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 MAPI: 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 Rank Is 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 Moria MUNI: 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 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 1a2 1a21a2

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: 1a2 b 1a2 1a2 b 1a

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 E Entities in the world R Fusion sets in the result C Correct fusion sets in the result

A Case Study: Hotels in Tel-Aviv The traditional nearest neighbor (Best results) Mutually nearest Proba- bilistic method Normal- ized weights method Recall Precision All three methods perform much better than the nearest-neighbor method Our three methods State 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 Thank you!