Approximate String Matching Algorithm Research Articles

Survey on Context Based Identification of Customer Name Variations using ML Techniques

Abstract: The demand for automated validation of new customer company accounts, with a specific emphasis on resolving name variations within large databases, is steadily increasing. This surge in demand is a direct response to the significant scale at which data for new customers is being input into CRM systems, necessitating manual validation and correction processes. The primary objective of this survey paper is to identify machine learning methods capable of addressing the challenge of resolving name variations in extensive databases. To achieve this goal, we propose a multi-step approach. Firstly, we employ an approximate string matching algorithm with high computational speed to identify matches with a very high percentage of accuracy. In cases where such matches are not found in the system, we utilize named entity recognition techniques to annotate company names and their suffixes, such as INC, PVT, LTD, CORP, and CO, which may appear in various forms. To resolve abbreviation disambiguity, we explore the application of machine learning algorithms, including the naïve Bayes classifier, decision trees, and Support Vector Machines. In this survey paper, we conclude by presenting potential approximate string matching algorithms, a named entity recognition method, and a model for resolving abbreviation disambiguity. Our review not only provides a comprehensive overview of the current state of research in this area but also highlights gaps in the existing knowledge, offering valuable insights for future research and practical application.

An FPGA Based Energy-Efficient Read Mapper With Parallel Filtering and In-Situ Verification.

In the assembly pipeline of Whole Genome Sequencing (WGS), read mapping is a widely used method to re-assemble the genome. It employs approximate string matching and dynamic programming-based algorithms on a large volume of data and associated structures, making it a computationally intensive process. Currently, the state-of-the-art data centers for genome sequencing incur substantial setup and energy costs for maintaining hardware, data storage and cooling systems. To enable low-cost genomics, we propose an energy-efficient architectural methodology for read mapping using a single system-on-chip (SoC) platform. The proposed methodology is based on the q-gram lemma and designed using a novel architecture for filtering and verification. The filtering algorithm is designed using a parallel sorted q-gram lemma based method for the first time, and it is complemented by an in-situ verification routine using parallel Myers bit-vector algorithm. We have implemented our design on the Zynq Ultrascale+ XCZU9EG MPSoC platform. It is then extensively validated using real genomic data to demonstrate up to 7.8× energy reduction and up to 13.3× less resource utilization when compared with the state-of-the-art software and hardware approaches.

Hardware-Algorithm Codesign for Fast and Energy Efficient Approximate String Matching on FPGA for Computational Biology.

Myers bit-vector algorithm for approximate string matching (ASM) is a dynamic programming based approach that takes advantage of bit-parallel operations. It is one of the fastest algorithms to find the edit distance between two strings. In computational biology, ASM is used at various stages of the computational pipeline, including proteomics and genomics. The computationally intensive nature of the underlying algorithms for ASM operating on the large volume of data necessitates the acceleration of these algorithms. In this paper, we propose a novel ASM architecture based on Myers bit-vector algorithm for parallel searching of multiple query patterns in the biological databases. The proposed parallel architecture uses multiple processing engines and hardware/software codesign for an accelerated and energy-efficient design of ASM algorithm on hardware. In comparison with related literature, the proposed design achieves 22× better performance with a demonstrative energy efficiency of ∼500×109 cell updates per joule.

Association of Patient-Centered Medical Home designation and quality indicators within HRSA-funded community health center delivery sites

BackgroundPatient-Centered Medical Home (PCMH) adoption is an important strategy to help improve primary care quality within Health Resources and Service Administration (HRSA) community health centers (CHC), but evidence of its effect thus far remains mixed. A limitation of previous evaluations has been the inability to account for the proportion of CHC delivery sites that are designated medical homes.MethodsRetrospective cross-sectional study using HRSA Uniform Data System (UDS) and certification files from the National Committee for Quality Assurance (NCQA) and the Joint Commission (JC). Datasets were linked through geocoding and an approximate string-matching algorithm. Predicted probability scores were regressed onto 11 clinical performance measures using 10% increments in site-level designation using beta logistic regression.ResultsThe geocoding and approximate string-matching algorithm identified 2615 of the 6851 (41.8%) delivery sites included in the analyses as having been designated through the NCQA and/or JC. In total, 74.7% (n = 777) of the 1039 CHCs that met the inclusion criteria for the analysis managed at least one NCQA- and/or JC-designated site. A proportional increase in site-level designation showed a positive association with adherence scores for the majority of all indicators, but primarily among CHCs that designated at least 50% of its delivery sites. Once this threshold was achieved, there was a stepwise percentage point increase in adherence scores, ranging from 1.9 to 11.8% improvement, depending on the measure.ConclusionGeocoding and approximate string-matching techniques offer a more reliable and nuanced approach for monitoring the association between site-level PCMH designation and clinical performance within HRSA’s CHC delivery sites. Our findings suggest that transformation does in fact matter, but that it may not appear until half of the delivery sites become designated. There also appears to be a continued stepwise increase in adherence scores once this threshold is achieved.

Keywords Search Correction Using Damerau Levenshtein Distance Algorithm

Searching is one of the important features on the website, but it is not uncommon for users to make typos when typing keywords. Typing errors of these keywords is usually referred to as typo. This study aims to build a system by providing suggestions for correcting typos in the search feature. Keywords search correction are obtained using the Damerau-Levenshtein Distance Approximate String Matching algorithm by to calculate the editing distance of each word in a keywords with each word in the Indonesian word dictionary. Testing was carried out as many as 40 experiments, with 10 keywords and 250 articles taken randomly. The test results show the Damerau-Levenshtein Distance algorithm is able to provide precision and recall values of 91.24% and 89.58% in providing keyword improvement suggestions. With the improvement of the system, each trial increases with precision value of 0.80 and recall value of 0.98

Ffgrep: Scalable Approximate String Matching

Approximate substring searching is a common but computationally demanding task in bioinformatics and text analysis. We present a new approach that recasts string search as a multiple convolution problem, then exploits highly efficient fast Fourier convolution techniques. This approach, which we call ffgrep, computes and caches the spectra of a target corpora, drastically reducing the cost of subsequent searches. Like other approaches, this algorithm is embarrassingly parallelizable; unlike other approaches, it is capable of operating on not only raw strings, but also word embeddings. ffgrep is applied to an original corpus of imperfect automatic transcriptions of campaign speeches in the 2012 U.S. presidential election. We contrast our approach with agrep, an industry-standard meta-algorithm that selects the optimal member from a number of highly optimized approximate string matching algorithms. Searching for approximate recurrences of a manually curated set of candidate catchphrases, we show that ffgrep speeds computation by up to a factor of 60x in typical settings, with increasing gains as alignments grow longer or more complex. Moreover, these computational gains come at little cost in performance. Taking agrep search results as ground truth, over a wide range of agrep parameters, we show that ffgrep is capable of recovering highly similar results with accuracies exceeding 0.94 and F1 of 0.84–0.9. Finally, we demonstrate how efficient substring matching enables new substantive research by identifying candidate catchphrases without human supervision. By rapidly computing and organizing 90 billion pairwise string comparisons, our proposed method automatically learns that the phrases “kick children off of Head Start or eliminate health insurance for the poor” and “kick students are [sic] financial aid or get rid of funding for Planned Parenthood or eliminate health care for millions on Medicaid” — along with 32 other campaign appeals — all map onto a single recurring theme, President Barack Obama’s critique of a proposed Medicare reform.

A Novel Algorithm for Online Inexact String Matching and its FPGA Implementation

Among the basic cognitive skills of the biological brain in humans and other mammals, a fundamental one is the ability to recognize inexact patterns in a sequence of objects or events. Accelerating inexact string matching procedures is of utmost importance when dealing with practical applications where huge amounts of data must be processed in real time, as usual in bioinformatics or cybersecurity. Inexact matching procedures can yield multiple shadow hits, which must be filtered, according to some criterion, to obtain a concise and meaningful list of occurrences. The filtering procedures are often computationally demanding and are performed offline in a post-processing phase. This paper introduces a novel algorithm for online approximate string matching (OASM) able to filter shadow hits on the fly, according to general purpose priority rules that greedily assign priorities to overlapping hits. A field-programmable gate array (FPGA) hardware implementation of OASM is proposed and compared with a serial software version. Even when implemented on entry-level FPGAs, the proposed procedure can reach a high degree of parallelism and superior performance in time compared to the software implementation, while keeping low the usage of logic elements. This makes the developed architecture very competitive in terms of both performance and cost of the overall computing system.

Exact String Matching Algorithms: Survey, Issues, and Future Research Directions

String matching has been an extensively studied research domain in the past two decades due to its various applications in the fields of text, image, signal, and speech processing. As a result, choosing an appropriate string matching algorithm for current applications and addressing challenges is difficult. Understanding different string matching approaches (such as exact string matching and approximate string matching algorithms), integrating several algorithms, and modifying algorithms to address related issues are also difficult. This paper presents a survey on single-pattern exact string matching algorithms. The main purpose of this survey is to propose new classification, identify new directions and highlight the possible challenges, current trends, and future works in the area of string matching algorithms with a core focus on exact string matching algorithms.

Mining Query Plans for Finding Candidate Queries and Sub-Queries for Materialized Views in BI Systems Without Cube Generation

Materialized views are important for optimizing Business Intelligence (BI) systems when they are designed without data cubes. Selecting candidate queries from large number of queries for materialized views is a challenging task. Most of the work done in the past involves finding out frequent queries from the past workload and creating materialized views from such queries by either manually analyzing workload or using approximate string matching algorithms using query text. Most of the existing methods suggest complete queries but ignore query components such as sub queries for creation of materialized views. This paper presents a novel method to determine on which queries and query components materialized views can be created to optimize aggregate and join queries by mining database of query execution plans which are in the form of binary trees. The proposed algorithm showed significant improvement in terms of more number of optimized queries because it is using the execution plan tree of the query as a basis of selection of query to be optimized using materialized views rather than choosing query text which is used by traditional methods. For selecting a correct set of queries to be optimized using materialized views, the paper proposes efficient specialized frequent tree component mining algorithm with novel heuristics to prune search space. These frequent components are used to determine the possible set of candidate queries for creation of materialized views. Experimentation on standard, real and synthetic data sets, and also the theoretical basis, proved that the proposed method is able to optimize a large number of queries with less number of materialized views and showed a significant improvement in performance compared to traditional methods.

BAŞLICA BİYOİNFORMATİK ALGORİTMALARI İÇİN WEB ARA YÜZÜ VE YENİ OTOMAT TABANLI YAKLAŞIK DESEN EŞLEŞTİRME YAKLAŞIMI

In this study, we present a new web interface for major bioinformatics algorithms and introduce a novel approximate string matching algorithm. Our web interface executes major algorithms on the field for the use of computational biologists, students or any other interested researchers. In the web interface, algorithms come under three sections: Sequence alignment, pattern matching and motif finding. In each section, we introduce algorithms in order to find best fitting one for specific dataset and problem. The interface introduces execution time, memory usage and context specific results of algorithms such as alignment score. The interface utilizes emerging open source languages and tools. In order to develop light and user-friendly interface, all parts of the interface coded with Python language. On the other hand, Django is used for web interface. Second contribution of the study is novel A-BOM algorithm, which is designed for approximate pattern matching problem. The algorithm is approximate matching variation of Backward Oracle Matching. We compare our algorithm with popular approximate string matching algorithms. Results denote that A-BOM introduces %30 to %80 short runtime improvement when compared to current approximate pattern matching algorithms on long patterns.

Data mining algorithm for pre-processing biopharmaceutical drug product manufacturing records

The quality of data plays a crucial role in providing a reliable decision-making process when improving processes and operations under uncertainty. We present a data mining-based algorithm for robustly pre-processing the manufacturing records of biopharmaceutical batch processes. The algorithm can identify the time intervals in which the process is in commercial operation, and can characterize process failures automatically. An approximate string-matching algorithm, a decision tree classifier and a constrained clustering is applied to sequence the raw data, to classify the noise and identify each single batches; finally process failure are characterized. The algorithm was applied to the records of the process named as “cleaning- and sterilizing-in-place”, which is an essential process in manufacturing environment, in a case study. The algorithm was training on state of the art manual pre-processing outcome and was applied reducing the execution time of the activity down to 11.7% while maintaining high data quality and integrity.

IMPLEMENTASI ALGORITMA APPROXIMATE STRING MATCHING PADA APLIKASI FILOSOFI BERBASIS ANDROID

Philosophy is a critical thinking framework to find solutions to all problems. The solution found to overcome a problem through critical thinking is the fruit of philosophical thinking. If the solution to a problem is not thought out carefully and critically, then it is not the fruit of philosophical thinking but only the sound. Philosophy is useful to achieve a better life, especially in a job so that you can understand the reason why the order in a job must be carried out. In understanding philosophical terms generally use foreign-language scholarship because the term philosophy is the absorption word from English, namely philosophy. While the word philosophy itself is an absorption word from the Greek language. In Greek the combination of the word philo means love and sophós, which means wisdom makes a special word, philosophia, which means love wisdom. To learn the term philosophy is very difficult, especially for ordinary people. Based on this research the author designed the Android-based Philosophy application using eclipse juno and sqlite as the database. To facilitate the search process the meaning of philosophical terms in the application the author applies the Approximate string matching algorithm. Approximate algorithm (Estimated string matching) is a problem in computer science that is applied in text search, pattern recognition and signal processing applications. For the text T [1..n] and the pattern P [1 ... m], we are expected to find all occurrences of the pattern in the text with the most edit distance is K. The edit distance between two strings is defined as the minimum number of character insertions, deletions and the replacement needed to make it the same. The results expected in this study is an application that implements the Approximate Algorithm so that the process of searching quickly in the application can help the user to more easily understand philosophical terms.Keywords: Application Philosophy, Android, String Matching, Approximate Algorithm.

Correction to: New algorithms for fixed-length approximate string matching and approximate circular string matching under the Hamming distance

Correction to: New algorithms for fixed-length approximate string matching and approximate circular string matching under the Hamming distance

New algorithms for fixed-length approximate string matching and approximate circular string matching under the Hamming distance

This paper proposes new algorithms for fixed-length approximate string matching and approximate circular string matching under the Hamming distance. Fixed-length approximate string matching and app...

A parallel approximate string matching under Levenshtein distance on graphics processing units using warp-shuffle operations

Approximate string matching with k-differences has a number of practical applications, ranging from pattern recognition to computational biology. This paper proposes an efficient memory-access algorithm for parallel approximate string matching with k-differences on Graphics Processing Units (GPUs). In the proposed algorithm, all threads in the same GPUs warp share data using warp-shuffle operation instead of accessing the shared memory. Moreover, we implement the proposed algorithm by exploiting the memory structure of GPUs to optimize its performance. Experiment results for real DNA packages revealed that the performance of the proposed algorithm and its implementation archived up to 122.64 and 1.53 times compared to that of sequential algorithm on CPU and previous parallel approximate string matching algorithm on GPUs, respectively.

Parallelizing Exact and Approximate String Matching via Inclusive Scan on a GPU

In this study, to substantially improve the runtimes of exact and approximate string matching algorithms, we propose a tribrid parallel method for bit-parallel algorithms such as the Shift-Or and Wu-Manber algorithms. Our underlying idea is to interpret bit-parallel algorithms as inclusive-scan operations, which allow these bit-parallel algorithms to run efficiently on a graphics processing unit (GPU); we achieve this speed-up here because inclusive-scan operations not only eliminate duplicate searches between threads but also realize a GPU-friendly memory access pattern that maximizes memory read/write throughput. To realize our ideas, we first define two binary operators and then present a proof regarding the associativity of these operators, which is necessary for the parallelization of the inclusive-scan operations. Finally, we integrate the inclusive-scan scheme into a previous segmentation-based scheme to maximize search throughput, identifying the best tradeoff point between synchronization cost and duplicate work. Through our experiments, we compared our proposed method with previous segmentation-based methods and indexing-based sequence aligners. For online string matching, our proposed method performed 6.7-16.7 times faster than previous methods, achieving a search throughput of up to 1.88 terabits per second (Tbps) on a GeForce GTX TITAN X GPU. We therefore conclude that our proposed method is quite effective for decreasing the runtimes of online string matching of short patterns.

A consensus algorithm for approximate string matching and its application to QRS complex detection

In this paper, a novel algorithm for approximate string matching (ASM) is proposed. The novelty resides in the fact that, unlike most other methods, the proposed algorithm is not based on the Hamming or Levenshtein distances, but instead computes a score for each symbol in the search text based on a consensus measure. Those symbols with sufficiently high scores will likely correspond to approximate instances of the pattern string. To demonstrate the usefulness of the proposed method, it has been applied to the detection of QRS complexes in electrocardiographic signals with competitive results when compared against the classic Pan-Tompkins (PT) algorithm. The proposed method outperformed PT in 72% of the test cases, with no extra computational cost.

New rule-based phishing detection method

In this paper, we present a new rule-based method to detect phishing attacks in internet banking. Our rule-based method used two novel feature sets, which have been proposed to determine the webpage identity. Our proposed feature sets include four features to evaluate the page resources identity, and four features to identify the access protocol of page resource elements. We used approximate string matching algorithms to determine the relationship between the content and the URL of a page in our first proposed feature set. Our proposed features are independent from third-party services such as search engines result and/or web browser history. We employed support vector machine (SVM) algorithm to classify webpages. Our experiments indicate that the proposed model can detect phishing pages in internet banking with accuracy of 99.14% true positive and only 0.86% false negative alarm. Output of sensitivity analysis demonstrates the significant impact of our proposed features over traditional features. We extracted the hidden knowledge from the proposed SVM model by adopting a related method. We embedded the extracted rules into a browser extension named PhishDetector to make our proposed method more functional and easy to use. Evaluating of the implemented browser extension indicates that it can detect phishing attacks in internet banking with high accuracy and reliability. PhishDetector can detect zero-day phishing attacks too.

An efficient pruning strategy for approximate string matching over suffix tree

Approximate string matching over suffix tree with depth-first search (ASM_ST_DFS), a classical algorithm in the field of approximate string matching, was originally proposed by Ricardo A. Baeza-Yates and Gaston H. Gonnet in 1990. The algorithm is one of the most excellent algorithms for approximate string matching if combined with other indexing techniques. However, its time complexity is sensitive to the length of pattern string because it searches $$m+k$$m+k characters on each path from the root before backtracking. In this paper, we propose an efficient pruning strategy to solve this problem. We prove its correctness and efficiency in theory. Particularly, we proved that if the pruning strategy is adopted, it averagely searches O(k) characters on each path before backtracking instead of O(m). Considering each internal node of suffix tree has multiple branches, the pruning strategy should work very well. We also experimentally show that when k is much smaller than m, the efficiency improves hundreds of times, and when k is not much smaller than m, it is still several times faster. This is the first paper that tries to solve the backtracking problem of ASM_ST_DFS in both theory and practice.

Approximate String Matching Algorithms: A Brief Survey and Comparison

Many database applications require similarity based retrieval on stored text and/or multimedia objects. This is an area of increasing research interest in the sectors of database, data mining, information retrieval and knowledge discovery. This paper presents a brief survey on the existing approximate string matching algorithms by primarily demonstrating three families of algorithms — the Brute force, the Lipschitz Embeddings and the Ball Partitioning algorithms. While Brute Force performs approximate string matching based on distance measures of the query object from each string stored in the database, Lipschitz Embeddings uses a far more efficient approach which embeds the stored strings in database in vector space so that the distances of embedded strings approximates the actual distances. Ball Partitioning algorithm, much more efficient than Brute force but less efficient than Lipschitz algorithm, performs search in approximate string matching based on distances where queries operate on an arbitrary search hierarchy. The paper compares and makes an analysis of these three algorithms which are suitable for approximate matching of strings stored in database text files, an issue much required in the context of similarity based retrieval of objects. The work can be extended for future work by taking into account a larger number of algorithms suited to approximate string matching for the benefit of a wider scope of comparisons and picking out the most optimal one. General Terms Algorithms for Approximate String Matching.