using the dbv model to maintain versions of multi-scale...

Using the DBV model to maintain versions of multi-scale geospatial data

João Sávio C. Longo

Luís Theodoro O. Camargo

Claudia Bauzer Medeiros

André Santanchè

Laboratory of Information Systems (LIS)

Institute of Computing – UNICAMP – Brazil

2

Outline

1. Motivation and goal

2. Central idea – extend the DBV model

3. Our approach

4. Conclusions and future work

3

Motivation and goal

4

Multi-scale data

Scale 1:250k Scale 1:1M

5

Problems attacked

1)How to manage multi-scale data?

2)How to trace real world evolution through time?

3)How to represent alternative scenarios?

6

Current approaches

• Generalization algorithms (computation centric)– real time computation – e.g. agents

– costly if there are continuous updates to the data

• Multi-representation databases (MRDB) (data centric)– data structures to store and link multi-representation data

– costly if there are many scales to store

• Hybrid – store some scales, compute others

– more flexible, but still lots of issues to solve

7

Goal

Propose a data-centric model that supports generalization plus MRDBs to:– manage multi-scale data

– trace real world evolution

– represent alternative scenarios for a given scale

8

Central idea – extend the DBV model

9

DBV model

Physical storage

...

...

DBV 1 DBV 2

10

DBV model

• DBV (Database Version)– represents a possible state or version of the database

• Not only temporal versioning– any stored modification can be a version

• Save storage space– only the changes must be stored

– unchanged data are shared from previous DBVs

11

Derivation tree

d1

d2 d3

0

0.1

0.1.1 0.1.2

d0

12

ExampleDBV Data

d0 -

0 d0

13

ExampleDBV Data

d0 -

d1 - watershed polygons

d1

0

0.1

d0

14

ExampleDBV Data

d0 -


d2 - watershed polygons- rivers

d1

d2

0

0.1

0.1.1

d0

15

ExampleDBV Data

d0 -


d2 - watershed polygons- rivers

d3 - watershed polygons- main river

d1

d2 d3

0

0.1

0.1.1 0.1.2

d0

16

Our approach

17

Overview

It is an extension of the DBV model, in which:– each scale has its own derivation tree

– all trees grow and shrink together

– scenario – set of DBVs with same version stamp

18

DBV multi-scale platform

• Open source

• http://code.google.com/p/dbv-ms-api– plataform

– sample project

– web manager

19

General architecture

Web manager

DBV multi-scale platform

Create and store new scales Visualize a given scale

Create, modify, delete multiversion objects

Create, modify, delete DBVs

20

Case study

• Watershed multi-scale data – Rio Pardo/Brazil– 1:250k

– 1:1M

• Content – 5641 geometric objects:– watershed polygons

– rivers

Building scenario 0

scale id

Starting from an empty database, with two possible stored scales


d01

0 d02

0

22

Building scenario 0

1:250k 1:1M

Building scenario 0.1

- scenario – set of DBVs, each corresponding to a given stored scale, at the same time- trees grow and shrink together

d01

d11

0

0.1

d02

d12

0

0.1


24

Building scenario 0.1

1:250k 1:1M

Building scenario 0.1.1


d01

d11

d21

0

0.1

0.1.1

d02

d12

d22

0.1.1

0

0.1

26


1:250k 1:1M


Alternative multi-scale

representations


d01

d11

d21

d31

0

0.1

0.1.1 0.1.2

d02

d12

d22

d32

0.1.1 0.1.2

0

0.1

Alternative mono-scale

representations

28


1:250k 1:1M

29

Conclusions and future work

30

Conclusions

1)How to manage multi-scale data?– DBV multi-scale platform

• flexible MRDB – links through version stamps

2)How to trace real world evolution through time?– derivation tree

3)How to represent alternative scenarios?– creating branches

– DBV model (in other approaches, evolution requires replication)

31

Future work

• Versioning along the temporal scale

• Specification and management of integrity constraints across scales, to determine rules for update propagation

32

Acknowledgments

• LIS (Laboratory of Information Systems)

• UNICAMP - IC

• FAPESP 2011/14280-0

• FAPESP – Microsoft Research Navscales

• Capes

• Embrapa

• INCT - Web Science

• CNPq

• MuZoo

• PRONEX

33

Thank you!

34

UML model

- control over the types of versioned objects

35

Architecture

36

MRDB

Link stored multi-representation data


37

MRDB

1:10k

1:20k

1:50k

38

MRDB

1:10k

1:20k

1:50k

39

Example

• There are many real world objects in the example:– watershed

– main river

– all other rivers with name

• Each stored geometry is a physical version of an object

• So, in a DBV d, a logical version of a multiversion object o is represented by a physical version pv

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Back to scenario 0.1.1

1:250k 1:1M

41

Scenario 0.1.1.1

1:250k 1:1M

using the dbv model to maintain versions of multi-scale...

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