Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Score Aggregation

  • Ronald Fagin
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_80678

Assume that there is a fixed collection O of objects and that there are m attributes of the objects. Assume that for attribute i (with 1 ≤ i ≤ m), there is a function fi that assigns a score fi(x) to each object x in O. Typically we have 0 ≤ fi(x) ≤ 1. Intuitively, fi(x) tells the extent to which object x has attribute i. For example, if attribute i represents “redness” (telling how red an object is), then a redness score fi(x) near 1 means that object x is very red and a redness score fi(x) near 0 means that object x is far from being red.

We assume that there is a scoring function (or aggregation function) F with m arguments, so that F (f1(x), …, fm(x)) gives the overall score of object i (the result of aggregating the scores of object x over all of the attributes). It is natural to assume that F is monotone, in the sense that if yi ≤ zi, for 1 ≤ i ≤ m, then F (y1, …, ym) ≤ F (z1, …, zm). Typical scoring functions are the min, which is used in fuzzy logic [2] to represent the...

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Recommended Reading

  1. 1.
    Fagin R, Lotem A, Naor M. Optimal aggregation algorithms for middleware. J Comput Syst Sci. 2003;66(4):614–56.MathSciNetzbMATHCrossRefGoogle Scholar
  2. 2.
    Zadeh LA. Fuzzy sets. Inf Control. 1969;8:338–53.zbMATHCrossRefGoogle Scholar
  3. 3.
    Zimmermann HJ. Fuzzy set theory. 3rd ed. Boston: Kluwer Academic Publishers; 1996.zbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.IBM Almaden Research CenterSan JoseUSA

Section editors and affiliations

  • Ihab F. Ilyas
    • 1
  1. 1.Cheriton School of Computer ScienceUniversity of WaterlooWaterlooCanada