Post on 06-Jan-2016
description
Spatiotemporal Data Indexing using hBπ-tree
Evangelos Kanoulas, Georgios EvangelidisDepartment of Applied Informatics, University of Macedonia, Hellas
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Spatial and Temporal Data
• Linear data
• Spatial data
• Temporal data
Applications, which require to support past, current and future data.
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Transaction Time Databases
transaction time
records (tuples) time-invariant key time – variant attributes time interval
valid records
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TSB-tree 1 - structure
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TSB-tree 3 – splitting
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hBπ – tree (Data nodes)
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hBπ – tree (Index nodes)
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Data Records – an example
TShB-tree – transaction time
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TShB-tree - splitting
• Index nodes
•Using the D/fp algorithm
• Data nodes
•Time split
•Key split
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D/fp – Τ/Κ (1)
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D/fp – Τ/Κ (2)
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D/fp – Τ/ΤΚ (1)
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D/fp – Τ/ΤΚ (2)
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% historical data nodes (node capacity 4K)
0 0.35
4.6
13.4
22.28
34.46 34.47 3537.4
42.72
0
5
10
15
20
25
30
35
40
45
70 75 80 85 90
% active records that indicate the type of node splitting
%
Τ/Κ Τ/ΤΚ
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Index nodes efficiency (node capacity 4Κ)
69.78 69.78 69.17
64.96
59.51
46.33 46.35 46.76 46.29
43.14
40
45
50
55
60
65
70
75
70 75 80 85 90
% active records that indicate the type of node splitting
%
Τ/Κ Τ/ΤΚ
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% historical data nodes
13
46
94
37
54
76
78 93
0
10
20
30
40
50
60
70
80
90
100
0 5 10 15 20 25 30 35
update - insertion rate
%
T/K T/TK
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Data nodes efficiency
64.96
53.57
47.72 49.2
43.9745.7146.29
49.57
0
10
20
30
40
50
60
70
0 5 10 15 20 25 30 35
update - insertion rate
%
T/K T/TK