Similarity Search: The Metric Space Approach
Springer Science & Business Media, 7 jun. 2006 - 220 páginas
The area of similarity searching is a very hot topic for both research and c- mercial applications. Current data processing applications use data with c- siderably less structure and much less precise queries than traditional database systems. Examples are multimedia data like images or videos that offer query by example search, product catalogs that provide users with preference based search, scientific data records from observations or experimental analyses such as biochemical and medical data, or XML documents that come from hetero- neous data sources on the Web or in intranets and thus does not exhibit a global schema. Such data can neither be ordered in a canonical manner nor meani- fully searched by precise database queries that would return exact matches. This novel situation is what has given rise to similarity searching, also - ferred to as content based or similarity retrieval. The most general approach to similarity search, still allowing construction of index structures, is modeled in metric space. In this book. Prof. Zezula and his co authors provide the first monograph on this topic, describing its theoretical background as well as the practical search tools of this innovative technology.
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Metric Searching in Large Collections of Data
Basic Partitioning Principles
Policies for Avoiding Distance Computations
Metric Space Transformations
Approximate Similarity Search
SURVEY OF EXISTING APPROACHES
APPROXIMATE SIMILARITY SEARCH
PARALLEL AND DISTRIBUTED INDEXES 161
Otras ediciones - Ver todo
accessed ACM Press actual nearest neighbor applied approach approximate nearest neighbor approximate similarity search ball partitioning ball regions bucket Chapter Ciaccia cluster covering radius D-index data objects database objects dataset deﬁned deﬁnition disk distance computations distance d(q distance function distance measure edit distance efﬁcient enddo entry error on distances Euclidean distance evaluation example Figure ﬁle ﬁnd ﬁrst Hausdorff distance index structure insertion internal node iteration leaf node Lemma lower bound M-tree metric function metric space metric tree modiﬁed nearest neighbor queries nearest neighbor search number of distance number of objects object q overlap parameter peer performance priority queue proximity qualifying objects query execution query object query radius query region radii random range search relative error respect response set result-set retrieved search algorithm Section similarity queries similarity search algorithm speciﬁc split stored strategy subsets subtree techniques transformation triangle inequality values vector space Zezula