A Portfolio Selection Method Based on Pattern Matching with Dual Information of Direction and Distance

Pattern matching method is one of the classic classifications of existing online portfolio selection strategies. This article aims to study the key aspects of this method—measurement of similarity and selection of similarity sets, and proposes a Portfolio Selection Method based on Pattern Matching with Dual Information of Direction and Distance (PMDI). By studying different combination methods of indicators such as Euclidean distance, Chebyshev distance, and correlation coefficient, important information such as direction and distance in stock historical price information is extracted, thereby filtering out the similarity set required for pattern matching based investment portfolio selection algorithms. A large number of experiments conducted on two datasets of real stock markets have shown that PMDI outperforms other algorithms in balancing income and risk. Therefore, it is suitable for the financial environment in the real world.

Exact Graph Pattern Matching:Applications,Progress and Prospects

Graph pattern matching(GPM)can be used to mine the key information in graphs.Exact GPM is one of the most commonly used methods among all the GPM-related methods,which aims to exactly find all subgraphs for a given query graph in a data graph.The exact GPM has been widely used in biological data analyses,social network analyses and other fields.In this paper,the applications of the exact GPM were first introduced,and the research progress of the exact GPM was summarized.Then,the related algorithms were introduced in detail,and the experiments on the state-of-the-art exact GPM algorithms were conducted to compare their performance.Based on the experimental results,the applicable scenarios of the algorithms were pointed out.New research opportunities in this area were proposed.

Fast algorithm on string cross pattern matching

Given a set U which is consisted of strings defined on alphabet Σ, string cross pattern matching is to find all the matches between every two strings in U. It is utilized in text processing like removing the duplication of strings. This paper presents a fast string cross pattern matching algorithm based on extracting high frequency strings. Compared with existing algorithms including single-pattern algorithms and multi-pattern matching algorithms, this algorithm is featured by both low time complexity and low space complexity. Because Chinese alphabet is large and the average length of Chinese words is much short, this algorithm is more suitable to process the text written by Chinese, especially when the size of Σ is large and the number of strings is far more than the maximum length of strings of set U.

A Fast Pattern Matching Algorithm Using Changing Consecutive Characters

Pattern matching is a very important algorithm used in many applications such as search engine and DNA analysis. They are aiming to find a pattern in a text. This paper proposes a Pattern Matching Algorithm Using Changing Consecutive Characters (PMCCC) to make the searching pro- cess of the algorithm faster. PMCCC enhances the shift process that determines how the pattern moves in case of the occurrence of the mismatch between the pattern and the text. It enhances the Berry Ravindran (BR) shift function by using m consecutive characters where m is the pattern length. The formal basis and the algorithms are presented. The experimental results show that PMCCC made enhancements in searching process by reducing the number of comparisons and the number of attempts. Comparing the results of PMCCC with other related algorithms has shown significant enhancements in average number of comparisons and average number of attempts.

New multi-pattern matching algorithm

The traditional multiple pattern matching algorithm, deterministic finite state automata, is implemented by tree structure. A new algorithm is proposed by substituting sequential binary tree for traditional tree. It is proved by experiment that the algorithm has three features, its construction process is quick, its cost of memory is small. At the same time, its searching process is as quick as the traditional algorithm. The algorithm is suitable for the application which requires preprocessing the patterns dynamically.

Location estimation of autonomous driving robot and 3D tunnel mapping in underground mines using pattern matched LiDAR sequential images

In this study,a machine vision-based pattern matching technique was applied to estimate the location of an autonomous driving robot and perform 3D tunnel mapping in an underground mine environment.The autonomous driving robot continuously detects the wall of the tunnel in the horizontal direction using the light detection and ranging(Li DAR)sensor and performs pattern matching by recognizing the shape of the tunnel wall.The proposed method was designed to measure the heading of the robot by fusion with the inertial measurement units sensor according to the pattern matching accuracy;it is combined with the encoder sensor to estimate the location of the robot.In addition,when the robot is driving,the vertical direction of the underground mine is scanned through the vertical Li DAR sensor and stacked to create a 3D map of the underground mine.The performance of the proposed method was superior to that of previous studies;the mean absolute error achieved was 0.08 m for the X-Y axes.A root mean square error of 0.05 m^(2)was achieved by comparing the tunnel section maps that were created by the autonomous driving robot to those of manual surveying.

POINT PATTERN MATCHING ALGORITHM BASED ON POINT PAIR TOPOLOGICAL CHARACTERISTICS AND SPECTRAL MATCHING

Most of the Point Pattern Matching (PPM) algorithm performs poorly when the noise of the point's position and outliers exist. This paper presents a novel and robust PPM algorithm which combined Point Pair Topological Characteristics (PPTC) and Spectral Matching (SM) together to solve the afore mentioned issues. In which PPTC, a new shape descriptor, is firstly proposed. A new comparability measurement based on PPTC is defined as the matching probability. Finally, the correct matching results are achieved by the spectral matching method. The synthetic data experiments show its robustness by comparing with the other state-of-art algorithms and the real world data experiments show its effectiveness.

A Novel Mathematical Model for Similarity Search in Pattern Matching Algorithms

Modern applications require large databases to be searched for regions that are similar to a given pattern. The DNA sequence analysis, speech and text recognition, artificial intelligence, Internet of Things, and many other applications highly depend on pattern matching or similarity searches. In this paper, we discuss some of the string matching solutions developed in the past. Then, we present a novel mathematical model to search for a given pattern and it’s near approximates in the text.

A Fast Multi-Pattern Matching Algorithm for Mining Big Network Data

The rapid development of mobile network brings opportunities for researchers to analyze user behaviors based on largescale network traffic data. It is important for Internet Service Providers(ISP) to optimize resource allocation and provide customized services to users. The first step of analyzing user behaviors is to extract information of user actions from HTTP traffic data by multi-pattern URL matching. However, the efficiency is a huge problem when performing this work on massive network traffic data. To solve this problem, we propose a novel and accurate algorithm named Multi-Pattern Parallel Matching(MPPM) that takes advantage of HashMap in data searching for extracting user behaviors from big network data more effectively. Extensive experiments based on real-world traffic data prove the ability of MPPM algorithm to deal with massive HTTP traffic with better performance on accuracy, concurrency and efficiency. We expect the proposed algorithm and it parallelized implementation would be a solid base to build a high-performance analysis engine of user behavior based on massive HTTP traffic data processing.

Fast Multi-Pattern Matching Algorithm on Compressed Network Traffic

Pattern matching is a fundamental approach to detect malicious behaviors and information over Internet, which has been gradually used in high-speed network traffic analysis. However, there is a performance bottleneck for multi-pattern matching on online compressed network traffic(CNT), this is because malicious and intrusion codes are often embedded into compressed network traffic. In this paper, we propose an online fast and multi-pattern matching algorithm on compressed network traffic(FMMCN). FMMCN employs two types of jumping, i.e. jumping during sliding window and a string jump scanning strategy to skip unnecessary compressed bytes. Moreover, FMMCN has the ability to efficiently process multiple large volume of networks such as HTTP traffic, vehicles traffic, and other Internet-based services. The experimental results show that FMMCN can ignore more than 89.5% of bytes, and its maximum speed reaches 176.470MB/s in a midrange switches device, which is faster than the current fastest algorithm ACCH by almost 73.15 MB/s.

Four Sliding Windows Pattern Matching Algorithm (FSW)

This paper presents an efficient pattern matching algorithm (FSW). FSW improves the searching process for a pattern in a text. It scans the text with the help of four sliding windows. The windows are equal to the length of the pattern, allowing multiple alignments in the searching process. The text is divided into two parts;each part is scanned from both sides simultaneously using two sliding windows. The four windows slide in parallel in both parts of the text. The comparisons done between the text and the pattern are done from both of the pattern sides in parallel. The conducted experiments show that FSW achieves the best overall results in the number of attempts and the number of character comparisons compared to the pattern matching algorithms: Two Sliding Windows (TSW), Enhanced Two Sliding Windows algorithm (ETSW) and Berry-Ravindran algorithm (BR). The best time case is calculated and found to be??while the average case time complexity is??.

Enhancing ERS-A Algorithm for Pattern Matching (EERS-A)

Pattern matching is a very important topic in computer science. It has been used in various applications such as information retrieval, virus scanning, DNA sequence analysis, data mining, machine learning, network security and pattern recognition. This paper has presented a new pattern matching algorithm—Enhanced ERS-A, which is an improvement over ERS-S algorithm. In ERS-A, two sliding windows are used to scan the text from the left and the right simultaneously. The proposed algorithm also scans the text from the left and the right simultaneously as well as making comparisons with the pattern from both sides simultaneously. The comparisons done between the text and the pattern are done from both sides in parallel. The shift technique used in the Enhanced ERS-A is the four consecutive characters in the text immediately following the pattern window. The experimental results show that the Enhanced ERS-A has enhanced the process of pattern matching by reducing the number of comparisons performed.

Assessing Risk in Chemical Plant with Pattern Matching

This paper discusses potential application of fuzzy set theory, more specifically, pattern matching, in assessing risk in chemical plants. Risk factors have been evaluated using linguistic representations of the quantity of the hazardous substance involved, its frequency of interaction with the environment, severity of its impact and the uncertainty involved in its detection in advance. For each linguistic value there is a corresponding membership function ranging over a universe of discourse. The risk scenario created by a hazard/hazardous situation having highest degree of featural value is taken as the known pattern. Each sample pattern of hazard/hazardous situation with their known featural values is then matched with the known pattern. The concept of multifeature pattern matching based on fuzzy logic is used to derive the rank ordering of process hazards. In multifeature pattern recognition/matching, a sample pattern is compared to a number of known data patterns or a known pattern is compared to a number of sample data patterns. The process assesses which known pattern resembles most closely data sample using Wang's approaching degree method. A methodology has been developed and the same has been exemplified by presenting a case example with a limited number of hazards.

A Single Pattern Matching Algorithm Based on Character Frequency

Based on the study of single pattern matching, MBF algorithm is proposed by imitating the string searching procedure of human. The algorithm preprocesses the pattern by using the idea of Quick Search algorithm and the already-matched pattern psefix and suffix information. In searching phase, the algorithm makes use of the!character using frequency and the continue-skip idea. The experiment shows that MBF algorithm is more efficient than other algorithms.

A Tree Pattern Matching Algorithm for XML Queries with Structural Preferences

In the XML community, exact queries allow users to specify exactly what they want to check and/or retrieve in an XML document. When they are applied to a semi-structured document or to a document with an overly complex model, the lack or the ignorance of the explicit document model (DTD—Document Type Definition, Schema, etc.) increases the risk of obtaining an empty result set when the query is too specific, or, too large result set when it is too vague (e.g. it contains wildcards such as “*”). The reason is that in both cases, users write queries according to the document model they have in mind;this can be very far from the one that can actually be extracted from the document. Opposed to exact queries, preference queries are more flexible and can be relaxed to expand the search space during their evaluations. Indeed, during their evaluation, certain constraints (the preferences they contain) can be relaxed if necessary to avoid precisely empty results;moreover, the returned answers can be filtered to retain only the best ones. This paper presents an algorithm for evaluating such queries inspired by the TreeMatch algorithm proposed by Yao et al. for exact queries. In the proposed algorithm, the best answers are obtained by using an adaptation of the Skyline operator (defined in relational databases) in the context of documents (trees) to incrementally filter into the partial solutions set, those which satisfy the maximum of preferential constraints. The only restriction imposed on documents is No-Self-Containment.

An Automata-Based Approach to Pattern Matching

Due to its importance in security, syntax analysis has found usage in many high-level programming languages. The Lisp language has its share of operations for evaluating regular expressions, but native parsing of Lisp code in this way is unsupported. Matching on lists requires a significantly more complicated model, with a different programmatic approach than that of string matching. This work presents a new automata-based approach centered on a set of functions and macros for identifying sequences of Lisp S-expressions using finite tree automata. The objective is to test that a given list is an element of a given tree language. We use a macro that takes a grammar and generates a function that reads off the leaves of a tree and tries to parse them as a string in a context-free language. The experimental results indicate that this approach is a viable tool for parsing Lisp lists and expressions in the abstract interpretation

Carving Thumbnail/s and Embedded JPEG Files Using Image Pattern Matching

Images (typically JPEG) are used as evidence against cyber perpetrators. Typically the file is carved using standard patterns. Many concentrate on carving JPEG files and overlook the important of thumbnail in assisting forensic investigation. However, a new unique pattern is used to detect thumbnail/s and embedded JPEG file. This paper is to introduce a tool call PattrecCarv to recognize thumbnail/s or embedded JPEG files using unique hex patterns (UHP). A tool called PattrecCarv is developed to automatically carve thumbnail/s and embedded JPEG files using DFRWS 2006 and DFRWS 2007 datasets. The tool successfully recovers 11.5% more thumbnails and embedded JPEG files than PredClus.

Revisiting Multiple Pattern Matching Algorithms for Multi-Core Architecture

Due to the huge size of patterns to be searched,multiple pattern searching remains a challenge to several newly-arising applications like network intrusion detection.In this paper,we present an attempt to design efficient multiple pattern searching algorithms on multi-core architectures.We observe an important feature which indicates that the multiple pattern matching time mainly depends on the number and minimal length of patterns.The multi-core algorithm proposed in this paper leverages this feature to decompose pattern set so that the parallel execution time is minimized.We formulate the problem as an optimal decomposition and scheduling of a pattern set,then propose a heuristic algorithm,which takes advantage of dynamic programming and greedy algorithmic techniques,to solve the optimization problem.Experimental results suggest that our decomposition approach can increase the searching speed by more than 200% on a 4-core AMD Barcelona system.

Pattern Matching with Flexible Wildcards

Pattern matching with wildcards（PMW） has great theoretical and practical significance in bioinformatics,information retrieval, and pattern mining. Due to the uncertainty of wildcards, not only is the number of all matches exponential with respect to the maximal gap flexibility and the pattern length, but the matching positions in PMW are also hard to choose. The objective to count the maximal number of matches one by one is computationally infeasible. Therefore,rather than solving the generic PMW problem, many research efforts have further defined new problems within PMW according to different application backgrounds. To break through the limitations of either fixing the number or allowing an unbounded number of wildcards, pattern matching with flexible wildcards（PMFW） allows the users to control the ranges of wildcards. In this paper, we provide a survey on the state-of-the-art algorithms for PMFW, with detailed analyses and comparisons, and discuss challenges and opportunities in PMFW research and applications.