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

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Object Fusion in Geographic Information Systems

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

  2. 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

  3. 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

  4. 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

  5. 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?

  6. 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

  7. Inaccuracy  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

  8. Fusion methods 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

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

  10. 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 + +

  11. 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 + +

  12. The Mutually-Nearest Method • 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

  13. The Probabilistic Method • 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

  14. Mutual Influences Between Probabilities 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

  15. 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)

  16. Measuring the Quality of the Result R Fusion sets in the result E Entities in the world C Correct fusion sets in the result

  17. A Case Study: Hotels in Tel-Aviv State of the art Our three methods All three methods perform much better than the nearest-neighbor method

  18. Extensive tests on synthesized data are described in the paper

  19. 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. Thank you! You are invited to visit our poster And our web site http://gis.cs.huji.ac.il/

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