Pattern Matching Algorithm Research Articles

An Efficient Algorithm for 2-Dimensional Pattern Matching Problem

Abstract Pattern matching is the area of computer science which deals with security and analysis of data. This work proposes two 2D pattern matching algorithms based on two different input domains. The first algorithm is for the case when the given pattern contains only two symbols, that is, binary symbols 0 and 1. The second algorithm is in the case when the given pattern contains decimal numbers, that is, the collection of symbols between 0 and 9. The algorithms proposed in this manuscript convert the given pattern into an equivalent binary or decimal number, correspondingly find the cofactors of the same dimension and convert these cofactors into numbers if a particular cofactor number matches indicate the matching of the pattern. Furthermore, the algorithm is enhanced for decimal numbers. In the case of decimal numbers, each row of the pattern is changed to its decimal equivalent, and then, modulo with a suitable prime number changes the decimal equivalent into a number less than the prime number. If the number mismatched pattern does not exist, the complexity of the proposed algorithm is very low as compared to other traditional algorithms.

A Novel Cryptosystem Based on Steganography and Automata Technique for Searchable Encryption

In this paper we first propose a new cryptosystem based on our data hiding scheme introduced in 2019 with high security, where encrypting and hiding are done at once, the ciphertext does not depend on the input image size as existing hybrid techniques of cryptography and steganography. We then exploit our automata approach presented in 2019 to design two algorithms for exact and approximate pattern matching on secret data encrypted by our cryptosystem. Theoretical analyses remark that these algorithms both have O(n) time complexity in the worst case, where for the approximate algorithm, we assume that it uses processors, where , m and n are the error of our string similarity measure and lengths of the pattern and secret data, respectively. In searchable encryption, our cryptosystem is used by users and our pattern matching algorithms are performed by cloud providers.

On modification of Boyer-Moore-horspool's algorithm for tree pattern matching in linearised trees

Tree pattern matching on ordered trees is an important problem in Computer Science. Ordered trees can be represented as strings with additional properties via various linearisations. We present a backward tree pattern matching algorithm for ordered trees for various linear representations of trees and tree patterns. The algorithm adaptations find all occurrences of a single given tree pattern which match an input tree regardless of the chosen linearisation. The algorithms preserve the properties and advantages of standard backward string pattern matching using Boyer-Moore-Horspool's bad character shift heuristics. The number of symbol comparisons in the backward tree pattern matching can be sublinear in the size of the input tree. As in the case of the string version of Boyer-Moore-Horspool's matching algorithm, the size of the bad character shift table used by the algorithm is linear in the size of the alphabet. We compare the algorithm adaptations with the algorithm using originally chosen linear representation and with the best performing previously existing algorithms based on (non-linearised) tree pattern matching using finite tree automata or stringpath matchers. We show that the presented backward tree pattern matching algorithms outperform the non-linearising ones for single pattern matching and they perform among themselves comparably.

Measurements and Analysis of Radio Propagation at 28 GHz in Vegetated Areas of Typical Residential Environments

This communication presents foliage channel measurements at 28 GHz using a directional channel sounder. The measurements were conducted in vegetated areas of typical residential environments in Daejeon, South Korea. We first propose a pattern matching algorithm that allows us to overcome the angle resolution of a directional antenna and to estimate a more accurate receiving angle. Then propagation paths received from the vegetation and the surrounding buildings are classified and their power ratios are analyzed. Furthermore, foliage attenuation was determined from the measurement data according to the foliage depth, and appropriate models based on the modified exponential decay (MED) model and the maximum attenuation (MA) model are presented. In addition, the effect of foliage attenuation on seasonal variation is studied. These results will be useful for predicting path losses through the vegetated areas at 28 GHz by modeling the foliage loss as an additional path loss term. Finally, spatial-temporal channel characteristics in a vegetated environment were statistically analyzed including delay spread, coherence bandwidth, and angular spreads.

Reconstruction of Simulated Magnetic Resonance Fingerprinting Using Accelerated Distance Metric Learning

Purpose: Magnetic Resonance Fingerprinting (MRF) is a novel framework that uses a random acquisition to acquire a unique tissue response, or fingerprint. Through a pattern-matching algorithm, every voxel-vise fingerprint is matched with a pre-calculated dictionary of simulated fingerprints to obtain MR parameters of interest. Currently, a correlation algorithm performs the MRF matching, which is time-consuming. Moreover, MRF suffers from highly undersampled k-space data, thereby reconstructed images have aliasing artifact, propagated to the estimated quantitative maps. We propose using a distance metric learning method as a matching algorithm and a Singular Value Decomposition (SVD) to compress the dictionary, intending to promote the accuracy of MRF and expedite the matching process. Materials and Methods: In this investigation, a distance metric learning method, called the Relevant Component Analysis (RCA) was used to match the fingerprints from the undersampled data with a compressed dictionary to create quantitative maps accurately and rapidly. An Inversion Recovery Fast Imaging with Steady-State (IR-FISP) MRF sequence was simulated based on an Extended Phase Graph (EPG) on a digital brain phantom. The performance of our work was compared with the original MRF paper. Results: Effectiveness of our method was evaluated with statistical analysis. Compared with the correlation algorithm and full-sized dictionary, this method acquires tissue parameter maps with more accuracy and better computational speed. Conclusion: Our numerical results show that learning a distance metric of the undersampled training data accompanied by a compressed dictionary improves the accuracy of the MRF matching and overcomes the computation complexity.

Methods to improve the quality of smoking records in a primary care EMR database: exploring multiple imputation and pattern-matching algorithms

BackgroundPrimary care electronic medical record (EMR) data are emerging as a useful source for secondary uses, such as disease surveillance, health outcomes research, and practice improvement. These data capture clinical details about patients’ health status, as well as behavioural risk factors, such as smoking. While the importance of documenting smoking status in a healthcare setting is recognized, the quality of smoking data captured in EMRs is variable. This study was designed to test methods aimed at improving the quality of patient smoking information in a primary care EMR database.MethodsEMR data from community primary care settings extracted by two regional practice-based research networks in Alberta, Canada were used. Patients with at least one encounter in the previous 2 years (2016–2018) and having hypertension according to a validated definition were included (n = 48,377). Multiple imputation was tested under two different assumptions for missing data (smoking status is missing at random and missing not-at-random). A third method tested a novel pattern matching algorithm developed to augment smoking information in the primary care EMR database. External validity was examined by comparing the proportions of smoking categories generated in each method with a general population survey.ResultsAmong those with hypertension, 40.8% (n = 19,743) had either no smoking information recorded or it was not interpretable and considered missing. Those with missing smoking data differed statistically by demographics, clinical features, and type of EMR system used in the clinic. Both multiple imputation methods produced fully complete smoking status information, with the proportion of current smokers estimated at 25.3% (data missing at random) and 12.5% (data missing not-at-random). The pattern-matching algorithm classified 18.2% of patients as current smokers, similar to the population-based survey (18.9%), but still resulted in missing smoking information for 23.6% of patients. The algorithm was estimated to be 93.8% accurate overall, but varied by smoking status category.ConclusionMultiple imputation and algorithmic pattern-matching can be used to improve EMR data post-extraction but the recommended method depends on the purpose of secondary use (e.g. practice improvement or epidemiological analyses).

Developing a Surgical Site Infection Surveillance System Based on Hospital Unstructured Clinical Notes and Text Mining

Background: Electronic surveillance using clinical and administrative data from multiple sources has been reported as a tool for surveillance of surgical site infections (SSIs), but experiences are limited. In this study, we aimed to assess the accuracy of a text-searching algorithm to detect SSIs in children based on the application of regular expressions of unstructured clinical notes collected through different information systems. Methods: We developed an information system data warehouse that integrates data provided by electronic health and administrative records for patients who underwent surgical procedures in index weeks when active SSIs surveillances was conducted. To capture whether the patient developed an SSI, we developed a customized application to analyze clinical notes and code descriptions applying a pattern-matching algorithm based on regular expressions. We described the SSI cases detected by the active surveillance and the text-searching algorithm. To assess the accuracy in identifying the SSIs through the two methods, we adopted a reference standard that calculated the total number of SSIs as those detected by active surveillance plus those derived by the text-searching algorithm that was missed by active surveillance. Results: Compared with the total number of SSIs used as a reference standard, both methods had a specificity of 100%, a positive predictive value of 100%, and a negative predictive value >99.5%. Sensitivity was 70% for the text-mining algorithm and 60% for the active surveillance. Accuracy was >99% with both methods. The kappa value was 0.46. Conclusions: Compared with conventional surveillance of SSIs, a text-searching algorithm is a valid tool for case finding that has the potential to reduce drastically the workload of conventional surveillance, which involved direct contact with all families.

Incremental Pattern Matching for Regular Expressions

Graph pattern matching lies at the heart of any graph transformation-based system. Incremental pattern matching is one approach proposed for reducingthe overall cost of pattern matching over successive transformations by preserving the matches that stay relevant after a rule application. An important issue in any matching scheme, is the ability to properly and consistently deal with various facilities that add to the expressiveness of a GT-tool’s rule language. One such feature is the support for regular path expressions, which would let two nodes to be consideredas a “match”, if a certain path of edges exists between them. In this paper, the incorporation of regular expression support into incremental pattern matching is discussed within the context of the GROOVE tool set. This includes laying down a formal foundation for incremental pattern matching for regular expressions which is then used to justify the extension proposed to add regular expression support to a well-known pattern matching algorithm.

Functional Programming in Pattern-Match-Oriented Programming Style

Throughout the history of functional programming, recursion has emerged as a natural method for describing loops in programs. However, there does often exist a substantial cognitive distance between the recursive definition and the simplest explanation of an algorithm even for the basic list processing functions such as map, concat, or unique; when we explain these functions, we seldom use recursion explicitly as we do in functional programming. For example, map is often explained as follows: the map function takes a function and a list and returns a list of the results of applying the function to all the elements of the list. This paper advocates a new programming paradigm called pattern-match-oriented programming for filling this gap. An essential ingredient of our method is utilizing pattern matching for non-free data types. Pattern matching for non-free data types features non-linear pattern matching with backtracking and extensibility of pattern-matching algorithms. Several non-standard pattern constructs, such as not-patterns, loop patterns, and sequential patterns, are derived from this pattern-matching facility. Based on that result, this paper introduces many programming techniques that replace explicit recursions with an intuitive pattern by confining recursions inside patterns. We classify these techniques as pattern-match-oriented programming design patterns. These programming techniques allow us to redefine not only the most basic functions for list processing such as map, concat, or unique more elegantly than the traditional functional programming style, but also more practical mathematical algorithms and software such as a SAT solver, computer algebra system, and database query language that we had not been able to implement concisely.

A novel pattern matching algorithm for genomic patterns related to protein motifs

Background: Patterns on proteins and genomic sequences are vastly analyzed, extracted and collected in databases. Although protein patterns originate from genomic coding regions, very few works have directly or indirectly dealt with coding region patterns induced from protein patterns. Results: In this paper, we have defined a new genomic pattern structure suitable for representing induced patterns from proteins. The provided pattern structure, which is called "Consecutive Positions Scoring Matrix (CPSSM)", is a replacement for protein patterns and profiles in the genomic context. CPSSMs can be identified, discovered, and searched in genomes. Then, we have presented a novel pattern matching algorithm between the defined genomic pattern and genomic sequences based on dynamic programming. In addition, we have modified the provided algorithm to support intronic gaps and huge sequences. We have implemented and tested the provided algorithm on real data. The results on Saccharomyces cerevisiae's genome show 132% more true positives and no false negatives and the results on human genome show no false negatives and 10 times as many true positives as those in previous works. Conclusion: CPSSM and provided methods could be used for open reading frame detection and gene finding. The application is available with source codes to run and download at http://app.foroughmand.ir/cpssm/.

Streaming k-mismatch with error correcting and applications

We present a new streaming algorithm for the k-Mismatch problem, one of the most basic problems in pattern matching. Given a pattern and a text, the task is to find all substrings of the text that are at the Hamming distance at most k from the pattern. Our algorithm is enhanced with an important new feature called Error Correcting, and its complexities for k=1 and for a general k are comparable to those of the solutions for the k-Mismatch problem by Porat and Porat (FOCS 2009) and Clifford et al. (SODA 2016). In parallel to our research, a yet more efficient algorithm for the k-Mismatch problem with the Error Correcting feature was developed by Clifford et al. (SODA 2019). Using the new feature and recent work on streaming Multiple Pattern Matching we develop a series of streaming algorithms for pattern matching on weighted strings, which are a commonly used representation of uncertain sequences in molecular biology. We also show that these algorithms are space-optimal up to polylog factors.A preliminary version of this work was published at DCC 2017 conference [24].

Pattern Matching: A Quantum Oriented Approach

This article influences the quantum based solutions for traditional pattern matching problem, as the quantum machines can accelerate the computation speed by inherently supporting the parallel executions. The problem identifies solution by suggesting effective algorithms, and as per the classical machines several algorithms are qualified and become criterion for other, importantly noted as Knuth Morris Pratt and Boyer Moore which solves such problem in O(M + N) time. A quantum identical pattern matching algorithms are based upon the principle of high speed Grover's quantum search method which searches an element over unstructured database of "N" elements in O(√N) time. The standard quantum algorithm was proposed by Ramesh and Vinay that could obtain computational speed and provides solution in O(√M + √N) time. We review two already existing quantum based exact and approximate pattern matching algorithms and then by combining the logic of both, a new exact pattern matching algorithm is being proposed which overcomes the algorithmic constraints and substantially proves to be equivalently better than the existing classical and quantum benchmark algorithms. So, this article includes existing and proposed quantum pattern matching algorithms, their flowcharts and examples, mathematical justification over complexity analysis. At last we discuss suitable application domains and further possible variations over the proposed work.

Simple and Efficient Pattern Matching Algorithms for Biological Sequences

The remarkable growth of biological data is a motivation to accelerate the discovery of solutions in many domains of computational bioinformatics. In different phases of the computational pipelines, pattern matching is a very practical operation. For example, pattern matching enables users to find the locations of particular DNA subsequences in a database or DNA sequence. Furthermore, in these expanding biological databases, some patterns are updated over time. To perform faster searches, high-speed pattern matching algorithms are needed. The present paper introduces three pattern matching algorithms that are specially formulated to speed up searches on large DNA sequences. The proposed algorithms raise performance by utilizing word processing (in place of the character processing presented in previous works) and also by searching the least frequent word of the pattern in the sequence. In terms of time cost, the experimental results demonstrate the superiority of the presented algorithms over the other simulated algorithms.

Метод швидкого пошуку патерна в потоці бітів

A family of algorithms for fast pattern matching in a bitstream is described. A bitstream is assumed to be stored in a memory and divided into bytes and machine words. The proposed algorithms are based on RZ-family of algorithms for fast pattern matching in a 256-ary text and inherit such their properties as ‘1.5-byte read’, double skip loop, right-to-left text processing as well as a special technique of multiple sliding windows. Beyond that, the new algorithms provide longer average shifts than the best known Binary Faro-Lecroq algorithm and more efficient technique of match checking. Applied to a text close to random, our algorithms outperform all known analogues for all pattern lengths from 20 up to 500.

REAL-TIME TRAFFIC DETECTION AND ANALYSIS OF NETWORK SECURITY INTRUSION ATTACK: SNORT INTRUSION PREVENTION SYSTEM

Intrusion detection is very important for network security. In this study, the structure and design of the Snort system were introduced briefly, and then the ability of data acquisition was improved by a third-party interface. In the part of intrusion detection, the pattern matching algorithm was improved to improve the detection effect of the system. The experimental results showed that the data packet capture ability of the improved system was significantly improved, and the packet loss rate was 97.41% lower than that of the ordinary system; in the intrusion detection, the detection efficiency was kept at 75 M/s, which was significantly higher than other algorithms; for 20 attack traffic, the improved system could realize all alarms, and the maximum response time was only 0.3 s. The experimental results show that the improved Snort system is effective in intrusion prevention and it is worth to be widely used in practice.

An ontological approach to threats pattern collection and classification: a preliminary study to security management

This study presents an agent based approach to resolve issues related to the collection and classification of software application anomalies and misuses with the aim of facilitating the reappraisal of security controls of information system (IS). The proposed system is assumed to be integrated with the existing IS in order to enhance information system security maintenance by continuously collecting identified threat behaviour from the application intrusion detection system (IDS). The system comprises of several functional agents like the input collector agent, classifier agent, and tracking agent. The collector agent collects the identified threats by the IDS, the categoriser agent categorises according to STRIDE model using pattern matching algorithm on the content of security knowledge base. The security knowledge repository is developed based on existing security ontology. The classifier classifies based on the threats IP address while the tracking agent collates all the threats profile. The collect-categorise-classify-track (C3T) model is hereby presented. The potential usability of this work is demonstrated by a case study and its useful integration with further studies is also discussed.

Systematic Partitioning and Labeling XML Subtrees for Efficient Processing of XML Queries in IoT Environments

With the advent of IoT, storing, indexing and querying XML data efficiently is critical. To minimize the cost of querying XML data, researchers have proposed many indexing techniques. Nearly all the techniques, partition the XML data into a number of data-streams. To evaluate a query, existing twig pattern matching algorithms process a subset of the data-streams simultaneously. Processing many data-streams simultaneously results in some or all of the following four problems, namely, the accessing of many data nodes which don’t appear in the final solution of a given query, the generation of duplicate results, the generation of huge number of intermediate results, and the cost of merging the generated intermediate results. To the best of our knowledge, all the existing twig pattern matching algorithms suffer from some or all of the above mentioned problems. This paper proposes a new twig pattern matching algorithm called MatchQTP which processes one data-stream at a time and avoids all the above mentioned four problems. It also proposes a new indexing technique called RLP-Index and a new XML node labeling scheme called RLP-Scheme, both of which are used by MatchQTP. Unlike the existing indexing techniques, RLP-Index stores a subset of the data nodes. The rest of the data nodes can be generated efficiently. This minimizes storage space utilization and query processing time and makes RLP-Index the first of its kind. Many experiments were conducted to study the performance of MatchQTP. The results show that MatchQTP is very efficient and highly scalable. It was also compared with four algorithms, three of which are used frequently in the literature to compare the performance of new algorithms and the fourth algorithm is the state-of-the-art algorithm. MatchQTP significantly and consistently outperformed all of them

基于深度学习的散斑投影轮廓术

Traditional single speckle pattern matching algorithms always suffer from the low measurement accuracy and cannot be used to measure complex surface objects. A speckle projection profilometry with deep learning was proposed to realize the pixel-by-pixel matching. The siamese convolutional neural network structure was applied and extended where the main speckle pattern and the auxiliary speckle pattern were fed into the neural network patch by patch. It was expected that the feature from the speckle pattern patches could be extracted by the convolution operation. In this way, the features were fused and the matching coefficient between the two patches was obtained, which could be further used to formulate the disparity data and then the three-dimensional (3D) object was reconstructed. The experiment results demonstrate that with the proposed method 3D measurement with an accuracy of about 290 μm could be achieved through a single speckle pattern.

Hand Gesture Recognition for Deaf and Mute

Communication is the fundamental channel between individuals to speak with each other. In the world of communication gestures and signals is a great deal and a lot of research work has been done in the course of past decades. With the end goal to enhance the recognition rate of systems, numerous specialists have conveyed strategies, for example, HMM, Artificial Neural Networks, and Kinect stage. Effective algorithms for segmentation, classification, pattern matching, and recognition have evolved. Gesture-based communication is generally utilized by people with hearing disabilities to speak with one another helpfully utilizing hand motions. The system uses image processing technology and neural networking for the capturing and conversion of gestures. A number of python packages are used to process and generate the results. The application uses laptop webcam for capturing gestures and recognize gestures shown by the user. The application uses TensorFlow and Keras to generate the model for datasets. The gestures shown by the user are compared with stored gestures and the corresponding output is generated along with speech output. The application thus eliminates the communication barrier between hearing impaired-mute and normal people.

Explicit Data Correlations-Directed Metadata Prefetching Method in Distributed File Systems

Metadata performance in distributed file systems (DFS) is critical, due to the following trends: (a) the growing size of modern storage systems is expected to exceed billions of files and most files are small; (b) over half of the file accesses are metadata operations. In this work, we present SMeta, a metadata prefetching method that is seamlessly integrated into DFS for easy-of-use and significantly scales the metadata performance. Previous prefetching proposals primarily focus on mining groups of files that tend to be accessed together from the access history. Nevertheless, our study discovered that these solutions likely miss a huge number of correlated files whose co-occurrence frequency is not high enough. Unlike access correlations, we take a novel and completely different approach to explore explicit data correlations by understanding the reference relationships between files encoded in some forms of hyperlinks, which naturally exist in many applications. To embrace this new concept, SMeta explores correlations upon files are written via a light-weight pattern matching algorithm, stores correlations in the reserved extended attributes of file metadata to avoid changes in DFS APIs, and collapses multiple I/O rounds for accessing metadata of the target file and its data-correlated files into one round. A cost-efficient adaptive feedback mechanism is introduced to improve prefetching accuracy. We implemented SMeta atop of Ceph and evaluated it using synthetic and real system workloads. Compared to baselines, SMeta provides better metadata performance in terms of latency, throughput and scalability.