Data Mining is the process of discovering new correlations, patterns, and trends by digging into (mining) large amounts of data stored in warehouses, using artificial intelligence, statistical and mathematical techniques. Data mining can also be defined as the process of extracting knowledge hidden from large volumes of raw data i.e. the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. The alternative name of Data Mining is Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, etc.
This document discusses knowledge discovery in databases (KDD) through the LON-CAPA online educational system. [1] It defines KDD and data mining, describing the tasks, methods, and applications of KDD. [2] The goals are to obtain predictive models of students, help students and instructors use resources more effectively, and provide information to increase student learning. [3] It then discusses the KDD process and data mining methods like classification, clustering, and dependency modeling that can be applied to discover knowledge from educational data.
The Survey of Data Mining Applications And Feature Scope IJCSEIT Journal
In this paper we have focused a variety of techniques, approaches and different areas of the research which
are helpful and marked as the important field of data mining Technologies. As we are aware that many MNC’s
and large organizations are operated in different places of the different countries. Each place of operation
may generate large volumes of data. Corporate decision makers require access from all such sources and
take strategic decisions .The data warehouse is used in the significant business value by improving the
effectiveness of managerial decision-making. In an uncertain and highly competitive business
environment, the value of strategic information systems such as these are easily recognized however in
today’s business environment, efficiency or speed is not the only key for competitiveness. This type of huge
amount of data’s are available in the form of tera- to peta-bytes which has drastically changed in the areas
of science and engineering. To analyze, manage and make a decision of such type of huge amount of data
we need techniques called the data mining which will transforming in many fields. This paper imparts more
number of applications of the data mining and also o focuses scope of the data mining which will helpful in
the further research.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
This document summarizes a research paper that uses genetic algorithms for data mining of mass spectrometry data. The paper applies genetic algorithms to the data mining step of the KDD (Knowledge Discovery in Databases) process. Specifically, it uses genetic algorithms to extract optimal features or peaks from mass spectrometry data that can distinguish cancer patients from control patients. The genetic algorithm searches for discriminative features, which are ion intensity levels at specific mass-charge values. Over generations of the genetic algorithm, the paper finds significant patterns of masses that can differentiate between normal and cancer patients.
Additive gaussian noise based data perturbation in multi level trust privacy ...IJDKP
This document discusses a technique called additive Gaussian noise based data perturbation for privacy preserving data mining. The technique introduces multiple perturbed copies of data for different trust levels of data miners to prevent diversity attacks. Gaussian noise is added to the original data and correlated between copies so that combining copies does not provide additional information about the original data. The goal is to limit what information adversaries can learn from individual or combined copies to within what the data owner intends to share, while still allowing accurate data mining. Experiments on banking customer data show the approach controls the normalized estimation error from individual and combined copies.
PERFORMING DATA MINING IN (SRMS) THROUGH VERTICAL APPROACH WITH ASSOCIATION R...Editor IJMTER
This system technique is used for efficient data mining in SRMS (Student Records
Management System) through vertical approach with association rules in distributed databases. The
current leading technique is that of Kantarcioglu and Clifton[1]. In this system I deal with two
challenges or issues, one that computes the union of private subsets that each of the interacting users
hold, and another that tests the inclusion of an element held by one user in a subset held by another.
The existing system uses different techniques for data mining purpose like Apriori algorithm. The
Fast Distributed Mining (FDM) algorithm of Cheung et al. [2], which is an unsecured distributed
version of the Apriori algorithm. Proposed system offers enhanced privacy and data mining with
respect to the Encryption techniques and Association rule with Fp-Growth Algorithm in private
cloud (system contains different files of subjects with respect to their branches). Due to this above
techniques the expected effect on this system is that, it is simpler and more efficient in terms of
communication cost and combinational cost. Due to these techniques it will affect the parameter like
time consumption for execution, length of the code is decrease, find the data fast, extracting hidden
predictive information from large databases and the efficiency of this proposed system should
increase by the 20%.
TUPLE VALUE BASED MULTIPLICATIVE DATA PERTURBATION APPROACH TO PRESERVE PRIVA...IJDKP
Huge volume of data from domain specific applications such as medical, financial, library, telephone,
shopping records and individual are regularly generated. Sharing of these data is proved to be beneficial
for data mining application. On one hand such data is an important asset to business decision making by
analyzing it. On the other hand data privacy concerns may prevent data owners from sharing information
for data analysis. In order to share data while preserving privacy, data owner must come up with a solution
which achieves the dual goal of privacy preservation as well as an accuracy of data mining task –
clustering and classification. An efficient and effective approach has been proposed that aims to protect
privacy of sensitive information and obtaining data clustering with minimum information loss
This document summarizes an article from the International Journal of Computer Engineering and Technology (IJCET) that discusses applications of data mining in medical databases. It begins by noting that large amounts of patient data have been collected in hospital information systems, and data mining techniques can be used to extract valuable hidden information from this data. The document then provides an overview of common data mining methods like neural networks, decision trees, and cluster detection that are applicable to medical data. It also discusses the process of knowledge discovery in databases and some considerations for preprocessing medical data from different sources before performing data mining analysis.
Predicted the activities performed by the user (Bending, Walking etc.) using Classification technique in Data mining.
Performed data cleaning, data pre-processing (removing false predictors, identifying important attributes) in Weka.
Created a model with 77% accuracy by performing Activity Recognition Classification and cross-checked it using Experimental design by adding noise, ROC Curves and Principal component analysis to create new attributes in Weka.
This document discusses knowledge discovery in databases (KDD) through the LON-CAPA online educational system. [1] It defines KDD and data mining, describing the tasks, methods, and applications of KDD. [2] The goals are to obtain predictive models of students, help students and instructors use resources more effectively, and provide information to increase student learning. [3] It then discusses the KDD process and data mining methods like classification, clustering, and dependency modeling that can be applied to discover knowledge from educational data.
The Survey of Data Mining Applications And Feature Scope IJCSEIT Journal
In this paper we have focused a variety of techniques, approaches and different areas of the research which
are helpful and marked as the important field of data mining Technologies. As we are aware that many MNC’s
and large organizations are operated in different places of the different countries. Each place of operation
may generate large volumes of data. Corporate decision makers require access from all such sources and
take strategic decisions .The data warehouse is used in the significant business value by improving the
effectiveness of managerial decision-making. In an uncertain and highly competitive business
environment, the value of strategic information systems such as these are easily recognized however in
today’s business environment, efficiency or speed is not the only key for competitiveness. This type of huge
amount of data’s are available in the form of tera- to peta-bytes which has drastically changed in the areas
of science and engineering. To analyze, manage and make a decision of such type of huge amount of data
we need techniques called the data mining which will transforming in many fields. This paper imparts more
number of applications of the data mining and also o focuses scope of the data mining which will helpful in
the further research.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
This document summarizes a research paper that uses genetic algorithms for data mining of mass spectrometry data. The paper applies genetic algorithms to the data mining step of the KDD (Knowledge Discovery in Databases) process. Specifically, it uses genetic algorithms to extract optimal features or peaks from mass spectrometry data that can distinguish cancer patients from control patients. The genetic algorithm searches for discriminative features, which are ion intensity levels at specific mass-charge values. Over generations of the genetic algorithm, the paper finds significant patterns of masses that can differentiate between normal and cancer patients.
Additive gaussian noise based data perturbation in multi level trust privacy ...IJDKP
This document discusses a technique called additive Gaussian noise based data perturbation for privacy preserving data mining. The technique introduces multiple perturbed copies of data for different trust levels of data miners to prevent diversity attacks. Gaussian noise is added to the original data and correlated between copies so that combining copies does not provide additional information about the original data. The goal is to limit what information adversaries can learn from individual or combined copies to within what the data owner intends to share, while still allowing accurate data mining. Experiments on banking customer data show the approach controls the normalized estimation error from individual and combined copies.
PERFORMING DATA MINING IN (SRMS) THROUGH VERTICAL APPROACH WITH ASSOCIATION R...Editor IJMTER
This system technique is used for efficient data mining in SRMS (Student Records
Management System) through vertical approach with association rules in distributed databases. The
current leading technique is that of Kantarcioglu and Clifton[1]. In this system I deal with two
challenges or issues, one that computes the union of private subsets that each of the interacting users
hold, and another that tests the inclusion of an element held by one user in a subset held by another.
The existing system uses different techniques for data mining purpose like Apriori algorithm. The
Fast Distributed Mining (FDM) algorithm of Cheung et al. [2], which is an unsecured distributed
version of the Apriori algorithm. Proposed system offers enhanced privacy and data mining with
respect to the Encryption techniques and Association rule with Fp-Growth Algorithm in private
cloud (system contains different files of subjects with respect to their branches). Due to this above
techniques the expected effect on this system is that, it is simpler and more efficient in terms of
communication cost and combinational cost. Due to these techniques it will affect the parameter like
time consumption for execution, length of the code is decrease, find the data fast, extracting hidden
predictive information from large databases and the efficiency of this proposed system should
increase by the 20%.
TUPLE VALUE BASED MULTIPLICATIVE DATA PERTURBATION APPROACH TO PRESERVE PRIVA...IJDKP
Huge volume of data from domain specific applications such as medical, financial, library, telephone,
shopping records and individual are regularly generated. Sharing of these data is proved to be beneficial
for data mining application. On one hand such data is an important asset to business decision making by
analyzing it. On the other hand data privacy concerns may prevent data owners from sharing information
for data analysis. In order to share data while preserving privacy, data owner must come up with a solution
which achieves the dual goal of privacy preservation as well as an accuracy of data mining task –
clustering and classification. An efficient and effective approach has been proposed that aims to protect
privacy of sensitive information and obtaining data clustering with minimum information loss
This document summarizes an article from the International Journal of Computer Engineering and Technology (IJCET) that discusses applications of data mining in medical databases. It begins by noting that large amounts of patient data have been collected in hospital information systems, and data mining techniques can be used to extract valuable hidden information from this data. The document then provides an overview of common data mining methods like neural networks, decision trees, and cluster detection that are applicable to medical data. It also discusses the process of knowledge discovery in databases and some considerations for preprocessing medical data from different sources before performing data mining analysis.
Predicted the activities performed by the user (Bending, Walking etc.) using Classification technique in Data mining.
Performed data cleaning, data pre-processing (removing false predictors, identifying important attributes) in Weka.
Created a model with 77% accuracy by performing Activity Recognition Classification and cross-checked it using Experimental design by adding noise, ROC Curves and Principal component analysis to create new attributes in Weka.
This document discusses privacy-preserving techniques for data stream mining. It proposes a hybrid method that uses both rotation and translation based data perturbation to anonymize sensitive attributes in data streams. The key steps are:
1) Select attribute pairs and set security thresholds for perturbation.
2) Apply rotation transformations to selected attribute pairs to distort the data within the security thresholds.
3) Also apply translation perturbations by adding or subtracting random noise values to other attributes.
The goal is to anonymize the data enough to preserve privacy while maintaining accuracy for data stream mining tasks like clustering. Evaluation focuses on balancing privacy protections with preserving data utility for analysis.
Herbal plant recognition using deep convolutional neural networkjournalBEEI
This paper investigates the application of deep convolutional neural network (CNN) for herbal plant recognition through leaf identification. Traditional plant identification is often time-consuming due to varieties as well as similarities possessed within the plant species. This study shows that a deep CNN model can be created and enhanced using multiple parameters to boost recognition accuracy performance. This study also shows the significant effects of the multi-layer model on small sample sizes to achieve reasonable performance. Furthermore, data augmentation provides more significant benefits on the overall performance. Simple augmentations such as resize, flip and rotate will increase accuracy significantly by creating invariance and preventing the model from learning irrelevant features. A new dataset of the leaves of various herbal plants found in Malaysia has been constructed and the experimental results achieved 99% accuracy.
This document summarizes statistical disclosure control techniques for protecting private data, specifically microaggregation. Microaggregation involves clustering individual records into small groups to anonymize the data before release. It aims to minimize information loss while preventing re-identification of individuals. The document discusses challenges with multivariate microaggregation and reviews different heuristic approaches. It also covers related topics like k-anonymity algorithms, various clustering techniques for microaggregation like k-means, and using genetic algorithms to handle large datasets.
Software Bug Detection Algorithm using Data mining TechniquesAM Publications
The main aim of software development is to develop high quality software and high quality software is
developed using enormous amount of software engineering data. The software engineering data can be used to gain
empirically based understanding of software development. The meaning full information can be extracted using
various data mining techniques. As Data Mining for Secure Software Engineering improves software productivity and
quality, software engineers are increasingly applying data mining algorithms to various software engineering tasks.
However mining software engineering data poses several challenges, requiring various algorithms to effectively mine
sequences, graphs and text from such data. Software engineering data includes code bases, execution traces,
historical code changes, mailing lists and bug data bases. They contains a wealth of information about a projectsstatus,
progress and evolution. Using well established data mining techniques, practitioners and researchers can
explore the potential of this valuable data in order to better manage their projects and do produce higher-quality
software systems that are delivered on time and within budget
Characterizing and Processing of Big Data Using Data Mining TechniquesIJTET Journal
The document discusses big data and techniques for processing it, including data mining. It begins by defining big data and its key characteristics of volume, variety, and velocity. It then discusses various data mining techniques that can be used to process big data, including clustering, classification, and prediction. It introduces the HACE theorem for characterizing big data based on its huge size, heterogeneous and diverse sources, decentralized control, and complex relationships within the data. The document proposes a big data processing model involving data set aggregation, pre-processing, connectivity-based clustering, and subset selection to efficiently retrieve relevant data. It evaluates the performance of subset selection versus deterministic search methods.
IRJET- Detection of Plant Leaf Diseases using Image Processing and Soft-C...IRJET Journal
This document presents a method for detecting plant leaf diseases using image processing and soft computing techniques. It involves taking images of plant leaves using a digital camera, pre-processing the images, segmenting the images to identify infected regions, extracting features from the infected regions, and classifying the disease based on the features. The method was tested on various plant leaf image datasets with an accuracy of 63% and was able to identify diseases for tomatoes, corn, grapes, peaches and peppers. The automatic detection technique can help identify diseases at an early stage with less time and effort compared to manual detection methods.
International Journal of Computational Engineering Research(IJCER) ijceronline
nternational Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Privacy Preservation and Restoration of Data Using Unrealized Data SetsIJERA Editor
In today’s world, there is an improved advance in hardware technology which increases the capability to store and record personal data about consumers and individuals. Data mining extracts knowledge to support a variety of areas as marketing, medical diagnosis, weather forecasting, national security etc successfully. Still there is a challenge to extract certain kinds of data without violating the data owners’ privacy. As data mining becomes more enveloping, such privacy concerns are increasing. This gives birth to a new category of data mining method called privacy preserving data mining algorithm (PPDM). The aim of this algorithm is to protect the easily affected information in data from the large amount of data set. The privacy preservation of data set can be expressed in the form of decision tree. This paper proposes a privacy preservation based on data set complement algorithms which store the information of the real dataset. So that the private data can be safe from the unauthorized party, if some portion of the data can be lost, then we can recreate the original data set from the unrealized dataset and the perturbed data set.
Privacy Preserving Clustering on Distorted dataIOSR Journals
- The document discusses privacy-preserving clustering on distorted data using singular value decomposition (SVD) and sparsified singular value decomposition (SSVD).
- It applies SVD and SSVD to distort a real-world dataset of 100 terrorists with 42 attributes, generating distorted datasets.
- K-means clustering is then performed on the original and distorted datasets for different numbers of clusters (k). The results show that SSVD more effectively groups the data objects into clusters compared to the original and SVD-distorted datasets, while preserving data privacy as measured by various metrics.
The document provides a list of over 100 URLs. It appears to be sharing backlink opportunities for a contest related to search engine optimization (SEO). The URLs are from a variety of sites and domains and cover different topics. The purpose seems to be to gather links from these sites for a marketing or SEO campaign.
The document is part one of a story that aims to summarize ITIL v3 concepts related to IT service management in an accessible way. It introduces the concepts of incident management and problem management in ITIL and how addressing incidents efficiently can save a company money by minimizing downtime. It sets up a scenario where a business user is affected by a system incident and support is needed to resolve the issue and identify the root cause. The story aims to illustrate ITIL processes and terminology to help diverse audiences better understand IT service management.
Resumo das principais funcionalidades para developers, bem como novos recursos para administradores como o grid link for rac e o active cache. também abordamos recursos de grid como o coherence
WebLion Hosting: Leveraging Laziness, Impatience, and HubrisErik Rose
Behind the scenes of WebLion's Plone hosting service, which uses Debian packages and a custom repository to deliver reliable, unattended updates to a cluster of heterogeneous departmental virtual servers. And it's all available for your own use for free.
The document provides information about higher education systems in Estonia, Latvia, and Lithuania. It discusses the organization of studies in each country, including academic calendars, admission procedures, tuition fees, assessment and grading systems, scholarships and grants available, and higher education institutions. The higher education systems in the three Baltic countries follow a bachelor's and master's degree structure and provide academic and professional education opportunities.
The Django Book - Chapter 6 the django admin siteVincent Chien
The document discusses customizing the Django admin site. It describes how to register models with the admin to make them editable, how to make fields optional by adding blank and null attributes, and how to customize field labels by adding a verbose_name. It also mentions customizing the display of objects on the admin change list by defining a custom ModelAdmin class and customizing the list_display attribute.
The document is a directory for UHY, a global network of independent accounting and business advisory firms. It provides information about UHY's presence in 86 countries worldwide, with over 7,100 professionals operating from more than 270 business centers. UHY member firms offer a range of services including audit, tax, accounting, and business advisory services. The directory then lists each country that UHY has a presence in, providing contact details for each member firm.
SAIC's employees are dedicated to delivering innovative solutions to support clients worldwide, particularly those on the front lines of homeland security and the war in Iraq. The document discusses several ways SAIC supports homeland security, including through emergency preparedness and response training, securing borders and transportation, and responding to nuclear, biological, and chemical threats. SAIC has extensive experience supporting government agencies and was chosen to integrate the new Department of Homeland Security's data network.
Condor overview - glideinWMS Training Jan 2012Igor Sfiligoi
An overview of the Condor Workload Management System, with emphasis on how it is used within the glideinWMS.
Part of the glideinWMS Training session held in Jan 2012 at UCSD.
The event page is http://hepuser.ucsd.edu/twiki2/bin/view/Main/GlideinFrontend1201
Video available at http://www.youtube.com/watch?v=tpaedg09VMM
This issue of the EastAlgarve magazine discusses various upcoming events in November, including music performances, theater productions, exhibits, and fairs. It also provides information on emergency numbers and introduces the owners of Casa do Polvo restaurant. The front page article previews stories on olive processing, gardening, golf tips, and properties that will be featured within.
Be2Awards and Be2Talks 2013 - event slidesBe2camp Admin
The document summarizes the agenda and award winners for the 2013 Be2Awards event. It provides details on the schedule of speakers and award categories. The event recognized the best uses of social media and collaboration applications in the architecture, engineering, and construction industries. It included seven speaker presentations and the announcement of award winners in 13 categories, including best use of Twitter, blogs, and collaboration platforms.
This document discusses security issues with cloud-based electronic health records (EHRs) and proposes a new security architecture. It notes that while EHR systems aim to improve healthcare and reduce costs, outsourcing medical data to cloud providers complicates privacy and security. The proposed architecture enhances existing solutions by focusing on client platform security in addition to network security and access control. It aims to properly address privacy regulations while accommodating real-world medical work flows.
This document discusses privacy-preserving techniques for data stream mining. It proposes a hybrid method that uses both rotation and translation based data perturbation to anonymize sensitive attributes in data streams. The key steps are:
1) Select attribute pairs and set security thresholds for perturbation.
2) Apply rotation transformations to selected attribute pairs to distort the data within the security thresholds.
3) Also apply translation perturbations by adding or subtracting random noise values to other attributes.
The goal is to anonymize the data enough to preserve privacy while maintaining accuracy for data stream mining tasks like clustering. Evaluation focuses on balancing privacy protections with preserving data utility for analysis.
Herbal plant recognition using deep convolutional neural networkjournalBEEI
This paper investigates the application of deep convolutional neural network (CNN) for herbal plant recognition through leaf identification. Traditional plant identification is often time-consuming due to varieties as well as similarities possessed within the plant species. This study shows that a deep CNN model can be created and enhanced using multiple parameters to boost recognition accuracy performance. This study also shows the significant effects of the multi-layer model on small sample sizes to achieve reasonable performance. Furthermore, data augmentation provides more significant benefits on the overall performance. Simple augmentations such as resize, flip and rotate will increase accuracy significantly by creating invariance and preventing the model from learning irrelevant features. A new dataset of the leaves of various herbal plants found in Malaysia has been constructed and the experimental results achieved 99% accuracy.
This document summarizes statistical disclosure control techniques for protecting private data, specifically microaggregation. Microaggregation involves clustering individual records into small groups to anonymize the data before release. It aims to minimize information loss while preventing re-identification of individuals. The document discusses challenges with multivariate microaggregation and reviews different heuristic approaches. It also covers related topics like k-anonymity algorithms, various clustering techniques for microaggregation like k-means, and using genetic algorithms to handle large datasets.
Software Bug Detection Algorithm using Data mining TechniquesAM Publications
The main aim of software development is to develop high quality software and high quality software is
developed using enormous amount of software engineering data. The software engineering data can be used to gain
empirically based understanding of software development. The meaning full information can be extracted using
various data mining techniques. As Data Mining for Secure Software Engineering improves software productivity and
quality, software engineers are increasingly applying data mining algorithms to various software engineering tasks.
However mining software engineering data poses several challenges, requiring various algorithms to effectively mine
sequences, graphs and text from such data. Software engineering data includes code bases, execution traces,
historical code changes, mailing lists and bug data bases. They contains a wealth of information about a projectsstatus,
progress and evolution. Using well established data mining techniques, practitioners and researchers can
explore the potential of this valuable data in order to better manage their projects and do produce higher-quality
software systems that are delivered on time and within budget
Characterizing and Processing of Big Data Using Data Mining TechniquesIJTET Journal
The document discusses big data and techniques for processing it, including data mining. It begins by defining big data and its key characteristics of volume, variety, and velocity. It then discusses various data mining techniques that can be used to process big data, including clustering, classification, and prediction. It introduces the HACE theorem for characterizing big data based on its huge size, heterogeneous and diverse sources, decentralized control, and complex relationships within the data. The document proposes a big data processing model involving data set aggregation, pre-processing, connectivity-based clustering, and subset selection to efficiently retrieve relevant data. It evaluates the performance of subset selection versus deterministic search methods.
IRJET- Detection of Plant Leaf Diseases using Image Processing and Soft-C...IRJET Journal
This document presents a method for detecting plant leaf diseases using image processing and soft computing techniques. It involves taking images of plant leaves using a digital camera, pre-processing the images, segmenting the images to identify infected regions, extracting features from the infected regions, and classifying the disease based on the features. The method was tested on various plant leaf image datasets with an accuracy of 63% and was able to identify diseases for tomatoes, corn, grapes, peaches and peppers. The automatic detection technique can help identify diseases at an early stage with less time and effort compared to manual detection methods.
International Journal of Computational Engineering Research(IJCER) ijceronline
nternational Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Privacy Preservation and Restoration of Data Using Unrealized Data SetsIJERA Editor
In today’s world, there is an improved advance in hardware technology which increases the capability to store and record personal data about consumers and individuals. Data mining extracts knowledge to support a variety of areas as marketing, medical diagnosis, weather forecasting, national security etc successfully. Still there is a challenge to extract certain kinds of data without violating the data owners’ privacy. As data mining becomes more enveloping, such privacy concerns are increasing. This gives birth to a new category of data mining method called privacy preserving data mining algorithm (PPDM). The aim of this algorithm is to protect the easily affected information in data from the large amount of data set. The privacy preservation of data set can be expressed in the form of decision tree. This paper proposes a privacy preservation based on data set complement algorithms which store the information of the real dataset. So that the private data can be safe from the unauthorized party, if some portion of the data can be lost, then we can recreate the original data set from the unrealized dataset and the perturbed data set.
Privacy Preserving Clustering on Distorted dataIOSR Journals
- The document discusses privacy-preserving clustering on distorted data using singular value decomposition (SVD) and sparsified singular value decomposition (SSVD).
- It applies SVD and SSVD to distort a real-world dataset of 100 terrorists with 42 attributes, generating distorted datasets.
- K-means clustering is then performed on the original and distorted datasets for different numbers of clusters (k). The results show that SSVD more effectively groups the data objects into clusters compared to the original and SVD-distorted datasets, while preserving data privacy as measured by various metrics.
The document provides a list of over 100 URLs. It appears to be sharing backlink opportunities for a contest related to search engine optimization (SEO). The URLs are from a variety of sites and domains and cover different topics. The purpose seems to be to gather links from these sites for a marketing or SEO campaign.
The document is part one of a story that aims to summarize ITIL v3 concepts related to IT service management in an accessible way. It introduces the concepts of incident management and problem management in ITIL and how addressing incidents efficiently can save a company money by minimizing downtime. It sets up a scenario where a business user is affected by a system incident and support is needed to resolve the issue and identify the root cause. The story aims to illustrate ITIL processes and terminology to help diverse audiences better understand IT service management.
Resumo das principais funcionalidades para developers, bem como novos recursos para administradores como o grid link for rac e o active cache. também abordamos recursos de grid como o coherence
WebLion Hosting: Leveraging Laziness, Impatience, and HubrisErik Rose
Behind the scenes of WebLion's Plone hosting service, which uses Debian packages and a custom repository to deliver reliable, unattended updates to a cluster of heterogeneous departmental virtual servers. And it's all available for your own use for free.
The document provides information about higher education systems in Estonia, Latvia, and Lithuania. It discusses the organization of studies in each country, including academic calendars, admission procedures, tuition fees, assessment and grading systems, scholarships and grants available, and higher education institutions. The higher education systems in the three Baltic countries follow a bachelor's and master's degree structure and provide academic and professional education opportunities.
The Django Book - Chapter 6 the django admin siteVincent Chien
The document discusses customizing the Django admin site. It describes how to register models with the admin to make them editable, how to make fields optional by adding blank and null attributes, and how to customize field labels by adding a verbose_name. It also mentions customizing the display of objects on the admin change list by defining a custom ModelAdmin class and customizing the list_display attribute.
The document is a directory for UHY, a global network of independent accounting and business advisory firms. It provides information about UHY's presence in 86 countries worldwide, with over 7,100 professionals operating from more than 270 business centers. UHY member firms offer a range of services including audit, tax, accounting, and business advisory services. The directory then lists each country that UHY has a presence in, providing contact details for each member firm.
SAIC's employees are dedicated to delivering innovative solutions to support clients worldwide, particularly those on the front lines of homeland security and the war in Iraq. The document discusses several ways SAIC supports homeland security, including through emergency preparedness and response training, securing borders and transportation, and responding to nuclear, biological, and chemical threats. SAIC has extensive experience supporting government agencies and was chosen to integrate the new Department of Homeland Security's data network.
Condor overview - glideinWMS Training Jan 2012Igor Sfiligoi
An overview of the Condor Workload Management System, with emphasis on how it is used within the glideinWMS.
Part of the glideinWMS Training session held in Jan 2012 at UCSD.
The event page is http://hepuser.ucsd.edu/twiki2/bin/view/Main/GlideinFrontend1201
Video available at http://www.youtube.com/watch?v=tpaedg09VMM
This issue of the EastAlgarve magazine discusses various upcoming events in November, including music performances, theater productions, exhibits, and fairs. It also provides information on emergency numbers and introduces the owners of Casa do Polvo restaurant. The front page article previews stories on olive processing, gardening, golf tips, and properties that will be featured within.
Be2Awards and Be2Talks 2013 - event slidesBe2camp Admin
The document summarizes the agenda and award winners for the 2013 Be2Awards event. It provides details on the schedule of speakers and award categories. The event recognized the best uses of social media and collaboration applications in the architecture, engineering, and construction industries. It included seven speaker presentations and the announcement of award winners in 13 categories, including best use of Twitter, blogs, and collaboration platforms.
This document discusses security issues with cloud-based electronic health records (EHRs) and proposes a new security architecture. It notes that while EHR systems aim to improve healthcare and reduce costs, outsourcing medical data to cloud providers complicates privacy and security. The proposed architecture enhances existing solutions by focusing on client platform security in addition to network security and access control. It aims to properly address privacy regulations while accommodating real-world medical work flows.
This document contains a list of cookie files exported from an Internet Explorer browser for Netscape browsers. There are cookies from various domains with information like counters, dates and identifiers. The cookies track information for sites like scribd.com, zylom.com, atdmt.com and others.
EdCamp News & UpDates
By now, it is safe to say that EdCamp is fast consolidating itself as the conference you go to when you don't want to sit passively in an audience of your peers and listen to the wisdom of the "guru" of the moment.
You go to an EdCamp near you.
You want to be active, share what you are doing in your classroom, look for real world solutions to real world problems, collaborate and cooperate with peers.
You go to an EdCamp near you...
EdCamp is free, democratic, conversation-based, participant-driven professional development for teachers by teachers...
According to the EdCamp Foundation, an authentic Edcamp has the following features:
"free: Edcamps should be free to all attendees. This helps ensure that all different types of teachers and educational stakeholders can attend.
non-commercial and with a vendor free presence: Edcamps should be about learning, not selling. Educators should feel free to express their ideas without being swayed or influenced by sales pitches for educational books or technology.
hosted by any organization or anyone:
Anyone can host an Edcamp. School districts, educational stakeholders, and teams of teachers have hosted Edcamps. YOU could be the next Edcamp organizer!
made up of sessions that are determined on the day of the event:
<strong>Edcamps do not have scheduled presentations</strong>.
During the morning of the event, the schedule is created in conjunction with everyone there. I know it sounds crazy, but it works! Sessions end up being spontaneous, interactive, and responsive to participants’ needs.
events where anyone who attends can be a presenter:
Anyone who attends an Edcamp is able to be a presenter. All teachers and educational stakeholders are viewed as professionals worthy of sharing their expertise in a collaborative setting.
reliant on the law of two feet that encourages participants to find a session that meets their needs:
As anyone can host a session, it is critical that participants can actively self-select the best content and sessions. <strong>Edcampers are encouraged to leave sessions that do not meet their needs.</strong>
This provides a uniquely effective way of “<strong>weeding out</strong>” sessions that are not based on appropriate research or not delivered in an engaging format."
Source: <a>http://edcampfoundation.org/</a>
You go to an EdCamp near you...
Nca career wise detailer edition march 2010guest9c4d5d
The document provides information about various Navy career news items from February-March 2010, including:
1) A detailing pilot program expanding on March 5th that aims to help sailors get answers to career questions from the NPC Customer Service Center instead of directly contacting detailers.
2) Free tax filing available to military members and families through Military OneSource until April 15.
3) The Navy is accepting nominations for the Spirit of Hope award until April 1st.
4) The Navy will eliminate paper field service records by September 30, 2010 and transition to electronic records accessible online.
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See you at the ISC 2014 from April 2 to 4th at the Sands Expo Las Vegas!
Our pavilion is located in the designated area for Global Expo on the RIGHT side of the exhibit hall from the entrance, please come by and say HELLO at the KOTRA booth #40718
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IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
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The Transpose Technique On Number Of Transactions Of...Amanda Brady
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Enhanced K-Mean Algorithm to Improve Decision Support System Under Uncertain ...IJMER
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The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
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Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
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https://bit.ly/Automation_Student_Kickstart
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What is RPA? Benefits of RPA?
RPA Applications
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UiPath Studio CE Installation and Setup
💻 Extra training through UiPath Academy:
Introduction to Automation
UiPath Business Automation Platform
Explore automation development with UiPath Studio
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This LF Energy webinar took place June 20, 2024. It featured:
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-Hallie Cramer, Google
-Daniel Roesler, UtilityAPI
-Henry Richardson, WattTime
In response to the urgency and scale required to effectively address climate change, open source solutions offer significant potential for driving innovation and progress. Currently, there is a growing demand for standardization and interoperability in energy data and modeling. Open source standards and specifications within the energy sector can also alleviate challenges associated with data fragmentation, transparency, and accessibility. At the same time, it is crucial to consider privacy and security concerns throughout the development of open source platforms.
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LF Energy Webinar: Carbon Data Specifications: Mechanisms to Improve Data Acc...
Datamining
1. DATAMINING
PROJECT REPORT
Submitted by SHY AM KUMAR S MTHIN
GOPINADH AJITH JOHN ALIAS RI TO
GEORGE CHERIAN
1
INTRODUCTION
1.1 ABOUT THE TOPIC
Data Mining is the process of discovering new correlations, patterns, and trends by digging into
(mining) large amounts of data stored in warehouses, using artificial intelligence, statistical and
mathematical techniques. Data mining can also be defined as the process of extracting knowledge hidden
from large volumes of raw data i.e. the nontrivial extraction of implicit, previously unknown, and
potentially useful information from data. The alternative name of Data Mining is Knowledge discovery
(mining) in databases (KDD), knowledge extraction, data/pattern analysis, etc.
Data mining is the principle of sorting through large amounts of data and picking out relevant
information. It is usually used by business intelligence organizations, and financial analysts, but it is
increasingly used in the sciences to extract information from the enormous data sets generated by modern
experimental and observational methods, it has been described as "the nontrivial extraction of implicit,
previously unknown, and potentially useful information from data" and "the science of extracting useful
information from large data sets or databases".
1.2 ABOUT THE PROJECT
The Project has been developed in our college in an effort to identify the most frequently visited
sites, the site from where the most voluminous downloading has taken place and the sites that have been
denied access when referred to by the users.
1
2. Our college uses the Squid proxy server and our aim is to extract useful knowledge from one of
the log files in it. After a combined scrutiny of the log files the log named access.log was decided to be
used as the database. Hence our project was to mine the contents ofaccess.log .
3. Finally the PERL programming language was used for manipulating the contents of the log file.
PERL EXPRESS 2.5 was the platform used to develop the mining application.
The log file content is in the form of standard text file requiring extensive and quick siring
manipulation to retrieve the necessary contents. The programs were required to sort the mined contents
in the descending order of its frequency of usage and size.
CHAPTER 2 REQUIREMENT
ANALYSIS
2.1 INTRODUCTION
Requirement analysis is the process of gathering and interpreting facts, diagnosing problems and
using the information lo recommend improvements on the system. It is a problem solving activity that
requires intensive communication between the system users and system developers.
Requirement analysis or study is an important phase of any system development process. The
system is studied to the minutest detail and analyzed. The system analyst plays the role of an interrogator
and dwells deep into the working of the present system. The system is viewed as a whole and the inputs
to the system are identified. The outputs from the organization are traced through the various processing
that the inputs phase through in the organization.
A detailed study of these processes must be made by various techniques like Interviews,
Questionnaires etc. The data collected by these sources must be scrutinized to arrive to a conclusion. The
conclusion is an understanding of how the system functions. This system is called the existing system.
Now, the existing system is subjected to close study and the problem areas are identified. The designer
now functions as a problem solver and tries to sort out the difficulties that the enterprise faces. The
solutions are given as a proposal.
The proposal is then weighed with the existing system analytically and the best one is
selected. The proposal is presented to the user for an endorsement by the user. The proposal is
reviewed on user request and suitable changes are made. This loop ends as soon as the user is
satisfied with the proposal.
3
4. 2.2 PROPOSED SYSTEM
In order to make the programming strategy optimal, complete and least complex a detailed
understanding of data mining, related concepts and associated algorithms are required. This is to be
followed by effective implementation of the algorithm using the best possible alternative.
2.3 DATAM1NING (KDD PROCESS)
The Knowledge Discovery from Data process involved / includes relevant prior knowledge and
goals of applications: Creating a large dataset, Preprocessing of the data, Filtering or clearing, data
transformation, identifying dimcnsionally and useful feature. It also involves classification, association,
regression, clustering and summarization. Choosing the mining algorithm is the most important parameter
for the process.
The final stage includes pattern evaluation which means visualization, transformation, removing
redundant pattern etc. use of discovery knowledge of the process.
DM Technology and System: Data mining methods involves neural network, evolutionary
programming, memory base programming, Decision trees. Genetic Algorithms, Nonlinear regression
methods these work also involve fuzzy logic, which is a superset of conventional Boolean logic that has
been extended handle the concept of partial truth, partial false between completely true and complete
false.
The term data mining is often used to apply to the two separate processes of knowledge discovery
and prediction. Knowledge discovery provides explicit information that has a readable form and can be
understood by a user. Forecasting, or predictive modeling provides predictions of future events and may
be transparent and readable in some approaches (e.g. rule based systems) and opaque in others such as
neural networks. Moreover, some data mining systems such as neural networks are inherently geared
towards prediction and pattern recognition, rather than knowledge discovery.
Metadata, or data about a given data set, are often expressed in a condensed data mine-able format,
or one that facilitates the practice of data mining. Common examples include executive summaries and
scientific abstracts.
4
5. Data Mining is the process of discovering new correlations, patterns, and trends by digging into
(mining) large amounts of data stored in warehouses, using artificial intelligence, statistical and
mathematical techniques.
Data mining can also be defined as the process of extracting knowledge hidden from large
volumes of raw data i.e. the nontrivial extraction of implicit, previously unknown, and potentially useful
information from data. The alternative name of Data Mining is Knowledge discovery (mining) in
databases (KDD), knowledge extraction, data/pattern analysis, etc. The importance of collecting data thai
reflect your business or scientific activities to achieve competitive advantage is widely recognized now.
Powerful systems for collecting data and managing it in large databases are in place in all large and mid-
range companies.
LOG files
Preprocessing
Data cleaning
Session identification
Data conversion
mjnsup
Frequent mjnsup
Frequent mjnsup
Frequent
Iternset Sequence Subtree
Discovery Discovery Discovery
| Pattern RESULTS i
Analysis
Figure 2.3.1 : Process of web usage mining
However, the bottleneck of turning this data into your success is the difficulty of extracting
knowledge about the system you study from the collected data. DSS are computerize tools develop assist
decision makers through the process of making of decision. This is inherently prescription which
enhances decision making in some way. DSS are closely related to the concept of rationality which means
the tendency to act in a reasonable'way to make good decision. To produce the key decision for an
organization involve product/service, distribution of the product using different distribution channel,
calculation /computation of the output on different time and space, prediction/trend of the output for
5
6. individual product or service with in estimated time frame and finally the schedule of the production on
the basis of demand, capacity and resource.
The main aim and objective of the work is to develop a system on dynamic decision which depend
on product life cycle individual characteristics graph analysis has been done to give enhance and advance
thought to analysis the pattern of the product. The system has been reviewed in terms of local and global
aspect.
2.4 WORKING OF DATAMINTNG
While large-scale information technology has been evolving separate transaction and analytical
systems, data mining provides the link between the two. Data mining software analyzes relationships and
patterns in stored transaction data based on open-ended user queries. Several types of analytical software
are available: statistical, machine learning, and neural networks. Generally, any of four types of
relationships are sought:
Classes: Stored data is used to locate data in predetermined groups. For example, a restaurant
chain could mine customer purchase data to determine when customers visit and what they typically
order. This information could be used to increase traffic by having daily specials.
Clusters: Data items are grouped according to logical relationships or consumer preferences. For
example, data can be mined to identify market segments or consumer affinities.
Associations: Data can be mined to identify associations. The beer-diaper example is an example
of associative mining.
Sequential patterns: Data is mined to anticipate behavior patterns and trends. For example, an
otitdoor equipment retailer could predict the likelihood of a backpack being purchased based on a
consumer's purchase of sleeping bags and hiking shoes. Data mining consists of five major elements:
•Extract, transform, and load transaction data onto the data warehouse system.
•Store and manage the data in a multidimensional database system.
•Provide data access to business analysts and information technology professionals.
6
7. •Analyze the data by application software.
•Present the data in a useful format, such as a graph or table.
1 .Classification and Regression Trees (CART) and Chi Square
2.Detection (CHAID) : CART and CHAID are decision tree techniques used for classification
of a dataset. They provide a set of rules that you can apply to a new (unclassified) dataset to predict
which records will have a given outcome. CART' segments a dataset by creating 2-way splits while
CHAID segments using chi square tests to create multi-way splits. CART typically requires less data
preparation than CHAID.
•Nearest neighbor method: A technique that classifies each record in a dataset based on a combination of
the classes of the k record(s) most similar to it in a historical dataset. Sometimes called the A:-nearest
neighbor technique.
•Rule induction: The extraction of useful if-then rules from data based on statistical significance.
• Data visualization: The visual interpretation of complex relationships in multidimensional data.
Graphics tools are used to illustrate data relation.
2.5 DATA MINING ALGORITHMS
The data mining algorithm is the mechanism that creates mining models. To create a model, an
algorithm first analyzes a set of data, looking for specific patterns and trends. The algorithm then uses
the results of this analysis to define the parameters of the mining model.
The mining model that an algorithm creates can take various forms, including:
•A set of rules that describe how products are grouped together in a transaction.
•A decision tree that predicts whether a particular customer will buy a product.
•A mathematical model that forecasts sales.
• A set of clusters that describe how the cases in a dataset are related.
7
8. Microsoft SQL Server 2005 Analysis Services (SSAS) provides several algorithms for use in your
data mining solutions. These algorithms are a subset of all the algorithms that can be used for data
mining. You can also use third-party algorithms that comply with the OLE DB for Data Mining
specification. For more information about third-party algorithms, see Plugin Algorithms.
Analysis Services includes the following algorithm types:
•Classification algorithms predict one or more discrete variables, based on the other attributes in
the dataset. An example of a classification algorithm is the Decision Trees Algorithm.
•Regression algorithms predict one or more continuous variables, such as profit or loss, based on
other attributes in the dataset. An example of a regression algorithm is the Time Series
Algorithm.
•Segmentation algorithms divide data into groups, or clusters, of items that have similar
properties. An example of a segmentation algorithm is the Clustering Algorithm.
•Association algorithms find correlations between different attributes in a dataset. The most
common application of this kind of algorithm is for creating association rules, which can be
used in a market basket analysis.
» Sequence analysis algorithms summarize frequent sequences or episodes in data, such as a Web
path How. An example of a sequence analysis algorithm is the Sequence Clustering
Algorithm.
2.6 SOFTWARE REQUIREMENTS
OPERATION SYSTEM WINDOWS XP SP2
PERL COMPILER. PERL ACTIVE PERL
SCRIPT EDITOR PERL EXPRESS
SERVER SOFTWARE IIS SERVER
8
9. 2.7 FUZZY LOGIC
Fuzzy logic is a form of multi-valued logic derived from fuzzy set theory to deal with
reasoning that is approximate rather than precise. Just as in fuzzy set theory the set membership values
can range (inclusively) between 0 and 1, in fuzzy logic the degree of truth of a statement can range
between 0 and 1 and is not constrained to the two truth values ftrue, false} as in classic predicate logic.
And when linguistic variables are used, these degrees may be managed by specific functions, as
discussed below.
Both fuzzy degrees of truth and probabilities range between 0 and 1 and hence may seem
similar at first. However, they are distinct conceptually; fuzzy truth represents membership in vaguely
defined sets, not likelihood of some event or condition as in probability theory. For example, if a 100-ml
glass contains 30 ml of water, then, for two fuzzy sets, Empty and Full, one might define the glass as
being 0.7 empty and 0.3 full.
Note that the concept of emptiness would be subjective and thus would depend on the observer
or designer. Another designer might equally well design a set membership function where the glass
would be considered full for all values down to 50 ml. A probabilistic setting would first define a
scalar variable for the fullness of the glass, and second, conditional distributions describing the
probability that someone would call the glass full given a specific fullness level. Note that the
conditioning can be achieved by having a specific observer that randomly selects ihe label for the
glass, a distribution over deterministic observers, or both. While fuzzy logic avoids talking about
randomness in this context, this simplification at the same time obscures what is exactly meant by the
statement the 'glass is 0.3 full'.
2.7.1 APPLYING FUZZY TRUTH VALUES
A basic application might characterize sub ranges of a continuous variable. For instance, a
temperature measurement for anti-lock brakes might have several separate membership functions
defining particular temperature ranges needed to control the brakes properly. Each function maps the
same temperature value to a truth value in the 0 to I range. These truth values can then be used to
determine how the brakes should be controlled.
In this image, cold, warm, and hot are functions mapping a temperature scale. A point on that
scale has three "truth values" — one for each of the three functions. The vertical line in the image
represents a particular temperature that the three arrows (truth values) gauge. Since the red arrow
9
10. points to zero, this temperature may be interpreted as "not hot". The orange arrow (pointing at 0.2)
may describe it as "slightly warm" and the blue arrow (pointing at 0.8) "fairly cold".
2.7.2 FUZZY LINGUISTIC VARIABLES
While variables in mathematics usually take numerical values, in fuzzy logic applications, the
non-numeric linguistic variables are often used to facilitate the expression of rules and facts.
A linguistic variable such as age may have a value such as young or its opposite defined as old.
ITowever, the great utility of linguistic variables is that they can be modified via linguistic operations
on the primary terms. For instance, if young is associated with the value 0.7 then very young is
automatically deduced as having the value 0.7 * 0.7 = 0.49. And not very young gets the value (l - 0.49),
i.e. 0.51.
In this example, the operator very(X) was defined as X * X, however in general these operators
may be uniformly, but flexibly defined to fit the application, resulting in a great deal of power for the
expression of both rules and fuzzy facts.
CHAPTER 3
SYSTEM DESIGN
System design is the solution to the creation of a new system. This phase is composed of several
systems. This phase focuses on the detailed implementation of the feasible system. Its emphasis is on
translating design specifications to performance specification. System design has two phases of
development logical and physical design.
During logical design phase the analyst describes inputs (sources), out puts (destinations),
databases (data sores) and procedures (data flows) all in a format that meats the uses requirements. The
analyst also specifies the user needs and at a level that virtually determines the information How into and
10
11. out of the system and the data resources. Here the logical design is done through data flow diagrams and
database design.
The physical design is followed by physical design or coding. Physical design produces the working
system by defining the design specifications, which tell the programmers exactly what the candidate system
must do. The programmers write the necessary programs that accept input from the user, perform necessary
processing on accepted data through call and produce the required report on a hard copy or display it on the
screen.
3.1 DATABASE DESIGN
The data mining process involves the manipulation of large data sets. Hence, a large database is a
key requirement in the mining operation. Ordered set of information is now to be extracted from this
database.
The overall objective in the development of database technology has been to treat data as an
organizational resource and as an integrated whole. DBMS allow data to be protected and organized
separately from other resources.
Database is an integrated collection of data. The most significant form of data as seen by the
programmers is data as stored on the direct access storage devices. This is the difference between logical
and physical data.
Database files are the key source of information into the system. It is the process of designing
database files, which are the key source of information to the system. The files should be properly designed
and planned for collection, accumulation, editing and retrieving the required information.
The organization of data in database aims to achieve three major objectives: -
•Data integration.
•Data integrity.
•Data independence.
11
12. A large data set is difficult to parse and to interpret the knowledge contained in it. Since the data
base used in this project is the log file of a proxy server called SQUID, a detailed study of the squid style
transaction logging is also required.
3.2 PKOXY SERVER
A proxy server is a server (a computer system or an application program) which services the
requests of its clients by forwarding requests to other servers. A client connects to the proxy server,
requesting some service, such as a file, connection, web page, or other resource, available from a different
server. The proxy server provides the resource by connecting to the specified server and requesting the
service on behalf of the client. A proxy server may optionally alter the client's request or the server's
response, and sometimes it may serve the request without contacting the specified server. In this case, it
would 'cache' the first request to the remote server, so it could save the information for later, and make
everything as fast as possible.
A proxy server that passes all requests and replies unmodified is usually called a gateway or
sometimes tunneling proxy. A proxy server can be placed in the user's local computer or at specific key
points between the user and the destination servers or the Internet.
• Caching proxy server
A proxy server can service requests without contacting the specified server, by retrieving content
saved from a previous request, made by the same client or even other clients. This is called caching.
• Web proxy
A proxy that focuses on WWW traffic is called a "web proxy". The most common use of a web
proxy is to serve as a web cache. Most proxy programs (e.g. Squid, Net Cache) provide a means to deny
access to certain URLs in a blacklist, thus providing content filtering.
• Content Filtering Web Proxy
A content filtering web proxy server provides administrative control over the content that may be
relayed through the proxy. It is commonly used in commercial and non-commercial organizations
(especially schools) to ensure that Internet usage conforms to acceptable use policy.
• Anonymizing proxy server
12
13. An anonymous proxy server (sometimes called a web proxy) generally attempts to anonymize web
surfing. These can easily be overridden by site administrators, and thus rendered useless in some cases.
There are different varieties of anonymizers.
• Hostile proxy
Proxies can also be installed by online criminals, in order to eavesdrop upon the dataflow between
the client machine and the web. All accessed pages, as well as all forms submitted, can be captured and
analyzed by the proxy operator.
3.3 THE SQUID PROXY SERVER
Squid is a caching proxy for the Web supporting HTTP, HTTPS, FTP, and more. It reduces
bandwidth and improves response times by caching and reusing frequently-requested web pages. Squid has
extensive access controls and makes a great server accelerator. It runs on Unix and Windows and is
licensed under the GNU GPL. Squid is used by hundreds of Internet Providers world-wide to provide their
users with the best possible web access.
Squid optimizes the data flow between client and server to improve performance and caches
frequently-used content to save bandwidth. Squid can also route content requests to servers in a wide
variety of ways to build cache server hierarchies which optimize network throughput.
Thousands of web-sites around the Internet use Squid to drastically increase their content delivery.
Squid can reduce your server load and improve delivery speeds to clients. Squid can also be used to deliver
content from around the world - copying only the content being used, rather than inefficiently copying
everything. Finally, Squid's advanced content routing configuration allows you to build content clusters to
route and load balance requests via a variety of web servers.
Squid is a fully-featured HTTP/1.0 proxy which is almost HTTP/1.1 compliant. Squid offers a rich
access control, authorization and logging environment to develop web proxy and content serving
applications. Squid is one of the projects which grew out of the initial content distribution and caching
work in the mid-90s.
It has grown to include extra features such as powerful access control, authorization, logging,
content distribution/replication, traffic management and shaping and more. It has many, many work -
arounds, new and old. to deal with incomplete and incorrect HTTP implementations.
13
14. Squid allows Internet Providers to save on their bandwidth through content caching. Cached
content means data is served locally and users will see this through faster download speeds with
frequently-used content.
A well-tuned proxy server (even without caching!) can improve user speeds purely by optimizing
TCP flows. Its easy to tune servers to deal with the wide variety of latencies found on the internet -
something that desktop environments just aren't tuned for.
Squid allows ISPs to avoid needing to spend large amounts of money on upgrading core equipment
and transit links to cope with ever-demanding content growth. It also allows ISPs to prioritize and control
certain web content types where dictated by technical or economic reasons.
3.3.1 SQUID STYLE TRANSACTION-LOGGING
Transaction logs allow administrators to view the traffic that has passed through the Content
Engine. Typical fields in the transaction log are the date and time when a request was made, the URL that
was requested, whether it was a cache-hit or a cache-miss, the type of request, the number of bytes
transferred, and the source IP.
High-performance caching presents additional challenges other than how to quickly retrieve objects
from storage, memory, or the web. Administrators of caches are often interested in what requests have been
made of the cache and what the results of these requests were. This information is then used for such
applications as:
•Problem identification and solving
•Load monitoring
•Billing
•Statistical analysis
•Security problems
• Cost analysis and provisioning
14
15. Squid log file format is:
time elapsed remotehost code/status bytes method URL rfc931 peerstatus/peerhost type A Squid log
format example looks like this:
1012429341.115 100 172.16.100.152 TCP REFRESHJVIISS/304 1100 GET http://www.cisco.com/iiiiages/
homepage/news.gif - DlRECT/www.cisco.com -
Squid logs are a valuable source of information about cache workloads and performance. The logs
record not only access information but also system configuration errors and resource consumption, such as
memory and disk space.
15
16. Field Description
lme
UNIX time stamp as Coordinated Jniversal
Time (UTC) seconds with a millisecond ■esolution.
Elapsed
Length of time in milliseconds that the
ache was busy with the transaction.
Note Entries are logged after the reply
las been sent, not during the lifetime of the
transaction.
Remote Host IP address of the requesting instance.
Code/Status
Two entries separated by a slash. The first
mtry contains information on the result of the
xansaction: the kind of request, how it was
satisfied, or in what way it failed. The second ■
mtry contains the HTTP result codes.
Bytes
Amount of data delivered to the client.
This does not constitute the net object size,
because headers are also counted. Also, failed
■equests may deliver an error page, the size of
which is also logged here.
16
17. Method
i
........................................ ...ARequest method to obtain an object for
jxample, GET.URLURL requested.Rfc93 1Contains the authentication
server's identification or lookup names of the requesting ;lient. This field
will always be a "-" (dash).Peerstatus/Peerhost
I Two entries separated by a slash. The first ;ntry represents a code that explains
how the •equest was handled, for example, by forwarding t to a peer, or returning
the request to the source. The second entry contains the name of the host rrom
which the object was requested. This host nay be the origin site, a parent, or any
other peer. Mso note that the host name may be numerical.Type
i1
! ..................................Content type of the object as seen in the HITTP
reply header. In the ACNS 4.1 software, :his field will always contain a "-"
(dash).
Table 3.3.1.1 : Squid-Style Format
18. 3.3.2 SQUID LOG FILES
The logs are a valuable source of information about Squid workloads and performance. The logs
record not only access information, but also system configuration errors and resource consumption (eg,
memory, disk space). There are several log file maintained by Squid. Some have 10 be explicitly
activated during compile time, others can safely be deactivated during run-time.
There are a few basic points common to all log files. The lime stamps logged into the log files are
usually UTC seconds unless stated otherwise. The initial time stamp usually contains a millisecond
extension.
SQUID.OUT
If we run your Squid from the Run Cache script, a file squid.out contains the Squid startup times,
and also all fatal errors, e.g. as produced by an assertQ failure. If we are not using Run Cache, you will
not see such a file.
CACHE.LOG
The cache.log file contains the debug and error messages that Squid generates. If we start your
Squid using the default RunCache .script, or start it with the -s command line option, a copy of certain
messages will go into your syslog facilities. It is a matter of personal preferences to use a separate file
for the squid log data.
From the area of automatic log file analysis, the cache.log file does not have much to offer. We
will usually look into this file for automated error reports, when programming Squid, testing new
features, or searching for reasons of a perceived misbehavior, etc.
USERAGENT.LOG
The user agent log file is only maintained, if
l.We configure the compile time —enable-useragent-log option, and
18
19. 2.We pointed the useragentjog configuration option to a file.
From the user agent log file you are able to find out about distribution of browsers of your clients.
Using this option in conjunction with a loaded production squid might not be the best of all ideas.
STORE.LOG
The store.log file covers the objects currently kept on disk or removed ones. As a kind of
transaction log it is usually used for debugging purposes. A definitive statement, whether an object
resides on your disks is only possible after analyzing the complete log file. The release (deletion) of an
object may be logged at a later time than the swap out (save to disk).
The store.log file may be of interest to log file analysis which looks into the objects on your
disks and the time they spend there, or how many times a hot object was accessed. The latter may be
covered by another log file, too. With knowledge of the cache_dir configuration option, this log file
allows for a URL to filename mapping without recurring your cache disks. However, the Squid
developers recommend to treat store.log primarily as a debug file, and so should you, unless you know
what you are doing.
2.0
20. HIERARCHY.LOG
This log file exists for Squid-1.0 only. The format is
[date] URL peer status peer host
ACCESS.LOG
Most log file analysis program are based on the entries in access.log. Currently, there are two file
formats possible for the log file, depending on your configuration for the emulate^ httpd Jog option. By
default, Squid will log in its native log file format. If the above option is enabled. Squid will log in the
common log file format as defined by the CER'N web daemon.
'The Common Logfile Format is used by numerous HTTP servers. This format consists of the
following seven fields:
remote host rfc931 authuser [date] "method URL" status bytes
It is pars able by a variety of tools. The common format contains different information than the
native log file format. The HTTP version is logged, which is not logged in native log file format.
The log contents include the site name, the IP address of the requesting instance, date and time
in unix time format, bytes transferred, the requesting method and other such features. Log files are
usually large in size, large enough to be mined. However, the values of an entire line of input changes
with the change in header.
The common log file format contains other information than the native log file, and less. The
native format contains more information for the admin interested in cache evaluation. The access.log is
the squid log that has been made use of in this project. The log file was in the form of a text file shown
below :
20
22. A valid copy of the requested object was in the
cache. TCP_MISS
The requested object was not in the
cache. TCP REFRESH HIT
The requested object was cached but STALE. The IMS query for the object resulted in "304
not modi lied".
TCP REFFAILHIT
The requested object was cached but STALE. The IMS query failed and the stale object was
delivered.
TCPREFRESHJVHSS
The requested object was cached but STALE. The IMS query returned the new content.
TCP CLIENTJREFRESH MISS
The client issued a "no-cache" pragma, or some analogous cache control command along
with the request. Thus, the cache has to-prefect the object.
22
23. TCP IMS_HIT
The client issued an IMS request for an object which was in the cache and fresh. TCP
SWAPFAIL MISS
The object was believed to be in the cache, but could not be accessed.
TCPNEGATIVEHIT
Request for a negatively cached object, e.g. "404 not found", for which the cache believes to know
that it is inaccessible. Also refer to the explanations for negative^ ttl in your squid.conf file.
TCPMEMHIT
A valid copy of the requested object was in the cache and it was in memory, thus avoiding disk
accesses.
TCPDENIED
Access was denied for this request.
TCP_OFFLINE_IIIT
The requested object was retrieved from the cache during offline mode. The offline mode never
validates any object.
UDP HIT
A valid copy of the requested object was in the cache.
UDP MISS
The requested object is not in this cache.
UDPDENIED
Access was denied for this request.
UDP_IN VALID An invalid request
was received. UDP_MISS_NOFEl
CH
23
24. During "-Y" startup, or during frequent failures, a cache in hit only mode will return either
UDPJHIT or this code. Neighbors will thus only fetch hits.
NONE
Seen with errors and cache manager requests.
3.4 HTTP RESULT CODES
These are taken from RFC 2616 and verified for Squid. Squid-2 uses almost all codes except
307 (Temporary Redirect), 416 (Request Range Not Satisfactory), and 417 (Expectation Failed).
Extra codes include 0 for a result code being unavailable, and. 600 to signal an invalid header, a
proxy error. Also, some definitions were added as for RFC 2518. Yes, there are really two entries
for status code 424, compare with http_status in src/enums.h;
000 USED MOSTLY WITH UDP TRAFFIC
100 CONTINUE
101 SWITCHING PROTOCOLS
102 PROCESSING
200 OK
201CREATED
202ACCEPTED
203NON-AUTHORITATIVE INFORMATION
204NO CONTENT
205RESET CONTENT
206PARTIAL CONTENT
207MULTI STATUS
24
25. 300MULTIPLE CHOICES
301MOVED PERMANENTLY
302MOVED TEMPORARILY
304NOT MODIFIED
305USE PROXY
307 TEMPORARY REDIRECT
400BAD REQUEST
401UNAUTHORIZED
402PAYMENT REQUIRED
403FORBIDDEN
404NOT FOUND
405METHOD NOT ALLOWED
406NOT ACCEPTABLE
407PROXY AUTHENTICATION REQUIRED
408REQUEST TIMEOUT
409CONFLICT
410GONE
411LENGTH REQUIRED
412PRECONDITION FAILED
413REQUEST ENTITY TOO LARGE
414REQUEST URI TOO LARGE
415UNSUPPORTED MEDIA TYPE
416REQUEST RANGE NOT SATISFIABLE
25
26. 417 EXPECTATION FAILED
424 LOCKED
424 FAILED DEPENDENCY
433 UNPROCESSABLE ENTITY
500INTERNAL SERVER ERROR
501NOT IMPLEMENTED
502BAD GATEWAY TABLE 3.4.1 : HTTP
result codes
3.5 HTTP REQUEST METHODS
Squid recognizes several request methods as defined in RFC 2616. Newer versions o Squid
also recognize RFC 2518 "HTTP Extensions for Distributed Authoring WEBDAV extensions.
GET OBJECT RETRIEVAL AND SIMPLE SEARCHES.
HEAD METADATA RETRIEVAL.
'OST SUBMIT DATA (TO A PROGRAM).
PUT UPLOAD DATA (E.G. TO A FILE).
DELETE REMOVE RESOURCE (E.G. FILE).
TRACE APPLN LAYER TRACE OF REQUEST ROUTE.
OPTIONS REQUEST AVAILABLE COMM. OPTIONS.
CONNECT TUNNEL SSL CONNECTION.
PROPF1ND RETRIEVE PROPERTIES OF AN OBJEC
26
27. PROPATCH CHANGE PROPERTIES OF AN OBJECT
COPY CREATE A DUPLICATE OF SRC IN DST.
MOVE ATOMICALLY MOVE SRC TO DST.
LOCK LOCK AN OBJECT AGAINST MODIFICATIONS.
UNLOCK UNLOCK AN OBJECT.
TABLE 3.4.2 : HTTP request methods
CHAPTER 4
CODING
4.1 FEATURES OF LANGUAGE (PERL)Practical Extraction and Reporting Language is an interpreted
language optimized for scanning arbitrary text files, extracting information from those text files, and
printing reports based on that information, it's also a good language for many system management
tasks.
•The language is intended to be practical (easy to use, efficient, complete) rather than beautiful
(tiny, elegant, minimal).
•It combines (in the author's opinion, anyway) some of the best features of c, sed, awk, and sh, so
people familiar with those languages should have little difficulty with it. (language historians
will also note some vestiges of Pascal and even basic-plus.)
•Unlike most UNIX utilities, Perl does not arbitrarily limit the size of our data — if we have got
the memory, Perl can slurp in our whole file as a single string, recursion is of unlimited depth.
•The hash tables used by associative arrays grow as necessary to prevent degraded performance.
Perl uses sophisticated pattern matching techniques to scan large amounts of data very quickly.
•Although optimized for scanning text, Perl can also deal with binary data, and can make dbm
files look like associative arrays (where dbm is available).Setuid Perl scripts are safer than c
programs through a dataflow tracing mechanism which prevents many stupid security holes.
27
28. •The overall structure of Perl derives broadly from C. Perl is procedural in nature, with variables,
expressions, assignment statements, brace-delimited code blocks, control structures, and
subroutines.
•Perl also takes features from shell programming. All variables are marked with leading sigils.
which unambiguously identify the data type (scalar, array, hash, etc.) of the variable in context.
Importantly, sigils allow variables to be interpolated directly into strings.
•Perl has many built-in functions which provide tools often used in shell programming (though
many of these tools are implemented by programs external to the shell) like sorting, and calling
on system facilities.
•Perl takes lists from Lisp, associative arrays (hashes) from AWK, and regular expressions
from sed. These simplify and facilitate many parsing, text handling, and data management
tasks.
•In Perl 5, features were added that support complex data structures, first-class functions (i.e.,
closures as values), and an object-oriented programming model. These include references,
packages, class-based method dispatch, and lexically scoped variables, along with compiler
directives .
•All versions of Perl do automatic data typing and memory management. The interpreter knows
the type and storage requirements of every data object in the program; it allocates and frees
storage for them as necessary using reference counting (so it cannot reallocate circular data
structures without manual intervention). Legal type conversions -for example, conversions
from number to string—are done automatically at run time; illegal type conversions are fatal
errors.
•Perl has a context-sensitive grammar which can be affected by code executed during an
intermittent run-time phase. Therefore Perl cannot be parsed by a straight Lex/Yacc
lexer/parser combination. Instead, the interpreter implements its own laxer, which coordinates
with a modified GNU bison parser to resolve ambiguities in the language.
•The execution of a Perl program divides broadly into two phases: compile-timc and runtime.
At compile time, the interpreter parses the program text into a syntax tree. At run time, it
executes the program by walking the tree.
28
29. 4.2 PERL CODE FOR MINING
6 :
i
2
12 j nptn (DAT, Sdi.uifiJ .-f ! ! 1.1 f ?iile content-<LiU>;
]:eM7h * line ft'".
Ltiop(f line);
?.U | (5ET,?tP,iC3,SBYTt;,;MT,8KAHi:,;P:;;H.: ^1| peint
"*NA«E"; 32 : print "n"; 83! inumfgarray, "SWAHr'.i ;
■2*1 ! ! -<:S ■
j 27 : £uie dch (IJaEt ttyj
icounc»<5 )++;
teach $Weye (keys '
MC- i |ii iiit "-ii • :;;sor.rn- n; o- frequency of usAGEnnW
[ 43 jforeaeh Ske-; (Kort hashValuePeaceiiCtingNum (ktty* ('* hash)))
...j
FIGURE 4.2.1: PERL Program for mining
The Perl code to mine access.log makes use of the construct splitf) which is
required to split a line of text in the log file. The extracted site name is pushed into an array for
comparison purposes. After the required comparison to determine the number of times that a site has
been repeated, both the site and its corresponding count is inserted into a hash array.
The Hashed array is now utilized for sorting the site name in the descending
order of its count. The count and the corresponding site name is displayed as the output.
4.3 DISPLAYED OUTPUT
. -
He "dt vm Rut feUM* Pflri Serve Mndm ti*>
(«"j:."l61.I53:4«
'login.lC3.eom:443
Ihttp;/fvvv.google.com/
l.t.tp://w««.around-]apan.net/c:gi-bin,/tarikyacce33.cgi?
http://rebiaail.cliartec.net/iniages/portal/IIaiHd.jP9 6ttp://»«».club-
support.net/cgi-bio/cank'ing/ranklink.cgii? ■ login.icq.com:413
http://«vv.2hue.com.cti/d*i'.a/cuuDetit'/Add Conwent.asp
30. http;//iww,ti(jogla.com/
http://Biysstudio.crjm/proxy5/checJt.php
ht tp://ZOZ. 86.4.199/conf ig/ ispverify_u3er 7
fcttp://nuhost- into/eye■php
http://nuhost.info/eye.php
http://E09.191.92.64/conIig/isp_verify_usei-?
http://5marteh.com.ru/proxy checker/proxy de3t.php
http://nobilel.lcjgin.vip.den.yahoo.com/coiifig/pHtoken gut?
http://«qrl.diyocarte.cc^S !Jp/514222/2/ind-2732062/3707270.html
tttp://i»r*.BW*>?L. in. yahoo, cui/coni ig/pirtoken_get?
httpj//wwb.arcartebanaers.com/banners.php?
http://pod-o-lee.inymiriicity.fr/sec
http://shebiog3.p«oplt:aggreqati:t:.riet/ct'ntent.php?
.15.188.153.97:413 V.tp://mamono. 2ch.net/test/read, cai/cvoV
1200928402/1
FIGURE 4.2.2 : VISITED SITES
'.-■:i>; "ir,.priM i"
used otif once.
v.--rr»""- - - - ---------------^aaJi pv.ia ............-; - ""■*** *■ ■
This is the output to the program in figure 4. It displays only the sites that have been
reqtiested for, visited and even those that have been denied access from the proxy server. Hence, the
log records all the transactions that have been successful and those that have failed.
fen Run Oatahjie
{* 511 Input TOTAL SITES VISITED : 5238
SITES SORTED IN ORDER OF FREQUENCY OF
USiGF.:
200 11
93 11
80 11
69 10
53 10
51 10
50
11
31
26
24
23
23
22
20
19
19
18
18
17
15
14
13
13
13
13
13
12
11
33. CHAPTER 5
TESTING
5.1 SYSTEM TESTING
Testing is a set activity that can be planned and conducted systematically. Testing begins at the
module level and work towards the integration of entire computers based system. Nothing is complete
without testing, as it is vital success of the system.
Testing Objectives:
There are several rides that can serve as testing objectives, they are Testing is a process of
executing a program with the intent of finding an error A good test case is one that has high
probability of finding an undiscovered error. A successful test is one that uncovers an
undiscovered error.
If testing is conducted successfully according to the objectives as stated above, it would
uncover errors in the software. Also testing demonstrates that software functions appear to the working
according to the specification, that performance requirements appear to have been met.
There are three ways to test a program
•For Correctness
•For Implementation efficiency
•For Computational Complexity.
Tests for correctness are supposed to verify that a program does exactly what it was designed
to do. This is much more difficult than it may at first appear, especially for large programs.
Tests for implementation efficiency attempt to find ways to make a correct program faster or
use less storage. It is a code-refining process, which reexamines the implementation phase of algorithm
development.
Tests for computational complexity amount to an experimental analysis of the complexity of an
algorithm or an experimental comparison of two or more algorithms, which solve the same problem.
Testing Correctness
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34. The following ideas should be a part of any testing plan:
•Preventive Measures
•Spot checks
•Testing all parts of the program
•Test Data
•Looking for trouble
•Time for testing
•Re Testing
The data is entered in all forms separately and whenever an error occurred, it is corrected
immediately. A quality team deputed by the management verified all the necessary documents and
tested the Software while entering the data at all levels. The entire testing process can be divided into
3 phases
Unit Testing
Integrated Testing
Final/ System testing
5.1.1 UNIT TESTING
As this system was partially GUI based WINDOWS application, the following were tested in this
phase
Tab Order
Reverse Tab Order
Field length
Front end validations
In our system, Unit testing has been successfully handled. The test data was given to each and
every module in all respects and got the desired output. Each module has been tested found working
properly.
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35. 5.1.2 INTEGRATION TESTING
Test data should be prepared carefully since the data only determines the efficiency and accuracy
of the system. Artificial data are prepared solely for testing. Every program validates the input data
5.1.3 VALIDATION TESTING
In this, all the Code Modules were tested individually one after the other. The following were
tested in all the modules
Loop testing
Boundary Value analysis
Equivalence Partitioning Testing
In our case all the modules were combined and given the test data. The combined module
works successfully with out any side effect on other programs. Everything was found tine working.
5.1.4 OUTPUT TESTING
This is the final step in testing. In this the entire system was tested as a whole with all forms,
code, modules and class modules. This form of testing is popularly known as Black Box testing or
system testing.
Black Box testing methods focus on the functional requirement of the software. That is, Black
Box testing enables the software engineer to derive sets of input conditions that will fully exercise all
functional requirements for a program.
Black Box testing attempts to find errors in the following categories; incorrect or missing
functions, interface errors, errors in data structures or external database access, performance errors and
initialization errors and termination errors.
CHAPTER 6
CONCLUSION
The project report entitled "DATAMINING USING FUZZY LOGIC" has come to its final
stage. The system has been developed with much care that it is free of errors and at the same time it is
efficient and less time consuming. The important thing is that the system is robust. We have tried our
level best to make the complete the project with all its required features.
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36. However due to time constraints the fuzzy implementation over the mined data has not been
possible. Since, the queries related to mining require the proper retrieval of data, actual connl is
preferred over applying fuzziness into count.
APPENDICES
OVERVIEW OF PERL EXPRESS 2.5
PERL EXPRESS 2.5 is a free integrated development environment (IDE) for Perl with multiple
tools for writing and debugging your scripts. It features multiple CGI scripts for editing, running, and
debugging; multiple input fdes; full server simulation; queries created from an internal Web browser or
query editor; test MySQL, MS Access scripts: interactive I/O; directory window; code library; and
code templates.
Perl Express allows us to set environment variables used for running and debugging script. It
has a customizable code editor with syntax highlighting, unlimited text size, printing, line numbering,
bookmarks, column selection, a search-and-replace engine, multilevel undo/redo operations. Version
2.5 adds command line and bug fixes.
RESUME
The developed system is flexible and changes can be made easily. The system is developed
with an insight into the necessary modification that may be required in the future. Hence the system
can be maintained successfully without much rework.
One of the main future enhancements of our system is to include fuzzy logic which is a form
of multi-valued logic derived from fuzzy set theory to deal with reasoning that is approximate rather
than precise.
REFERENCES
1.frequent Pattern Mining in Web Log Data - Renata Ivancsy, lstvan Vajk
2.Squid-Style Transaction Logging (log formats) - http://www.cisco.com/
3.Mining interesting knowledge from weblogs: a survey - Federico Michele Facca,
Pier Luca lanzi.
4.http://software.techrepublic.com.com/abstract.aspx
5.http://en.wikipedia.org/
6.http://msdn.microsoft.com/
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