Object Fusion in Geographic Information Systems Catriel Beeri, Yaron Kanza, Eliyahu Safra, Yehoshua Sagiv Hebrew University Jerusalem Israel.

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-AvivMAPI: the survey of Israel

MAPA: commercial corporation

MUNI: The municipally of Tel-Aviv

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

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

Hotel RankIs there a nearby parking lot?

polygon

points

MAPI: cadastral and building information

MAPA: tourist information

MUNI: Municipal information

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

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

MAPI: cadastral and building information

MAPA: tourist information

Radison MoriaMUNI: Municipal information

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 are keys: – Each spatial object includes location attributes

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

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

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

Inaccuracy Difficult to Use Locations

• It is difficult to distinguish between: 1. A pair of objects that represent close entities

2. A pair of objects that represent the same entity

• Partial coverage complicates the problem

+

+

1 a 2

?

Fusion methods

Assumptions

• There are only two 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

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

Fusion Sets

• 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

• 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 result includes– All mutually-nearest pairs– All singletons, when an object is not part of pair

Fusion setsinput Finding nearest objects

nearest

nearest

nearest

1 a 2 1 a 2 1 a 2

The Probabilistic Method

+ Confidence – the probability of the mutual choice

A threshold value is used to discard fusion sets with low confidence

• 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 +

Mutual Influences Between Probabilities

Case II: we expect

Case I:

1 a 2

b

1 a 2

1 a 2b

1 a 2

0.3 0.2

0.050.8

The Normalized-Weights Method

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

||

||

result thein sets all #

result thein setscorrect #

R

CPrecision

EEntities in the world

RFusion sets in

the result

CCorrect

fusion setsin the result

||

||

entities #

result thein setscorrect #

E

CRecall

A Case Study: Hotels in Tel-Aviv

The traditional nearest neighbor

(Best results)

Mutually nearest

Proba-bilistic method

Normal-ized weights method

Recall0.480.770.800.85

Precision

0.560.850.800.90

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

Our three methodsState of the art

Extensive tests on synthesized data are

described in the paper

Conclusions

The novelty of our approach is in developing efficient

methods that find fusion sets with high recall and

precision, using only location of objects.

You are invited to visit our poster

And our web site

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

Thank you!Thank you!