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International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
This document summarizes previous work on modeling and analyzing network traffic at the source level. It discusses how traffic at network aggregation points has been found to exhibit self-similarity and long-range dependence, which can be explained by the superposition of many ON/OFF sources with heavy-tailed distributions. The document then describes an experimental study where the authors collected traffic traces from various sources, analyzed the statistical properties, and fitted distributions to metrics like interarrival times and packet sizes in order to model source traffic for simulation purposes.
Extended pso algorithm for improvement problems k means clustering algorithmIJMIT JOURNAL
The clustering is a without monitoring process and one of the most common data mining techniques. The
purpose of clustering is grouping similar data together in a group, so were most similar to each other in a
cluster and the difference with most other instances in the cluster are. In this paper we focus on clustering
partition k-means, due to ease of implementation and high-speed performance of large data sets, After 30
year it is still very popular among the developed clustering algorithm and then for improvement problem of
placing of k-means algorithm in local optimal, we pose extended PSO algorithm, that its name is ECPSO.
Our new algorithm is able to be cause of exit from local optimal and with high percent produce the
problem’s optimal answer. The probe of results show that mooted algorithm have better performance
regards as other clustering algorithms specially in two index, the carefulness of clustering and the quality
of clustering.
A Novel Framework for Short Tandem Repeats (STRs) Using Parallel String MatchingIJERA Editor
This document presents a novel framework for identifying Short Tandem Repeats (STRs) using parallel string matching. It begins with background on STRs and challenges with existing sequential algorithms. It then describes a two-phase methodology - first applying a basic improved right prefix algorithm sequentially, then applying it in parallel using multi-threading on multicore processors. Results show the basic algorithm outperforms Boyer-Moore, Knuth-Morris-Pratt and brute force algorithms sequentially. When applied in parallel, processing time is reduced from 80ms sequentially to 40ms in parallel on multicore systems. The parallel STR identification framework allows efficient searching of repeats in large genomes.
An Approach for Project Scheduling Using PERT/CPM and Petri Nets (PNs) ToolsIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
This document presents and analyzes algorithms for finding maximal vectors in large data sets. It introduces a cost model and assumptions for average-case analysis. It reviews existing algorithms such as double divide-and-conquer (DD&C) and linear divide-and-conquer (LD&C), and analyzes their runtimes. It also presents a new algorithm called LESS and proves it has average-case runtime of O(kn).
Research Inventy : International Journal of Engineering and Scienceinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Packet Classification using Support Vector Machines with String KernelsIJERA Editor
Since the inception of internet many methods have been devised to keep untrusted and malicious packets away
from a user’s system . The traffic / packet classification can be used
as an important tool to detect intrusion in the system. Using Machine Learning as an efficient statistical based
approach for classifying packets is a novel method in practice today . This paper emphasizes upon using an
advanced string kernel method within a support vector machine to classify packets .
There exists a paper related to a similar problem using Machine Learning [2]. But the researches mentioned in
their paper are not up-to date and doesn’t account for modern day
string kernels that are much more efficient . My work extends their research by introducing different approaches
to classify encrypted / unencrypted traffic / packets .
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
This document summarizes previous work on modeling and analyzing network traffic at the source level. It discusses how traffic at network aggregation points has been found to exhibit self-similarity and long-range dependence, which can be explained by the superposition of many ON/OFF sources with heavy-tailed distributions. The document then describes an experimental study where the authors collected traffic traces from various sources, analyzed the statistical properties, and fitted distributions to metrics like interarrival times and packet sizes in order to model source traffic for simulation purposes.
Extended pso algorithm for improvement problems k means clustering algorithmIJMIT JOURNAL
The clustering is a without monitoring process and one of the most common data mining techniques. The
purpose of clustering is grouping similar data together in a group, so were most similar to each other in a
cluster and the difference with most other instances in the cluster are. In this paper we focus on clustering
partition k-means, due to ease of implementation and high-speed performance of large data sets, After 30
year it is still very popular among the developed clustering algorithm and then for improvement problem of
placing of k-means algorithm in local optimal, we pose extended PSO algorithm, that its name is ECPSO.
Our new algorithm is able to be cause of exit from local optimal and with high percent produce the
problem’s optimal answer. The probe of results show that mooted algorithm have better performance
regards as other clustering algorithms specially in two index, the carefulness of clustering and the quality
of clustering.
A Novel Framework for Short Tandem Repeats (STRs) Using Parallel String MatchingIJERA Editor
This document presents a novel framework for identifying Short Tandem Repeats (STRs) using parallel string matching. It begins with background on STRs and challenges with existing sequential algorithms. It then describes a two-phase methodology - first applying a basic improved right prefix algorithm sequentially, then applying it in parallel using multi-threading on multicore processors. Results show the basic algorithm outperforms Boyer-Moore, Knuth-Morris-Pratt and brute force algorithms sequentially. When applied in parallel, processing time is reduced from 80ms sequentially to 40ms in parallel on multicore systems. The parallel STR identification framework allows efficient searching of repeats in large genomes.
An Approach for Project Scheduling Using PERT/CPM and Petri Nets (PNs) ToolsIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
This document presents and analyzes algorithms for finding maximal vectors in large data sets. It introduces a cost model and assumptions for average-case analysis. It reviews existing algorithms such as double divide-and-conquer (DD&C) and linear divide-and-conquer (LD&C), and analyzes their runtimes. It also presents a new algorithm called LESS and proves it has average-case runtime of O(kn).
Research Inventy : International Journal of Engineering and Scienceinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Packet Classification using Support Vector Machines with String KernelsIJERA Editor
Since the inception of internet many methods have been devised to keep untrusted and malicious packets away
from a user’s system . The traffic / packet classification can be used
as an important tool to detect intrusion in the system. Using Machine Learning as an efficient statistical based
approach for classifying packets is a novel method in practice today . This paper emphasizes upon using an
advanced string kernel method within a support vector machine to classify packets .
There exists a paper related to a similar problem using Machine Learning [2]. But the researches mentioned in
their paper are not up-to date and doesn’t account for modern day
string kernels that are much more efficient . My work extends their research by introducing different approaches
to classify encrypted / unencrypted traffic / packets .
New Data Association Technique for Target Tracking in Dense Clutter Environme...CSCJournals
Improving data association process by increasing the probability of detecting valid data points (measurements obtained from radar/sonar system) in the presence of noise for target tracking are discussed in manuscript. We develop a novel algorithm by filtering gate for target tracking in dense clutter environment. This algorithm is less sensitive to false alarm (clutter) in gate size than conventional approaches as probabilistic data association filter (PDAF) which has data association algorithm that begin to fail due to the increase in the false alarm rate or low probability of target detection. This new selection filtered gate method combines a conventional threshold based algorithm with geometric metric measure based on one type of the filtering methods that depends on the idea of adaptive clutter suppression methods. An adaptive search based on the distance threshold measure is then used to detect valid filtered data point for target tracking. Simulation results demonstrate the effectiveness and better performance when compared to conventional algorithm.
Data mining projects topics for java and dot netredpel dot com
This document discusses several papers related to data mining and machine learning techniques. It begins with a brief summary of each paper, discussing the key contributions and findings. The summaries cover topics such as differential privacy-preserving data anonymization, fault detection in power systems using decision trees, temporal pattern searching in event data, high dimensional indexing for similarity search, landmark-based approximate shortest path computation, feature selection for high dimensional data, temporal pattern mining in data streams, data leakage detection, keyword search in spatial databases, analyzing relationships on Wikipedia, improving recommender systems using user-item subgroups, decision trees for uncertain data, and building confidential query services in the cloud using data perturbation.
SECURE & EFFICIENT AUDIT SERVICE OUTSOURCING FOR DATA INTEGRITY IN CLOUDSGyan Prakash
Cloud-based outsourced storage relieves the client’s load for storage management and maintenance by providing a comparably low-cost, scalable, location-independent platform. Though, the information that clients no longer have physical control of data specifies that they are facing a potentially formidable risk for missing or corrupted data. To avoid the security risks, inspection services are serious to ensure the integrity and availability of outsourced data and to achieve digital forensics and reliability on cloud computing. Provable data possession (PDP), which is a cryptographic method for validating the reliability of data without retrieving it at an untrusted server, can be used to realize audit services. In this project, profiting from the interactive zero-knowledge proof system, the construction of an interactive PDP protocol to prevent the fraudulence of prover (soundness property) and the leakage of verified data (zero knowledge property).To prove that our construction holds these properties based on the computation Diffie–Hellman assumption and the rewindable black-box knowledge extractor. An efficient mechanism on probabilistic queries and periodic verification is proposed to reduce the audit costs per verification and implement abnormal detection timely. Also, we present an efficient method for choosing an optimal parameter value to reduce computational overheads of cloud audit services.
This document discusses using topological data analysis to analyze the spread of contagions on networks. It introduces contagion maps, which embed network nodes as point clouds based on contagion transmission times. The topology, geometry, and dimensionality of these point clouds are then analyzed and compared to the underlying network manifold. This reveals insights into whether contagion dynamics follow the network's geometric structure. Numerical experiments on simulated networks demonstrate that contagion maps can recover the underlying manifold when wavefront propagation dominates.
IRJET- Sampling Selection Strategy for Large Scale Deduplication of Synthetic...IRJET Journal
This document proposes a two-stage sampling selection strategy (T3S) for large-scale data deduplication using Apache Spark. T3S reduces the labeling effort for training data by first selecting balanced subsets of candidate pairs, then removing redundant pairs to produce a smaller, more informative training set. It detects fuzzy region boundaries using this training set to classify candidate pairs. The approach is implemented in a distributed manner using Apache Spark and shows better performance than an existing method by reducing the training set size.
A Template Matching Approach to Classification of QAM Modulation using Geneti...CSCJournals
The automatic recognition of the modulation format of a detected signal, the intermediate step between signal detection and demodulation, is a major task of an intelligent receiver, with various civilian and military applications. Obviously, with no knowledge of the transmitted data and many unknown parameters at the receiver, such as the signal power, carrier frequency and phase offsets, timing information, etc., blind identification of the modulation is a difficult task. This becomes even more challenging in real-world. In this paper modulation classification for QAM is performed by Genetic Algorithm followed by Template matching, considering the constellation of the received signal. In addition this classification finds the decision boundary of the signal which is critical information for bit detection. I have proposed and implemented a technique that casts modulation recognition into shape recognition. Constellation diagram is a traditional and powerful tool for design and evaluation of digital modulations. The simulation results show the capability of this method for modulation classification with high accuracy and appropriate convergence in the presence of noise.
IRJET- Clustering of Hierarchical Documents based on the Similarity Deduc...IRJET Journal
This document discusses techniques for clustering hierarchical documents based on their structural similarity. It summarizes several existing approaches:
1) A tree edit distance-based method that represents trees as paths and computes the distance between subtrees. However, it requires trees to have a pre-specified structure.
2) Chawathe's algorithm that uses pre-order tree traversal and transforms trees into sequences of node labels and depths to calculate distances. It allows efficient assignment of new documents to clusters.
3) The XCLSC algorithm that clusters documents in two phases - grouping structurally similar documents and then searching to further improve clustering results and performance. However, it has high computational requirements.
4) The XPattern and PathXP
018 20160902 Machine Learning Framework for Analysis of Transport through Com...Ha Phuong
This document proposes a machine learning framework to analyze fluid flow through porous media. It involves:
1) Discrete element modeling of granular materials to generate pore structure data.
2) Finite element modeling of fluid flow simulations to calculate permeability.
3) Construction of pore and contact networks from structure data.
4) Calculation of network features like centrality measures related to permeability.
5) Feature selection and machine learning models to predict permeability from network features.
Signal Processing Approach for Recognizing Identical Reads From DNA Sequencin...IOSR Journals
The document discusses a signal processing approach using wavelet transforms to identify identical reads from DNA sequencing data. It proposes representing DNA sequences numerically based on electron-ion interaction pseudo potentials and applying multi-level Haar wavelet transforms. This reduces sequence lengths to one-eighth their original size, allowing efficient element-by-element comparisons to find identical reads. Testing on Bacillus sequencing data showed the method identified up to 17% identical reads, reducing redundant data by up to 11% and computation time compared to string processing methods.
The document discusses using word sense disambiguation (WSD) in concept identification for ontology construction. It describes implementing an approach that forms concepts from terms by meeting certain criteria, such as having an intentional definition and instances. WSD is needed to identify the sense of terms related to the domain when forming concepts. The Lesk algorithm is discussed as one method for WSD and concept disambiguation, involving calculating similarity between terms and WordNet senses. Evaluation shows the approach identified domain-specific concepts with reasonable precision and recall compared to other methods. Choosing the best WSD algorithm depends on factors like the problem nature and performance metrics.
Clustering sentence level text using a novel fuzzy relational clustering algo...JPINFOTECH JAYAPRAKASH
This paper presents a novel fuzzy clustering algorithm that operates on relational input data in the form of a pairwise similarity matrix between data objects. The algorithm uses a graph representation and models graph centrality as likelihood in an expectation-maximization framework. The algorithm, called FRECCA, is capable of identifying overlapping clusters of semantically related sentences, which makes it useful for text mining tasks. It offers advantages over existing hard clustering methods by allowing sentences to belong to multiple clusters and handles the high dimensionality of similarity matrices better. The algorithm is evaluated on sentence clustering tasks and other domains, demonstrating superior performance to benchmark algorithms.
This document proposes improvements to domain-specific term extraction for ontology construction. It discusses issues with existing term extraction approaches and presents a new method that selects and organizes target and contrastive corpora. Terms are extracted using linguistic rules on part-of-speech tagged text. Statistical distributions are calculated to identify terms based on their frequency across multiple contrastive corpora. The approach achieves better precision in extracting simple and complex terms for computer science and biomedical domains compared to existing methods.
SVD BASED LATENT SEMANTIC INDEXING WITH USE OF THE GPU COMPUTATIONSijscmcj
The purpose of this article is to determine the usefulness of the Graphics Processing Unit (GPU) calculations used to implement the Latent Semantic Indexing (LSI) reduction of the TERM-BY DOCUMENT matrix. Considered reduction of the matrix is based on the use of the SVD (Singular Value Decomposition) decomposition. A high computational complexity of the SVD decomposition - O(n3), causes that a reduction of a large indexing structure is a difficult task. In this article there is a comparison of the time complexity and accuracy of the algorithms implemented for two different environments. The first environment is associated with the CPU and MATLAB R2011a. The second environment is related to graphics processors and the CULA library. The calculations were carried out on generally available benchmark matrices, which were combined to achieve the resulting matrix of high size. For both considered environments computations were performed for double and single precision data.
This document discusses using particle swarm optimization based on variable neighborhood search (PSO-VNS) to attack classical cryptography ciphers. PSO is a population-based optimization algorithm inspired by bird flocking behavior. VNS is a metaheuristic algorithm that explores neighborhoods of solutions to escape local optima. The paper proposes improving PSO with VNS to find better solutions. It evaluates PSO-VNS on substitution and transposition ciphers, finding it recovers keys better than standard PSO and other variants.
Optimal Configuration of Network Coding in Ad Hoc Networks1crore projects
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
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Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
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Phone : +91 97518 00789 / +91 72999 51536
Vchunk join an efficient algorithm for edit similarity joinsVijay Koushik
Similarity join is most important technique to
involve many applications such as data integration, record
linkage and pattern recognition. Here we introduce new
algorithm for similarity join with edit distance constraints.
Currently extracting overlapping grams from string and consider
only string that share certain gram as candidate. Now we propose
extracting non-overlapping substring or chunk from string.
Chunk scheme based on tail-restricted chunk boundary
dictionary (CBD). This approach integrated existing approach
for calculating similarity with several new filters unique to chunk
based method. Greedy algorithm automatically select good
chunking scheme from given data set. Then show the result our
method occupies less space and faster performance to compute
the value
The authors present two novel progressive duplicate detection algorithms called Progressive Sorted Neighborhood Method (PSNM) and Progressive Blocking (PB) that improve the efficiency of duplicate detection over traditional approaches. PSNM works best on small, clean datasets by sorting records and comparing those within a sliding window, prioritizing nearby records. PB works best on large, dirty datasets by progressively combining blocks of records based on likelihood of matching. Experiments show these algorithms can double the efficiency of traditional methods and outperform related work by finding more duplicate pairs earlier within a given time frame.
Commentz-Walter: Any Better than Aho-Corasick for Peptide Identification? IJORCS
An algorithm for locating all occurrences of a finite number of keywords in an arbitrary string, also known as multiple strings matching, is commonly required in information retrieval (such as sequence analysis, evolutionary biological studies, gene/protein identification and network intrusion detection) and text editing applications. Although Aho-Corasick was one of the commonly used exact multiple strings matching algorithm, Commentz-Walter has been introduced as a better alternative in the recent past. Comments-Walter algorithm combines ideas from both Aho-Corasick and Boyer Moore. Large scale rapid and accurate peptide identification is critical in computational proteomics. In this paper, we have critically analyzed the time complexity of Aho-Corasick and Commentz-Walter for their suitability in large scale peptide identification. According to the results we obtained for our dataset, we conclude that Aho-Corasick is performing better than Commentz-Walter as opposed to the common beliefs.
Splay trees based early packet rejection mechanism against do s traffic targe...Anh Phan
The document proposes a mechanism called Statistical Splaying Filters with Binary Search on Prefix Length (SSF-BSPL) to improve firewall performance against denial of service (DoS) attacks targeting the default security rule. SSF-BSPL optimizes the order of security policy filtering fields using traffic statistics and a multilevel filtering approach based on splay trees and hash tables. It aims to reject unwanted traffic earlier in the filtering process compared to the related Self Adjusting Binary Search on Prefix Length (SA-BSPL) technique. Simulation results showed the proposed mechanism significantly reduced the filtering processing time of DoS traffic targeting the firewall default security rule.
This paper presents a new clustering algorithm called Robust Fuzzy n-Means (RFNM) that can determine the optimal number of clusters in a dataset and is robust to outliers. RFNM is a modification of existing Robust Fuzzy c-Means Clustering (RFCM) and Fuzzy c-Means Clustering (FCM) algorithms. RFCM improves on FCM by making it more resistant to outliers, but requires the user to specify the number of clusters. RFNM retains RFCM's robustness to outliers and does not require the user to specify the number of clusters in advance, allowing it to determine the optimal number of clusters automatically.
El documento describe los esfuerzos de la organización no gubernamental Educo para combatir la pobreza y malnutrición infantil en España debido a la crisis económica. Educo ha lanzado un nuevo proyecto de becas comedor para garantizar una comida saludable diaria a niños de familias con dificultades en 17 comunidades autónomas. Los datos muestran que el número de niños en riesgo de pobreza ha aumentado en medio millón desde 2007 a 2,5 millones, y uno de cada cuatro niños españoles sufre mal
New Data Association Technique for Target Tracking in Dense Clutter Environme...CSCJournals
Improving data association process by increasing the probability of detecting valid data points (measurements obtained from radar/sonar system) in the presence of noise for target tracking are discussed in manuscript. We develop a novel algorithm by filtering gate for target tracking in dense clutter environment. This algorithm is less sensitive to false alarm (clutter) in gate size than conventional approaches as probabilistic data association filter (PDAF) which has data association algorithm that begin to fail due to the increase in the false alarm rate or low probability of target detection. This new selection filtered gate method combines a conventional threshold based algorithm with geometric metric measure based on one type of the filtering methods that depends on the idea of adaptive clutter suppression methods. An adaptive search based on the distance threshold measure is then used to detect valid filtered data point for target tracking. Simulation results demonstrate the effectiveness and better performance when compared to conventional algorithm.
Data mining projects topics for java and dot netredpel dot com
This document discusses several papers related to data mining and machine learning techniques. It begins with a brief summary of each paper, discussing the key contributions and findings. The summaries cover topics such as differential privacy-preserving data anonymization, fault detection in power systems using decision trees, temporal pattern searching in event data, high dimensional indexing for similarity search, landmark-based approximate shortest path computation, feature selection for high dimensional data, temporal pattern mining in data streams, data leakage detection, keyword search in spatial databases, analyzing relationships on Wikipedia, improving recommender systems using user-item subgroups, decision trees for uncertain data, and building confidential query services in the cloud using data perturbation.
SECURE & EFFICIENT AUDIT SERVICE OUTSOURCING FOR DATA INTEGRITY IN CLOUDSGyan Prakash
Cloud-based outsourced storage relieves the client’s load for storage management and maintenance by providing a comparably low-cost, scalable, location-independent platform. Though, the information that clients no longer have physical control of data specifies that they are facing a potentially formidable risk for missing or corrupted data. To avoid the security risks, inspection services are serious to ensure the integrity and availability of outsourced data and to achieve digital forensics and reliability on cloud computing. Provable data possession (PDP), which is a cryptographic method for validating the reliability of data without retrieving it at an untrusted server, can be used to realize audit services. In this project, profiting from the interactive zero-knowledge proof system, the construction of an interactive PDP protocol to prevent the fraudulence of prover (soundness property) and the leakage of verified data (zero knowledge property).To prove that our construction holds these properties based on the computation Diffie–Hellman assumption and the rewindable black-box knowledge extractor. An efficient mechanism on probabilistic queries and periodic verification is proposed to reduce the audit costs per verification and implement abnormal detection timely. Also, we present an efficient method for choosing an optimal parameter value to reduce computational overheads of cloud audit services.
This document discusses using topological data analysis to analyze the spread of contagions on networks. It introduces contagion maps, which embed network nodes as point clouds based on contagion transmission times. The topology, geometry, and dimensionality of these point clouds are then analyzed and compared to the underlying network manifold. This reveals insights into whether contagion dynamics follow the network's geometric structure. Numerical experiments on simulated networks demonstrate that contagion maps can recover the underlying manifold when wavefront propagation dominates.
IRJET- Sampling Selection Strategy for Large Scale Deduplication of Synthetic...IRJET Journal
This document proposes a two-stage sampling selection strategy (T3S) for large-scale data deduplication using Apache Spark. T3S reduces the labeling effort for training data by first selecting balanced subsets of candidate pairs, then removing redundant pairs to produce a smaller, more informative training set. It detects fuzzy region boundaries using this training set to classify candidate pairs. The approach is implemented in a distributed manner using Apache Spark and shows better performance than an existing method by reducing the training set size.
A Template Matching Approach to Classification of QAM Modulation using Geneti...CSCJournals
The automatic recognition of the modulation format of a detected signal, the intermediate step between signal detection and demodulation, is a major task of an intelligent receiver, with various civilian and military applications. Obviously, with no knowledge of the transmitted data and many unknown parameters at the receiver, such as the signal power, carrier frequency and phase offsets, timing information, etc., blind identification of the modulation is a difficult task. This becomes even more challenging in real-world. In this paper modulation classification for QAM is performed by Genetic Algorithm followed by Template matching, considering the constellation of the received signal. In addition this classification finds the decision boundary of the signal which is critical information for bit detection. I have proposed and implemented a technique that casts modulation recognition into shape recognition. Constellation diagram is a traditional and powerful tool for design and evaluation of digital modulations. The simulation results show the capability of this method for modulation classification with high accuracy and appropriate convergence in the presence of noise.
IRJET- Clustering of Hierarchical Documents based on the Similarity Deduc...IRJET Journal
This document discusses techniques for clustering hierarchical documents based on their structural similarity. It summarizes several existing approaches:
1) A tree edit distance-based method that represents trees as paths and computes the distance between subtrees. However, it requires trees to have a pre-specified structure.
2) Chawathe's algorithm that uses pre-order tree traversal and transforms trees into sequences of node labels and depths to calculate distances. It allows efficient assignment of new documents to clusters.
3) The XCLSC algorithm that clusters documents in two phases - grouping structurally similar documents and then searching to further improve clustering results and performance. However, it has high computational requirements.
4) The XPattern and PathXP
018 20160902 Machine Learning Framework for Analysis of Transport through Com...Ha Phuong
This document proposes a machine learning framework to analyze fluid flow through porous media. It involves:
1) Discrete element modeling of granular materials to generate pore structure data.
2) Finite element modeling of fluid flow simulations to calculate permeability.
3) Construction of pore and contact networks from structure data.
4) Calculation of network features like centrality measures related to permeability.
5) Feature selection and machine learning models to predict permeability from network features.
Signal Processing Approach for Recognizing Identical Reads From DNA Sequencin...IOSR Journals
The document discusses a signal processing approach using wavelet transforms to identify identical reads from DNA sequencing data. It proposes representing DNA sequences numerically based on electron-ion interaction pseudo potentials and applying multi-level Haar wavelet transforms. This reduces sequence lengths to one-eighth their original size, allowing efficient element-by-element comparisons to find identical reads. Testing on Bacillus sequencing data showed the method identified up to 17% identical reads, reducing redundant data by up to 11% and computation time compared to string processing methods.
The document discusses using word sense disambiguation (WSD) in concept identification for ontology construction. It describes implementing an approach that forms concepts from terms by meeting certain criteria, such as having an intentional definition and instances. WSD is needed to identify the sense of terms related to the domain when forming concepts. The Lesk algorithm is discussed as one method for WSD and concept disambiguation, involving calculating similarity between terms and WordNet senses. Evaluation shows the approach identified domain-specific concepts with reasonable precision and recall compared to other methods. Choosing the best WSD algorithm depends on factors like the problem nature and performance metrics.
Clustering sentence level text using a novel fuzzy relational clustering algo...JPINFOTECH JAYAPRAKASH
This paper presents a novel fuzzy clustering algorithm that operates on relational input data in the form of a pairwise similarity matrix between data objects. The algorithm uses a graph representation and models graph centrality as likelihood in an expectation-maximization framework. The algorithm, called FRECCA, is capable of identifying overlapping clusters of semantically related sentences, which makes it useful for text mining tasks. It offers advantages over existing hard clustering methods by allowing sentences to belong to multiple clusters and handles the high dimensionality of similarity matrices better. The algorithm is evaluated on sentence clustering tasks and other domains, demonstrating superior performance to benchmark algorithms.
This document proposes improvements to domain-specific term extraction for ontology construction. It discusses issues with existing term extraction approaches and presents a new method that selects and organizes target and contrastive corpora. Terms are extracted using linguistic rules on part-of-speech tagged text. Statistical distributions are calculated to identify terms based on their frequency across multiple contrastive corpora. The approach achieves better precision in extracting simple and complex terms for computer science and biomedical domains compared to existing methods.
SVD BASED LATENT SEMANTIC INDEXING WITH USE OF THE GPU COMPUTATIONSijscmcj
The purpose of this article is to determine the usefulness of the Graphics Processing Unit (GPU) calculations used to implement the Latent Semantic Indexing (LSI) reduction of the TERM-BY DOCUMENT matrix. Considered reduction of the matrix is based on the use of the SVD (Singular Value Decomposition) decomposition. A high computational complexity of the SVD decomposition - O(n3), causes that a reduction of a large indexing structure is a difficult task. In this article there is a comparison of the time complexity and accuracy of the algorithms implemented for two different environments. The first environment is associated with the CPU and MATLAB R2011a. The second environment is related to graphics processors and the CULA library. The calculations were carried out on generally available benchmark matrices, which were combined to achieve the resulting matrix of high size. For both considered environments computations were performed for double and single precision data.
This document discusses using particle swarm optimization based on variable neighborhood search (PSO-VNS) to attack classical cryptography ciphers. PSO is a population-based optimization algorithm inspired by bird flocking behavior. VNS is a metaheuristic algorithm that explores neighborhoods of solutions to escape local optima. The paper proposes improving PSO with VNS to find better solutions. It evaluates PSO-VNS on substitution and transposition ciphers, finding it recovers keys better than standard PSO and other variants.
Optimal Configuration of Network Coding in Ad Hoc Networks1crore projects
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Vchunk join an efficient algorithm for edit similarity joinsVijay Koushik
Similarity join is most important technique to
involve many applications such as data integration, record
linkage and pattern recognition. Here we introduce new
algorithm for similarity join with edit distance constraints.
Currently extracting overlapping grams from string and consider
only string that share certain gram as candidate. Now we propose
extracting non-overlapping substring or chunk from string.
Chunk scheme based on tail-restricted chunk boundary
dictionary (CBD). This approach integrated existing approach
for calculating similarity with several new filters unique to chunk
based method. Greedy algorithm automatically select good
chunking scheme from given data set. Then show the result our
method occupies less space and faster performance to compute
the value
The authors present two novel progressive duplicate detection algorithms called Progressive Sorted Neighborhood Method (PSNM) and Progressive Blocking (PB) that improve the efficiency of duplicate detection over traditional approaches. PSNM works best on small, clean datasets by sorting records and comparing those within a sliding window, prioritizing nearby records. PB works best on large, dirty datasets by progressively combining blocks of records based on likelihood of matching. Experiments show these algorithms can double the efficiency of traditional methods and outperform related work by finding more duplicate pairs earlier within a given time frame.
Commentz-Walter: Any Better than Aho-Corasick for Peptide Identification? IJORCS
An algorithm for locating all occurrences of a finite number of keywords in an arbitrary string, also known as multiple strings matching, is commonly required in information retrieval (such as sequence analysis, evolutionary biological studies, gene/protein identification and network intrusion detection) and text editing applications. Although Aho-Corasick was one of the commonly used exact multiple strings matching algorithm, Commentz-Walter has been introduced as a better alternative in the recent past. Comments-Walter algorithm combines ideas from both Aho-Corasick and Boyer Moore. Large scale rapid and accurate peptide identification is critical in computational proteomics. In this paper, we have critically analyzed the time complexity of Aho-Corasick and Commentz-Walter for their suitability in large scale peptide identification. According to the results we obtained for our dataset, we conclude that Aho-Corasick is performing better than Commentz-Walter as opposed to the common beliefs.
Splay trees based early packet rejection mechanism against do s traffic targe...Anh Phan
The document proposes a mechanism called Statistical Splaying Filters with Binary Search on Prefix Length (SSF-BSPL) to improve firewall performance against denial of service (DoS) attacks targeting the default security rule. SSF-BSPL optimizes the order of security policy filtering fields using traffic statistics and a multilevel filtering approach based on splay trees and hash tables. It aims to reject unwanted traffic earlier in the filtering process compared to the related Self Adjusting Binary Search on Prefix Length (SA-BSPL) technique. Simulation results showed the proposed mechanism significantly reduced the filtering processing time of DoS traffic targeting the firewall default security rule.
This paper presents a new clustering algorithm called Robust Fuzzy n-Means (RFNM) that can determine the optimal number of clusters in a dataset and is robust to outliers. RFNM is a modification of existing Robust Fuzzy c-Means Clustering (RFCM) and Fuzzy c-Means Clustering (FCM) algorithms. RFCM improves on FCM by making it more resistant to outliers, but requires the user to specify the number of clusters. RFNM retains RFCM's robustness to outliers and does not require the user to specify the number of clusters in advance, allowing it to determine the optimal number of clusters automatically.
El documento describe los esfuerzos de la organización no gubernamental Educo para combatir la pobreza y malnutrición infantil en España debido a la crisis económica. Educo ha lanzado un nuevo proyecto de becas comedor para garantizar una comida saludable diaria a niños de familias con dificultades en 17 comunidades autónomas. Los datos muestran que el número de niños en riesgo de pobreza ha aumentado en medio millón desde 2007 a 2,5 millones, y uno de cada cuatro niños españoles sufre mal
This document provides a frame-by-frame analysis of the music video for "Juicebox" by The Strokes. It analyzes the shots used in each frame, the mise-en-scene elements including costumes, lighting, and settings. It notes that the shots are used to show the band members singing and playing instruments, introduce the radio DJ character, and depict scenes meant to convey the grungy rock genre through images like a man vomiting in an alley. The analysis suggests the video was aimed at portraying the band and scenes the target rock audience would find appealing.
Este documento describe las plataformas de comercio electrónico y los elementos clave a considerar al seleccionar una. Explica que una plataforma tiene dos vistas, el back-end para la administración y el front-end para los usuarios. También cubre alojamiento en línea, sistemas de gestión de contenido como Joomla y WordPress, y factores importantes para elegir un hosting como disponibilidad, seguridad y soporte técnico.
The document discusses ancient Indian research methodology as seen through various texts. It outlines the medium of expression as sutras, classification based on sound physics, and the use of Sanskrit grammar. Styles of presentation included verse and prose formats. Research followed an anubandha chatustaya structure covering the subject, utility, recipient, and new theory. Nyaya shastra focused on logic, definitions, valid proofs, and fallacies. Examples discussed logic applied to atoms, elements, and change. Advice for students included speaking truth, following dharma, and not neglecting study and teaching.
This document discusses the zodiac signs and their associated planets. It states that Mars is associated with Aries and Scorpio, Venus with Taurus and Libra, Jupiter with Sagittarius and Pisces, and Mercury with Gemini and Virgo. It also associates Saturn with Capricorn, whose additional name could be Shaneeshvar, and Aquarius, whose name is Shani. The Moon is associated with Cancer and the Sun with Leo.
During the Gilded Age from 1870-1900, the US government did little domestically under a policy of laissez-faire. However, there was widespread corruption, as evidenced by numerous scandals that plagued the Grant administration. Political machines also exercised influence through patronage and graft. Reform efforts attempted to establish merit-based civil service and curb the excesses of political bosses and corruption. By the late 1800s, Americans increasingly wanted the federal government to address growing economic and social problems through acts like the Interstate Commerce Act and Sherman Antitrust Act.
Este documento habla brevemente sobre los sentimientos humanos y cómo manejarlos de manera saludable. Reconoce que a veces los sentimientos pueden ser intensos pero que acostumbrándonos a ellos gradualmente y buscando formas positivas de expresarlos y distraernos podemos lidiar con ellos de manera eficaz sin prolongarlos en exceso ni de manera contraindicada.
El documento describe los conceptos básicos de estímulos, respuestas y sistemas sensoriales. Explica que los estímulos pueden ser internos o externos y provocan respuestas musculares o glandulares. Describe los órganos sensoriales principales como los ojos, oídos, piel, nariz y lengua, y cómo cada uno capta diferentes estímulos. También resume la estructura básica del sistema nervioso, incluidas las neuronas, y cómo coordina los movimientos voluntarios e involuntarios del cuerpo.
The document discusses perception and the perceptual process. It defines perception as how individuals interpret and make sense of sensory information from their environment. The perceptual process involves four stages: sensation, selection, organization, and interpretation. Several factors can influence perception, including characteristics of the perceiver, characteristics of the target or object being perceived, and aspects of the situational context. The document also examines how people group stimuli into meaningful patterns through principles of figure-ground perception and perceptual grouping. Finally, it discusses concepts related to person perception such as attribution theory, heuristics like the halo effect, and cognitive shortcuts like stereotyping.
Educo es una nueva ONG formada por la fusión de Intervida y Educación Sin Fronteras. La fusión se produjo para poder ayudar a niños en situación de riesgo tanto en el extranjero como en España, donde la crisis está aumentando la pobreza infantil. Educo ha decidido iniciar un programa de becas comedor en las escuelas españolas para garantizar que los niños reciban al menos una comida saludable al día.
IDENTIFICATION AND INVESTIGATION OF THE USER SESSION FOR LAN CONNECTIVITY VIA...ijcseit
This paper mainly presents some technical discussions on the identification and analyze of “LAN usersessions”.
The identification of a user-session is non trivial. Classical methods approaches rely on
threshold based mechanisms. Threshold based techniques are very sensitive to the value chosen for the
threshold, which may be difficult to set correctly. Clustering techniques are used to define a novel
methodology to identify LAN user-sessions without requiring an a priori definition of threshold values. We
have defined a clustering based approach in detail, and also we discussed positive and negative of this
approach, and we apply it to real traffic traces. The proposed methodology is applied to artificially
generated traces to evaluate its benefits against traditional threshold based approaches. We also analyzed
the characteristics of user-sessions extracted by the clustering methodology from real traces and study
their statistical properties.
Extended pso algorithm for improvement problems k means clustering algorithmIJMIT JOURNAL
The clustering is a without monitoring process and one of the most common data mining techniques. The
purpose of clustering is grouping similar data together in a group, so were most similar to each other in a
cluster and the difference with most other instances in the cluster are. In this paper we focus on clustering
partition k-means, due to ease of implementation and high-speed performance of large data sets, After 30
year it is still very popular among the developed clustering algorithm and then for improvement problem of
placing of k-means algorithm in local optimal, we pose extended PSO algorithm, that its name is ECPSO.
Our new algorithm is able to be cause of exit from local optimal and with high percent produce the
problem’s optimal answer. The probe of results show that mooted algorithm have better performance
regards as other clustering algorithms specially in two index, the carefulness of clustering and the quality
of clustering.
The document proposes streaming algorithms for performing Pearson's chi-square goodness-of-fit test in a streaming setting with minimal assumptions. It presents algorithms for the one-sample and two-sample continuous chi-square tests that use O(K^2log(N)√N) space, where K is the number of bins and N is the stream length. It also shows that no sublinear solution exists for the categorical chi-square test and provides a heuristic algorithm. The algorithms are validated on real and synthetic data and can detect deviations from distributions or differences between streams with low memory requirements.
The document discusses using region-based techniques for process discovery from event logs. It proposes incorporating region information into cycle detection algorithms to more efficiently identify complex cycles when converting event logs into automata. This enables better application of region-based techniques to discover process models from industrial event logs. The experimental results suggest the techniques can significantly improve applying region theory for process mining in industry scenarios.
The document discusses using region-based techniques for process discovery from event logs. It proposes incorporating region information into cycle detection algorithms to more efficiently identify complex cycles when converting event logs into automata. This enables better application of region-based techniques to discover process models from industrial event logs. The experimental results suggest the techniques can significantly improve applying region theory for process mining in industry scenarios.
Android region-based foldings in process discoveryecway
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The document discusses using region-based techniques for process discovery from event logs. It proposes incorporating region information into cycle detection algorithms to more efficiently identify complex cycles when constructing an automaton from event traces. This enables better application of region-based techniques to discover process models from industrial event logs. The experimental results suggest the techniques can significantly improve applying region theory for process mining in industry scenarios.
Java region-based foldings in process discoveryecwayerode
The document discusses using region-based techniques for process discovery from event logs. It proposes incorporating region information into cycle detection algorithms to more efficiently identify complex cycles when converting event logs into automata. This enables better application of region-based techniques to discover process models from industrial event logs. The experimental results suggest the techniques can significantly improve applying region theory for process mining in industry scenarios.
Ijricit 01-002 enhanced replica detection in short time for large data setsIjripublishers Ijri
Similarity check of real world entities is a necessary factor in these days which is named as Data Replica Detection.
Time is an critical factor today in tracking Data Replica Detection for large data sets, without having impact over quality
of Dataset. In this we primarily introduce two Data Replica Detection algorithms , where in these contribute enhanced
procedural standards in finding Data Replication at limited execution periods.This contribute better improvised state
of time than conventional techniques . We propose two Data Replica Detection algorithms namely progressive sorted
neighborhood method (PSNM), which performs best on small and almost clean datasets, and progressive blocking (PB),
which performs best on large and very grimy datasets. Both enhance the efficiency of duplicate detection even on very
large datasets.
Congestion Control in Wireless Sensor Networks Using Genetic AlgorithmEditor IJCATR
Sensor network consists of a large number of small nods, strongly interacting with the physical environment, takes
environmental data through sensors, and reacts after processing on information. Wireless network technologies are widely used in most
applications. As wireless sensor networks have many activities in the field of information transmission, network congestion cannot be
thus avoided. So it seems necessary that some new methods can control congestion and use existing resources for providing better traffic
demands. Congestion increases packet loss and retransmission of removed packets and also wastes of energy. In this paper, a novel
method is presented for congestion control in wireless sensor networks using genetic algorithm. The results of simulation show that the
proposed method, in comparison with the algorithm LEACH, can significantly improve congestion control at high speeds.
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Dotnet region-based foldings in process discoveryEcwaytech
The document discusses region-based process discovery techniques. It aims to obtain a formal model from a set of system executions to represent the processes conducted in a system. The theory of regions is useful for process discovery by learning a formal model (Petri nets) from a set of event traces. Revealing underlying cycles and folding an initial automaton can significantly reduce the complexity of region-based techniques. The paper incorporates region information into a cycle detection algorithm to efficiently identify complex cycles not obtainable with current techniques. Experimental results suggest the presented techniques widen the application of region theory in process mining for industrial scenarios.
Android region-based foldings in process discoveryEcwayt
The document discusses region-based process discovery techniques. It aims to obtain a formal model from a set of system executions to represent the processes conducted in a system. The theory of regions is useful for process discovery by learning a formal model (Petri nets) from a set of event traces. Revealing underlying cycles and folding an initial automaton can significantly reduce the complexity of region-based techniques. The paper incorporates region information into a cycle detection algorithm to efficiently identify complex cycles not obtainable with current techniques. Experimental results suggest the presented techniques widen application of region theory for industrial process mining scenarios.
Android region-based foldings in process discoveryEcwaytech
The document discusses region-based process discovery techniques. It aims to obtain a formal model from a set of system executions to represent the processes conducted in a system. The theory of regions is useful for process discovery by learning a formal model (Petri nets) from a set of event traces. Revealing underlying cycles and folding an initial automaton can significantly reduce the complexity of region-based techniques. The paper incorporates region information into a cycle detection algorithm to efficiently identify complex cycles not obtainable with current techniques. Experimental results suggest the presented techniques widen the application of region theory in process mining for industrial scenarios.
Android region-based foldings in process discoveryEcwaytechnoz
The document discusses region-based process discovery techniques. It aims to obtain a formal model from a set of system executions to represent the processes conducted in a system. The theory of regions is useful for process discovery by learning a formal model (Petri nets) from a set of event traces. Revealing underlying cycles and folding an initial automaton can significantly reduce the complexity of region-based techniques. The paper incorporates region information into a cycle detection algorithm to efficiently identify complex cycles not obtainable with current techniques. Experimental results suggest the presented techniques widen the application of region theory in process mining for industrial scenarios.
Android region-based foldings in process discoveryEcwaytechnoz
The document discusses region-based process discovery techniques. It aims to obtain a formal model from a set of system executions to represent the processes conducted in a system. The theory of regions is useful for process discovery by learning a formal model (Petri nets) from a set of event traces. Revealing underlying cycles and folding an initial automaton can significantly reduce the complexity of region-based techniques. The paper incorporates region information into a cycle detection algorithm to efficiently identify complex cycles not obtainable with current techniques. Experimental results suggest the presented techniques widen application of the theory of regions for industrial Process Mining scenarios.
Android region-based foldings in process discoveryEcway2004
The document discusses region-based process discovery techniques. It aims to obtain a formal model from a set of system executions to represent the processes conducted in a system. The theory of regions is useful for process discovery by learning a formal model (Petri nets) from a set of event traces. Revealing underlying cycles and folding an initial automaton can significantly reduce the complexity of region-based techniques. The paper incorporates region information into a cycle detection algorithm to efficiently identify complex cycles not obtainable with current techniques. Experimental results suggest the presented techniques widen the application of region theory in process mining for industrial scenarios.
Android region-based foldings in process discoveryEcwaytechnoz
The document discusses region-based process discovery techniques. It aims to obtain a formal model from a set of system executions to represent the processes conducted in a system. The theory of regions is useful for process discovery by learning a formal model (Petri nets) from a set of event traces. Revealing underlying cycles and folding an initial automaton can significantly reduce the complexity of region-based techniques. The paper incorporates region information into a cycle detection algorithm to efficiently identify complex cycles not obtainable with current techniques. Experimental results suggest the presented techniques widen the application of region theory in process mining for industrial scenarios.
Android region-based foldings in process discoveryEcway2004
The document discusses region-based process discovery techniques. It aims to obtain a formal model from a set of system executions to represent the processes conducted in a system. The theory of regions is useful for process discovery by learning a formal model (Petri nets) from a set of event traces. Revealing underlying cycles and folding an initial automaton can significantly reduce the complexity of region-based techniques. The paper incorporates region information into a cycle detection algorithm to efficiently identify complex cycles not obtainable with current techniques. Experimental results suggest the presented techniques widen application of region theory for industrial process mining scenarios.
Android region-based foldings in process discoveryEcwayt
The document discusses region-based process discovery techniques. It aims to obtain a formal model from a set of system executions to represent the processes conducted in a system. The theory of regions is useful for process discovery by learning a formal model (Petri nets) from a set of event traces. Revealing underlying cycles and folding an initial automaton can significantly reduce the complexity of region-based techniques. The paper incorporates region information into a cycle detection algorithm to efficiently identify complex cycles not obtainable with current techniques. Experimental results suggest the presented techniques widen application of the theory of regions for industrial Process Mining scenarios.
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JAVA 2013 IEEE DATAMINING PROJECT Region based foldings in process discovery
1. Region-Based Foldings in Process Discovery
ABSTRACT
A central problem in the area of Process Mining is to obtain a formal model that represents the processes
that are conducted in a system. If realized, this simple motivation allows for powerful techniques that can be
used to formally analyze and optimize a system, without the need to resort to its semiformal and sometimes
inaccurate specification. The problem addressed in this paper is known as Process Discovery: to obtain a formal
model from a set of system executions. The theory of regions is a valuable tool in process discovery: it aims at
learning a formal model (Petri nets) from a set of traces. On its genuine form, the theory is applied on an
automaton and therefore one should convert the traces into an acyclic automaton in order to apply these
techniques. Given that the complexity of the region-based techniques depends on the size of the input automata,
revealing the underlying cycles and folding the initial automaton can incur in a significant complexity
alleviation of the region-based techniques. In this paper, we follow this idea by incorporating region
information in the cycle detection algorithm, enabling the identification of complex cycles that cannot be
obtained efficiently with state-of-the-art techniques. The experimental results obtained by the devised tool
suggest that the techniques presented in this paper are a big step into widening the application of the theory of
regions in Process Mining for industrial scenarios.
Existing System
The global patterns that can be used to make predictions about the future has been one of the key
elements that have brought Data Mining to be one of the most relevant research areas in the last decades. Data
mining techniques can be applied naturally on large amount of data like databases or even the Internet, and with
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2. the help of other disciplines like statistics or machine learning, can effectively reveal important patterns in many
scenarios such as health care, business or transportation. As in data mining, Process Discovery tries to reveal
patterns. However, the patterns aimed by Process Discovery techniques are process models, i.e., formal
representations of the processes of a system. Due to its different focus, Process Discovery techniques apply
disciplines different from the ones used in data mining, to allow for the derivation of both the statics and the
dynamics of a system process. Depending on the emphasis, different dimensions can be considered ranging
from social (the identification of communities) to control-flow (the identification of the complex interplay
between system’s tasks). In this work we consider the latter: discover a Petri net from a log, that is from a set of
traces corresponding to executions of a system. The first method to obtain a Petri net from a log was presented.
Disadvantages
To overcome this limitation, several extensions have been presented in the literature to widen the class
of Petri nets that the algorithm can discover.
The theory of regions was initially proposed to solve the synthesis problem: obtain a Petri net that has a
behavior equivalent to a given transition system.
Proposed System
The theory of regions was initially proposed to solve the synthesis problem: obtain a Petri net that has a
behavior equivalent to a given transition system. three conversions from a language to a TS were proposed,
namely sequence, multiset, and set. The main difference between them is how it is decided whether the
occurrence of an event in a trace produces a new state in the TS or just introduces an arc to an existing state.
Together with these conversions, a number of additional conversions producing smaller TSs by means of
abstractions have been proposed in the literature. Besides the sequence and multiset conversions, other
conversions have been proposed that can yield smaller TSs at the cost of sacrificing regions. We use the term
abstraction techniques to refer to them. The fundamental difference between all these methods and our proposal
is that, in our case, the set of sacrificed regions is controlled considering bounds that are already used by
process discovery tools, thus the compression of the TS does not involve a quality reduction.
3. Advantages
An advantage of region theory for process discovery is that it allows to perform label splitting.
The advantages offered by the theory of regions, there are two main reasons that hamper a wider
adoption of region-based Process Discovery methodologies in an industrial setting. One is their
sensitivity to noise.
The other hand the benefits for rbminer are twofold, since a smaller region basis reduces the amount of
regions to explore. In this case, both advantages (state and basis reduction) combine to achieve orders of
magnitude speedups.
Module
1. Get Input Text File
2. Discovery Sentence Word
3. Decided Sentence
4. Tandem Repeats
5. Sequence And Multiset Conversions
6. Counting Data
Module Description
Get Input Text File
The Process Discovery differs from synthesis in the knowledge assumption: while in synthesis one
assumes a complete description of the system, only a partial description of the system is assumed in Process
Discovery. Therefore, equivalence or bisimulation is no longer a goal to achieve. Instead, obtaining
approximations that succinctly represent the log under consideration are more valuable.
Discovery Sentence Word
The fact that a discovery algorithm returns a PN with a smaller language than desired is referred as
overfitting. A classical strategy to avoid overfitting is to allow the algorithms to restrict their output to k-
bounded PNs (kbounded discovery), usually for small values of k, as nets with high numbers of tokens are
considered harder to understand for humans than nets with fewer tokens. The particular k used in each case can
4. be either determined from the desired level of complexity of the resulting PN1 or the number of available
resources in the system (since places can represent resources).
Decided Sentence
The conversions from a language to a TS were proposed, namely sequence, multiset, and set. The main
difference between them is how it is decided whether the occurrence of an event in a trace produces a new state
in the TS or just introduces an arc to an existing state.
Tandem Repeats
The detection of unfolded cycles in an acyclic TS is a problem related to finding consecutively repeated patterns
in a string. The latter problem has been studied in several fields with many variations and under different
names, although it is often referred as the finding tandem repeats problem.
Sequence And Multiset Conversions
The sequence and multiset conversions, other conversions have been proposed that can yield smaller
TSs at the cost of sacrificing regions. We use the term abstraction techniques to refer to them. The fundamental
difference between all these methods and our proposal is that, in our case, the set of sacrificed regions is
controlled considering bounds that are already used by process discovery tools, thus the compression of the TS
does not involve a quality reduction.
Counting Data
The region-based approaches yield PNs that never reject a trace of the log, they are extremely sensitive
to noise. Hence, to be applicable, the approach presented in this paper must be preceded by a noise filtering
phase. The filtering can be done by clustering techniques or by outlier detection. Also, considering the
frequencies of the states is a possibility in our approach to distinguish between real and noisy states, because the
latter have often low frequency. For instance, only Parikh vector differences between frequent states could be
taken into account to differentiate real folding opportunities from spurious cycle unfoldings caused by noise. An
advantage of region theory for process discovery is that it allows to perform label splitting (i.e., to change the
label of some arcs in the TS so that an event is actually represented by a set of different events). Label splitting
is a technique that can help into improving the visualization of the PN, but also into avoiding to generalize too
much. This technique can also be used with the TSs produced by our approach. However, the splitting options
might be reduced as a consequence of arcs with the same label in the original TS that have been now merged
into one arc in the folded TS.
5. FLOW CHART
Region-Based Process Discovery
Get The Input Text File
Discovery Sentence Word
Sequence and Multiset Tandem Repeats Counting Data
6. CONCLUSION
The presents a novel technique for compacting a TS, one of the objects typically used in process discovery
algorithms. The two main characteristics of this technique makes it very attractive in the context of region-
based k-bounded process discovery: first, it is one of the most aggressive folding techniques in the literature,
and second, it preserves the important regions that are crucial for PN derivation. The use of folding techniques
that are region-aware like the one presented in this paper may be a crucial step to use region-based algorithms
for process discovery in industrial scenarios.
REFFERENCE
[1] W. van der Aalst, H. Reijers, and M. Song, “Discovering Social Networks from Event Logs,” Computer
Supported Cooperative Work, vol. 14, no. 6, pp. 549-593, 2005.
[2] W. van der Aalst, T. Weijters, and L. Maruster, “Workflow Mining: Discovering Process Models from
Event Logs,” IEEE Trans. Knowledge Data Eng., vol. 16, no. 9, pp. 1128-1142, Sept. 2004.
[3] A. de Medeiros, W. van der Aalst, and A. Weijters, “Workflow Mining: Current Status and Future
Directions,” Proc. On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE, pp. 389-
406, 2003.
[4] L. Wen, W. van der Aalst, J. Wang, and J. Sun, “Mining Process Models with Non-Free-Choice
Constructs,” Data Mining and Knowledge Discovery, vol. 15, no. 2, pp. 145-180, 2007.
[5] W. van der Aalst, A. de Medeiros, and A. Weijters, “Genetic Process Mining,” Proc. 26th Int’l Conf.
Applications and Theory of Petri Nets (ICATPN), pp. 48-69, 2005.
[6] A. Ehrenfeucht and G. Rozenberg, “Partial (Set) 2-Structures. Part I, II,” Acta Informatica, vol. 27, pp. 315-
368, 1990.