Read e-book online Approximate String Processing PDF

By Marios Hadjieleftheriou, Divesh Srivastava

ISBN-10: 1601984189

ISBN-13: 9781601984180

Essentially the most very important primitive facts kinds in smooth information processing is textual content. textual content information are recognized to have various inconsistencies (e.g., spelling error and representational variations). for this reason, there exists a wide physique of literature relating to approximate processing of textual content. Approximate String Processing focuses in particular at the challenge of approximate string matching and surveys indexing ideas and algorithms in particular designed for this goal. It concentrates on inverted indexes, filtering ideas, and tree info buildings that may be used to judge quite a few set dependent and edit dependent similarity services. the focal point 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 advent and end, Chapters 2 to five speak about intimately the elemental primitives that signify any approximate string matching indexing method. the subsequent 3 chapters, 6 to nine, are devoted to really good indexing thoughts and algorithms for approximate string matching.

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T. W (λv1 ) ≥ · · · ≥ W (λvm ), and v threshold 0 < θ ≤ m i=1 W (λi ). Let |v| W (λvi ) ≥ θ. 2 Top-k Selection Queries 323 Let Pθ (v) = {λv1 , . . , λvπ } be the prefix of v and Sθ (v) = {λvπ+1 , . . , λvm } be the suffix of v. t. s ∩ Pθ (v) = ∅, I(s, v) < θ. Proof. 1. Hence, the only viable candidates have to appear in at least one of the lists in the prefix L(λv1 ), . . , L(λvπ ). The heaviest first algorithm exhaustively scans the prefix lists to find all viable candidates and then probes the suffix lists to complete their scores using random access.

Since the sequential round robin algorithm computes similarity incrementally, it has to temporarily store in main memory information about strings whose similarity has only been partially computed. Hence, the algorithm has a high book-keeping cost, given that the candidate set needs to be maintained as a hash table organized by string identifiers. The aggressive random access based algorithm computes the similarity of strings in one step and hence does not need to store any information in main memory.

We divide the lists into two groups, one with the τ longest lists and one with the rest of the lists. We use the aforementioned optimized multiway merge algorithm on the short lists to identify candidate strings that appear at least θ − τ times. Then, we probe the τ long lists to verify the candidates. Depending on the query and the data at hand we can find an appropriate value τ that will result in a good tradeoff between the cost of running the multiway merge algorithm on the short lists and the subsequent probing of the long lists.

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Approximate String Processing by Marios Hadjieleftheriou, Divesh Srivastava

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