Structural Process Pattern Matching Based on Graph Morphism Detection

2017 ◽  
Vol 27 (02) ◽  
pp. 153-189 ◽  
Author(s):  
Veronica Gacitua-Decar ◽  
Claus Pahl
Keyword(s):  
Pattern Matching ◽  
Time Complexity ◽  
Search Space ◽  
Pattern Detection ◽  
Workflow Systems ◽  
Detection Techniques ◽  
Subgraph Matching ◽  
Process Pattern ◽  
Similarity Computation ◽  
Match Accuracy

Context: Processes are central to the operation of many systems or organizations. Process-centric systems, ranging from enterprise workflow systems to open distributed service compositions, have significantly increased in number and complexity. Objective: Designers of process-centric systems can benefit from process abstractions (including patterns) capturing and allowing the reuse of designs for frequent operational problems. Existing process patterns detection techniques have efficiency problems and difficulties to identify partial and inexact pattern instances. Method: We propose a process pattern detection technique based on a family of subgraph matching algorithms. The algorithms implement surjective graph morphism detection and a mechanism to incorporate semantic similarity computation for types and attributes of process graph elements. Results: Efficiency is addressed using simplified data structures, reducing the search space and its exploration. Match accuracy and time-complexity are demonstrated in an experimental study. Conclusions: Using process patterns allows business and technical processes to be provided as sharable service resources. Patterns can help to manage processes as configurable resources where a pattern can define a family of concrete customizable processes.

scholarly journals Computerized or Automated Object Recognition and Analysis of Pattern Matching in Runways Using Surface Track Data

2021 ◽  
Vol 23 (11) ◽  
pp. 159-165
Author(s):  
JAYANTH DWIJESH H P ◽  
◽  
SANDEEP S V ◽  
RASHMI S ◽  
◽  
...  
Keyword(s):  
Edge Detection ◽  
Object Detection ◽  
Pattern Matching ◽  
Aerial Images ◽  
3 Dimensional ◽  
Detection Techniques ◽  
Detection Algorithms ◽  
Detection Analysis ◽  
Efficient Detection ◽  
Minimal Damage

In today’s world, accurate and fast information is vital for safe aircraft landings. The purpose of an EMAS (Engineered Materials Arresting System) is to prevent an aeroplane from overrunning with no human injury and minimal damage to the aircraft. Although various algorithms for object detection analysis have been developed, only a few researchers have examined image analysis as a landing assist. Image intensity edges are employed in one system to detect the sides of a runway in an image sequence, allowing the runway’s 3-dimensional position and orientation to be approximated. A fuzzy network system is used to improve object detection and extraction from aerial images. In another system, multi-scale, multiplatform imagery is used to combine physiologically and geometrically inspired algorithms for recognizing objects from hyper spectral and/or multispectral (HS/MS) imagery. However, the similarity in the top view of runways, buildings, highways, and other objects is a disadvantage of these methods. We propose a new method for detecting and tracking the runway based on pattern matching and texture analysis of digital images captured by aircraft cameras. Edge detection techniques are used to recognize runways from aerial images. The edge detection algorithms employed in this paper are the Hough Transform, Canny Filter, and Sobel Filter algorithms, which result in efficient detection.

scholarly journals Sentence Similarity Calculation Based on Probabilistic Tolerance Rough Sets

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ruiteng Yan ◽  
Dong Qiu ◽  
Haihuan Jiang
Keyword(s):  
Language Processing ◽  
Rough Set ◽  
Time Complexity ◽  
Computation Model ◽  
Text Data ◽  
Efficient Performance ◽  
Sentence Similarity ◽  
Similarity Calculation ◽  
Tolerance Rough Set ◽  
Similarity Computation

Sentence similarity calculation is one of the important foundations of natural language processing. The existing sentence similarity calculation measurements are based on either shallow semantics with the limitation of inadequately capturing latent semantics information or deep learning algorithms with the limitation of supervision. In this paper, we improve the traditional tolerance rough set model, with the advantages of lower time complexity and becoming incremental compared to the traditional one. And then we propose a sentence similarity computation model from the perspective of uncertainty of text data based on the probabilistic tolerance rough set model. It has the ability of mining latent semantics information and is unsupervised. Experiments on SICK2014 task and STSbenchmark dataset to calculate sentence similarity identify a significant and efficient performance of our model.

scholarly journals Subgraph-Indexed Sequential Subdivision for Continuous Subgraph Matching on Dynamic Knowledge Graph

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Yunhao Sun ◽  
Guanyu Li ◽  
Mengmeng Guan ◽  
Bo Ning
Keyword(s):  
Empirical Studies ◽  
Search Space ◽  
Knowledge Graph ◽  
Dynamic Graph ◽  
Matching Problem ◽  
Flow Graph ◽  
Query Graph ◽  
Subgraph Matching ◽  
Wide Range ◽  
Multiple Edges

Continuous subgraph matching problem on dynamic graph has become a popular research topic in the field of graph analysis, which has a wide range of applications including information retrieval and community detection. Specifically, given a query graph q , an initial graph G 0 , and a graph update stream △ G i , the problem of continuous subgraph matching is to sequentially conduct all possible isomorphic subgraphs covering △ G i of q on G i (= G 0   ⊕   △ G i ). Since knowledge graph is a directed labeled multigraph having multiple edges between a pair of vertices, it brings new challenges for the problem focusing on dynamic knowledge graph. One challenge is that the multigraph characteristic of knowledge graph intensifies the complexity of candidate calculation, which is the combination of complex topological and attributed structures. Another challenge is that the isomorphic subgraphs covering a given region are conducted on a huge search space of seed candidates, which causes a lot of time consumption for searching the unpromising candidates. To address these challenges, a method of subgraph-indexed sequential subdivision is proposed to accelerating the continuous subgraph matching on dynamic knowledge graph. Firstly, a flow graph index is proposed to arrange the search space of seed candidates in topological knowledge graph and an adjacent index is designed to accelerate the identification of candidate activation states in attributed knowledge graph. Secondly, the sequential subdivision of flow graph index and the transition state model are employed to incrementally conduct subgraph matching and maintain the regional influence of changed candidates, respectively. Finally, extensive empirical studies on real and synthetic graphs demonstrate that our techniques outperform the state-of-the-art algorithms.

scholarly journals JavaDL: automatically incrementalizing Java bug pattern detection

2021 ◽  
Vol 5 (OOPSLA) ◽  
pp. 1-31
Author(s):  
Alexandru Dura ◽  
Christoph Reichenbach ◽  
Emma Söderberg
Keyword(s):  
Pattern Matching ◽  
General Purpose ◽  
Specification Language ◽  
Pattern Detection ◽  
Software Developers ◽  
Syntactic Pattern ◽  
Benchmark Suite ◽  
Support Software ◽  
Logical Rules ◽  
Java Code

Static checker frameworks support software developers by automatically discovering bugs that fit general-purpose bug patterns. These frameworks ship with hundreds of detectors for such patterns and allow developers to add custom detectors for their own projects. However, existing frameworks generally encode detectors in imperative specifications, with extensive details of not only what to detect but also how . These details complicate detector maintenance and evolution, and also interfere with the framework’s ability to change how detection is done, for instance, to make the detectors incremental. In this paper, we present JavaDL, a Datalog-based declarative specification language for bug pattern detection in Java code. JavaDL seamlessly supports both exhaustive and incremental evaluation from the same detector specification. This specification allows developers to describe local detector components via syntactic pattern matching , and nonlocal (e.g., interprocedural) reasoning via Datalog-style logical rules . We compare our approach against the well-established SpotBugs and Error Prone tools by re-implementing several of their detectors in JavaDL. We find that our implementations are substantially smaller and similarly effective at detecting bugs on the Defects4J benchmark suite, and run with competitive runtime performance. In our experiments, neither incremental nor exhaustive analysis can consistently outperform the other, which highlights the value of our ability to transparently switch execution modes. We argue that our approach showcases the potential of clear-box static checker frameworks that constrain the bug detector specification language to enable the framework to adapt and enhance the detectors.

Dynamic Generalized Suffix Arrays

2012 ◽  
Vol 263-266 ◽  
pp. 1398-1401
Author(s):  
Song Feng Lu ◽  
Hua Zhao
Keyword(s):  
Data Structure ◽  
Pattern Matching ◽  
Time Complexity ◽  
Document Retrieval ◽  
Suffix Array ◽  
Index Structure ◽  
Suffix Arrays ◽  
Basic Task ◽  
Insertion And Deletion ◽  
Dynamic Version

Document retrieval is the basic task of search engines, and seize amount of attention by the pattern matching community. In this paper, we focused on the dynamic version of this problem, in which the text insertion and deletion is allowable. By using the generalized suffix array and other data structure, we proposed a new index structure. Our scheme achieved better time complexity than the existing ones, and a bit more space overhead is needed as return.

scholarly journals A Survey of parallel intrusion detection on graphical processors

2014 ◽  
Vol 4 (4) ◽  
Author(s):  
Liberios Vokorokos ◽  
Michal Ennert ◽  
Marek >Čajkovský ◽  
Ján Radušovský
Keyword(s):  
Intrusion Detection ◽  
Pattern Matching ◽  
Graphics Processing Units ◽  
Academic Research ◽  
General Purpose ◽  
Intrusion Detection Systems ◽  
Detection Techniques ◽  
Detection Systems ◽  
Common Technique ◽  
Graphics Processing

AbstractIntrusion detection is enormously developing field of informatics. This paper provides a survey of actual trends in intrusion detection in academic research. It presents a review about the evolution of intrusion detection systems with usage of general purpose computing on graphics processing units (GPGPU). There are many detection techniques but only some of them bring advantages of parallel computing implementation to graphical processors (GPU). The most common technique transformed into GPU is the technique of pattern matching. There is a number of intrusion detection tools using GPU tested in real network traffic.

An Effective Pattern Matching Algorithm for Intrusion Detection

2014 ◽  
Vol 556-562 ◽  
pp. 3010-3013
Author(s):  
Qing Qing Zhang ◽  
Qian Zhang ◽  
Yue Jiang Feng
Keyword(s):  
Intrusion Detection ◽  
Pattern Matching ◽  
Intrusion Detection System ◽  
Time Complexity ◽  
Detection System ◽  
Space Complexity ◽  
Experiment Data ◽  
Matching Algorithm ◽  
Bm Algorithm ◽  
Pattern Matching Algorithm

This paper makes a summary of pattern matching algorithm in Intrusion Detection System: KMP algorithm, BM algorithm and BMH algorithm algorithm. The performances of various algorithms are analyzed, and then through the experiment data is verified. Last an improved algorithm based on the BM algorithm: BMD is proposed. BMD algorithm can reduce the space complexity and maintain the time complexity by reducing a pretreatment function and recording the number of times that a bad char found in the pattern.

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