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.
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
Intent-Aware Temporal Query Modeling for Keyword SuggestionFindwise
This paper presents a data-driven approach for capturing the temporal variations in user search behaviour by modeling the dynamic query relationships using query-log data. The dependence between different queries (in terms of the query words and latent user intent) is represented using hypergraphs which allows us to explore more complex relationships compared to graph-based approaches. This time-varying dependence is modeled using the framework of probabilistic graphical models. The inferred interactions are used for query keyword suggestion - a key task in web information retrieval. Preliminary experiments using query logs collected from internal search engine of a large health care organization yield promising results. In particular, our model is able to capture temporal variations between queries relationships that reflect known trends in disease occurrence. Further, hypergraph-based modeling captures relationships significantly better compared to graph-based approaches.
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.
A new hybrid algorithm for business intelligence recommender systemIJNSA Journal
Business Intelligence is a set of methods, process and technologies that transform raw data into meaningful
and useful information. Recommender system is one of business intelligence system that is used to obtain
knowledge to the active user for better decision making. Recommender systems apply data mining
techniques to the problem of making personalized recommendations for information. Due to the growth in
the number of information and the users in recent years offers challenges in recommender systems.
Collaborative, content, demographic and knowledge-based are four different types of recommendations
systems. In this paper, a new hybrid algorithm is proposed for recommender system which combines
knowledge based, profile of the users and most frequent item mining technique to obtain intelligence.
An Iterative Improved k-means ClusteringIDES Editor
Clustering is a data mining (machine learning),
unsupervised learning technique used to place data elements
into related groups without advance knowledge of the group
definitions. One of the most popular and widely studied
clustering methods that minimize the clustering error for
points in Euclidean space is called K-means clustering.
However, the k-means method converges to one of many local
minima, and it is known that the final results depend on the
initial starting points (means). In this research paper, we have
introduced and tested an improved algorithm to start the kmeans
with good starting points (means). The good initial
starting points allow k-means to converge to a better local
minimum; also the numbers of iteration over the full dataset
are being decreased. Experimental results show that initial
starting points lead to good solution reducing the number of
iterations to form a cluster.
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
Intent-Aware Temporal Query Modeling for Keyword SuggestionFindwise
This paper presents a data-driven approach for capturing the temporal variations in user search behaviour by modeling the dynamic query relationships using query-log data. The dependence between different queries (in terms of the query words and latent user intent) is represented using hypergraphs which allows us to explore more complex relationships compared to graph-based approaches. This time-varying dependence is modeled using the framework of probabilistic graphical models. The inferred interactions are used for query keyword suggestion - a key task in web information retrieval. Preliminary experiments using query logs collected from internal search engine of a large health care organization yield promising results. In particular, our model is able to capture temporal variations between queries relationships that reflect known trends in disease occurrence. Further, hypergraph-based modeling captures relationships significantly better compared to graph-based approaches.
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.
A new hybrid algorithm for business intelligence recommender systemIJNSA Journal
Business Intelligence is a set of methods, process and technologies that transform raw data into meaningful
and useful information. Recommender system is one of business intelligence system that is used to obtain
knowledge to the active user for better decision making. Recommender systems apply data mining
techniques to the problem of making personalized recommendations for information. Due to the growth in
the number of information and the users in recent years offers challenges in recommender systems.
Collaborative, content, demographic and knowledge-based are four different types of recommendations
systems. In this paper, a new hybrid algorithm is proposed for recommender system which combines
knowledge based, profile of the users and most frequent item mining technique to obtain intelligence.
An Iterative Improved k-means ClusteringIDES Editor
Clustering is a data mining (machine learning),
unsupervised learning technique used to place data elements
into related groups without advance knowledge of the group
definitions. One of the most popular and widely studied
clustering methods that minimize the clustering error for
points in Euclidean space is called K-means clustering.
However, the k-means method converges to one of many local
minima, and it is known that the final results depend on the
initial starting points (means). In this research paper, we have
introduced and tested an improved algorithm to start the kmeans
with good starting points (means). The good initial
starting points allow k-means to converge to a better local
minimum; also the numbers of iteration over the full dataset
are being decreased. Experimental results show that initial
starting points lead to good solution reducing the number of
iterations to form a cluster.
In today’s competitive world that thrives on the thirst for profits through excellence, obtaining greater efficiency by optimum utilization of resources and better decision-making through analytical data mining methods has become the backbone of every industry. This highlights the importance of the powerful tools and concepts of Data Mining and Warehousing, which when applied effectively can revolutionize the face of any industry. The role of normalization techniques has become extremely pivotal for identifying patterns and maintaining the consistency of database
The role of materialized views is becoming vital in today’s distributed Data warehouses. Materialization is
where parts of the data cube are pre-computed. Some of the real time distributed architectures are
maintaining materialization transparencies in the sense the users are not known with the materialization at
a node. Usually what all followed by them is a cache maintenance mechanism where the query results are
cached. When a query requesting materialization arrives at a distributed node it checks in its cache and if
the materialization is available answers the query. What if materialization is not available- the node
communicates the query in the network until a node answering the requested materialization is available.
This type of network communication increases the number of query forwarding’s between nodes. The aim
of this paper is to reduce the multiple redirects. In this paper we propose a new CB-pattern tree indexing to
identify the exact distributed node where the needed materialization is available.
A Review on Reversible Data Hiding Scheme by Image Contrast EnhancementIJERA Editor
In present world demand of high quality images, security of the information on internet is one of the most important issues of research. Data hiding is a method of hiding a useful information by embedding it on another image (cover image) to provide security and only the authorize person is able to extract the original information from the embedding data. This paper is a review which describes several different algorithms for Reversible Data Hiding (RDH). Previous literature has shown that histogram modification, histogram equalization (HE) and interpolation are the most common methods for data hiding. To improve security these methods are used in encrypted images. This paper is a comprehensive study of all the major reversible data hiding approaches implemented as found in the literature.
A statistical data fusion technique in virtual data integration environmentIJDKP
Data fusion in the virtual data integration environment starts after detecting and clustering duplicated
records from the different integrated data sources. It refers to the process of selecting or fusing attribute
values from the clustered duplicates into a single record representing the real world object. In this paper, a
statistical technique for data fusion is introduced based on some probabilistic scores from both data
sources and clustered duplicates
Enhancement techniques for data warehouse staging areaIJDKP
Poor performance can turn a successful data warehousing project into a failure. Consequently, several
attempts have been made by various researchers to deal with the problem of scheduling the Extract-
Transform-Load (ETL) process. In this paper we therefore present several approaches in the context of
enhancing the data warehousing Extract, Transform and loading stages. We focus on enhancing the
performance of extract and transform phases by proposing two algorithms that reduce the time needed in
each phase through employing the hidden semantic information in the data. Using the semantic
information, a large volume of useless data can be pruned in early design stage. We also focus on the
problem of scheduling the execution of the ETL activities, with the goal of minimizing ETL execution time.
We explore and invest in this area by choosing three scheduling techniques for ETL. Finally, we
experimentally show their behavior in terms of execution time in the sales domain to understand the impact
of implementing any of them and choosing the one leading to maximum performance enhancement.
Frequent Item set Mining of Big Data for Social MediaIJERA Editor
Big data is a term for massive data sets having large, more varied and complex structure with the difficulties of storing, analyzing and visualizing for further processes or results. Bigdata includes data from email, documents, pictures, audio, video files, and other sources that do not fit into a relational database. This unstructured data brings enormous challenges to Bigdata.The process of research into massive amounts of data to reveal hidden patterns and secret correlations named as big data analytics. Therefore, big data implementations need to be analyzed and executed as accurately as possible. The proposed model structures the unstructured data from social media in a structured form so that data can be queried efficiently by using Hadoop MapReduce framework. The Bigdata mining is essential in order to extract value from massive amount of data. MapReduce is efficient method to deal with Big data than traditional techniques.The proposed Linguistic string matching Knuth-Morris-Pratt algorithm and K-Means clustering algorithm gives proper platform to extract value from massive amount of data and recommendation for user.Linguistic matching techniques such as Knuth–Morris–Pratt string matching algorithm are very useful in giving proper matching output to user query. The K-Means algorithm is one which works on clustering data using vector space model. It can be an appropriate method to produce recommendation for user
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
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
Quality - Security Uncompromised and Plausible Watermarking for Patent Infrin...CSCJournals
The most quoted applications for digital watermarking is in the context of copyright-protection of digital (multi-)media. In this paper we offer a new digital watermarking technique, which pledges both Security and Quality for the image for the Patent protection. This methodology uses tale techniques like Shuffling, Composition & Decomposition, and Encryption & Decryption to record the information of a protected primary image and the allied watermarks. The quadtree can aid the processing of watermark and AES provides added security to information. Besides that, we intend a novel architecture for Patent Protection that holds promise for a better compromise between practicality and security for emerging digital rights management application. Security solutions must seize a suspicious version of the application-dependent restrictions and competing objectives.
Adaptive Neural Fuzzy Inference System for Employability AssessmentEditor IJCATR
Employability is potential of a person for gaining and maintains employment. Employability is measure through the
education, personal development and understanding power. Employability is not the similar as ahead a graduate job, moderately it
implies something almost the capacity of the graduate to function in an employment and be capable to move between jobs, therefore
remaining employable through their life. This paper introduced a new adaptive neural fuzzy inference system for assessment of
employability with the help of some neuro fuzzy rules. The purpose and scope of this research is to examine the level of employability.
The concern research use both fuzzy inference systems and artificial neural network which is known as neuro fuzzy technique for
solve the problem of employability assessment. This paper use three employability skills as input and find a crisp value as output
which indicates the glassy of employee. It uses twenty seven neuro fuzzy rules, with the help of Sugeno type inference in Mat-lab and
finds single value output. The proposed system is named as Adaptive Neural Fuzzy Inference System for Employability Assessment
(ANFISEA).
In today’s competitive world that thrives on the thirst for profits through excellence, obtaining greater efficiency by optimum utilization of resources and better decision-making through analytical data mining methods has become the backbone of every industry. This highlights the importance of the powerful tools and concepts of Data Mining and Warehousing, which when applied effectively can revolutionize the face of any industry. The role of normalization techniques has become extremely pivotal for identifying patterns and maintaining the consistency of database
The role of materialized views is becoming vital in today’s distributed Data warehouses. Materialization is
where parts of the data cube are pre-computed. Some of the real time distributed architectures are
maintaining materialization transparencies in the sense the users are not known with the materialization at
a node. Usually what all followed by them is a cache maintenance mechanism where the query results are
cached. When a query requesting materialization arrives at a distributed node it checks in its cache and if
the materialization is available answers the query. What if materialization is not available- the node
communicates the query in the network until a node answering the requested materialization is available.
This type of network communication increases the number of query forwarding’s between nodes. The aim
of this paper is to reduce the multiple redirects. In this paper we propose a new CB-pattern tree indexing to
identify the exact distributed node where the needed materialization is available.
A Review on Reversible Data Hiding Scheme by Image Contrast EnhancementIJERA Editor
In present world demand of high quality images, security of the information on internet is one of the most important issues of research. Data hiding is a method of hiding a useful information by embedding it on another image (cover image) to provide security and only the authorize person is able to extract the original information from the embedding data. This paper is a review which describes several different algorithms for Reversible Data Hiding (RDH). Previous literature has shown that histogram modification, histogram equalization (HE) and interpolation are the most common methods for data hiding. To improve security these methods are used in encrypted images. This paper is a comprehensive study of all the major reversible data hiding approaches implemented as found in the literature.
A statistical data fusion technique in virtual data integration environmentIJDKP
Data fusion in the virtual data integration environment starts after detecting and clustering duplicated
records from the different integrated data sources. It refers to the process of selecting or fusing attribute
values from the clustered duplicates into a single record representing the real world object. In this paper, a
statistical technique for data fusion is introduced based on some probabilistic scores from both data
sources and clustered duplicates
Enhancement techniques for data warehouse staging areaIJDKP
Poor performance can turn a successful data warehousing project into a failure. Consequently, several
attempts have been made by various researchers to deal with the problem of scheduling the Extract-
Transform-Load (ETL) process. In this paper we therefore present several approaches in the context of
enhancing the data warehousing Extract, Transform and loading stages. We focus on enhancing the
performance of extract and transform phases by proposing two algorithms that reduce the time needed in
each phase through employing the hidden semantic information in the data. Using the semantic
information, a large volume of useless data can be pruned in early design stage. We also focus on the
problem of scheduling the execution of the ETL activities, with the goal of minimizing ETL execution time.
We explore and invest in this area by choosing three scheduling techniques for ETL. Finally, we
experimentally show their behavior in terms of execution time in the sales domain to understand the impact
of implementing any of them and choosing the one leading to maximum performance enhancement.
Frequent Item set Mining of Big Data for Social MediaIJERA Editor
Big data is a term for massive data sets having large, more varied and complex structure with the difficulties of storing, analyzing and visualizing for further processes or results. Bigdata includes data from email, documents, pictures, audio, video files, and other sources that do not fit into a relational database. This unstructured data brings enormous challenges to Bigdata.The process of research into massive amounts of data to reveal hidden patterns and secret correlations named as big data analytics. Therefore, big data implementations need to be analyzed and executed as accurately as possible. The proposed model structures the unstructured data from social media in a structured form so that data can be queried efficiently by using Hadoop MapReduce framework. The Bigdata mining is essential in order to extract value from massive amount of data. MapReduce is efficient method to deal with Big data than traditional techniques.The proposed Linguistic string matching Knuth-Morris-Pratt algorithm and K-Means clustering algorithm gives proper platform to extract value from massive amount of data and recommendation for user.Linguistic matching techniques such as Knuth–Morris–Pratt string matching algorithm are very useful in giving proper matching output to user query. The K-Means algorithm is one which works on clustering data using vector space model. It can be an appropriate method to produce recommendation for user
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
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
Quality - Security Uncompromised and Plausible Watermarking for Patent Infrin...CSCJournals
The most quoted applications for digital watermarking is in the context of copyright-protection of digital (multi-)media. In this paper we offer a new digital watermarking technique, which pledges both Security and Quality for the image for the Patent protection. This methodology uses tale techniques like Shuffling, Composition & Decomposition, and Encryption & Decryption to record the information of a protected primary image and the allied watermarks. The quadtree can aid the processing of watermark and AES provides added security to information. Besides that, we intend a novel architecture for Patent Protection that holds promise for a better compromise between practicality and security for emerging digital rights management application. Security solutions must seize a suspicious version of the application-dependent restrictions and competing objectives.
Adaptive Neural Fuzzy Inference System for Employability AssessmentEditor IJCATR
Employability is potential of a person for gaining and maintains employment. Employability is measure through the
education, personal development and understanding power. Employability is not the similar as ahead a graduate job, moderately it
implies something almost the capacity of the graduate to function in an employment and be capable to move between jobs, therefore
remaining employable through their life. This paper introduced a new adaptive neural fuzzy inference system for assessment of
employability with the help of some neuro fuzzy rules. The purpose and scope of this research is to examine the level of employability.
The concern research use both fuzzy inference systems and artificial neural network which is known as neuro fuzzy technique for
solve the problem of employability assessment. This paper use three employability skills as input and find a crisp value as output
which indicates the glassy of employee. It uses twenty seven neuro fuzzy rules, with the help of Sugeno type inference in Mat-lab and
finds single value output. The proposed system is named as Adaptive Neural Fuzzy Inference System for Employability Assessment
(ANFISEA).
DATA MINING IN EDUCATION : A REVIEW ON THE KNOWLEDGE DISCOVERY PERSPECTIVEIJDKP
Knowledge Discovery in Databases is the process of finding knowledge in massive amount of data where
data mining is the core of this process. Data mining can be used to mine understandable meaningful patterns from large databases and these patterns may then be converted into knowledge.Data mining is the process of extracting the information and patterns derived by the KDD process which helps in crucial decision-making.Data mining works with data warehouse and the whole process is divded into action plan to be performed on data: Selection, transformation, mining and results interpretation. In this paper, we have reviewed Knowledge Discovery perspective in Data Mining and consolidated different areas of data
mining, its techniques and methods in it.
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.
We are living in a world, where a vast amount of digital data which is called big data. Plus as the world becomes more and more connected via the Internet of Things (IoT). The IoT has been a major influence on the Big Data landscape. The analysis of such big data brings ahead business competition to the next level of innovation and productivity.
Data mining referred to extracting the hidden predictive information from huge amount of data set. Recently, there are number of private institution are came into existence and they put their efforts to get fruitful admissions. In this paper, the techniques of data mining are used to analyze the mind setup of student after matriculate. One of the best tools of data mining is known as WEKA (Waikato Environment Knowledge Analysis), is used to formulate the process of analysis.
Data mining referred to extracting the hidden predictive information from huge amount of data set. Recently, there are number of private institution are came into existence and they put their efforts to get fruitful admissions. In this paper, the techniques of data mining are used to analyze the mind setup of student after matriculate. One of the best tools of data mining is known as WEKA (Waikato Environment Knowledge Analysis), is used to formulate the process of analysis.
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.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
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/
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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.
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.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
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.
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.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
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.
2. ISSN: 2277 – 9043
International Journal of Advanced Research in Computer Science and Electronics Engineering
Volume 1, Issue 4, June 2012
• Using this knowledge for promoting the performance of the probabilities or correlation to specify the strengths
system and Resolving any potential conflicts with previously (quantitative level) of dependencies (3).
held beliefs or extracted Knowledge These are the steps that
all KDD and data mining tasks progress through. Mixed tasks
II Data Mining Methods There are some tasks in data mining that have both
descriptive and predictive aspects. Using these tasks, we can
The objective of data mining is both prediction and move from basic descriptive tasks toward higher-order
description. That is, to predict Unknown or future values of predictive tasks. Here, we indicate two of them:
the attributes of interest using other attributes in the • Association Rule Discovery – Given a set of records each
Databases, while describing the data in a manner of which contain some number of items from a given
understandable and interpretable to Humans. Predicting the collection, produce dependency rules which will predict the
sale amounts of a new product based on advertising occurrence of an item based on patterns found in the data.
expenditure, or predicting wind velocities as a function of • Sequential Pattern Discovery – Given a set of objects,
temperature, humidity, air pressure, etc., are examples of where each object is associated with its own timeline of
tasks with a predictive goal in data mining. Describing the events, find rules that predict strong sequential dependencies
different terrain groupings that emerge in a sampling of among different events. Rules are formed by first
satellite imagery is an example of a descriptive goal for a data discovering patterns followed by event occurrences which
mining task. The relative importance of description and are governed by timing constraints found within those
prediction can vary between different applications. These patterns.
two goals can be fulfilled by any of a number data mining
tasks including: classification, regression, clustering, So far we briefly described the main concepts of data mining.
summarization, dependency modeling, and deviation Chapter two focuses on methods and algorithms of data
detection. (2) mining in the context of descriptive and predictive tasks. The
research background of both the association rule and
Predictive tasks sequential pattern mining – newer techniques in data mining,
that deserve a separate discussion . Data mining does not
The following are general tasks that serve predictive data take place in a vacuum. In other words, any application of
mining goals: this method of analysis is dependent upon the context in
• Classification – to segregate items into several predefined which it takes place. Therefore, it is necessary to know the
classes. Given a Collection of training samples, this type of environment in which we are going to use data mining
task can be designed to find a Model for class attributes as a methods. The next section provides a brief overview of the
function of the values of other attributes (3). LON-CAPA system.
• Regression – to predict a value of a given continuously
valued variable based On the values of other variables, Online Education systems
assuming either a linear or nonlinear model of Dependency.
These tasks are studied in statistics and neural network fields Several Online Education systems1 such as Blackboard,
(4). WebCT, Virtual University (VU), and some other similar
• Deviation Detection – to discover the most significant systems have been developed to focus on course management
changes in data from Previously measured or normative issues. The objectives of these systems are to present courses
values (5). Explicit information outside the data, like and instructional programs through the web and other
integrity constraints or predefined patterns, is used for technologically enhanced media. These new technologies
deviation detection. Arning et al., (1996) approached the make it possible to offer instruction without the limitations of
problem from the inside of the data, using the implicit time and place found in traditional university programs.
redundancy. However, these systems tend to use existing materials and
present them as a static package via the Internet. There is
Descriptive tasks another approach, pursued in LON-CAPA, to construct
more-or-less new courses using newer network technology.
• Clustering – to identify a set of categories, or clusters, that In this model of content creation, college faculty, K-12
describe the data (1). teachers, and students interested in collaboration can access
• Summarization – to find a concise description for a subset a database of hypermedia software
of data. Tabulating the mean and standard deviations for all
fields is a simple example of summarization. There are more LON-CAPA, System Overview
sophisticated techniques for summarization and they are
usually applied to facilitate automated report generation and LON-CAPA is a distributed instructional management
interactive data analysis (2). system, which provides students with personalized problem
• Dependency modeling – to find a model that describes sets, quizzes, and exams. Personalized (or individualized)
significant dependencies between variables. For example, homework means that each student sees a slightly different
probabilistic dependency networks use conditional computer-generated problem. LON-CAPA provides students
independence to specify the structural level of the model and and instructors with immediate feedback on conceptual
understanding and correctness of solutions. It also provides
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4. ISSN: 2277 – 9043
International Journal of Advanced Research in Computer Science and Electronics Engineering
Volume 1, Issue 4, June 2012
• A Content Page displays course content. It is essentially a
conventional html
page. These resources use the extension “.html”.
• A Problem resource represents problems for the students to
solve, with answers stored in the system. These resources are
stored in files that must use the extension “.problem”.
• A Page is a type of Map which is used to join other
resources together into one HTML page. For example, a page
of problems will appear as a problem set. These resources are
stored in files that must use the extension “.page”.
• A Sequence is a type of Map, which is used to link other Figure 1.4 Directory listing of course’s home directory
resources together. Sequences are stored in files that must
use the extension “.sequence”. Sequences can contain other An example of course data is shown in Figure 1.4. classlist is
sequences and pages. the list of students in the course, environment includes the
Authors create these resources and publish them in library course’s full name, etc, and resourcedata are deadlines, etc.
servers. Then, instructors use these resources in online The parameters for homework problems are stored in these
courses. The LON-CAPA system logs any access to these files . To identify a specific instance of a resource,
resources as well as the sequence and frequency of access in LON-CAPA uses symbols or “symbs.” These identifiers are
relation to the successful completion of any assignment. All built from the URL of the map, the resource number of the
these accesses are logged. resource in the map, and the URL of the resource itself. The
latter is somewhat redundant, but might help if maps change.
LON-CAPA Strategy for Data Storage An example is
gec/dkd/parts/part1.sequence___19___gec/dkd/tests/part12.
Internally, the student data is stored in a directory: problem The respective map entry is
/home/httpd/lonUsers/domain/1st.char/2nd.char/3rd.char/use <resource id="19" src="/res/gec/dkd/tests/part12.problem"
rname/ title="Problem 2"> </resource>
For example /home/httpd/lonUsers/gec/m/i/n/minaeibi/ Symbs are used by the random number generator, as well as
Figure 1.3 shows a list of a student’s data. Files ending with to store and restore data specific to a certain instance of a
.db are GDBM files (Berkeley database), while those with a problem. More details of the stored data and their exact
course-ID as name, for example structures will be explained in chapter three, when we will
gec_12679c3ed543a25msul1.db, store performance data for describe the data acquisition of the system.
that student in the course.
Resource Evaluation in LON-CAPA
One of the most challenging aspects of the system is to
provide instructors with information concerning the quality
and effectiveness of the various materials in the resource pool
on student understanding of concepts. These materials can
include web pages, demonstrations, simulations, and
individualized problems designed for use on homework
assignments, quizzes, and examinations. The system
generates a range of statistics that can be useful in evaluating
the degree to which individual problems are effective in
promoting formative learning for students. For example, each
exam problem contains attached metadata that catalog its
Figure 1.3 Directory listing of user’s home directory degree of difficulty and discrimination for students at
different phases in their education (i.e., introductory college
Courses are assigned to users, not vice versa. Internally, courses, advanced college courses, and so on). To evaluate
courses are handled like users without login privileges. The resource pool materials, a standardized format is required so
username is a unique ID, for example that materials from different sources can be compared. This
msu_12679c3ed543a25msul1 – every course in every helps resource users to select the most effective materials
semester has a unique ID, and there is no semester transition. available.
The user-data of the course includes the full name of the LON-CAPA has also provided questionnaires which are
course, a pointer to its top-level resource map (“course completed by faculty and students who use the educational
map”), and any associated deadlines, spreadsheets, etc., as materials to assess the quality and efficacy of resources.
well as a course enrollment list. The latter is somewhat In addition to providing the questionnaires and using the
redundant, since in principle, this list could be produced by statistical reports, we investigate here methods to find
going through the roles of all users, and looking for the valid criteria for classifying students and grouping problems by
role for a student in that course. examining logged data such as: time spent on a particular
resource, resources visited (other web pages), due date for
each homework, the difficulty of problems (observed
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