EAN: 9783031007231

SYNTHESIS LECTURES ON DATA MANAGEMENT SIMILAR...

State-of-the-art database systems manage and process a variety of complex objects  including
strings and trees. For such objects equality comparisons are often not meaningful and must be
replaced by similarity comparisons. This book describes the concepts and techniques to
incorporate similarity into database systems. We start out by discussing the properties of
strings and trees  and identify the edit distance as the de facto standard for comparing
complex objects. Since the edit distance is computationally expensive  token-based distances
have been introduced to speed up edit distance computations. The basic idea is to decompose
complex objects into sets of tokens that can be compared efficiently. Token-based distances are
used to compute an approximation of the edit distance and prune expensive edit distance
calculations. A key observation when computing similarity joins is that many of the object
pairs  for which the similarity is computed  are very different from each other. Filters
exploit this property to improve the performance of similarity joins. A filter preprocesses the
input data sets and produces a set of candidate pairs. The distance function is evaluated on
the candidate pairs only. We describe the essential query processing techniques for filters
based on lower and upper bounds. For token equality joins we describe prefix  size  positional
and partitioning filters  which can be used to avoid the computation of small intersections
that are not needed since the similarity would be too low.

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