PiDuce: A Process Calculus with Native XML Datatypes

Pattern matching algorithms have gained a lot of importance in computer science, primarily because they are used in various domains such as computational biology, video retrieval, intrusion detection systems, and fraud detection. Finding one or more patterns in a given text is known as pattern matching. Two important things that are used to judge how well exact pattern matching algorithms work are the total number of attempts and the character comparisons that are made during the matching process. The primary focus of our proposed method is reducing the size of both components wherever possible. Despite sprinting, hash-based pattern matching algorithms may have hash collisions. The Efficient Hashing Method (EHM) algorithm is improved in this research. Despite the EHM algorithm’s effectiveness, it takes a lot of time in the preprocessing phase, and some hash collisions are generated. A novel hashing method has been proposed, which has reduced the preprocessing time and hash collision of the EHM algorithm. We devised the Hashing Approach for Pattern Matching (HAPM) algorithm by taking the best parts of the EHM and Quick Search (QS) algorithms and adding a way to avoid hash collisions. The preprocessing step of this algorithm combines the bad character table from the QS algorithm, the hashing strategy from the EHM algorithm, and the collision-reducing mechanism. To analyze the performance of our HAPM algorithm, we have used three types of datasets: E. coli, DNA sequences, and protein sequences. We looked at six algorithms discussed in the literature and compared our proposed method. The Hash-q with Unique FNG (HqUF) algorithm was only compared with E. coli and DNA datasets because it creates unique bits for DNA sequences. Our proposed HAPM algorithm also overcomes the problems of the HqUF algorithm. The new method beats older ones regarding average runtime, number of attempts, and character comparisons for long and short text patterns, though it did worse on some short patterns.

EPMA: Efficient pattern matching algorithm for DNA sequences

Intrusion Detection System has become the important part of the computer and network security because it can effectively compensate for the lack of network security measures. However, the intrusion detection technology relies heavily on pattern matching algorithms, as the choice of pattern matching algorithms directly affect the detection rate, so it is more important. First the paper is detailed analysis the characteristics of the four kinds of single pattern matching which based on prefix search, and then it tests by the number of different pattern strings. The experimental results show that the KMP algorithm can improve effectively the speed of pattern matching. So the application of KMP algorithm can improve the efficiency of intrusion detection.

Discontinuous and Pattern Matching algorithm to measure deformation having discontinuities

Pattern matching is essential to a wide range of applications such as network intrusion detection, virus scanning, etc. Pattern matching algorithms normally rely on state machines to detect predefined patterns. Recently, parallel pattern matching engines, based on ASICs, FPGAs or network processors, perform matching with multiple state machines. The state migration in the matching procedure incurs intensive memory accesses. Thus, it is critical to minimize the storage of state machines such that they can be fit in on-chip or other fast memory modules to achieve high-speed pattern matching. This paper proposes novel optimization techniques, namely state re-labeling and memory partition, to reduce state machine storage. The paper also presents architectural designs based on the optimization strategy. We evaluate our design using realistic pattern sets, and the results show state machine memory reduction up to 80.1%.

The need for efficient intrusion detection systems increases every day to protect network traffic against emerging attacks. Unfortunately, increasing network speeds and number of signatures makes it harder for the existing signature-based intrusion detection systems to keep up. This makes those systems the weak link and the bottleneck which decreases the overall network performance. Researchers found that 30%–60% of the overall processing time of signature-based intrusion detection systems is spent on pattern matching operations [1]. In this paper, we present a novel and fast software-based pattern matching algorithm to reduce the number of times to perform pattern matching. This new algorithm introduces an exclusion-inclusion filter programmed only with signatures prefixes. It filters out the clean traffic without requiring pattern matching and weeds out suspicious packets to be searched using a specially modified Wu-Manber pattern matching algorithm. The exclusion-inclusion filter is a modified Bloom filter that produces a list of probable matching signatures for each suspect packet. The remaining few suspicious packets are searched only for the probable matches. Compared to the Wu-Manber algorithm used in intrusion detection systems, the experimental results indicate a speed up of 3.4 times on average, 5.5 times for regular traffic, and 1.6 times for worst case traffic. The memory overhead added by the algorithm was limited to 0.11%.

Pattern matching with wildcards is very important in many fields such as information retrieval and bioinformatics. Suffix trees are used in pattern matching with variable length wildcards. But the construction of a suffix tree needs significant time and space overload. This paper presents a new pattern matching algorithm, PST, based on multiple suffix trees. The PST algorithm uses a cutting process to divide a string S into several parts firstly, and then establishes a suffix tree for each part of S respectively. If multiple patterns are to be retrieved, the suffix trees should be adjusted according to the cutting points: prefix sequence deletion and suffix sequence addition; prefix sequence addition and suffix sequence deletion. Theoretical analysis and experiments show that the PST algorithm can decrease the time and space overload than other peers.

We propose a document-searching architecture based on high-speed hardware pattern matching to increase the throughput of an information retrieval system. We also propose a new parallel VLSI pattern-matching algorithm called the Data Parallel Pattern Matching (DPPM) algorithm, which serially broadcasts and compares the pattern to a block of data in parallel. The DPPM algorithm utilizes the high degree of integration of VLSI technology to attain very high-speed processing through parallelism. Performance of the DPPM has been evaluated both analytically and by simulation. Based on the simulation statistics and timing analysis on the hardware design, a search rate of multiple gigabytes per second is achievable using 2-μm CMOS technology. The potential performance of the proposed document-searching architecture is also analyzed using the simulation statistics of the DPPM algorithm.

Journal Article A Digest and Pattern Matching-Based Intrusion Detection Engine Get access Zhongqiang Chen, Zhongqiang Chen * 1Yahoo! Inc., Santa Clara, CA 95054, USA *Corresponding author: zqchen@yahoo-inc.com Search for other works by this author on: Oxford Academic Google Scholar Yuan Zhang, Yuan Zhang 2Department of Mathematics, Florida State University, Tallahassee, FL 32306, USA Search for other works by this author on: Oxford Academic Google Scholar Zhongrong Chen, Zhongrong Chen 3ProMetrics Inc., King of Prussia, PA 19406, USA Search for other works by this author on: Oxford Academic Google Scholar Alex Delis Alex Delis 4Department of Informatics and Telecommunications, University of Athens, Athens, 15784, Greece Search for other works by this author on: Oxford Academic Google Scholar The Computer Journal, Volume 52, Issue 6, August 2009, Pages 699–723, https://doi.org/10.1093/comjnl/bxp026 Published: 15 April 2009 Article history Received: 05 October 2008 Revision received: 14 March 2009 Published: 15 April 2009

With the remarkable increase in the number of nucleotide and acid sequences, it is necessary to study pattern matching in querying sequence patterns in the biological sequence database. To further raise the performance of the pattern matching algorithm, an improved BM algorithm (called BMBR) is presented. It bases on the method of BM algorithm and combines with the shift function of BR algorithm, thus reaches the best shift distance and improves the performance. The best and worst cases in time complexities of the proposed algorithm are also discussed. The experimental results show that the algorithm is faster than other compared algorithms for small alphabets and long patterns, and thus the proposed algorithm is quite suitable for pattern matching in biological sequences.

A superimposed code is a set of binary vectors having the property that no vector is contained in a boolean sum (i.e. bitwise OR) of a small number of others. Such codes are used in information retrieval for constructing so-called signature files; they also have applications in other areas. In this paper we introduce a new notion of data-dependent superimposed codes and give a deterministic algorithm for constructing short such codes. We then show that these codes can be used to achieve an almost optimal de-randomization of several pattern matching algorithms, including the almost-linear algorithm for tree pattern matching developed recently. Thus, we give the first almost-linear time deterministic algorithms for these problems.

Enhancing particle image tracking performance with a sequential Monte Carlo method: The bootstrap filter

The performance of the pattern matching algorithms based on bit-parallelism degrades when the input pattern length exceeds the computer word size. Although several divide-and-conquer methods have been proposed to overcome that limitation, the resulting schemes are not that much efficient and hard to implement. This study introduces a new fast bit-parallel pattern matching algorithm that is capable of searching patterns of any length in a common bit-parallel fashion. The proposed bit-parallel length invariant matcher (BLIM) is compared with the Shift-Or and bit-parallel non-deterministic matching (BNDM) algorithms along with the standard Boyer-Moore and Sunday's quick search, which are known to be the very fast in general. Benchmarks have been conducted on natural language, DNA sequence, and binary alphabet random texts. Besides the length invariant architecture of the algorithm, experimental results indicate that on the average BLIM is 18%, 44%, and 6% faster than BNDM, which is accepted as one of the fastest algorithms of this genre, on natural language, DNA sequence and binary random texts respectively.

Generation of Pattern-Matching Algorithms by Extended Regular Expressions

This project explored the use of quantum-assisted algorithms for pattern matching in sub-atomic physics experiments. Pattern matching algorithms are commonly employed to prune data of random noise and to help discriminate between signals generated by particle tracks of interest and signals generated by background events. The quantum-assisted algorithms explored in this project were based on an Ising formulation of quantum associative model (QAMM) recall and quantum content-addressable memory (QCAM) recall. The recall is performed by comparing a probe pattern with those stored in a library of patterns encoded in the QAMM/QCAM model. The classification accuracy of QAMM and QCAM recall was determined as a function of detector resolution, noise, and efficiency and pattern density, where pattern density is defined as the ratio of the number of reference signal patterns encoded in the library to each pattern’s length. We found that QAMM achieved high classification accuracy when applied to datasets with low pattern density. QCAM achieved high classification accuracy for datasets with high pattern density and was found to be more robust to detector noise. The project methodology and results are described in detail in our arXiv preprint (arXiv:2011.11848) . This project was conducted by scientists at the Johns Hopkins University Applied Physics Laboratory and Oak Ridge National Laboratory from August 2018 to August 2020 and was supported by DOE grant DE-SC0019497.