By Marios Hadjieleftheriou, Divesh Srivastava
Probably the most vital primitive information kinds in glossy info processing is textual content. textual content information are recognized to have a number of inconsistencies (e.g., spelling error and representational variations). therefore, there exists a wide physique of literature regarding approximate processing of textual content. Approximate String Processing focuses in particular at the challenge of approximate string matching and surveys indexing strategies and algorithms particularly designed for this goal. It concentrates on inverted indexes, filtering thoughts, and tree facts buildings that may be used to judge quite a few set established and edit dependent similarity features. the focal point is on all-match and top-k flavors of choice and subscribe to queries, and it discusses the applicability, merits and drawbacks of every method for each question sort. Approximate String Processing is geared up into 9 chapters. Sandwiched among the creation and end, Chapters 2 to five speak about intimately the basic primitives that signify any approximate string matching indexing approach. the following 3 chapters, 6 to nine, are devoted to really good indexing strategies and algorithms for approximate string matching.
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Extra resources for Approximate String Processing
We pop from the heap another θ − 1 − x elements, identify the new top element of the heap, let it be r¨, and directly skip to the ﬁrst element s¨ ≥ r¨ in every token list (we can identify such elements using binary search on the respective lists). 1 All-Match Selection Queries 303 to the heap, and repeat. An important observation here is that an element that was popped from the heap might end up subsequently being re-inserted to the heap, if it happens to be equal to the top element of the heap after the θ − 1 total removals.
L(λvm )} be the m lists corresponding to the query tokens. t. λvi ∈ s. The simplest algorithm for evaluating the similarity between the query and all the strings in lists Lv , is to perform a multiway merge of the string identiﬁers in Lv , to compute all intersections v ∩ s. This will directly yield the Weighted Intersection similarity. Whether we compute the weighted intersection on sequences, frequency-sets or sets depends on whether the initial construction of the inverted index was done on sequences, frequency-sets, or sets.
Let N k be the k-th smallest similarity score in heap H. t. N (s, v) ≥ N k , hence the algorithm can stop inserting new strings in M . The algorithm can also evict from M strings whose best-case similarity is smaller than N k , as both N k and the best-case similarity of strings in M keep getting tighter. After the frontier condition has been met, the algorithm needs to simply complete the partial similarity scores of strings already in M in order to determine the ﬁnal top-k answers. The important question is how to seed the algorithm with a good set of k initial candidates.