In our series on The Yosemite Project, we explore RDF as a data standard for health data. In this presentation, we will discuss with Claude Nanjo, a Software Architect at Cognitive Medical Systems, ways to expose clinical knowledge as OWL and RDF resources on the Web in order to promote greater convergence in the representation of health knowledge in the longer term. We will also explore how one might rally and coordinate the community to seed the Web with a core set of high-value resources and technologies that could greatly enhance health interoperability.
In our series on The Yosemite Project, we explore RDF as a data standard for health data. In this presentation, we will discuss with Claude Nanjo, a Software Architect at Cognitive Medical Systems, ways to expose clinical knowledge as OWL and RDF resources on the Web in order to promote greater convergence in the representation of health knowledge in the longer term. We will also explore how one might rally and coordinate the community to seed the Web with a core set of high-value resources and technologies that could greatly enhance health interoperability.
Keynote on software sustainability given at the 2nd Annual Netherlands eScience Symposium, November 2014.
Based on the article
Carole Goble ,
Better Software, Better Research
Issue No.05 - Sept.-Oct. (2014 vol.18)
pp: 4-8
IEEE Computer Society
http://www.computer.org/csdl/mags/ic/2014/05/mic2014050004.pdf
http://doi.ieeecomputersociety.org/10.1109/MIC.2014.88
http://www.software.ac.uk/resources/publications/better-software-better-research
Scalable keyword search on large rdf data
Keyword search is a useful tool for exploring large RDF datasets. Existing techniques either rely on constructing a distance matrix for pruning the search space or building summaries from the RDF graphs for query processing. Existing techniques have serious limitations in dealing with realistic, large RDF data with tens of millions of triples
This presentation was provided by Carolyn Hansen of the University of Cincinnati during the NISO Training Thursday event, Metadata and the IR, held on Thursday, February 23, 2017.
LIBER Webinar: Are the FAIR Data Principles really fair?LIBER Europe
The FAIR Data Principles are a hot topic in research data managment. Their adoption within the H2020 funding programme means researchers now have to pay much more attention to how their share, publish and archive their data.
In this light, how can libraries help their research communities implement the FAIR principles? And write better data management plans?
This questions were addressed in a LIBER webinar containing some guidance and reflections on the principles themselves. Presented by Alastair Dunning, Head Research Data Services at the TU Delft (hosts of the 4TU.Centre for Research Data), it is based on a study of 37 data repositories (from subject specific repositories, to generic data archives, to national infrastructures), seeing how far they comply with each of the individual facets of the Data principles.
Sources of Change in Modern Knowledge Organization SystemsPaul Groth
Talk covering how knowledge graphs are making us rethink how change occurs in Knowledge Organization Systems. Based on https://arxiv.org/abs/1611.00217
This presentation was provided by Tim McGeary of Duke University during the NISO virtual conference, Open Data Projects, held on Wednesday, June 13, 2018.
Efficient Practices for Large Scale Text Mining ProcessOntotext
Text mining is a need when managing large scale textual collections. It facilitates access to, otherwise, hard to organise unstructured and heterogeneous documents, allows for extraction of hidden knowledge and opens new dimensions in data exploration.
In this webinar, Ivelina Nikolova, PhD, shares best practices and text analysis examples from successful text mining process in domains like news, financial and scientific publishing, pharma industry and cultural heritage.
Content + Signals: The value of the entire data estate for machine learningPaul Groth
Content-centric organizations have increasingly recognized the value of their material for analytics and decision support systems based on machine learning. However, as anyone involved in machine learning projects will tell you the difficulty is not in the provision of the content itself but in the production of annotations necessary to make use of that content for ML. The transformation of content into training data often requires manual human annotation. This is expensive particularly when the nature of the content requires subject matter experts to be involved.
In this talk, I highlight emerging approaches to tackling this challenge using what's known as weak supervision - using other signals to help annotate data. I discuss how content companies often overlook resources that they have in-house to provide these signals. I aim to show how looking at a data estate in terms of signals can amplify its value for artificial intelligence.
There are high expectations for Linked Government Data—the practice of publishing public sector information on the Web using Linked Data formats. This slideset reviews some of the ongoing work in the US, UK, and within W3C, as well as activities within my institute (DERI, National University of Ireland, Galway).
Tim Davies, SMART Infrastructure Facility E-Research Coordinator, presented the SMART Data Management Systement as part of the SMART Seminar Series on Thursday, 5th March 2015.
Keynote on software sustainability given at the 2nd Annual Netherlands eScience Symposium, November 2014.
Based on the article
Carole Goble ,
Better Software, Better Research
Issue No.05 - Sept.-Oct. (2014 vol.18)
pp: 4-8
IEEE Computer Society
http://www.computer.org/csdl/mags/ic/2014/05/mic2014050004.pdf
http://doi.ieeecomputersociety.org/10.1109/MIC.2014.88
http://www.software.ac.uk/resources/publications/better-software-better-research
Scalable keyword search on large rdf data
Keyword search is a useful tool for exploring large RDF datasets. Existing techniques either rely on constructing a distance matrix for pruning the search space or building summaries from the RDF graphs for query processing. Existing techniques have serious limitations in dealing with realistic, large RDF data with tens of millions of triples
This presentation was provided by Carolyn Hansen of the University of Cincinnati during the NISO Training Thursday event, Metadata and the IR, held on Thursday, February 23, 2017.
LIBER Webinar: Are the FAIR Data Principles really fair?LIBER Europe
The FAIR Data Principles are a hot topic in research data managment. Their adoption within the H2020 funding programme means researchers now have to pay much more attention to how their share, publish and archive their data.
In this light, how can libraries help their research communities implement the FAIR principles? And write better data management plans?
This questions were addressed in a LIBER webinar containing some guidance and reflections on the principles themselves. Presented by Alastair Dunning, Head Research Data Services at the TU Delft (hosts of the 4TU.Centre for Research Data), it is based on a study of 37 data repositories (from subject specific repositories, to generic data archives, to national infrastructures), seeing how far they comply with each of the individual facets of the Data principles.
Sources of Change in Modern Knowledge Organization SystemsPaul Groth
Talk covering how knowledge graphs are making us rethink how change occurs in Knowledge Organization Systems. Based on https://arxiv.org/abs/1611.00217
This presentation was provided by Tim McGeary of Duke University during the NISO virtual conference, Open Data Projects, held on Wednesday, June 13, 2018.
Efficient Practices for Large Scale Text Mining ProcessOntotext
Text mining is a need when managing large scale textual collections. It facilitates access to, otherwise, hard to organise unstructured and heterogeneous documents, allows for extraction of hidden knowledge and opens new dimensions in data exploration.
In this webinar, Ivelina Nikolova, PhD, shares best practices and text analysis examples from successful text mining process in domains like news, financial and scientific publishing, pharma industry and cultural heritage.
Content + Signals: The value of the entire data estate for machine learningPaul Groth
Content-centric organizations have increasingly recognized the value of their material for analytics and decision support systems based on machine learning. However, as anyone involved in machine learning projects will tell you the difficulty is not in the provision of the content itself but in the production of annotations necessary to make use of that content for ML. The transformation of content into training data often requires manual human annotation. This is expensive particularly when the nature of the content requires subject matter experts to be involved.
In this talk, I highlight emerging approaches to tackling this challenge using what's known as weak supervision - using other signals to help annotate data. I discuss how content companies often overlook resources that they have in-house to provide these signals. I aim to show how looking at a data estate in terms of signals can amplify its value for artificial intelligence.
There are high expectations for Linked Government Data—the practice of publishing public sector information on the Web using Linked Data formats. This slideset reviews some of the ongoing work in the US, UK, and within W3C, as well as activities within my institute (DERI, National University of Ireland, Galway).
Tim Davies, SMART Infrastructure Facility E-Research Coordinator, presented the SMART Data Management Systement as part of the SMART Seminar Series on Thursday, 5th March 2015.
Accelerating Insight - Smart Data Lake Customer Success StoriesCambridge Semantics
At Gartner Data & Analytics Summit 2017 Alok Prasad, President, was joined by Peter Horowitz of PricewaterhouseCoopers in presenting a session on how Cambridge Semantics' in-memory, massively parallel, semantic graph-based platform delivers an accelerating edge to data-driven organizations, while maintaining trust with security and governance.
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
International Journal of Data Mining Systems & Applications (IJDSA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Data Mining Systems & Applications . The journal focuses on all technical and practical aspects of Database Management Systems. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advancedData Mining Systems & Applications and establishing new collaborations in these areas.
Fractional differential equations Assignment Help and Fractional differential...john mayer
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Computer Network Assignment help , Computer Network Online tutorsjohn mayer
Get the 24/7 tutors for Computer Network Assignment help & Computer Network homework help. Computer Network tutors are available 24/7 in order to provide the complete academic assistance for the Computer Network assignments.
UML Diagram Assignment help , UML Diagram Online tutorsjohn mayer
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Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
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2. Data Mining
Relational Databases
Data Warehouses
Transactional Databases
Data Mining Functionalities
Mining Frequent Patterns, Associations, and
Correlations
Cluster Analysis
Outlier Analysis
Evolution Analysis
Classification of Data Mining Systems
3. Preprocess the Data
Measuring the Central Tendency
Measuring the Dispersion of Data
Data Cleaning
Missing Values
Data Integration and Transformation
Data Integration
Data Transformation
Data Reduction
Data Cube Aggregation
Dimensionality Reduction
Numerosity Reduction
4. Data Warehouse
A Multidimensional Data Model
Data Warehouse Architecture
Data Warehouse Back-End Tools and
Utilities
Types of OLAP Servers
Data Warehouse Implementation
Indexing OLAP Data
Data Warehouse Usage
5. Efficient Methods for Data Cube Computation
Multiway Array Aggregation for Full Cube
Computation
Precomputing Shell Fragments for Fast High-
Dimensional OLAP
Computing Cubes with Complex Iceberg
Conditions
Discovery-Driven Exploration of Data Cubes
Complex Aggregation at Multiple Granularity
Constrained Gradient Analysis in Data Cubes
Attribute-Oriented Induction
Presentation of the Derived Generalization
Mining Class Comparisons
6. Market Basket Analysis
The Apriori Algorithm
Mining Frequent Itemsets without Candidate
Generation
Mining Frequent Itemsets Using Vertical Data
Format
Mining Closed Frequent Itemsets
Mining Various Kinds of Association Rules
Mining Multilevel Association Rules
Mining Multidimensional Association Rules from
Relational Databases and Data Warehouses
Constraint-Based Association Mining
8. Cluster Analysis
Types of Data in Cluster Analysis
A Categorization of Major Clustering Methods
Constraint-Based Cluster Analysis
Clustering with Obstacle Objects
User-Constrained Cluster Analysis
Semi-Supervised Cluster Analysis
Outlier Analysis
Statistical Distribution-Based Outlier Detection
Distance-Based Outlier Detection
Density-Based Local Outlier Detection
Deviation-Based Outlier Detection
9. Mining Data Streams
Methodologies for Stream Data Processing and
Stream Data Systems
Stream OLAP and Stream Data Cubes
Frequent-Pattern Mining in Data Streams
Classification of Dynamic Data Streams
Mining Time-Series Data
Sequential Pattern Mining
Scalable Methods for Mining Sequential Patterns
Constraint-Based Mining of Sequential Patterns
Periodicity Analysis for Time-Related Sequence
Data
10. Graph Mining
Methods for Mining Frequent Subgraphs
Social Network Analysis
Social Network
Characteristics of Social Networks
Link Mining: Tasks and Challenges
ILP Approach to Multirelational Classification
Tuple ID Propagation
Multirelational Classification
Multirelational Clustering
11. Multidimensional Analysis and Descriptive
Mining of Complex Data Objects
Generalization of Structured Data
Aggregation and Approximation in Spatial and
Multimedia Data Generalization
Construction and Mining of Object Cubes
Generalization-Based Mining of Plan Databases
by Divide-and-Conquer
Spatial Data Mining atial Data Cube Construction
and Spatial OLAP
Mining Spatial Association and Co-location
Patterns
12. Spatial Classification and Spatial Trend Analysis
Multimedia Data Mining
Classification and Prediction Analysis of
Multimedia Data
Audio and Video Data Mining
Text Mining
Mining the World Wide Web
Mining the Web Page Layout Structure
Mining Multimedia Data on the Web
Web Usage Mining
13. Data Mining for Financial Data Analysis Data
Mining for the Retail Industry
Data Mining for the Telecommunication Industry
Data Mining for Biological Data Analysis
Data Mining in Other Scientific Applications
Data Mining for Intrusion Detection
Theoretical Foundations of Data Mining
Statistical Data Mining
Visual and Audio Data Mining
Trends in Data Mining
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