International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Description of four techniques for Data Cleaning:
1.DWCLEANER Framework
2.Data Mining Techniques include Association Rule and Functional Dependecies
,...
Data Mining System and Applications: A Reviewijdpsjournal
In the Information Technology era information plays vital role in every sphere of the human life. It is very important to gather data from different data sources, store and maintain the data, generate information, generate knowledge and disseminate data, information and knowledge to every stakeholder. Due to vast use of computers and electronics devices and tremendous growth in computing power and storage capacity, there is explosive growth in data collection. The storing of the data in data warehouse enables entire enterprise to access a reliable current database. To analyze this vast amount of data and drawing fruitful conclusions and inferences it needs the special tools called data mining tools. This paper gives overview of the data mining systems and some of its applications.
The development of data mining is inseparable from the recent developments in information technology that enables the accumulation of large amounts of data. For example, a shopping mall that records every sales transaction of goods using various POS (point of sales). Database data from these sales could reach a large storage capacity, even more being added each day, especially when the shopping center will develop into a nationwide network. The development of the internet at the moment also has a share large enough in the accumulation of data occurs. But the rapid growth of data accumulation it has created conditions that are often referred to as "data rich but information poor" because the data collected can not be used optimally for useful applications. Not infrequently the data set was left just seemed to be a "grave data". There are several techniques used in data mining which includes association, classification, and clustering. In this paper, the author will do a comparison between the performance of the technical classification methods naïve Bayes and C4.5 algorithms.
Applying K-Means Clustering Algorithm to Discover Knowledge from Insurance Da...theijes
Data mining works to extract information known in advance from the enormous quantities of data which can lead to knowledge. It provides information that helps to make good decisions. The effectiveness of data mining in access to knowledge to achieve the goal of which is the discovery of the hidden facts contained in databases and through the use of multiple technologies. Clustering is organizing data into clusters or groups such that they have high intra-cluster similarity and low inter cluster similarity. This paper deals with K-means clustering algorithm which collect a number of data based on the characteristics and attributes of this data, and process the Clustering by reducing the distances between the data center. This algorithm is applied using open source tool called WEKA, with the Insurance dataset as its input
Description of four techniques for Data Cleaning:
1.DWCLEANER Framework
2.Data Mining Techniques include Association Rule and Functional Dependecies
,...
Data Mining System and Applications: A Reviewijdpsjournal
In the Information Technology era information plays vital role in every sphere of the human life. It is very important to gather data from different data sources, store and maintain the data, generate information, generate knowledge and disseminate data, information and knowledge to every stakeholder. Due to vast use of computers and electronics devices and tremendous growth in computing power and storage capacity, there is explosive growth in data collection. The storing of the data in data warehouse enables entire enterprise to access a reliable current database. To analyze this vast amount of data and drawing fruitful conclusions and inferences it needs the special tools called data mining tools. This paper gives overview of the data mining systems and some of its applications.
The development of data mining is inseparable from the recent developments in information technology that enables the accumulation of large amounts of data. For example, a shopping mall that records every sales transaction of goods using various POS (point of sales). Database data from these sales could reach a large storage capacity, even more being added each day, especially when the shopping center will develop into a nationwide network. The development of the internet at the moment also has a share large enough in the accumulation of data occurs. But the rapid growth of data accumulation it has created conditions that are often referred to as "data rich but information poor" because the data collected can not be used optimally for useful applications. Not infrequently the data set was left just seemed to be a "grave data". There are several techniques used in data mining which includes association, classification, and clustering. In this paper, the author will do a comparison between the performance of the technical classification methods naïve Bayes and C4.5 algorithms.
Applying K-Means Clustering Algorithm to Discover Knowledge from Insurance Da...theijes
Data mining works to extract information known in advance from the enormous quantities of data which can lead to knowledge. It provides information that helps to make good decisions. The effectiveness of data mining in access to knowledge to achieve the goal of which is the discovery of the hidden facts contained in databases and through the use of multiple technologies. Clustering is organizing data into clusters or groups such that they have high intra-cluster similarity and low inter cluster similarity. This paper deals with K-means clustering algorithm which collect a number of data based on the characteristics and attributes of this data, and process the Clustering by reducing the distances between the data center. This algorithm is applied using open source tool called WEKA, with the Insurance dataset as its input
Data Mining, KDD Process, Data mining functionalities, Characterization,
Discrimination ,
Association,
Classification,
Prediction,
Clustering,
Outlier analysis, Data Cleaning as a Process
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%.
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.
key note address delivered on 23rd March 2011 in the Workshop on Data Mining and Computational Biology in Bioinformatics, sponsored by DBT India and organised by Unit of Simulation and Informatics, IARI, New Delhi.
I do not claim any originality either to slides or their content and in fact aknowledge various web sources.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Data Mining, KDD Process, Data mining functionalities, Characterization,
Discrimination ,
Association,
Classification,
Prediction,
Clustering,
Outlier analysis, Data Cleaning as a Process
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%.
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.
key note address delivered on 23rd March 2011 in the Workshop on Data Mining and Computational Biology in Bioinformatics, sponsored by DBT India and organised by Unit of Simulation and Informatics, IARI, New Delhi.
I do not claim any originality either to slides or their content and in fact aknowledge various web sources.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Weekly commodity-report 14-18 july by epic research pvt.ltd indoreEpic Research Limited
Epic Research has India's best experienced research analyst they keep on eyes 24*7 on market and update daily report of trading market in all market segments like Equity,Comex,Commodity,Forex etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The past two decades has seen a dramatic increase in the amount of information or data being stored in electronic format. This accumulation of data has taken place at an explosive rate. It has been estimated that the amount of information in the world doubles every 20 months and the size and number of databases are increasing even faster. The increase in use of electronic data gathering devices such as point-of-sale or remote sensing devices has contributed to this explosion of available data. Figure 1 from the Red Brick company illustrates the data explosion.
A SURVEY ON DATA MINING IN STEEL INDUSTRIESIJCSES Journal
In Industrial environments, huge amount of data is being generated which in turn collected indatabase anddata warehouses from all involved areas such as planning, process design, materials, assembly, production, quality, process control, scheduling, fault detection,shutdown, customer relation management, and so on. Data Mining has become auseful tool for knowledge acquisition for industrial process of Iron and steel making. Due to the rapid growth in Data Mining, various industries started using data mining technology to search the hidden patterns, which might further be used to the system with the new knowledge which might design new models to enhance the production quality, productivity optimum cost and maintenance etc. The continuous improvement of all steel production process regarding the avoidance of quality deficiencies and the related improvement of production yield is an essential task of steel producer. Therefore, zero defect strategy is popular today and to maintain it several quality assurancetechniques areused. The present report explains the methods of data mining and describes its application in the industrial environment and especially, in the steel industry.
This slide describe all the necessary topic on Data-Mining. Even this covered all the important Questions on Data Mining in Graduation Level. Basically it covers the actual 2 and 4 marks questions along with the answers that you will need after.
Introduction to feature subset selection methodIJSRD
Data Mining is a computational progression to ascertain patterns in hefty data sets. It has various important techniques and one of them is Classification which is receiving great attention recently in the database community. Classification technique can solve several problems in different fields like medicine, industry, business, science. PSO is based on social behaviour for optimization problem. Feature Selection (FS) is a solution that involves finding a subset of prominent features to improve predictive accuracy and to remove the redundant features. Rough Set Theory (RST) is a mathematical tool which deals with the uncertainty and vagueness of the decision systems.
Privacy preservation techniques in data miningeSAT Journals
Abstract In this paper different privacy preservation techniques are compared. Classification is the most commonly applied data mining technique, which employs a set of pre-classified examples to develop a model that can classify the population of records at large. Fraud detection and credit risk applications are particularly well suited to this type of analysis. This approach frequently employs decision tree or neural network-based classification algorithms. The data classification process involves learning and classification. In Learning the training data are analyzed by classification algorithm. In classification test data are used to estimate the accuracy of the classification rules. If the accuracy is acceptable the rules can be applied to the new data tuples . For a fraud detection application, this would include complete records of both fraudulent and valid activities determined on a record-by-record basis. The classifier-training algorithm uses these pre-classified examples to determine the set of parameters required for proper discrimination. The algorithm then encodes these parameters into a model called a classifier Index Terms: Data Mining, Privacy Preservation, Clustering, Classification Techniques, Naive Bayes.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Study and Analysis of K-Means Clustering Algorithm Using RapidminerIJERA Editor
Institution is a place where teacher explains and student just understands and learns the lesson. Every student has his own definition for toughness and easiness and there isn’t any absolute scale for measuring knowledge but examination score indicate the performance of student. In this case study, knowledge of data mining is combined with educational strategies to improve students’ performance. Generally, data mining (sometimes called data or knowledge discovery) is the process of analysing data from different perspectives and summarizing it into useful information. Data mining software is one of a number of analytical tools for data. It allows users to analyse data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational database. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters).This project describes the use of clustering data mining technique to improve the efficiency of academic performance in the educational institutions .In this project, a live experiment was conducted on students .By conducting an exam on students of computer science major using MOODLE(LMS) and analysing that data generated using RapidMiner(Datamining Software) and later by performing clustering on the data. This method helps to identify the students who need special advising or counselling by the teacher to give high quality of education.
Evaluating the efficiency of rule techniques for file classificationeSAT Journals
Abstract Text mining refers to the process of deriving high quality information from text. It is also known as knowledge discovery from text (KDT), deals with the machine supported analysis of text. It is used in various areas such as information retrieval, marketing, information extraction, natural language processing, document similarity, and so on. Document Similarity is one of the important techniques in text mining. In document similarity, the first and foremost step is to classify the files based on their category. In this research work, various classification rule techniques are used to classify the computer files based on their extensions. For example, the extension of computer files may be pdf, doc, ppt, xls, and so on. There are several algorithms for rule classifier such as decision table, JRip, Ridor, DTNB, NNge, PART, OneR and ZeroR. In this research work, three classification algorithms namely decision table, DTNB and OneR classifiers are used for performing classification of computer files based on their extension. The results produced by these algorithms are analyzed by using the performance factors classification accuracy and error rate. From the experimental results, DTNB proves to be more efficient than other two techniques. Index Terms: Data mining, Text mining, Classification, Decision table, DTNB, OneR
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
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G045033841
1. Ms. A J. Chamatkar et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 5( Version 3), May 2014, pp.38-41
www.ijera.com 38 | P a g e
Importance of Data Mining with Different Types of Data
Applications and Challenging Areas
Ms. Aruna J. Chamatkar*
, Dr. P.K. Butey**
* (Department of Electronics & Computer Science, RTM Nagpur University, Nagpur)
** ( Department of Computer Science, RTM Nagpur University ,Nagpur-32)
ABSTRACT
Data mining is an increasingly popular set of tools for dealing with large amounts of data, often collected in
haphazard fashion with many missing values. 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 there are need to techniques called the data mining
which will transforming in many fields. In Data Mining data sets will be explored to yield hidden and unknown
predictions which can be used in future for the efficient decision making. Data Mining that involves pattern
recognition, mathematical and statistical techniques to search data Warehouses and help the analyst in
recognizing significant trends, facts relationships and anomalies. In this paper we discuss the importance of
data mining , different challenging areas and application areas in data mining .
Keyword - data integration ,data mining , KDD, knowledge, OLAP
I. INTRODUCTION
Data mining is the extraction of useful
patterns and relationships from data sources, such as
databases, texts, the web. It has nothing to do
however with SQL, OLAP, data warehousing or any
of that kind of thing. It uses statistical and pattern
matching techniques. The concern in data mining are
noisy data, missing values, static data, sparse data,
dynamic data, relevance, interestingness,
heterogeneity, algorithm efficiency, size and
complexity of data. The data we have is often vast,
and noisy, meaning that it’s imprecise and the data
structure is complex. This is where a purely
statistical technique would not succeed, so data
mining is a solution. Data mining has become a
popular tool for analyzing large datasets. The
efficient database management systems have been
very important assets for management of a large
corpus of data and especially for effective and
efficient retrieval of particular information from a
large collection whenever needed. The proliferation
of database management systems has also contributed
to recent massive gathering of all sorts of
information. Information retrieval is simply not
enough anymore for decision-making.
II. What are Data Mining and
Knowledge Discovery?
With the enormous amount of data stored in
files, databases, and other repositories, it is
increasingly important, if not necessary, to develop
powerful means for analysis and perhaps
interpretation of such data and for the extraction of
interesting knowledge that could help in decision-
making[1].
Data Mining, also popularly known as
Knowledge Discovery in Databases (KDD), refers to
the nontrivial extraction of implicit, previously
unknown and potentially useful information from
data in databases. While data mining and knowledge
discovery in databases (or KDD) are frequently
treated as synonyms, data mining is actually part of
the knowledge discovery process. The following
figure (Figure 1.1) shows data mining as a step in an
iterative knowledge discovery process.
The Knowledge Discovery in Databases
process comprises of a few steps leading from raw
data collections to some form of new knowledge. The
iterative process consists of the following steps:
1. Data cleaning: It is also known as data cleansing,
it is a phase in which noise data and irrelevant
data are removed from the collection.
2. Data integration: In this stage, multiple data
sources, often heterogeneous, may be combined
in a common source.
3. Data selection: At this step, the data relevant to
the analysis is decided on and retrieved from the
data collection.
4. Data transformation: also known as data
consolidation, it is a phase in which the selected
data is transformed into forms appropriate for the
mining procedure.
RESEARCH ARTICLE OPEN ACCESS
2. Ms. A J. Chamatkar et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 5( Version 3), May 2014, pp.38-41
www.ijera.com 39 | P a g e
5. Data mining: it is the crucial step in which clever
techniques are applied to extract patterns
potentially useful.
6. Pattern evaluation: in this step, strictly
interesting patterns representing knowledge are
identified based on given measures.
7. Knowledge representation: is the final phase in
which the discovered knowledge is visually
represented to the user. This essential step uses
visualization techniques to help users understand
and interpret the data mining results.
It is common to combine some of these steps
together. For instance, data cleaning and data
integration can be performed together as a pre-
processing phase to generate a data warehouse. Data
selection and data transformation can also be
combined where the consolidation of the data is the
result of the selection, or, as for the case of data
warehouses, the selection is done on transformed
data.
The KDD is an iterative process. Once the
discovered knowledge is presented to the user, the
evaluation measures can be enhanced, the mining can
be further refined, new data can be selected or further
transformed, or new data sources can be integrated,
in order to get different, more appropriate results[2].
Data mining became the accepted customary term,
and very rapidly a trend that even overshadowed
more general terms such as knowledge discovery in
databases (KDD) that describe a more complete
process. Other similar terms referring to data mining
are: data dredging, knowledge extraction and pattern
discovery.
III. Five Major Elements in Data Mining
1) Extract, transform, and load transaction data onto
the data warehouse system.
2) Store and manage the data in a multidimensional
database system.
3) Provide data access to business analysts and
information technology professionals.
4) Analyze the data by application software.
5) Present the data in a useful format, such as a
graph or table.
IV. What can be discovered?
The kinds of patterns that can be discovered
depend upon the data mining tasks given. Two types
of data mining tasks are there descriptive data mining
tasks that describe the general properties of the
existing data, and predictive data mining tasks that
attempt to do predictions based on inference on
available data.
The data mining functionalities and the variety of
knowledge they discover are briefly presented in the
following list:
Characterization: Data characterization is a
summarization of general features of objects in a
target class, and produces what is called
characteristic rules. module to extract the essence
of the data at different levels of abstractions.
Discrimination: Data discrimination produces
what are called discriminate rules and is
basically the comparison of the general features
of objects between two classes referred to as the
target class and the contrasting class[4,5].
Association analysis: Association analysis is the
discovery of what are commonly called
association rules. It studies the frequency of
items occurring together in transactional
databases, and based on a threshold called
support, identifies the frequent item sets.
Classification: Classification analysis is the
organization of data in given classes. Also
known as supervised classification, the
classification uses given class labels to order the
objects in the data collection. Classification
approaches normally use a training set where all
objects are already associated with known class
labels. The classification algorithm learns from
the training set and builds a model. The model is
used to classify new objects.
3. Ms. A J. Chamatkar et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 5( Version 3), May 2014, pp.38-41
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Prediction: Prediction has attracted considerable
attention given the potential implications of
successful forecasting in a business context.
There are two major types of predictions: one
can either try to predict some unavailable data
values or pending trends, or predict a class label
for some data. The latter is tied to classification.
Clustering: Similar to classification, clustering is
the organization of data in classes. However,
unlike classification, in clustering, class labels
are unknown and it is up to the clustering
algorithm to discover acceptable classes.
Outlier analysis: Outliers are data elements that
cannot be grouped in a given class or cluster.
Also known as exceptions or surprises, they are
often very important to identify.
Evolution and deviation analysis: Evolution and
deviation analysis pertain to the study of time
related data that changes in time. Evolution
analysis models evolutionary trends in data,
which consent to characterizing, comparing,
classifying or clustering of time related data.
It is common that users do not have a clear
idea of the kind of patterns they can discover or need
to discover from the data at hand. It is therefore
important to have a versatile and inclusive data
mining system that allows the discovery of different
kinds of knowledge and at different levels of
abstraction. This also makes interactivity an
important attribute of a data mining system.
V. What are the kinds of data can be
mined ?
Flat files: Flat files are actually the most
common data source for data mining algorithms,
especially at the research level. Flat files are
simple data files in text or binary format with a
structure known by the data mining algorithm to
be applied. The data in these files can be
transactions, time-series data, scientific
measurements, etc.
Relational Databases: a relational database
consists of a set of tables containing either values
of entity attributes, or values of attributes from
entity relationships. Tables have columns and
rows, where columns represent attributes and
rows represent tuples. A tuple in a relational
table corresponds to either an object or a
relationship between objects and is identified by
a set of attribute values representing a unique
key
Transaction Databases: A transaction database is
a set of records representing transactions, each
with a time stamp, an identifier and a set of
items. Associated with the transaction files could
also be descriptive data for the items.
Multimedia Databases: Multimedia databases
include video, images, audio and text media.
They can be stored on extended object-relational
or object-oriented databases, or simply on a file
system. Multimedia is characterized by its high
dimensionality, which makes data mining even
more challenging.
Spatial Databases: Spatial databases are
databases that, in addition to usual data, store
geographical information like maps, and global
or regional positioning. Such spatial databases
present new challenges to data mining
algorithms.
Time-Series Databases: Time-series databases
contain time related data such stock market data
or logged activities. These databases usually
have a continuous flow of new data coming in,
which sometimes causes the need for a
challenging real time analysis. Data mining in
such databases commonly includes the study of
trends and correlations between evolutions of
different variables, as well as the prediction of
trends and movements of the variables in time.
World Wide Web: The World Wide Web is the
most heterogeneous and dynamic repository
available. Data in the World Wide Web is
organized in inter-connected documents[3].
VI. Challenging Problems In Data Mining
1. Developing a Unifying Theory of Data Mining
2. Scaling Up for High Dimensional Data and High
Speed Data Streams
3. Mining Sequence Data and Time Series Data
4. Mining Complex Knowledge from Complex
Data
5. Data Mining in a Network Setting
6. Distributed Data Mining and Mining Multi-agent
Data
7. Data Mining for Biological and Environmental
Problems
8. Data-Mining-Process Related Problems
9. Security, Privacy and Data Integrity
10. Dealing with Non-static, Unbalanced and Cost-
sensitive Data
VII. Application Of Data Mining
Now a days data mining are used in lots of
areas but In this section , here we mainly listed some
application areas for data mining[6,7].
1. Data mining Application in Healthcare
2. Future Direction of Health care system through
Data mining tools
3. Data mining used in many different areas in
manufacturing Engineering
4. Data mining is used for market basket analysis
5. Data mining is used an emerging trends in the
educational system
6. Data mining Application can be generic and
domain specific
7. Data mining techniques used in the CRM
4. Ms. A J. Chamatkar et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 5( Version 3), May 2014, pp.38-41
www.ijera.com 41 | P a g e
8. Large scope for application of data mining in
Medical Science
9. Data mining Methods are used in the Web
Application
10. Data mining method is used to classify the
network traffic control
11. Data mining and its techniques is used for an
application of Sports Center
12. Data mining methods are used for application in
a malicious executable is Threat i.e. in System
Security.
VIII. CONCLUSIONS
In this paper , briefly discuss the basic
concept related to the data mining, challenging and
application areas for data mining. Data mining is
more than running some complex queries on the data
you stored in your database.. Identifying the format
of the information that you need is based upon the
technique and the analysis that you want to do.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.
KDD is the actually the process of finding hidden
pattern of the repositories. The different method of
data mining are used to extract the pattern and thus
the knowledge from this variety databases. Data
mining should be applicable to any kind of
information repository. The challenges listed by
different types of data very significantly. Data
Mining methods ,tools and techniques are useful in
different application areas.
REFERENCES:
Journal Papers:
[1] M. Shiga, I. Takigawa, and H. Mamitsuka,
“A spectral clustering approach to optimally
combining numericalvectors with a modular
network,” in KDD, 2007, pp. 647–656.
[2] Osmer R. Zalane,CMPUT 690 principles of
knowledge discovery in databases”
Introduction to Data mining”.
[3] M.S. Chen.J.Han and P.S. Yu. Data mining :
An overview from a database perspective.
IEEE transactions on Knowledge and data
engineering 8:866.
[4] Feldman, Ronen, Will Klosgen, and Amir
Zilberstein. “Visualization techniques to
explore data mining results for document
collections.”, In Proceedings ofthe Third
Annual Conference on KnowledgeDiscovery
and Data Mining (KDD), Newport Beach,
1997
[5] Koperski, J. Adhikary and J. Han, "Spatial
Data Mining: Progress and Challenges",
SIGMOD'96Workshop on Research Issues
in Data Mining and Knowledge Discovery
DMKD'96, Montreal,Canada.
[6] N. Padhy, P.Mishra (IJCSEIT) “ The survey
of Data mining Application” vol.2 no. 3
June 2012 .
Books:
[7] Han, J. and M. Kamber, Data Mining:
Concepts and Techniques, Morgan
Kaufmann, 2001.
[8] Introduction to Data Mining with Case
Studies by Gupta G. k