Internet Search: the past, present and the futurePayamBarnaghi
The document discusses internet search from the past to the present and future. It covers early internet search, the need to find data once it is collected, patterns in time-series IoT data, and algorithms for segmenting time-series data. It proposes an IoT search engine to enable searching the vast amounts of data generated by internet-connected devices, highlighting the unique requirements and challenges of searching IoT data. The author is an expert in vision, speech, and signal processing focusing on IoT search and analysis of real-world data streams.
The need for a transparent data supply chainPaul Groth
1. The document discusses the need for transparency in data supply chains. It notes that data goes through multiple steps as it is collected, modeled, and applied in applications.
2. It illustrates the complexity of data supply chains using examples of how data is reused and integrated from multiple sources to build models and how bias can propagate.
3. The document argues that transparency is important to understand where data comes from, how it has been processed, and help address issues like bias, privacy, or other problems at their source in the data supply chain.
Keynote for Theory and Practice of Digital Libraries 2017
The theory and practice of digital libraries provides a long history of thought around how to manage knowledge ranging from collection development, to cataloging and resource description. These tools were all designed to make knowledge findable and accessible to people. Even technical progress in information retrieval and question answering are all targeted to helping answer a human’s information need.
However, increasingly demand is for data. Data that is needed not for people’s consumption but to drive machines. As an example of this demand, there has been explosive growth in job openings for Data Engineers – professionals who prepare data for machine consumption. In this talk, I overview the information needs of machine intelligence and ask the question: Are our knowledge management techniques applicable for serving this new consumer?
This document summarizes key points about data science and privacy regulation:
1. Regulation aims to alter behavior according to standards to achieve defined outcomes, and can involve standard-setting, information gathering, and modifying behavior.
2. With "big data", problems arise for the laissez-faire conception of privacy regulation due to market failures, insider threats, and mass surveillance capabilities.
3. Designing for privacy is important, such as data minimization, decentralization, consent requirements, and easy-to-use privacy interfaces. The "data exhaust" from ubiquitous data collection threatens privacy in Europe.
The literature contains a myriad of recommendations, advice, and strictures about what data providers should do to facilitate data reuse. It can be overwhelming. Based on recent empirical work (analyzing data reuse proxies at scale, understanding data sensemaking and looking at how researchers search for data), I talk about what practices are a good place to start for helping others to reuse your data.
This document discusses leveraging graph data structures to analyze variant data and related annotations from large genomic datasets in a scalable way. An in-memory graph database was used to model variants, annotations, and their relationships. Simple queries on the graph performed as well or better than a relational database. More complex queries and analysis, like spectral clustering of populations, were also possible with the graph model and helped identify patterns not feasible with relational approaches. The results indicate graph databases are a powerful tool for precision medicine research by enabling both known and novel analysis of large genomic datasets.
Combining Explicit and Latent Web Semantics for Maintaining Knowledge GraphsPaul Groth
A look at how the thinking about Web Data and the sources of semantics can help drive decisions on combining latent and explicit knowledge. Examples from Elsevier and lots of pointers to related work.
Internet Search: the past, present and the futurePayamBarnaghi
The document discusses internet search from the past to the present and future. It covers early internet search, the need to find data once it is collected, patterns in time-series IoT data, and algorithms for segmenting time-series data. It proposes an IoT search engine to enable searching the vast amounts of data generated by internet-connected devices, highlighting the unique requirements and challenges of searching IoT data. The author is an expert in vision, speech, and signal processing focusing on IoT search and analysis of real-world data streams.
The need for a transparent data supply chainPaul Groth
1. The document discusses the need for transparency in data supply chains. It notes that data goes through multiple steps as it is collected, modeled, and applied in applications.
2. It illustrates the complexity of data supply chains using examples of how data is reused and integrated from multiple sources to build models and how bias can propagate.
3. The document argues that transparency is important to understand where data comes from, how it has been processed, and help address issues like bias, privacy, or other problems at their source in the data supply chain.
Keynote for Theory and Practice of Digital Libraries 2017
The theory and practice of digital libraries provides a long history of thought around how to manage knowledge ranging from collection development, to cataloging and resource description. These tools were all designed to make knowledge findable and accessible to people. Even technical progress in information retrieval and question answering are all targeted to helping answer a human’s information need.
However, increasingly demand is for data. Data that is needed not for people’s consumption but to drive machines. As an example of this demand, there has been explosive growth in job openings for Data Engineers – professionals who prepare data for machine consumption. In this talk, I overview the information needs of machine intelligence and ask the question: Are our knowledge management techniques applicable for serving this new consumer?
This document summarizes key points about data science and privacy regulation:
1. Regulation aims to alter behavior according to standards to achieve defined outcomes, and can involve standard-setting, information gathering, and modifying behavior.
2. With "big data", problems arise for the laissez-faire conception of privacy regulation due to market failures, insider threats, and mass surveillance capabilities.
3. Designing for privacy is important, such as data minimization, decentralization, consent requirements, and easy-to-use privacy interfaces. The "data exhaust" from ubiquitous data collection threatens privacy in Europe.
The literature contains a myriad of recommendations, advice, and strictures about what data providers should do to facilitate data reuse. It can be overwhelming. Based on recent empirical work (analyzing data reuse proxies at scale, understanding data sensemaking and looking at how researchers search for data), I talk about what practices are a good place to start for helping others to reuse your data.
This document discusses leveraging graph data structures to analyze variant data and related annotations from large genomic datasets in a scalable way. An in-memory graph database was used to model variants, annotations, and their relationships. Simple queries on the graph performed as well or better than a relational database. More complex queries and analysis, like spectral clustering of populations, were also possible with the graph model and helped identify patterns not feasible with relational approaches. The results indicate graph databases are a powerful tool for precision medicine research by enabling both known and novel analysis of large genomic datasets.
Combining Explicit and Latent Web Semantics for Maintaining Knowledge GraphsPaul Groth
A look at how the thinking about Web Data and the sources of semantics can help drive decisions on combining latent and explicit knowledge. Examples from Elsevier and lots of pointers to related work.
The Roots: Linked data and the foundations of successful Agriculture DataPaul Groth
Some thoughts on successful data for the agricultural domain. Keynote at Linked Open Data in Agriculture
MACS-G20 Workshop in Berlin, September 27th and 28th, 2017 https://www.ktbl.de/inhalte/themen/ueber-uns/projekte/macs-g20-loda/lod/
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
This document discusses how semantic technologies can help link datasets to publications and institutions to enable new forms of data search and showcasing. It notes that standard schemas and formats are needed to allow linkages between data repositories. Knowledge graphs can help relate entities like papers, authors and institutions to facilitate disambiguation and multi-institutional search capabilities. Semantic technologies are seen as central to efficiently building these linkages at scale across the research data ecosystem.
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.
Semantic Web Development for Traditional Chinese MedicineTong Yu
This thesis is motivated by a case study of SSME, in which we digitalize and integrate cultural assets of TCM and provide Web-based knowledge services to medical experts. The major quest is to turn cultural assets from prolonged Chinese history into knowledge services contributing to modern biomedicine. In our view, the essence of knowledge service is cross-domain collaboration in knowledge discovery on the Web of data. Whereas the Service-Oriented Architecture enables interactions between Web agents, and the Semantic Web provides a knowledge representation and integration framework, the feasibility and benefits of Web-based collaborative knowledge discovery need to be further investigated. We propose a methodology named Semantic Graph Mining (SGM), which uses the semantic graph model to integrate graph mining and ontology reasoning for better analyzing biomedical complex networks (an important KDD problem). Potential methods of SGM include Web resource ranking, semantic association discovery, frequent subgraph mining, and clustering. The effectiveness of these methods is investigated in use cases such as TCM semantic search, TCM formulae analysis, drug-interaction analysis.
Knowledge graph construction for research & medicinePaul Groth
1) Elsevier aims to build knowledge graphs to help address challenges in research and medicine like high drug development costs and medical errors.
2) Knowledge graphs link entities like people, concepts, and events to provide answers by going beyond traditional bibliographic descriptions.
3) Elsevier constructs knowledge graphs using techniques like information extraction from text, integrating data sources, and predictive modeling of large patient datasets to identify statistical correlations.
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
Data Communities - reusable data in and outside your organization.Paul Groth
Description
Data is a critical both to facilitate an organization and as a product. How can you make that data more usable for both internal and external stakeholders? There are a myriad of recommendations, advice, and strictures about what data providers should do to facilitate data (re)use. It can be overwhelming. Based on recent empirical work (analyzing data reuse proxies at scale, understanding data sensemaking and looking at how researchers search for data), I talk about what practices are a good place to start for helping others to reuse your data. I put this in the context of the notion data communities that organizations can use to help foster the use of data both within your organization and externally.
The document discusses various challenges in social network analysis including collecting and extracting network data at scale from sources such as the web, validating automated data extraction methods, and developing algorithms and software that can analyze large and complex network datasets. It also outlines different network analysis methods, visualization and simulation techniques, and recommendations for how tools can better support networking, referrals, and workflows across multiple data sources and programs. Scaling methods and algorithms to very large network sizes and developing standards to integrate diverse data and tools are highlighted as key challenges.
Sci Know Mine 2013: What can we learn from topic modeling on 350M academic do...William Gunn
This document discusses topic modeling on 350 million documents from Mendeley. It describes how topic modeling can be used to categorize documents into topics and subcategories, though categorization is imperfect and topics change over time. It also discusses how topic modeling and metrics can help with fact discovery and reproducibility of research to build more robust datasets.
This document provides a summary of Himansu Bhusan Sahoo's background and experience in data analysis. He has over 10 years of experience analyzing petabytes of data using statistical analysis and machine learning techniques. Some of his skills include data cleaning, feature selection, predictive modeling with techniques like regression, decision trees and random forests. He has a Ph.D. in data analysis from the University of Hawaii and has worked on projects at Mississippi University and Argonne National Laboratory involving extracting rare signals from noisy data and developing shower reconstruction algorithms.
Peter Yun-Shao Sung is a computer science professional with a MS in computer science from NYU and a ME in biomedical engineering from Cornell University. He has over 10 years of experience in bioinformatics analysis at Memorial Sloan-Kettering Cancer Center and has published over 30 papers identifying genomic signatures related to sarcoma development. His technical skills include Python, C++, JavaScript, and machine learning algorithms like CNNs, RNNs, and LSTMs.
This document presents a case study on applying a data analytics approach to conducting a systematic literature review on master data management. It outlines the steps taken, including defining review questions, searching multiple databases and sources, combining and preprocessing the data, and performing descriptive and text analyses. The analyses addressed questions about trends in publications over time, primary databases, publication types, and frequent keywords. This provided insights into the progress and topics within the master data management research domain. The presented structured approach aims to improve the replicability of systematic literature reviews.
This presentation educates you about top data science project ideas for Beginner, Intermediate and Advanced. the ideas such as Fake News Detection Using Python, Data Science Project on, Detecting Forest Fire, Detection of Road Lane Lines, Project on Sentimental Analysis, Speech Recognition, Developing Chatbots, Detection of Credit Card Fraud and Customer Segmentations etc:
For more topics stay tuned with Learnbay.
This document summarizes a presentation by Christina Pikas on how librarians at special libraries like the Johns Hopkins Applied Physics Laboratory (APL) can provide bibliometric analysis services. Pikas discusses how librarians' domain knowledge, access to data, and understanding of ethics uniquely positions them to analyze research output and collaboration in a reliable way. She provides examples of bibliometric questions answered at APL and the tools used. Pikas concludes that librarians should leverage their skills and study bibliometrics to support research assessment activities.
This certificate certifies that an individual successfully completed a 12-hour online cybersecurity course from January 12 to February 23, 2016. The course was developed by faculty from MIT's Computer Science and Artificial Intelligence Laboratory in collaboration with MIT Professional Education. The certificate is signed by the Executive Director of MIT Professional Education and the Director and a Principal Research Scientist of the Computer Science and Artificial Intelligence Laboratory.
This document provides an overview of open brain imaging data resources that are available for machine learning research. It describes the different types of brain scan data, such as structural scans, diffusion scans, and functional scans. It then summarizes several existing datasets that contain brain imaging data for conditions like brain tumors, stroke, Alzheimer's disease, autism, and ADHD. The document recommends some large open datasets for transfer learning and notes opportunities for collaborating with other researchers at events like Brainhack Global San Francisco.
Semantics for Bioinformatics: What, Why and How of Search, Integration and An...Amit Sheth
Amit Sheth's Keynote at Semantic Web Technologies for Science and Engineering Workshop (held in conjunction with ISWC2003), Sanibel Island, FL, October 20, 2003.
With the explosion of interest in both enhanced knowledge management and open science, the past few years have seen considerable discussion about making scientific data “FAIR” — findable, accessible, interoperable, and reusable. The problem is that most scientific datasets are not FAIR. When left to their own devices, scientists do an absolutely terrible job creating the metadata that describe the experimental datasets that make their way in online repositories. The lack of standardization makes it extremely difficult for other investigators to locate relevant datasets, to re-analyse them, and to integrate those datasets with other data. The Center for Expanded Data Annotation and Retrieval (CEDAR) has the goal of enhancing the authoring of experimental metadata to make online datasets more useful to the scientific community. The CEDAR work bench for metadata management will be presented in this webinar. CEDAR illustrates the importance of semantic technology to driving open science. It also demonstrates a means for simplifying access to scientific data sets and enhancing the reuse of the data to drive new discoveries.
Preprint-ICDMAI,Defense Institute,20-22 January 2023.pdfChristo Ananth
Call for Papers- Special Session: Bio-Signal Processing using Deep Learning, 7th International Conference on Data Management, Analytics & Innovation (ICDMAI), Defence Institute of Advanced Technology, Pune-India Organized by Society For Data Science, Pune, India, 20-22 January 2023
This document discusses profiling linked open data. It outlines the research background, plan, and preliminary results of profiling linked open data. The research aims to automatically generate new statistics and knowledge patterns to provide dataset summaries and inspect data quality. Preliminary results include profiling Italian public administration websites for compliance with open data policies and automatically classifying over 1,000 linked data sets into 8 topics with over 80% accuracy. Future work involves enriching the framework with additional statistics and applying it to unstructured microdata.
The Roots: Linked data and the foundations of successful Agriculture DataPaul Groth
Some thoughts on successful data for the agricultural domain. Keynote at Linked Open Data in Agriculture
MACS-G20 Workshop in Berlin, September 27th and 28th, 2017 https://www.ktbl.de/inhalte/themen/ueber-uns/projekte/macs-g20-loda/lod/
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
This document discusses how semantic technologies can help link datasets to publications and institutions to enable new forms of data search and showcasing. It notes that standard schemas and formats are needed to allow linkages between data repositories. Knowledge graphs can help relate entities like papers, authors and institutions to facilitate disambiguation and multi-institutional search capabilities. Semantic technologies are seen as central to efficiently building these linkages at scale across the research data ecosystem.
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.
Semantic Web Development for Traditional Chinese MedicineTong Yu
This thesis is motivated by a case study of SSME, in which we digitalize and integrate cultural assets of TCM and provide Web-based knowledge services to medical experts. The major quest is to turn cultural assets from prolonged Chinese history into knowledge services contributing to modern biomedicine. In our view, the essence of knowledge service is cross-domain collaboration in knowledge discovery on the Web of data. Whereas the Service-Oriented Architecture enables interactions between Web agents, and the Semantic Web provides a knowledge representation and integration framework, the feasibility and benefits of Web-based collaborative knowledge discovery need to be further investigated. We propose a methodology named Semantic Graph Mining (SGM), which uses the semantic graph model to integrate graph mining and ontology reasoning for better analyzing biomedical complex networks (an important KDD problem). Potential methods of SGM include Web resource ranking, semantic association discovery, frequent subgraph mining, and clustering. The effectiveness of these methods is investigated in use cases such as TCM semantic search, TCM formulae analysis, drug-interaction analysis.
Knowledge graph construction for research & medicinePaul Groth
1) Elsevier aims to build knowledge graphs to help address challenges in research and medicine like high drug development costs and medical errors.
2) Knowledge graphs link entities like people, concepts, and events to provide answers by going beyond traditional bibliographic descriptions.
3) Elsevier constructs knowledge graphs using techniques like information extraction from text, integrating data sources, and predictive modeling of large patient datasets to identify statistical correlations.
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
Data Communities - reusable data in and outside your organization.Paul Groth
Description
Data is a critical both to facilitate an organization and as a product. How can you make that data more usable for both internal and external stakeholders? There are a myriad of recommendations, advice, and strictures about what data providers should do to facilitate data (re)use. It can be overwhelming. Based on recent empirical work (analyzing data reuse proxies at scale, understanding data sensemaking and looking at how researchers search for data), I talk about what practices are a good place to start for helping others to reuse your data. I put this in the context of the notion data communities that organizations can use to help foster the use of data both within your organization and externally.
The document discusses various challenges in social network analysis including collecting and extracting network data at scale from sources such as the web, validating automated data extraction methods, and developing algorithms and software that can analyze large and complex network datasets. It also outlines different network analysis methods, visualization and simulation techniques, and recommendations for how tools can better support networking, referrals, and workflows across multiple data sources and programs. Scaling methods and algorithms to very large network sizes and developing standards to integrate diverse data and tools are highlighted as key challenges.
Sci Know Mine 2013: What can we learn from topic modeling on 350M academic do...William Gunn
This document discusses topic modeling on 350 million documents from Mendeley. It describes how topic modeling can be used to categorize documents into topics and subcategories, though categorization is imperfect and topics change over time. It also discusses how topic modeling and metrics can help with fact discovery and reproducibility of research to build more robust datasets.
This document provides a summary of Himansu Bhusan Sahoo's background and experience in data analysis. He has over 10 years of experience analyzing petabytes of data using statistical analysis and machine learning techniques. Some of his skills include data cleaning, feature selection, predictive modeling with techniques like regression, decision trees and random forests. He has a Ph.D. in data analysis from the University of Hawaii and has worked on projects at Mississippi University and Argonne National Laboratory involving extracting rare signals from noisy data and developing shower reconstruction algorithms.
Peter Yun-Shao Sung is a computer science professional with a MS in computer science from NYU and a ME in biomedical engineering from Cornell University. He has over 10 years of experience in bioinformatics analysis at Memorial Sloan-Kettering Cancer Center and has published over 30 papers identifying genomic signatures related to sarcoma development. His technical skills include Python, C++, JavaScript, and machine learning algorithms like CNNs, RNNs, and LSTMs.
This document presents a case study on applying a data analytics approach to conducting a systematic literature review on master data management. It outlines the steps taken, including defining review questions, searching multiple databases and sources, combining and preprocessing the data, and performing descriptive and text analyses. The analyses addressed questions about trends in publications over time, primary databases, publication types, and frequent keywords. This provided insights into the progress and topics within the master data management research domain. The presented structured approach aims to improve the replicability of systematic literature reviews.
This presentation educates you about top data science project ideas for Beginner, Intermediate and Advanced. the ideas such as Fake News Detection Using Python, Data Science Project on, Detecting Forest Fire, Detection of Road Lane Lines, Project on Sentimental Analysis, Speech Recognition, Developing Chatbots, Detection of Credit Card Fraud and Customer Segmentations etc:
For more topics stay tuned with Learnbay.
This document summarizes a presentation by Christina Pikas on how librarians at special libraries like the Johns Hopkins Applied Physics Laboratory (APL) can provide bibliometric analysis services. Pikas discusses how librarians' domain knowledge, access to data, and understanding of ethics uniquely positions them to analyze research output and collaboration in a reliable way. She provides examples of bibliometric questions answered at APL and the tools used. Pikas concludes that librarians should leverage their skills and study bibliometrics to support research assessment activities.
This certificate certifies that an individual successfully completed a 12-hour online cybersecurity course from January 12 to February 23, 2016. The course was developed by faculty from MIT's Computer Science and Artificial Intelligence Laboratory in collaboration with MIT Professional Education. The certificate is signed by the Executive Director of MIT Professional Education and the Director and a Principal Research Scientist of the Computer Science and Artificial Intelligence Laboratory.
This document provides an overview of open brain imaging data resources that are available for machine learning research. It describes the different types of brain scan data, such as structural scans, diffusion scans, and functional scans. It then summarizes several existing datasets that contain brain imaging data for conditions like brain tumors, stroke, Alzheimer's disease, autism, and ADHD. The document recommends some large open datasets for transfer learning and notes opportunities for collaborating with other researchers at events like Brainhack Global San Francisco.
Semantics for Bioinformatics: What, Why and How of Search, Integration and An...Amit Sheth
Amit Sheth's Keynote at Semantic Web Technologies for Science and Engineering Workshop (held in conjunction with ISWC2003), Sanibel Island, FL, October 20, 2003.
With the explosion of interest in both enhanced knowledge management and open science, the past few years have seen considerable discussion about making scientific data “FAIR” — findable, accessible, interoperable, and reusable. The problem is that most scientific datasets are not FAIR. When left to their own devices, scientists do an absolutely terrible job creating the metadata that describe the experimental datasets that make their way in online repositories. The lack of standardization makes it extremely difficult for other investigators to locate relevant datasets, to re-analyse them, and to integrate those datasets with other data. The Center for Expanded Data Annotation and Retrieval (CEDAR) has the goal of enhancing the authoring of experimental metadata to make online datasets more useful to the scientific community. The CEDAR work bench for metadata management will be presented in this webinar. CEDAR illustrates the importance of semantic technology to driving open science. It also demonstrates a means for simplifying access to scientific data sets and enhancing the reuse of the data to drive new discoveries.
Preprint-ICDMAI,Defense Institute,20-22 January 2023.pdfChristo Ananth
Call for Papers- Special Session: Bio-Signal Processing using Deep Learning, 7th International Conference on Data Management, Analytics & Innovation (ICDMAI), Defence Institute of Advanced Technology, Pune-India Organized by Society For Data Science, Pune, India, 20-22 January 2023
This document discusses profiling linked open data. It outlines the research background, plan, and preliminary results of profiling linked open data. The research aims to automatically generate new statistics and knowledge patterns to provide dataset summaries and inspect data quality. Preliminary results include profiling Italian public administration websites for compliance with open data policies and automatically classifying over 1,000 linked data sets into 8 topics with over 80% accuracy. Future work involves enriching the framework with additional statistics and applying it to unstructured microdata.
The document discusses the growth of data-intensive science and the need for new computing infrastructures to manage the large amounts of data being produced. It covers three perspectives on infrastructure: grid computing which enables sharing of distributed resources over the internet, data centers which provide integrated storage and computing services, and e-science which combines grids, collaboration tools, and data analysis services. Examples are given of different scientific domains using these infrastructures.
Data Science and AI in Biomedicine: The World has ChangedPhilip Bourne
This document discusses the changing landscape of data science and AI in biomedicine. Some key points:
- We are at a tipping point where data science is becoming a driver of biomedical research rather than just a tool. Biomedical researchers need to become data scientists.
- Data science is interdisciplinary and touches every field due to the rise of digital data. It requires openness, translation of findings, and consideration of responsibilities like algorithmic bias.
- Advances like AlphaFold2 show the power of large collaborative efforts combining data, computing resources, engineering, and domain expertise. This points to the need for public-private partnerships and new models of open data sharing.
- The definition of
Trust and Accountability: experiences from the FAIRDOM Commons Initiative.Carole Goble
Presented at Digital Life 2018, Bergen, March 2018. In the Trust and Accountability session.
In recent years we have seen a change in expectations for the management and availability of all the outcomes of research (models, data, SOPs, software etc) and for greater transparency and reproduciblity in the method of research. The “FAIR” (Findable, Accessible, Interoperable, Reusable) Guiding Principles for stewardship [1] have proved to be an effective rallying-cry for community groups and for policy makers.
The FAIRDOM Initiative (FAIR Data Models Operations, http://www.fair-dom.org) supports Systems Biology research projects with their research data, methods and model management, with an emphasis on standards and sensitivity to asset sharing and credit anxiety. Our aim is a FAIR Research Commons that blends together the doing of research with the communication of research. The Platform has been installed by over 30 labs/projects and our public, centrally hosted FAIRDOMHub [2] supports the outcomes of 90+ projects. We are proud to support projects in Norway’s Digital Life programme.
2018 is our 10th anniversary. Over the past decade we learned a lot about trust between researchers, between researchers and platform developers and curators and between both these groups and funders. We have experienced the Tragedy of the Commons but also seen shifts in attitudes.
In this talk we will use our experiences in FAIRDOM to explore the political, economic, social and technical, social practicalities of Trust.
[1] Wilkinson et al (2016) The FAIR Guiding Principles for scientific data management and stewardship Scientific Data 3, doi:10.1038/sdata.2016.18
[2] Wolstencroft, et al (2016) FAIRDOMHub: a repository and collaboration environment for sharing systems biology research Nucleic Acids Research, 45(D1): D404-D407. DOI: 10.1093/nar/gkw1032
1. Developing a unifying theory of data mining that connects different tasks and approaches could help advance the field by providing a theoretical framework.
2. Scaling data mining methods to handle high dimensional and streaming data at massive scales is challenging due to limitations in current approaches for problems like concept drift.
3. Efficiently mining sequential, time series, and noisy time series data remains an important open problem, particularly for applications like financial and seismic predictions.
The document discusses future developments in cognitive-based knowledge acquisition systems using big data. It covers preparing students and the cognitive landscape for big data analytics through tools like concept maps and visualization. It also addresses challenges like determining where information comes from, whether humans or computers can best identify patterns in data, and whether autonomous systems will eventually replace human decision making.
Biomedical Data Science: We Are Not AlonePhilip Bourne
This document discusses biomedical data science and the opportunities and challenges presented by new developments in data science. Some key points:
- We are at a tipping point where biomedical research is no longer the sole leader in data science due to advances in many other fields. Biomedical researchers need to become data scientists to stay relevant.
- Data science is being driven by the massive growth of digital data and requires an interdisciplinary approach. It is touching every field and attracting many students.
- Developing effective data systems and infrastructure is a major challenge to enable open sharing and analysis of data. Initiatives are underway but more collaboration is needed across sectors.
- Advances in machine learning, like Alpha
[DSC Croatia 22] Writing scientific papers about data science projects - Mirj...DataScienceConferenc1
Data science is not only about numbers and how to crunch them; it is also about how to communicate project results with the various audience. Scientific journals and conferences are an excellent venue for getting a wider audience reach and gathering valuable comments. The talk will answer the questions: How to structure a scientific paper in data science? What are relevant venues for showcasing your work to gain the most relevant reach? To demystify the process of scientific writing, the case study will be presented: Messy process: Story of the birth of one data science paper.
Building Effective Visualization Shiny WVFOlga Scrivner
This document provides an overview of web visualization tools and frameworks for business intelligence and data visualization. It discusses reactive web frameworks, the Shiny application framework from RStudio, and the Web Visualization Framework (WVF) developed by the Cyberinfrastructure for Network Science Center. Examples of visualizations created with Shiny and WVF are presented, including Sankey diagrams, streamgraphs, heatmaps, and network maps. The document concludes by discussing the future outlook for WVF and promoting an online course on information visualization.
Thoughts on Knowledge Graphs & Deeper ProvenancePaul Groth
Thinking about the need for deeper provenance for knowledge graphs but also using knowledge graphs to enrich provenance. Presented at https://seminariomirianandres.unirioja.es/sw19/
Post 1We all know that our era belongs to technology; we are veanhcrowley
The document discusses how the gospel message is enculturated, or influenced by culture. It argues that the gospel is not acultural, but rather takes on aspects of the cultures it engages with. It notes how Jesus himself was enculturated as a first century Jewish man. When sharing the gospel, it is important to understand both one's own cultural influences and those of the person one is evangelizing to. The gospel must be communicated in a way that is understandable within a person's cultural frame of reference in order to be properly interpreted. Application of the gospel will also look different across cultural contexts. Evangelists themselves have a cultural accent or perspective that shapes how they understand and present the gospel.
Post 1We all know that our era belongs to technology; we are ve.docxstilliegeorgiana
Post 1:
We all know that our era belongs to technology; we are very much depending on technology. Data is everywhere, for every second lot of data is producing around us. In order to handle all these huge data we are relying on information technology to make use of the data. Below are the most essential directions for the information delivery
The Internet of things. Wireless communications and radio frequency identification (RFID) product tags will be used in every organization in the future days to track the physical objects (car parts, etc) as they are moving through the supply chain. Already Walmart started conducting large-scale trials with RFID with hundreds of its major suppliers. In future RFID replace the universal code (Langton 2004). As the usage of technologies are continuously increasing, so organizations will be able to track and they can remotely control the status of everything from the freshness of lettuce between the field and the store to the location of the hospital suppliers. Even though this technology is almost ready for prime time, most organizations are nowhere near ready to cope with making sense of such a large influx of information; this will be one of the biggest challenges of the future (Smith and Konsynski 2003).
Networkcentric operations: The improvement of the standardizing communication network protocols such as network devices, high- speed data will provide to access the data to collect, distribute, create and exploit very fast. There are three critical elements which must be in place to achieve this goal:
Sensor grids. Small sensor devices are connected to the computers to filter different types of data, highlighting areas and anomalies to which the organization should pay attention ((Watson et al. 2010).
High- quality visual information. Along with all the sophisticated modeling and the simulation capabilities and the displaying the technology, high- quality visualized information will provide dramatically create better awareness about the market place, operations and the environmental impact.
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Date: May 29, 2024
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ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
Search, Discovery and Analysis of Sensory Data Streams
1. Search, Discovery and Analysis
of Sensory Data Streams
1
Payam Barnaghi
Centre for Vision, Speech and Signal Processing (CVSSP), University of
Surrey
Care Technology & Research Centre, The UK Dementia Research
Institute (DRI)
SAW2019: 1st International Workshop on Sensors and Actuators on
the Web
2. 46 years ago on the 5th of November (submission
day)
2
Source: https://www.cs.princeton.edu/courses/archive/fall06/cos561/papers/cerf74.pdf
• A 32 bit IP address was
used of which the first 8
bits signified the network
and the remaining 24 bits
designated the host on
that network.
• The assumption was that
256 networks would be
sufficient for the
foreseeable future…
• Obviously this was before
LANs (Ethernet was
under development at
Xerox PARC at that time).
5. And there came Google!
55
Google says that the web has now over 30
trillion unique individual pages. It is
probably not even that relevant anymore;
lots of resources are dynamic…
17. Sensor Data Flow on the Web
17
P. Barnaghi, A. Sheth, “On Searching the Internet of Things: Requirements and Challenges”, IEEE Intelligent Systems, 2016.
28. The Crawling Challenge
− Uniform policy: re-visiting all pages in the collection with
the same frequency, regardless of their rates of change.
− Proportional policy: re-visiting more often the pages that
change more frequently. The visiting frequency is directly
proportional to the (estimated) change frequency.
28
Cho, Junghoo; Garcia-Molina, Hector (2003). "Effective page refresh policies for Web
crawlers". ACM Transactions on Database Systems. 28 (4): 390–426.
29. Web Crawling
− Cho and Garcia-Molina proved the surprising result that,
in terms of average freshness, the uniform policy
outperforms the proportional policy in both a simulated
Web and a real Web crawl.
− Allocating too many new crawls to rapidly changing
pages at the expense of less frequently updating pages.
− A proportional policy allocates more resources to
crawling frequently updating pages, but experiences less
overall freshness time from them.
29
Source: Wikipedia
30. Crawling and the Freshness Issue
− To improve freshness, the crawler should penalise the
elements that change too often.
− The optimal re-visiting policy is neither the uniform policy
nor the proportional policy.
− The optimal method for keeping average freshness high
includes ignoring the pages that change too often, and
the optimal for keeping average age low is to use access
frequencies that monotonically (and sub-linearly)
increase with the rate of change of each page.
30
Junghoo Cho; Hector Garcia-Molina (2003). "Estimating frequency of change". ACM
Transactions on Internet Technology. 3 (3): 256–290.
Source: Wikipedia
33. But the data is often multidimensional and
multivariate
33Credit: Shirin Enshaeifar, CR&T Centre, UK Dementia Research Institute/CVSSP, Uni of Surrey
34. Creating patterns from streaming data
34(Gonzalez-Vidal, Barnaghi, Skarmeta, IEEE TKDE, 2018)
39. Some of the Research Challenges
− Provenance monitoring and fact checking algorithms
and tools
− Dealing with noisy, incomplete and dynamic data.
− Handling and processing large data streams, search and
identification of patterns.
− Crawling, search and query of changing data
− Multi-modal information analysis and continual and
adaptive learning algorithms
− Security, privacy, trust and accessibility
− Solutions to keep (and make) the Web a safe, open,
inclusive and collaborative environment.
39
43. How stable are the models that you learn from
your data?
43
Credits: Roonak Rezvani, CR&T Centre, UK Dementia Research Institute/CVSSP, Uni of Surrey
44. Dynamicity and machine learning issue
44
Noise and missing data Pattern and change representation
Continual and adaptive learning Network and Causation analysis
47. References
− S. Enshaeifar et. al, "Health management and pattern analysis of daily living activities
of people with Dementia using in-home sensors and machine learning techniques",
PLoS ONE 13(5): e0195605, 2018.
− A. González Vidal, P. Barnaghi, A. F. Skarmeta, "BEATS: Blocks of Eigenvalues
Algorithm for Time series Segmentation", IEEE Transactions on Knowledge and Data
Engineering (TKDE), 2018.
− Y. Fathy, P. Barnaghi, R. Tafazolli, "An Online Adaptive Algorithm for Change
Detection in Streaming Sensory Data", IEEE Systems Journal, 2018.
− Y. Fathy, P. Barnaghi, R. Tafazolli, "Large-Scale Indexing, Discovery and Ranking for
the Internet of Things (IoT)", ACM Computing Surveys, 2017.
− S. A. Hosieni Tabatabaei, Y. Fathy, P. Barnaghi, C. Wang, R. Tafazolli, "A Novel
Indexing Method for Scalable IoT Source Lookup", IEEE Internet of Things Journal,
2018.
− Y. Fathy, P. Barnaghi, R. Tafazolli, "Distributed Spatial Indexing for the Internet of
Things Data Management", Proc. of IFIP/IEEE International Symposium on
Integrated Network Management, Lisbon, Portugal, May 2017.
47