Approximate string matching

About: Approximate string matching is a research topic. Over the lifetime, 1903 publications have been published within this topic receiving 62352 citations. The topic is also known as: fuzzy string-searching algorithm & fuzzy string-matching algorithm.

A fast parallel algorithm to determine edit distance

Abstract: We consider the problem of determining in parallel the cost of converting a source string to a destination string by a sequence of insert, delete and transform operations. Each operation has an integer cost in some fixed range. We present an algorithm that runs in <9(logmlogrt) time and uses mn processors on a CRCW PRAM, where m and n are the lengths of the strings. The best known sequential algorithm [MP83] runs in time 0(n/ log n) for strings of length n, indicating that our parallel algorithm (with time-processor product equal to 0(mn log m log n)) is nearly optimal. An instance of the edit distance problem is represented as a graph. The algorithm finds the shortest path in the graph using a path doubling method with efficient pruning due to the structure of the problem. Extensions of the algorithm solve approximate string matching and local best fit problems. The problem of finding the largest common submatrix of two matrices is considered and shown to be NP-hard. Finally we present an algorithm for exact two-dimensional pattern matching that runs in OClog n) time using n processors for a n x n search matrix.

Combination of rule and pattern based lithography unfriendly pattern detection in OPC flow

Abstract: Foundry companies encounter again and again the same or similar lithography unfriendly patterns (Hot-spots) in different designs within the same technology node and across different technology nodes, which eluded design rule check (DRC), but detected again and again in OPC verification step. Since Model-based OPC tool applies OPC on whole-chip design basis, individual hot-spot patterns are treated same as the rest of design patterns, regardless of its severity. We have developed a methodology to detect those frequently appeared hot-spots in pre-OPC design, as well as post OPC designs to separate them from the rest of designs, which provide the opportunity to treat them differently in early OPC flow. The methodology utilizes the combination of rule based and pattern based detection algorithms. Some hotspot patterns can be detected using rule-based algorithm, which offer the flexibility of detecting similar patterns within pre-defined ranges. However, not all patterns can be detected (or defined) by rules. Thus, a pattern-based approach is developed using defect pattern library concept. The GDS/OASIS format hot-spot patterns can be saved into a defect pattern library. Fast pattern matching algorithm is used to detect hot-spot patterns in a design using the library as a pattern template database. Even though the pattern matching approach lacks the flexibility to detect patterns’ similarity, but it has the capability to detect any patterns as long as a template exists. The pattern-matching algorithm can be either exact match or a fuzzy match. The rule based and pattern based hot-spot pattern detection algorithms complement each other and offer both speed and flexibility in hot spot pattern detection in pre-OPC and post-OPC designs. In this paper, we will demonstrate the methodology in our OPC flow and the benefits of such methodology application in production environment for 90nm designs. After the hot spot pattern detection, examples of special treatment to selected hot spot patterns will be shown.

Average-Case Optimal Approximate Circular String Matching

Abstract: Approximate string matching is the problem of finding all factors of a text \(t\) of length \(n\) that are at a distance at most \(k\) from a pattern \(x\) of length \(m\). Approximate circular string matching is the problem of finding all factors of \(t\) that are at a distance at most \(k\) from \(x\) or from any of its rotations. In this article, we present a new algorithm for approximate circular string matching under the edit distance model with optimal average-case search time \(\mathcal {O}(n(k + \log m) /m)\). Optimal average-case search time can also be achieved by the algorithms for multiple approximate string matching (Fredriksson and Navarro, 2004) using \(x\) and its rotations as the set of multiple patterns. Here we reduce the preprocessing time and space requirements compared to that approach.

Mapping processing system and method for solving problem of standard code control of medical data

Abstract: The invention provides a mapping processing system and method for solving a problem of standard code control of medical data. The mapping processing system comprises a resource word bank, a target value range bank, a simulation semantic word segmentation unit, a fuzzy matching unit, an accurate matching unit, a matching table and a labor management unit. The mapping processing method includes steps of acquiring data from the medical data, mapping the acquired data in an accurate match manner according to standard codes stored in the target value range bank or mapping matching rule information stored in the matching table; analyzing fuzzy semantics of the data acquired from a data source; subjecting simulation semantic word segmentation results to fuzzy matching to generate a mapping matching result; mapping the medical data into the standard codes according to the mapping matching rule information in the matching table and generating a medical data mapping processing result. The mapping processing system and method build an automatic mapping matching process, and high accuracy and matching accuracy of the mapping matching results are achieved by combination of fuzzy matching, labor check, mechanical training and the like.

Error tolerant autocompletion

Abstract: Techniques for error-tolerant autocompletion are described. While displaying characters of an input string as they are inputted by a user, when a character is added to the input string by the user, matching strings may be selected from among a set of candidate strings by determining which of the candidate strings have a prefix whose characters match the characters of the input string within a given edit distance of the input string.