String Pattern Matching Research Articles

Asymptotic normality for -dependent and constrained -statistics, with applications to pattern matching in random strings and permutations

AbstractWe study (asymmetric) $U$ -statistics based on a stationary sequence of $m$ -dependent variables; moreover, we consider constrained $U$ -statistics, where the defining multiple sum only includes terms satisfying some restrictions on the gaps between indices. Results include a law of large numbers and a central limit theorem, together with results on rate of convergence, moment convergence, functional convergence, and a renewal theory version.Special attention is paid to degenerate cases where, after the standard normalization, the asymptotic variance vanishes; in these cases non-normal limits occur after a different normalization.The results are motivated by applications to pattern matching in random strings and permutations. We obtain both new results and new proofs of old results.

On the Multiple Pattern String Matching in DNA Databases

On the Multiple Pattern String Matching in DNA Databases

Engineering faster double‐array Aho–Corasick automata

AbstractMultiple pattern matching in strings is a fundamental problem in text processing applications such as regular expressions or tokenization. This article studies efficient implementations of double‐array Aho–Corasick automata (DAACs), data structures for quickly performing the multiple pattern matching. The practical performance of DAACs is improved by carefully designing the data structure, and many implementation techniques have been proposed thus far. A problem in DAACs is that comprehensive descriptions and experimental analyses on their ideas are not provided. Engineers face difficulties in implementing an efficient DAAC. In this article, we review implementation techniques for DAACs and provide a comprehensive description of them. We also propose several new techniques for further improvement. We conduct exhaustive experiments through real‐world datasets and reveal the best combination of techniques to achieve a higher performance in DAACs. The best combination is different from those used in the most popular libraries of DAACs, which demonstrates that their performance can be further enhanced. On the basis of our experimental analysis, we developed a new Rust library for fast multiple pattern matching using DAACs, named Daachorse, as open‐source software at https://github.com/daac‐tools/daachorse. Experiments demonstrate that Daachorse outperforms other AC‐automaton implementations, indicating its suitability as a fast alternative for multiple pattern matching in many applications.

A Chaotic Image Encryption Scheme Based on Genetic Central Dogma and KMP Method

In this paper, an image cryptosystem based on genetic central dogma (GCD), Knuth–Morria–Pratt (KMP) algorithm and a chaotic system is developed. The KMP algorithm is firstly used to bind DNA strings to obtain the next array, which participates in the design of the chaotic initial condition, and then the secure chaotic sequences are produced by employing the sliding idea in pattern string matching. In the present procedure, a DNA-level two-way pixel’s shuffle is achieved by a shared stack push operation and it is adopted in the permutation module for the purpose of accelerating the overall pixel’s shuffle. Subsequently, the pixel values are substituted by simulating the process of protein synthesis in GCD, in which the DNA replication and RNA replication form the basis of the DNA-triploid mutation and new RNA mutation rules, respectively. Experimental simulations and extensive cryptanalysis fully vindicate that superior security effects in addition to satisfactory low time complexity can be simultaneously obtained by the proposed confusion-substitution scheme.

A Novel Approach for Pattern String Matching in Intrusion Detection System

String matching algorithms are critical for web usage devices which screen the packets and moves on the basis of load. Intrusion detection or prevention systems, web cleansing, anti-virus and junk preventive systems all have a necessity for effective algorithms when managing string matching scenarios. Some of the algorithms viz. Aho-Corasick, WuManber, Rabin-Karp, Commentz-Walter, Bit parallel etc. entails lot of space and time. AhoCorasick algorithm is built on the basis of finite state machines. On the shadow of KnuttMorris-Pratt, Commentz Walter developed an algorithm which in turn uses finite state machines. Bit parallel algorithms like shift-or takes the help of varied computer words ie. the registers in CPU, to compare the efforts. Rabin Karp implements hashing for discovering matching pattern strings in a given text. Wu-Manber looks text in chunks than of every character coalescing the work of Aho-Corasick and Boyer-Moore. Our algorithm reduces character comparisons and consumption of memory on the basis of graph evolution structure and search procedure by means of dynamic linked list. Theoretic study and trial results demonstrates that the projected work is much enhanced in terms of space and time consumption, when compared with previously known pattern-matching algorithms.

Mining naturalistic human behaviors in long-term video and neural recordings

BackgroundRecent technological advances in brain recording and machine learning algorithms are enabling the study of neural activity underlying spontaneous human behaviors, beyond the confines of cued, repeated trials. However, analyzing such unstructured data lacking a priori experimental design remains a significant challenge, especially when the data is multi-modal and long-term. New methodHere we describe an automated, behavior-first approach for analyzing simultaneously recorded long-term, naturalistic electrocorticography (ECoG) and behavior video data. We identify and characterize spontaneous human upper-limb movements by combining computer vision, discrete latent-variable modeling, and string pattern-matching on the video. ResultsOur pipeline discovers and annotates over 40,000 instances of naturalistic arm movements in long term (7–9 day) behavioral videos, across 12 subjects. Analysis of the simultaneously recorded brain data reveals neural signatures of movement that corroborate previous findings. Our pipeline produces large training datasets for brain–computer interfacing applications, and we show decoding results from a movement initiation detection task. Comparison with existing methodsSpontaneous movements capture real-world neural and behavior variability that is missing from traditional cued tasks. Building beyond window-based movement detection metrics, our unsupervised discretization scheme produces a queryable pose representation, allowing localization of movements with finer temporal resolution. ConclusionsOur work addresses the unique analytic challenges of studying naturalistic human behaviors and contributes methods that may generalize to other neural recording modalities beyond ECoG. We publish our curated dataset and believe that it will be a valuable resource for future studies of naturalistic movements.

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.

The Localization Method for Ship Network Nodes Based on High Performance Matching of Multi-pattern String TMWM

ABSTRACT Zhu, H. and Zhu, L., 2018. The localization method for ship network nodes based on high performance matching of multi-pattern string TMWM. In: Liu, Z.L. and Mi, C. (eds.), Advances in Sustainable Port and Ocean Engineering. Journal of Coastal Research, Special Issue No. 83, pp. 776–780. Coconut Creek (Florida), ISSN 0749-0208. The pattern string matching used in the current method is single pattern matching. The matching efficiency and accuracy are low, and a large number of CPU resources are used, which reduces the efficiency and accuracy of the localization of the ship network nodes. In this paper, a high performance matching method based on multi-pattern string TMWM is proposed to preprocess the ship network nodes and make multi-pattern matching, until all the strings are terminated. Application of multi-pattern matching algorithm improves the matching efficiency, and will not take up too much CPU resources, which can increase the efficiency and accuracy of localization. The use of mobile beac...

Efficient pattern matching in degenerate strings with the Burrows–Wheeler transform

A degenerate or indeterminate string on an alphabet Σ is a sequence of non-empty subsets of Σ. Given a degenerate string t of length n and its Burrows–Wheeler transform we present a new method for searching for a degenerate pattern of length m in t running in O(mn) time on a constant size alphabet Σ. Furthermore, it is a hybrid pattern matching technique that works on both regular and degenerate strings. A degenerate string is said to be conservative if its number of non-solid letters is upper-bounded by a fixed positive constant q; in this case we show that the search time complexity is O(qm2) for counting the number of occurrences and O(qm2+occ) for reporting the found occurrences where occ is the number of occurrences of the pattern in t. Experimental results show that our method performs well in practice.

Extended Horspool Algorithm and Its Application in Square Hmong String Pattern Matching

Extended Horspool Algorithm and Its Application in Square Hmong String Pattern Matching

Assuring String Pattern Matching in Outsourced Middleboxes

Modern enterprise networks heavily rely on the ubiquitous network middleboxes for advanced traffic processing functions. Recent advances in software packet processing and virtualization technologies are further pushing forward the paradigm of migrating middleboxes to third-party providers, e.g., clouds and ISPs, as virtualized services, with well-understood benefits on reduced maintenance cost and increased service scalability. Despite promising, outsourcing middleboxes raises new security challenges. Among others, this new service eliminates the enterprise’s direct control on outsourced network functions. Mechanisms assuring that those middleboxes consistently perform network functions as intended currently do not exist. In this paper, we propose the first practical system that enables runtime execution assurances of outsourced middleboxes with high confidence, helping enterprises to extend their visibility into untrusted service providers. As an initial effort, we target on pattern matching-based network functions, which cover a broad class of middlebox applications, such as instruction detection, Web firewall, and traffic classification. Our design follows the roadmap of probabilistic checking mechanisms that provide a tunable level of assurance, as in cloud and distributed computing literature. We show how to synthesize this design intuition in the context of outsourced middleboxes and the dynamic network effect. Specifically, we present diligent technical instantiations in the cases of the single middlebox and the composition (i.e., service chaining). We deploy our designs into off-the-shelf middlebox outsourcing architectures with full-fledged implementation on the click modular router. Evaluations demonstrate that high assurance levels are achieved by pre-processing only a few packets with marginal overhead.

Interval stabbing on the Automata Processor

The Automata Processor (AP) was designed for string-pattern matching. In this paper, we showcase its use to execute integer and floating-point comparisons and apply the same to accelerate interval stabbing queries. An interval stabbing query determines which of the intervals in a set overlap a query point. Such queries are often used in computational geometry, pattern matching, database management systems, and geographic information systems. The check for each interval is programmed as a single automaton and multiple automata are executed in parallel to provide significant performance gains. While handling 32-bit integers or single-precision floating-point numbers, up to 2.75 trillion comparisons can be executed per second, whereas 0.79 trillion comparisons per second can be completed for 64-bit integers or double-precision floating-point numbers. Additionally, our solution leaves the intervals in the set unordered allowing addition or deletion of an interval in constant time. This is not possible for contemporary solutions wherein the intervals are ordered, making update of intervals complex. Our automata designs are modular allowing them to become constituent parts of larger automata, where the numerical comparisons are part of the overall pattern matching operation. We have validated the designs on the hardware, and the routines to generate the necessary automata and execute them on the AP will be made available as software libraries shortly.

Real-time positioning of a specific object in the big data environment

Real-time positioning of a specific object in the big data environment can improve the monitoring and management capacity for network data. For the real-time positioning of the specific object, it is necessary to quickly search the network data representing a specific object and match its pattern strings and compare the corresponding Internet protocol (IP) address of the matched network data with the IP address library in real time, so as to determine the position of the specific object. When a traditional method is used for pattern string matching, it will occupy a lot of memories and network resources, thereby reducing the positioning effect of the specific object in the big data environment. A positioning method for a specific object of high performance and multi-pattern matching based on three indexes in the big data network environment is proposed in this paper. Firstly, the initialization of Modified Wu-Manber (MWM) algorithm was carried out, and the algorithm was used to match the network data continuously. Secondly, the three indexes were used to improve the MWM algorithm, and the real-time and fast positioning of a specific object in the big data environment was completed by the Third Index Modified Wu-Manber (TMWM). The experimental results show that compared with the traditional method, the proposed algorithm reduces the pattern string matching scope of network data representing the specific object, improves the search speed of the specific object, and locates the specific object in the big data environment in an effective and rapid manner.

Implementation of Multipattern String Matching Accelerated with GPU for Intrusion Detection System

As Internet-related security threats continue to increase in terms of volume and sophistication, existing Intrusion Detection System is also being challenged to cope with the current Internet development. Multi Pattern String Matching algorithm accelerated with Graphical Processing Unit is being utilized to improve the packet scanning performance of the IDS. This paper implements a Multi Pattern String Matching algorithm, also called Parallel Failureless Aho Corasick accelerated with GPU to improve the performance of IDS. OpenCL library is used to allow the IDS to support various GPU, including popular GPU such as NVIDIA and AMD, used in our research. The experiment result shows that the application of Multi Pattern String Matching using GPU accelerated platform provides a speed up, by up to 141% in term of throughput compared to the previous research.

Single and Multiple Pattern String Matching Algorithm

Background/Objectives: Information Retrieval Systems (IRS) are playing an eminent role in different applications like World Wide Web, DNA sequence retrieval, etc. Basically, the IRS systems use the string matching algorithms. Methods/ Statistical Analysis: Since IRS uses string matching algorithms. If string matching algorithms quality is improved then automatically information retrieval system will achieve the most relevant results. For this retrieval purpose in this paper, single pattern and multiple pattern string matching algorithms are proposed. Findings: To assess the efficiency of the proposed single pattern and multiple pattern string matching algorithm in this paper, DNA sequences of different monkeys datasets called Saimiri boliviensis (2.46 Gb), Pan paniscus (2.71 Gb), Macaca nemestrina (2.78 Gb), Colobus angolensis palliates(2.8 Gb), Tarsius syrichta (3.31 Gb) are considered and different tetra patterns TAGA, TCTG are searched in this data sets. From the experimental results, it is observed that proposed single pattern and multiple pattern string matching algorithms outperforms compared to other well-known string matching algorithms. Application/Improvements: It is also observed that multiple pattern string matching algorithm reduces search time and unnecessary comparisons when compared to single pattern string matching and other existing string matching algorithms. These proposed algorithms very useful when we searching about the multiple patterns.

Implementation of Multipattern String Matching Accelerated with GPU for Intrusion Detection System

Abstract. As Internet-related security threats continue to increase in terms of volume and sophistication, existing Intrusion Detection System is also being challenged to cope with the current Internet development. Multi Pattern String Matching algorithm accelerated with Graphical Processing Unit is being utilized to improve the packet scanning performance of the IDS. This paper implements a Multi Pattern String Matching algorithm, also called Parallel Failureless Aho Corasick accelerated with GPU to improve the performance of IDS. OpenCL library is used to allow the IDS to support various GPU, including popular GPU such as NVIDIA and AMD, used in our research. The experiment result shows that the application of Multi Pattern String Matching using GPU accelerated platform provides a speed up, by up to 141% in term of throughput compared to the previous research.

A Comparative Study of Wu Manber String Matching Algorithm and its Variations

String matching algorithms is become one of the most important topic in the computer science world. These algorithms are used in many real world problems like as scanning the threat in intrusion detection system, finding the pattern in text mining, match the similarity of the document in the plagiarism detection system, recognition in bio informatics and so on. String Matching Algorithms are broadly categorized into single pattern string matching and multiple pattern string matching algorithms. To get the faster solution of the problem most of the cases multiple pattern matching is the best choice. Aho-Corasick, Shift-OR, Robin Karp but Wu Manber Algorithm is better choice because it search the pattern faster(take less time) as well as occupies less space. This paper discus the various pit falls of the wu manber algorithm with their detailed explanation. Also discuss the various improved wu manber algorithm and comparison between these algorithms.

High performance parallelization of Boyer–Moore algorithm on many-core accelerators

Boyer---Moore (BM) algorithm is a single pattern string matching algorithm. It is considered as the most efficient string matching algorithm and used in many applications. The algorithm first calcu...

Boyer-Moore 알고리즘을 위한 GPU상에서의 병렬 최적화

The Boyer-Moore algorithm is a single pattern string matching algorithm that is widely used in various applications such as computer and internet security, and bioinformatics. This algorithm is computationally demanding and requires high-performance parallel processing. In this paper, we propose a parallelization and performance optimization methodology for the BM algorithm on a GPU. Our methodology adopts an algorithmic cascading technique. This results in significant reductions in the mapping overheads for the threads participating in the parallel string matching. It also results in the efficient utilization of the multithreading capability of the GPU which improves the load balancing among threads. Our experimental results show that this approach achieves a 45-times speedup at maximum, in comparison with a serial execution.

A Survey of Automatic Extraction of Personal Name Alias from the Web

The survey paper explains about the extraction and retrieval of personal name alias using various techniques from the web with the help of web crawls. The existing methods help to improve the depth of knowledge relevant to alias extraction and retrieval process. It also describes about how the aliases are ranked, then page counts on the web, word co-occurrence using anchor text and techniques like term frequency (tf), inverse document frequency (idf), log likelihood ratio. Chi-squared tests etc.., are used for measuring the association and similarities between words. The existing method consists of pattern extraction algorithm or string matching algorithm for extracting patterns from snippets instead of using these algorithms. The survey helps to discover a proposed method as graph mining to extract personal name aliases from the web.