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Object Fusion in Geographic Information Systems. Catriel Beeri, Yaron Kanza, Eliyahu Safra, Yehoshua Sagiv Hebrew University Jerusalem Israel. The Goal: Fusing Objects that Represent the Same Real-World Entity. Example: three data sources that provide information about hotels in Tel-Aviv.

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Object fusion in geographic information systems

Object Fusion in Geographic Information Systems

Catriel Beeri, Yaron Kanza,

Eliyahu Safra, Yehoshua Sagiv

Hebrew University

Jerusalem Israel


The goal fusing objects that represent the same real world entity
The Goal: Fusing Objects that Represent the Same Real-World Entity

Example: three data sources that provide information about hotels in Tel-Aviv

MAPI:

the survey of Israel

MAPA:

commercial corporation

MUNI: The municipally

of Tel-Aviv


The goal fusing objects that represent the same real world entity1

polygon

points

The Goal: Fusing Objects that Represent the Same Real-World Entity

MAPI: cadastral and building information

MUNI: Municipal information

MAPA: touristinformation

Is there a nearby parking lot?

Hotel Rank

Each data source provides data that the other sources do not provide


The goal fusing objects that represent the same real world entity2

Radison Moria

The Goal: Fusing Objects that Represent the Same Real-World Entity

MAPI: cadastral and building information

MUNI: Municipal information

MAPA: touristinformation

Object fusion enables us to utilize the different perspectives of the data sources


Why are locations used for fusion

There are no global keys to identify objects that should be fused

Names cannot be used

Change often

May be missing

May be in different languages

It seems that locations arekeys:

Each spatial object includes location attributes

In a “perfect world,” two objects that represent the same entity have the same location

Why Are Locations Used for Fusion?


Why is it difficult to use locations

For example, the Basel Hotel has three different locations, in the three data sources

Why is it Difficult to use Locations?

  • In real maps,

    locations are inaccurate

  • The map on the left is an overlay of the three data sources about hotels in Tel-Aviv


Inaccuracy difficult to use locations
Inaccuracy in the three data sources Difficult to Use Locations

  • It is difficult to distinguish between:

    • A pair of objects that represent close entities

    • A pair of objects that represent the same entity

  • Partial coverage complicates the problem

+

+

?

1

2

a


Fusion methods
Fusion methods in the three data sources

Assumptions

  • There are onlytwo data sources

  • Each data source has at most one object for each real-world entity – i.e., the matching is one-to-one


Corresponding Objects in the three data sources

  • Objects from two distinct sources that represent the same real-world entity


Fusion Sets in the three data sources

  • A fusion algorithm creates two types of fusion sets:

    • A set with a single object

    • A set with a pair of objects – one from each data source

+

+


Confidence in the three data sources

  • Our methods are heuristics  may produce incorrect fusion sets

  • A confidence value between 0 and 1 is attached to each fusion set

  • It indicates the degree of certainty in the correctness of the fusion set

Fusion sets with high confidence

Fusion sets with low confidence

+

+


The mutually nearest method
The Mutually-Nearest Method in the three data sources

  • The result includes

    • All mutually-nearest pairs

    • All singletons, when an object is not part of pair

Finding nearest objects

input

Fusion sets

nearest

nearest

1

a

2

1

a

2

1

a

2

nearest


The Probabilistic Method in the three data sources

  • An object from one dataset has a probability of choosing an object from the other dataset

  • The probability is inversely proportional to the distance

Confidence – the probability that

the object is not chosen by any +

Confidence – the probability of

the mutual choice

+

A threshold value is used to discard

fusion sets with low confidence


Mutual influences between probabilities
Mutual Influences Between Probabilities in the three data sources

Case I:

1

a

2

1

a

2

0.3

0.2

Case II: we expect

1

a

2

1

a

2

b

b

0.8

0.05


The normalized weights method
The Normalized-Weights Method in the three data sources

Normalization

captures mutual

influence

Iteration

brings to

equilibrium

Results are superior to those of the previous two methods (at a cost of only a small increase in the computation time)


Measuring the Quality of the Result in the three data sources

R

Fusion

sets in

the

result

E

Entities

in the

world

C

Correct

fusion sets

in the

result


A case study hotels in tel aviv
A Case Study: in the three data sourcesHotels in Tel-Aviv

State of the art

Our three methods

All three methods perform much better than the nearest-neighbor method



Conclusions
Conclusions paper

The novelty of our approach is in developing efficient

methods that find fusion sets with high recall and

precision, using only location of objects.

Thank you!

You are invited to visit our poster

And our web site

http://gis.cs.huji.ac.il/


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