Abstract/Details

New approaches to similarity searching in metric spaces


2006 2006

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Abstract (summary)

The complex and unstructured nature of many types of data, such as multimedia objects, text documents, protein sequences, requires the use of similarity search techniques for retrieval of information from databases. One popular approach for similarity searching is mapping database objects into feature vectors, which introduces an undesirable element of indirection into the process. A more direct approach is to define a distance function directly between objects. Typically such a function is taken from a metric space, which satisfies a number of properties, such as the triangle inequality. Index structures that can work for metric spaces have been shown to provide satisfactory performance, and were reported to outperform vector-based counterparts in many applications. Metric spaces also provide a more general framework, and for some domains defining a distance between objects can be accomplished more intuitively than mapping objects to feature vectors.

In this thesis we will investigate new efficient methods for similarity searching in metric spaces. We will first show that current solutions to indexing in metric spaces have several drawbacks. Tree-based solutions do not provide the best tradeoffs between construction time and query performance. Tree structures are also difficult to make dynamic without further degrading their performance. There is also a family of flat structures that address some of the deficiencies of tree-based indices, but they introduce their own unique problems in terms of higher construction cost, higher space usage, and extra CPU overhead.

In this thesis a new family of flat structures will be introduced, which are very flexible and simple. We will show that dynamic operations can easily be performed, and that they can be customized to work under different performance requirements. They also address many of the general drawbacks of flat structures as outlined above.

A new framework, composite metrics will also be introduced, which provides a more flexible similarity searching process by allowing several metrics to be combined in one search structure. Two indexing structures will be introduced that can handle similarity queries in this setting, and it will be shown that they provide competitive query performance with respect to data structures for standard metrics.

Indexing (details)


Subject
Computer science
Classification
0984: Computer science
Identifier / keyword
Applied sciences; Composite metrics; Flat structures; Metric spaces; Similarity searching
Title
New approaches to similarity searching in metric spaces
Author
Celik, Cengiz
Number of pages
139
Publication year
2006
Degree date
2006
School code
0117
Source
DAI-B 67/03, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
ISBN
9780542617317, 0542617315
Advisor
Mount, David
University/institution
University of Maryland, College Park
University location
United States -- Maryland
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
3212572
ProQuest document ID
305304891
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/305304891
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