A distributed vertex-centric approach for pattern matching in massive graphs

2013 ◽  
Author(s):  
Arash Fard ◽  
M. Usman Nisar ◽  
Lakshmish Ramaswamy ◽  
John A. Miller ◽  
Matthew Saltz
Keyword(s):  
Pattern Matching ◽  
Massive Graphs

scholarly journals A Survey on Distributed Graph Pattern Matching in Massive Graphs

2021 ◽  
Vol 54 (2) ◽  
pp. 1-35
Author(s):  
Sarra Bouhenni ◽  
Saïd Yahiaoui ◽  
Nadia Nouali-Taboudjemat ◽  
Hamamache Kheddouci
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Pattern Matching ◽  
Subgraph Isomorphism ◽  
Graph Pattern Matching ◽  
Np Completeness ◽  
Graph Pattern ◽  
Massive Graphs ◽  
Big Graphs

Besides its NP-completeness, the strict constraints of subgraph isomorphism are making it impractical for graph pattern matching (GPM) in the context of big data. As a result, relaxed GPM models have emerged as they yield interesting results in a polynomial time. However, massive graphs generated by mostly social networks require a distributed storing and processing of the data over multiple machines, thus, requiring GPM to be revised by adopting new paradigms of big graphs processing, e.g., Think-Like-A-Vertex and its derivatives. This article discusses and proposes a classification of distributed GPM approaches with a narrow focus on the relaxed models.

Pattern Recognition for Classification and Matching of Car Tires

2005 ◽  
Vol 33 (1) ◽  
pp. 2-17 ◽  
Author(s):  
D. Colbry ◽  
D. Cherba ◽  
J. Luchini
Keyword(s):  
Computer Vision ◽  
Feature Extraction ◽  
Pattern Matching ◽  
Fourier Transforms ◽  
Two Dimensions ◽  
Multiple Sources ◽  
Matching Process ◽  
Visual Searches ◽  
New Feature ◽  
2D Fft

Abstract Commercial databases containing images of tire tread patterns are currently used by product designers, forensic specialists and product application personnel to identify whether a given tread pattern matches an existing tire. Currently, this pattern matching process is almost entirely manual, requiring visual searches of extensive libraries of tire tread patterns. Our work explores a first step toward automating this pattern matching process by building on feature analysis techniques from computer vision and image processing to develop a new method for extracting and classifying features from tire tread patterns and automatically locating candidate matches from a database of existing tread pattern images. Our method begins with a selection of tire tread images obtained from multiple sources (including manufacturers' literature, Web site images, and Tire Guides, Inc.), which are preprocessed and normalized using Two-Dimensional Fast Fourier Transforms (2D-FFT). The results of this preprocessing are feature-rich images that are further analyzed using feature extraction algorithms drawn from research in computer vision. A new, feature extraction algorithm is developed based on the geometry of the 2D-FFT images of the tire. The resulting FFT-based analysis allows independent classification of the tire images along two dimensions, specifically by separating “rib” and “lug” features of the tread pattern. Dimensionality of (0,0) indicates a smooth treaded tire with no pattern; dimensionality of (1,0) and (0,1) are purely rib and lug tires; and dimensionality of (1,1) is an all-season pattern. This analysis technique allows a candidate tire to be classified according to the features of its tread pattern, and other tires with similar features and tread pattern classifications can be automatically retrieved from the database.

Parallel Combining Different Approaches to Multi-pattern Matching for Fpga-based Security Systems

2017 ◽  
Vol 5 (1) ◽  
pp. 8-15
Author(s):  
Sergii Hilgurt ◽  
Keyword(s):  
Information Security ◽  
Pattern Matching ◽  
Intrusion Detection System ◽  
Detection System ◽  
Finite Automata ◽  
Network Intrusion Detection ◽  
Security Systems ◽  
Analytical Technique ◽  
Network Intrusion ◽  
Pros And Cons

The multi-pattern matching is a fundamental technique found in applications like a network intrusion detection system, anti-virus, anti-worms and other signature- based information security tools. Due to rising traffic rates, increasing number and sophistication of attacks and the collapse of Moore’s law, traditional software solutions can no longer keep up. Therefore, hardware approaches are frequently being used by developers to accelerate pattern matching. Reconfigurable FPGA-based devices, providing the flexibility of software and the near-ASIC performance, have become increasingly popular for this purpose. Hence, increasing the efficiency of reconfigurable information security tools is a scientific issue now. Many different approaches to constructing hardware matching circuits on FPGAs are known. The most widely used of them are based on discrete comparators, hash-functions and finite automata. Each approach possesses its own pros and cons. None of them still became the leading one. In this paper, a method to combine several different approaches to enforce their advantages has been developed. An analytical technique to quickly advance estimate the resource costs of each matching scheme without need to compile FPGA project has been proposed. It allows to apply optimization procedures to near-optimally split the set of pattern between different approaches in acceptable time.

Oil to Source Rock Correlation Using Biomarker Data Through Pattern Matching and Fingerprinting Analysis in “Kitkat” Field, Jabung Block, South Sumatra Basin

2020 ◽  
Author(s):  
S., R. Muthasyabiha
Keyword(s):  
Pattern Matching ◽  
Source Rock ◽  
Vitrinite Reflectance ◽  
Hydrocarbon Exploration ◽  
Source Rocks ◽  
Maturity Level ◽  
Fingerprinting Analysis ◽  
Kerogen Type ◽  
Oil Characteristics ◽  
Biomarker Data

Geochemical analysis is necessary to enable the optimization of hydrocarbon exploration. In this research, it is used to determine the oil characteristics and the type of source rock candidates that produces hydrocarbon in the “KITKAT” Field and also to understand the quality, quantity and maturity of proven source rocks. The evaluation of source rock was obtained from Rock-Eval Pyrolysis (REP) to determine the hydrocarbon type and analysis of the value of Total Organic Carbon (TOC) was performed to know the quantity of its organic content. Analysis of Tmax value and Vitrinite Reflectance (Ro) was also performed to know the maturity level of the source rock samples. Then the oil characteristics such as the depositional environment of source rock candidate and where the oil sample develops were obtained from pattern matching and fingerprinting analysis of Biomarker data GC/GCMS. Moreover, these data are used to know the correlation of oil to source rock. The result of source rock evaluation shows that the Talangakar Formation (TAF) has all these parameters as a source rock. Organic material from Upper Talangakar Formation (UTAF) comes from kerogen type II/III that is capable of producing oil and gas (Espitalie, 1985) and Lower Talangakar Formation (LTAF) comes from kerogen type III that is capable of producing gas. All intervals of TAF have a quantity value from very good–excellent considerable from the amount of TOC > 1% (Peters and Cassa, 1994). Source rock maturity level (Ro > 0.6) in UTAF is mature–late mature and LTAF is late mature–over mature (Peters and Cassa, 1994). Source rock from UTAF has deposited in the transition environment, and source rock from LTAF has deposited in the terrestrial environment. The correlation of oil to source rock shows that oil sample is positively correlated with the UTAF.

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