Predefined spatial attributes hierarchies region nation city address 11
12 Geographical Data Warehouse Predefined spatial attributes hierarchies region nation city address 12
13 Geographical Data Warehouse Predefined spatial attributes hierarchies region nation city address 13
14 Geographical Data Warehouse region nation city address 14
15 Geographical Data Warehouse region nation city address
16 Geographical Data Warehouse 16
17 Geographical Data Warehouse Query Processing Star-join Spatial Predicate 17
18 Geographical Data Warehouse How does the spatial data redundancy affect query response time and storage requirements in a GDW? 18
19 Introduction Related Work Our Experimental Evaluation Indices Bitmap Index Our Index Structure Proposal: SB-index Experimental Evaluation applied to SB-index SB-index Enhancement for Redundant GDW Conclusions 19
20 Related Work 20
21 Related Work How does the spatial data redundancy affect query response time and storage requirements in a GDW? 21
22 Introduction Related Work Our Experimental Evaluation Indices Bitmap Index Our Index Structure Proposal: SB-index Experimental Evaluation applied to SB-index SB-index Enhancement for Redundant GDW Conclusions 22
23 Experimental Setup Benchmark Star Schema Benchmark + TIGER / Line shapefiles Workbench Redundant GDW Schema Hybrid GDW Schema Platform 2.8 GHz Pentium D processor 7200 RPM SATA 320 GB hard disk 2 GB of main memory PostgreSQL / PostGIS 23
24 Workbench Redundant GDW Schema Hybrid GDW Schema 24
25 Workbench Redundant GDW Schema Hybrid GDW Schema 25
26 Workbench Redundant GDW Schema Hybrid GDW Schema 150 GB 15 GB 26
27 Workload 5 complete roll-up operations 27
28 Performance Results Elapsed time (seconds) 28
29 Performance Results Elapsed time (seconds) < > > > < > > > Spatial data redundancy: Requires more storage space Deteriorates SOLAP query response time 29
30 Performance Results Elapsed time (seconds) 2.82% Hybrid GDW schema: Query processing was not affected by the spatial attribute granularity 30
31 Performance Results Elapsed time (seconds) Redundant GDW schema: 119% attribute granularity, query response time 31
32 Performance Results Elapsed time (seconds) Verdict Spatial data redundancy vs. Additional joins Larger performance losses (Redundant GDW) (Hybrid GDW) 32
33 Performance Results Elapsed time (seconds) ~47 min ~103 min Verdict Redundant and Hybrid schemas: prohibitive query response time! Ad-hoc query windows + Star-join Indices 33
34 Introduction Related Work Our Experimental Evaluation Indices Bitmap Index Our Index Structure Proposal: SB-index Experimental Evaluation applied to SB-index SB-index Enhancement for Redundant GDW Conclusions 34
35 Bitmap s_suppkey : s_address 1:1 35
36 Bitmap s_suppkey : s_address 1:1 36
37 Bitmap s_suppkey : s_address 1:1 37
38 Bitmap Region Nation City Address 38
39 Bitmap Region Nation City Address 39
40 Bitmap Region Nation City Address 40
41 Bitmap Region Nation City Address 41
42 Bitmap Query processing Fast bit-wise logical operations Avoids star-join 42
43 Bitmap Use in conventional DW Star-join and attribute hierarchies Advantages Multidimensionality does not drastically deteriorate performance Drawback High cardinality attributes Binning, Compression, Encoding Restriction Bitmap is not yet used to index geographical DW Opportunity! 43
44 Introduction Related Work Our Experimental Evaluation Indices Bitmap Index Our Index Structure Proposal: SB-index Experimental Evaluation applied to SB-index SB-index Enhancement for Redundant GDW Conclusions 44
45 SB-index Goal To enhance Star-join with spatial predicates Spatial DW Predefined spatial attribute hierarchies Ad-hoc query windows Resources Star-join Bitmap Index FastBit Binning, Compression and Encoding techniques Free Software 45
46 SB-index Definition Array (Sequential File) whose entries maintain One primary key value The MBR of the corresponding spatial object Star-join Bitmaps On spatial dimension tables primary keys One SB-index per spatial granularity level Region Nation City Address 46
47 SB-index: Building 1) Star-join Bitmaps Primary keys of Spatial Dimension Tables 2) Extraction Organized in disk pages 47
48 SB-index: Query Processing 1st Scanning SB-index and collecting candidates Sequential Scan MBR against the query window Candidates: key values 2nd Refinement and query re-writing Checks original spatial objects in the database New query: only conventional predicates 3rd FastBit performs the new query Star-join Bitmap 48
49 SB-index: Query Processing WHERE INTERSECTS (City, QW) 49
50 SB-index: Query Processing 1. Scan and Collection SB-index Disjoint 50
51 SB-index: Query Processing 1. Scan and Collection SB-index Intersects 51
52 SB-index: Query Processing 1. Scan and Collection SB-index Intersects 52
53 SB-index: Query Processing 1. Scan and Collection SB-index Intersects False candidate 53
54 SB-index: Query Processing 1. Scan and Collection SB-index Intersects False candidate 54
55 SB-index: Query Processing 1. Scan and Collection SB-index and so on: 4, 5, 6,... 55
56 SB-index: Query Processing 1. Scan and Collection SB-index 1st task complete! 56
57 SB-index: Query Processing 2. Refinement and Re-writing 57
58 SB-index: Query Processing 2. Refinement and Re-writing Candidates = {2,3,4,8,13} Answers = {2} INTERSECTS 58
59 SB-index: Query Processing 2. Refinement and Re-writing Candidates = {2,3,4,8,13} Answers = {2} DISJOINT! 59
60 SB-index: Query Processing 2. Refinement and Re-writing Candidates = {2,3,4,8,13} Answers = {2,4} INTERSECTS 60
61 SB-index: Query Processing 2. Refinement and Re-writing Candidates = {2,3,4,8,13} Answers = {2,4,8} INTERSECTS 61
62 SB-index: Query Processing 2. Refinement and Re-writing Candidates = {2,3,4,8,13} Answers = {2,4,8,13} INTERSECTS 62
63 SB-index: Query Processing 2. Refinement and Re-writing 2nd task complete! 63
64 SB-index: Query Processing 3. FastBit performs the new query See: Building 1st step WHERE p_brand = MFGR#2239 AND s_suppkey in {2,4,8,13} 3rd task complete! 64
65 SB-index Explores Predefined spatial attribute hierarchies Enhances Star-join and spatial predicate computation Makes viable Multidimensional analytical queries with spatial predicate Performs range queries Intersection, Containment, Enclosure 65
66 Introduction Related Work Our Experimental Evaluation Indices Bitmap Index Our Index Structure Proposal: SB-index Experimental Evaluation applied to SB-index SB-index Enhancement for Redundant GDW Conclusions 66
67 Workbench Firstly, Hybrid GDW Schema 67
68 Results Hybrid GDW Schema Star-join Bitmap Index: 3.4 GB SB-index: 0.10% addition 68
69 Results Hybrid GDW Schema Smaller indices due to spatial data redundancy avoidance Decreases the disk accesses Decreases query response time 69
70 Results Hybrid GDW Schema 0.001% 0.05% 0.1% 1% Granularity attribute granularity, user query window, selectivity 70
71 Results Hybrid GDW Schema Star-join aided by R-tree on spatial attribute Star-join aided by GiST on spatial attribute SB-index 71
72 Workbench And then, Redundant GDW Schema 72
73 Results Redundant GDW Schema Star-join Bitmap Index: 2.3 GB SB-index: 0.14% addition (per granularity level) 73
74 Results Redundant GDW Schema Spatial data redundancy: All SB-indices have the same size 74
75 Results Redundant GDW Schema Spatial data redundancy: The same MBR is evaluated several times 75
76 Results Redundant GDW Schema The same MBR is evaluated several times 76
77 Results Redundant GDW Schema The same MBR is evaluated several times Region Nation City Address 77
78 Results Redundant GDW Schema Spatial data redundancy: SB-index performance gains: from 25% up to 95% 78
79 Discussions Spatial data redundancy still deteriorates query performance The same MBR is evaluated several times Scan Refinement Motivation for a SB-index enhancement 79
80 Introduction Related Work Our Experimental Evaluation Indices Bitmap Index Our Index Structure Proposal: SB-index Experimental Evaluation applied to SB-index SB-index Enhancement for Redundant GDW Conclusions 80
81 SB-index Enhancement SB-index manipulates only distinct MBR Each MBR is related to a file Each file contains every primary key whose spatial object is represented by the corresponding MBR Scan IF (MBRi intersects QW1) THEN All keyvalues from Filei are considered candidates 81
82 SB-index Enhancement Star-join aided by R-tree on spatial attribute Star-join aided by GiST on spatial attribute SB-index 82
83 Introduction Related Work Our Experimental Evaluation Indices Bitmap Index Our Index Structure Proposal: SB-index Experimental Evaluation applied to SB-index SB-index Enhancement for Redundant GDW Conclusions 83
84 Conclusions How does spatial data redundancy affects? 1st Experiments GDW: redundant schema vs. hybrid schema Star-join + R-tree/GiST Hybrid/Redundant: prohibitive query response times 2nd SB-index Star-join + Spatial predicate computation Performance gains: 25% up to 95% Spatial data redundancy still affected query processing 84
85 Conclusions 3rd SB-index enhancement Specifically to redundant GDW schemas Distinct MBR manipulation Performance gains: 80% up to 95% Storage requirements Tiny addition Portability GDW redundant or hybrid (normalized) schemas 85
86 Future Work Data Structure Extending a multidimensional access method To avoid sequential scan To cluster spatial objects Evaluation Increasing data volumes DBMS Queries More than one query window Suppliers from QW1 and Customers from QW2 86
87 Investigating the Effects of Spatial Data Redundancy in Query Performance over Geographical Data Warehouses Thiago Luís Lopes Siqueira Ricardo Rodrigues Ciferri Valéria Cesário Times Cristina Dutra de Aguiar Ciferri (UFSCar) (UFPE) (USP) Observatório da Educação
88 Thanks to 88
89 Binning Wu, K., Stockinger, K. and Shosani, A. Breaking the Curse of Cardinality on Bitmap Indexes. Report LBNL-173E
90 References Beckmann, N., Kriegel, H.-P., Schneider, R., Seeger, B. (1990) The R*-Tree: An Efficient and Robust Access Method for Points and Rectangles. In: SIGMOD Conference. p Fidalgo, R. N. et al. (2004) GeoDWFrame: A Framework for Guiding the Design of Geographical Dimensional Schemas. In: 6th DaWak. p Gaede, V., Günther, O. (1998) Multidimensional Access Methods. In ACM Computing Surveys, v.30, n.2, p Guttman, A. (1984) R-Trees: A Dynamic Index Structure for Spatial Searching. In ACM SIGMOD Record, v.14, n.2, p Harinarayan, V., Rajaraman, A. and Ullman, J. D. (1996) Implementing Data Cubes Efficiently. In ACM SIGMOD Record, v.25, n.2, p Kimball, R. and Ross, M. (2002) The Data Warehouse Toolkit. Wiley, 2nd edition. Malinowski, E. and Zimányi, E. (2004) Representing Spatiality in a Conceptual Multidimensional Model. In: 12th ACM GIS. p O Neil, E., O Neil, P., and Wu, K. (2007) Bitmap Index Design Choices and Their Performance Implications. In: 11th IEEE IDEAS. p
91 References O Neil, P., and Graefe, G. (1995) Multi-Table Joins Through Bitmapped Join Indices. In ACM SIGMOD Record, v.24, n.3, p O Neil, P., O Neil, E. and Chen, X. (2007) The Star Schema Benchmark, July. O Neil, P. and Quass, D. (1997) Improved Query Performance with Variant Indexes, In: ACM SIGMOD Conference. p Papadias, D. et al. (2001) Efficient OLAP Operations in Spatial Data Warehouses. In: 7th Symposium on Spatial and Temporal Databases. p Sampaio, M. C., et al. (2006) Towards a logical multidimensional model for spatial data warehousing and OLAP. In: 8th ACM DOLAP, p Silva, J., Oliveira, A., Salgado, A. C., Times, V. C., Fidalgo, R., Souza C. (2008) A set of aggregation functions for spatial measures. In 11th ACM DOLAP. Siqueira, T. L. L., Ciferri, R. R., Times, V. C., Ciferri, C. D. A (2009) An Spatial Bitmap-Based Index for Geographical Data Warehouses. In: 24th ACM SAC. 91
92 References Stefanovic, N., et al. (2000) Object-Based Selective Materialization for Efficient Implementation of Spatial Data Cubes. In IEEE TKDE, v.12, n.6, p Stockinger, K. and Wu, K. (2007) Bitmap Indices for Data Warehouses. In: Data Warehouses and OLAP: Concepts, Architectures and Solutions, Edited by Robert Wrembel and Christian Koncilia. IRM Press, p Wu, K., Otoo, E. J. and Shoshani, A. (2006) Optimizing Bitmap Indices with Efficient Compression. In ACM TODS v.31, p Wu, K., Stockinger, K. and Shoshani, A. (2008) Breaking the Curse of Cardinality on Bitmap Indexes. July. 92
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