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