Transcript Spatiotemporal Data Indexing using hBπ-tree Evangelos Kanoulas, Georgios Evangelidis Department of Applied Informatics, University of Macedonia, Hellas.

Spatiotemporal Data Indexing
using hBπ-tree
Evangelos Kanoulas, Georgios Evangelidis
Department of Applied Informatics, University of Macedonia, Hellas
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)
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
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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π
hB
– tree (Data nodes)
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π
hB
– tree (Index nodes)
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TShB-tree – transaction time
Data Records – an example
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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)
45
42.72
40
37.4
35
34.46
35
34.47
30
%
25
22.28
20
15
13.4
10
5
4.6
0
0.35
0
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)
75
70
69.78
69.78
69.17
65
64.96
60
%
59.51
55
50
46.33
45
46.76
46.35
46.29
43.14
40
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
100
94
90
78
93
80
76
70
%
60
54
46
50
40
37
30
20
13
10
0
0
5
10
15
20
25
30
35
update - insertion rate
T/K
T/TK
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Data nodes efficiency
70
64.96
60
49.57
49.2
53.57
%
50
47.72
43.97
45.71
40 46.29
30
20
10
0
0
5
10
15
20
25
30
35
update - insertion rate
T/K
T/TK
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