By Marios Hadjieleftheriou, Divesh Srivastava
Some of the most very important primitive info kinds in smooth info processing is textual content. textual content info are identified to have a number of inconsistencies (e.g., spelling errors and representational variations). as a result, there exists a wide physique of literature relating to approximate processing of textual content. Approximate String Processing focuses particularly at the challenge of approximate string matching and surveys indexing innovations and algorithms particularly designed for this goal. It concentrates on inverted indexes, filtering ideas, and tree information constructions that may be used to judge a number of set established and edit dependent similarity services. the point of interest is on all-match and top-k flavors of choice and sign up for queries, and it discusses the applicability, benefits and drawbacks of every process for each question style. Approximate String Processing is equipped into 9 chapters. Sandwiched among the creation and end, Chapters 2 to five talk about intimately the elemental primitives that symbolize any approximate string matching indexing procedure. the following 3 chapters, 6 to nine, are devoted to really good indexing thoughts and algorithms for approximate string matching.
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Extra info for Approximate String Processing
In addition, all Lp -norm ﬁlters are still applicable: L1 -norm reduces to the L0 -norm and L2 -norm reduces to the square root of the L0 -norm. Surprisingly, the heaviest first strategy can also help prune candidates for Weighted Intersection similarity, based on the observation that strings containing the heaviest query tokens are more likely to exceed the threshold. , W (λv1 ) ≥ . . ≥ W (λvm )). Assume that there exists a string s that is contained only in a suﬃx L(λvk ), . . , L(λvm ) of token lists, whose aggregate weight m v i=k W (λi ) < θ.
Dice: θ v 2−θ 1 ≤ s 1 ≤ 2 v λ · · · λvm θ i+1 C(v, s) ≥ θ ⇒ θ v 2 ≤ s 2 ≤ ( λvi+1 · · · λvm 2 )2 . θ v 2 D(v, s) ≥ θ ⇒ • Cosine: Care needs to be taken though in order to accurately complete the partial similarity scores of all candidates already inserted in the candidate set from previous lists, which can have L1 -norms that do not satisfy the recomputed bounds for v . For that purpose the algorithm 320 Algorithms for Set Based Similarity Using Inverted Indexes identiﬁes the largest L1 -norm in the candidate set and scans subsequent lists using that bound.
Hence it has a very small book-keeping cost, but on the other hand it has to perform a large number of random accesses to achieve this. This could be a drawback on traditional storage devices (like hard drives) but unimportant in modern solid 306 Algorithms for Set Based Similarity Using Inverted Indexes state drives. Combination strategies try to strike a balance between low book-keeping and a small number of random accesses. 2. The algorithm keeps a candidate set M containing tuples consisting of a string identiﬁer, a partial similarity score, and a bit vector containing zeros for all query tokens that have not matched with the particular string identiﬁer yet, and ones for those that a match has been found.
Approximate String Processing by Marios Hadjieleftheriou, Divesh Srivastava