The document discusses integrating a research group's website data into the Web of Data. It describes motivating the project by making the website's unstructured data available as Linked Data. The proposed solution is a Joomla! extension that extracts publication data through regular expressions, DBLP queries, and Google Scholar scraping. The extension generates RDF that links publications to authors' FOAF profiles and enriches the data with information from external sources. The system architecture includes a Joomla! plugin for data extraction and an RDF store to centralize the Linked Data.
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Presentation slides of the LEAD (Learning Design – Designing for Learning) project. The research project aims to (1) bring design thinking to learning design and to (2) bring design expertise to the development process of technological learning solutions. In this project we understand learning situations widely, from traditional classroom situations to more informal learning settings. Project consortium is combination of Finnish leading universities with major international academic collaboration, active new start-ups and SMEs developing new solutions for educational institutes and organization for tackling the 21st century information management and learning challenges, and high-impact testbeds that act as a catalyst for companies to trial their solutions and competencies. The two year project includes collaboration with number of international research partners. The project is funded by Tekes – the Finnish Funding Agency for Technology and Innovation.
This document summarizes a presentation given by Katrina Pritchard and Rebecca Whiting on their e-research project. It discusses what e-research is, outlines their approach which included collecting data through alerts and tracking online conversations, and discusses some of the practical and ethical challenges they faced such as managing large amounts of digitally generated data and blurred boundaries between primary and secondary data. Key emergent ideas from their project included tracking online conversations and re-thinking relationships with research participants in an online context.
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The document summarizes the presentation given by Dr. Andreas Both on challenges related to big data and the data economy. It discusses Unister's evolution from an internet startup in 2002 to a large company with over 1500 employees in a decade. Unister succeeded by integrating diverse data sets to improve user experience, developing data analysis processes, and defining descriptive analysis processes to handle increasing data volumes, though analysis capabilities reached limits due to many business segments. Overall the presentation addressed the steps a startup must take regarding data access, integration and analysis, and emphasized that managing data is key for data-driven companies but challenging given the volume, variety and velocity of big data.
Big Data and Content Management. SkyDox and the European Court of Human Righ...SkyDox LTD
SkyDox Business Development Director, Josh Gilbertson & Head of IT at the ECHR John Hunter, discuss how SkyDox cloud-enabled file collaboration platform has improved the content management at the ECHR at Info360 in New York.
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This document discusses wikibility, which is defined as how effectively a wiki can be used, in innovation-oriented workplaces using the case of CERN. It begins by exploring the shift from Web 1.0 to Web 2.0 and how this enables new forms of knowledge management and collaboration. The research questions are identified as determining the key cultural drivers that make an organization wikible and how to audit an organization's wikibility. A model is presented that focuses on how organizational culture enables wiki use and drives innovation. Insights identify some initial cultural drivers like flexibility, sharing, and trust that wikis improve and that drive innovation through collaboration and openness to ideas.
Internal presentation for the Enterprise 2.0 Observatory (October 2007). Topics: Enterprise 2.0, Open Innovation, Mobility, Crowdsourcing, Social Network, and more...
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The document discusses enabling semantic web technologies in enterprise 2.0 environments. It provides an overview of enterprise 2.0 and how semantic technologies can help solve issues with current enterprise 2.0 systems. The tutorial goals are to explain how to implement a semantic web architecture for enterprise 2.0, including how to create, consume and mash up RDF data from multiple enterprise 2.0 services. Use cases will also be discussed.
LEAD - Learning Design – Design For Learning -project presentationTeemu Leinonen
Presentation slides of the LEAD (Learning Design – Designing for Learning) project. The research project aims to (1) bring design thinking to learning design and to (2) bring design expertise to the development process of technological learning solutions. In this project we understand learning situations widely, from traditional classroom situations to more informal learning settings. Project consortium is combination of Finnish leading universities with major international academic collaboration, active new start-ups and SMEs developing new solutions for educational institutes and organization for tackling the 21st century information management and learning challenges, and high-impact testbeds that act as a catalyst for companies to trial their solutions and competencies. The two year project includes collaboration with number of international research partners. The project is funded by Tekes – the Finnish Funding Agency for Technology and Innovation.
This document summarizes a presentation given by Katrina Pritchard and Rebecca Whiting on their e-research project. It discusses what e-research is, outlines their approach which included collecting data through alerts and tracking online conversations, and discusses some of the practical and ethical challenges they faced such as managing large amounts of digitally generated data and blurred boundaries between primary and secondary data. Key emergent ideas from their project included tracking online conversations and re-thinking relationships with research participants in an online context.
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The document summarizes the presentation given by Dr. Andreas Both on challenges related to big data and the data economy. It discusses Unister's evolution from an internet startup in 2002 to a large company with over 1500 employees in a decade. Unister succeeded by integrating diverse data sets to improve user experience, developing data analysis processes, and defining descriptive analysis processes to handle increasing data volumes, though analysis capabilities reached limits due to many business segments. Overall the presentation addressed the steps a startup must take regarding data access, integration and analysis, and emphasized that managing data is key for data-driven companies but challenging given the volume, variety and velocity of big data.
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SkyDox Business Development Director, Josh Gilbertson & Head of IT at the ECHR John Hunter, discuss how SkyDox cloud-enabled file collaboration platform has improved the content management at the ECHR at Info360 in New York.
Wikibility of Innovation Oriented Workplaces - The CERN CaseVince Cammarata
This document discusses wikibility, which is defined as how effectively a wiki can be used, in innovation-oriented workplaces using the case of CERN. It begins by exploring the shift from Web 1.0 to Web 2.0 and how this enables new forms of knowledge management and collaboration. The research questions are identified as determining the key cultural drivers that make an organization wikible and how to audit an organization's wikibility. A model is presented that focuses on how organizational culture enables wiki use and drives innovation. Insights identify some initial cultural drivers like flexibility, sharing, and trust that wikis improve and that drive innovation through collaboration and openness to ideas.
Internal presentation for the Enterprise 2.0 Observatory (October 2007). Topics: Enterprise 2.0, Open Innovation, Mobility, Crowdsourcing, Social Network, and more...
This document summarizes a research paper on big data and Hadoop. It begins by defining big data and explaining how the volume, variety and velocity of data makes it difficult to process using traditional methods. It then discusses Hadoop, an open source software used to analyze large datasets across clusters of computers. Hadoop uses HDFS for storage and MapReduce as a programming model to distribute processing. The document outlines some of the key challenges of big data including privacy, security, data access and analytical challenges. It also summarizes advantages of big data in areas like understanding customers, optimizing business processes, improving science and healthcare.
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Toward a System Building Agenda for Data Integration(and Dat.docxjuliennehar
Toward a System Building Agenda for Data Integration
(and Data Science)
AnHai Doan, Pradap Konda, Paul Suganthan G.C., Adel Ardalan, Jeffrey R. Ballard, Sanjib Das,
Yash Govind, Han Li, Philip Martinkus, Sidharth Mudgal, Erik Paulson, Haojun Zhang
University of Wisconsin-Madison
Abstract
We argue that the data integration (DI) community should devote far more effort to building systems,
in order to truly advance the field. We discuss the limitations of current DI systems, and point out that
there is already an existing popular DI “system” out there, which is PyData, the open-source ecosystem
of 138,000+ interoperable Python packages. We argue that rather than building isolated monolithic DI
systems, we should consider extending this PyData “system”, by developing more Python packages that
solve DI problems for the users of PyData. We discuss how extending PyData enables us to pursue an
integrated agenda of research, system development, education, and outreach in DI, which in turn can
position our community to become a key player in data science. Finally, we discuss ongoing work at
Wisconsin, which suggests that this agenda is highly promising and raises many interesting challenges.
1 Introduction
In this paper we focus on data integration (DI), broadly interpreted as covering all major data preparation steps
such as data extraction, exploration, profiling, cleaning, matching, and merging [10]. This topic is also known
as data wrangling, munging, curation, unification, fusion, preparation, and more. Over the past few decades, DI
has received much attention (e.g., [37, 29, 31, 20, 34, 33, 6, 17, 39, 22, 23, 5, 8, 36, 15, 35, 4, 25, 38, 26, 32, 19,
2, 12, 11, 16, 2, 3]). Today, as data science grows, DI is receiving even more attention. This is because many
data science applications must first perform DI to combine the raw data from multiple sources, before analysis
can be carried out to extract insights.
Yet despite all this attention, today we do not really know whether the field is making good progress. The
vast majority of DI works (with the exception of efforts such as Tamr and Trifacta [36, 15]) have focused on
developing algorithmic solutions. But we know very little about whether these (ever-more-complex) algorithms
are indeed useful in practice. The field has also built mostly isolated system prototypes, which are hard to use and
combine, and are often not powerful enough for real-world applications. This makes it difficult to decide what
to teach in DI classes. Teaching complex DI algorithms and asking students to do projects using our prototype
systems can train them well for doing DI research, but are not likely to train them well for solving real-world DI
problems in later jobs. Similarly, outreach to real users (e.g., domain scientists) is difficult. Given that we have
Copyright 0000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for
advertising or promotional purpose ...
FAIR data_ Superior data visibility and reuse without warehousing.pdfAlan Morrison
The advantages of semantic knowledge graphs over data warehousing when it comes to scaling quality, contextualized data for machine learning and advanced analytics purposes.
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An open source methodology called MIKE2.0 provides a framework for information management that can be applied to any project. It uses an online collaborative community and wiki to develop standards for information development. MIKE2.0 aims to create a common industry approach to tackling the growing complexity of information management in an increasingly connected world.
This document provides an overview of big data and Hadoop. It defines big data as large volumes of diverse data that cannot be processed by traditional systems. Key characteristics are volume, velocity, variety, and veracity. Popular sources of big data include social media, emails, videos, and sensor data. Hadoop is presented as an open-source framework for distributed storage and processing of large datasets across clusters of computers. It uses HDFS for storage and MapReduce as a programming model. Major tech companies like Google, Facebook, and Amazon are discussed as big players in big data.
Drinking from the Fire Hose: Practical Approaches to Big Data Preparation and...Inside Analysis
The Briefing Room with Robin Bloor and Pervasive Software
Slides from the Live Webcast on May 1, 2012
The old methods of delivering data for analysts and other business users will simply not scale to meet new demands. Hadoop is rapidly emerging as a powerful and economic platform for storing and processing Big Data. And yet, the biggest obstacle to implementing Hadoop solutions is the scarcity of Hadoop programming skills.
Check out this episode of The Briefing Room to learn from veteran Analyst Robin Bloor, who will explain why modern information architectures must embrace the new, massively parallel world of computing as it relates to several enterprise roles: traditional business analysts, data scientists, and line-of-business workers. He'll be briefed by David Inbar and Jim Falgout of Pervasive Software, who will explain how Pervasive RushAnalyzer™ was designed to accommodate the new reality of Big Data.
For more information visit: http://www.insideanalysis.com
Watch us on YouTube: http://www.youtube.com/playlist?list=PL5EE76E2EEEC8CF9E
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One of the hardest problems technically is to allow external collaborators to work with internal team effectively. Security and privacy are the chief concerns. With the new paradigm shift, it brings big opportunities for us to do this correctly.
Business Intelligence for normal peoplemark madsen
1. Web 2.0 applications allow for open access and open data, which encourages individual contribution and collective value through passive collaboration without central planning.
2. The web functions as a database that can be used flexibly to create mashups by combining different types of data and applications.
3. Mashups are easy to create situational applications that can address the "long tail of IT applications" and be made by domain experts without extensive developer help.
The document discusses new tools being developed by KBK Group to more efficiently search the growing amounts of multimedia data on the internet. The tools apply indexing and "DNA codes" to allow search by image, video, color, and other attributes. The tools can be integrated into clients' databases while keeping the original data confidential. KBK Group is seeking customers with large multimedia databases to pilot the new search capabilities.
The document discusses the importance of final year undergraduate projects and provides ideas and suggestions. It recommends using projects as an opportunity to gain hands-on experience with software engineering processes and emerging technologies like machine learning, Big Data, and mobile development. The document provides examples of project ideas involving knowledge management systems, algorithms as a service, clustering algorithms, and building databases. It also discusses strategies for successful project planning and completion, and notes that projects can provide chances to win prizes.
IRJET- Youtube Data Sensitivity and Analysis using Hadoop FrameworkIRJET Journal
This document discusses analyzing YouTube data using the Hadoop framework. It proposes a system to filter and analyze YouTube comment content to remove sensitive data using natural language processing and store the data in Hadoop Distributed File System (HDFS). MapReduce is used to extract key-value pairs from the data and Hadoop provides a scalable platform for analyzing the large-scale YouTube data.
The document summarized a panel on big data challenges and solutions, how big data requires new approaches, and polled attendees on their organization's progress with big data initiatives. Resources were also listed for continuing education on big data topics.
The document discusses how perfection is not achievable in the digital world and proposes that "good enough" should be the new standard. It outlines three strategies for success: 1) using automation to streamline processes like image capture and metadata extraction, 2) engaging with stakeholders through tools and communication, and 3) embracing evolution through education, professional development, and community involvement. The goal is to evolve digital project management practices away from unattainable perfection towards sustainable excellence.
A presentation of the underlying motivations and institutional context behind GeoNode, some of its major design decisions, and unresolved challenges for its sustainability.
I gave this talk at UC Berkeley School of Information's research seminar on Information and Communication Technology for Development (ICTD).
Much of the material comes from an older presentation I wrote with Rolando Peñate.
Slides from a presentation I gave at the 5th SOA, Cloud + Service Technology Symposium (September 2012, Imperial College, London). The goal of this presentation was to explore with the audience use cases at the intersection of SOA, Big Data and Fast Data. If you are working with both SOA and Big Data I would would be very interested to hear about your projects.
Response needed 1The paper is well placed on the issues of the.docxaudeleypearl
The document discusses the need for the data integration (DI) community to devote more effort to building DI systems in order to advance the field. It argues that rather than building isolated DI systems, the community should extend the existing PyData ecosystem of Python packages for data science. Specifically, it proposes developing Python packages that solve specific DI problems, fostering an ecosystem of DI packages called PyDI under PyData, and extending PyDI to cloud and collaborative settings. This would enable an integrated agenda of DI research, system building, education, and outreach to make practical impacts and position the community as a key player in data science.
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The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
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This document summarizes a research paper on big data and Hadoop. It begins by defining big data and explaining how the volume, variety and velocity of data makes it difficult to process using traditional methods. It then discusses Hadoop, an open source software used to analyze large datasets across clusters of computers. Hadoop uses HDFS for storage and MapReduce as a programming model to distribute processing. The document outlines some of the key challenges of big data including privacy, security, data access and analytical challenges. It also summarizes advantages of big data in areas like understanding customers, optimizing business processes, improving science and healthcare.
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Toward a System Building Agenda for Data Integration(and Dat.docxjuliennehar
Toward a System Building Agenda for Data Integration
(and Data Science)
AnHai Doan, Pradap Konda, Paul Suganthan G.C., Adel Ardalan, Jeffrey R. Ballard, Sanjib Das,
Yash Govind, Han Li, Philip Martinkus, Sidharth Mudgal, Erik Paulson, Haojun Zhang
University of Wisconsin-Madison
Abstract
We argue that the data integration (DI) community should devote far more effort to building systems,
in order to truly advance the field. We discuss the limitations of current DI systems, and point out that
there is already an existing popular DI “system” out there, which is PyData, the open-source ecosystem
of 138,000+ interoperable Python packages. We argue that rather than building isolated monolithic DI
systems, we should consider extending this PyData “system”, by developing more Python packages that
solve DI problems for the users of PyData. We discuss how extending PyData enables us to pursue an
integrated agenda of research, system development, education, and outreach in DI, which in turn can
position our community to become a key player in data science. Finally, we discuss ongoing work at
Wisconsin, which suggests that this agenda is highly promising and raises many interesting challenges.
1 Introduction
In this paper we focus on data integration (DI), broadly interpreted as covering all major data preparation steps
such as data extraction, exploration, profiling, cleaning, matching, and merging [10]. This topic is also known
as data wrangling, munging, curation, unification, fusion, preparation, and more. Over the past few decades, DI
has received much attention (e.g., [37, 29, 31, 20, 34, 33, 6, 17, 39, 22, 23, 5, 8, 36, 15, 35, 4, 25, 38, 26, 32, 19,
2, 12, 11, 16, 2, 3]). Today, as data science grows, DI is receiving even more attention. This is because many
data science applications must first perform DI to combine the raw data from multiple sources, before analysis
can be carried out to extract insights.
Yet despite all this attention, today we do not really know whether the field is making good progress. The
vast majority of DI works (with the exception of efforts such as Tamr and Trifacta [36, 15]) have focused on
developing algorithmic solutions. But we know very little about whether these (ever-more-complex) algorithms
are indeed useful in practice. The field has also built mostly isolated system prototypes, which are hard to use and
combine, and are often not powerful enough for real-world applications. This makes it difficult to decide what
to teach in DI classes. Teaching complex DI algorithms and asking students to do projects using our prototype
systems can train them well for doing DI research, but are not likely to train them well for solving real-world DI
problems in later jobs. Similarly, outreach to real users (e.g., domain scientists) is difficult. Given that we have
Copyright 0000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for
advertising or promotional purpose ...
FAIR data_ Superior data visibility and reuse without warehousing.pdfAlan Morrison
The advantages of semantic knowledge graphs over data warehousing when it comes to scaling quality, contextualized data for machine learning and advanced analytics purposes.
Research Methodology (how to choose Datasets ).pptxZainab Alhassani
This document provides summaries of several freely available datasets and data repositories for researchers. It describes BuzzFeed News, which shares datasets, analysis, tools and guides used in its articles on GitHub. It also describes Metatext, which aims to democratize access to AI through curated datasets for classification tasks. Paper with Code is described as sharing machine learning papers, code, datasets and evaluation tables to support NLP and ML. Datahub.io focuses on stock market and property data that is frequently updated. Finally, Google Dataset Search is presented as a search engine for datasets to make them universally accessible.
An open source methodology called MIKE2.0 provides a framework for information management that can be applied to any project. It uses an online collaborative community and wiki to develop standards for information development. MIKE2.0 aims to create a common industry approach to tackling the growing complexity of information management in an increasingly connected world.
This document provides an overview of big data and Hadoop. It defines big data as large volumes of diverse data that cannot be processed by traditional systems. Key characteristics are volume, velocity, variety, and veracity. Popular sources of big data include social media, emails, videos, and sensor data. Hadoop is presented as an open-source framework for distributed storage and processing of large datasets across clusters of computers. It uses HDFS for storage and MapReduce as a programming model. Major tech companies like Google, Facebook, and Amazon are discussed as big players in big data.
Drinking from the Fire Hose: Practical Approaches to Big Data Preparation and...Inside Analysis
The Briefing Room with Robin Bloor and Pervasive Software
Slides from the Live Webcast on May 1, 2012
The old methods of delivering data for analysts and other business users will simply not scale to meet new demands. Hadoop is rapidly emerging as a powerful and economic platform for storing and processing Big Data. And yet, the biggest obstacle to implementing Hadoop solutions is the scarcity of Hadoop programming skills.
Check out this episode of The Briefing Room to learn from veteran Analyst Robin Bloor, who will explain why modern information architectures must embrace the new, massively parallel world of computing as it relates to several enterprise roles: traditional business analysts, data scientists, and line-of-business workers. He'll be briefed by David Inbar and Jim Falgout of Pervasive Software, who will explain how Pervasive RushAnalyzer™ was designed to accommodate the new reality of Big Data.
For more information visit: http://www.insideanalysis.com
Watch us on YouTube: http://www.youtube.com/playlist?list=PL5EE76E2EEEC8CF9E
How To Connect To Your Customers, Partners Securely, Privately and EffectivelyAndy Harjanto
One of the hardest problems technically is to allow external collaborators to work with internal team effectively. Security and privacy are the chief concerns. With the new paradigm shift, it brings big opportunities for us to do this correctly.
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1. Web 2.0 applications allow for open access and open data, which encourages individual contribution and collective value through passive collaboration without central planning.
2. The web functions as a database that can be used flexibly to create mashups by combining different types of data and applications.
3. Mashups are easy to create situational applications that can address the "long tail of IT applications" and be made by domain experts without extensive developer help.
The document discusses new tools being developed by KBK Group to more efficiently search the growing amounts of multimedia data on the internet. The tools apply indexing and "DNA codes" to allow search by image, video, color, and other attributes. The tools can be integrated into clients' databases while keeping the original data confidential. KBK Group is seeking customers with large multimedia databases to pilot the new search capabilities.
The document discusses the importance of final year undergraduate projects and provides ideas and suggestions. It recommends using projects as an opportunity to gain hands-on experience with software engineering processes and emerging technologies like machine learning, Big Data, and mobile development. The document provides examples of project ideas involving knowledge management systems, algorithms as a service, clustering algorithms, and building databases. It also discusses strategies for successful project planning and completion, and notes that projects can provide chances to win prizes.
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This document discusses analyzing YouTube data using the Hadoop framework. It proposes a system to filter and analyze YouTube comment content to remove sensitive data using natural language processing and store the data in Hadoop Distributed File System (HDFS). MapReduce is used to extract key-value pairs from the data and Hadoop provides a scalable platform for analyzing the large-scale YouTube data.
The document summarized a panel on big data challenges and solutions, how big data requires new approaches, and polled attendees on their organization's progress with big data initiatives. Resources were also listed for continuing education on big data topics.
The document discusses how perfection is not achievable in the digital world and proposes that "good enough" should be the new standard. It outlines three strategies for success: 1) using automation to streamline processes like image capture and metadata extraction, 2) engaging with stakeholders through tools and communication, and 3) embracing evolution through education, professional development, and community involvement. The goal is to evolve digital project management practices away from unattainable perfection towards sustainable excellence.
A presentation of the underlying motivations and institutional context behind GeoNode, some of its major design decisions, and unresolved challenges for its sustainability.
I gave this talk at UC Berkeley School of Information's research seminar on Information and Communication Technology for Development (ICTD).
Much of the material comes from an older presentation I wrote with Rolando Peñate.
Slides from a presentation I gave at the 5th SOA, Cloud + Service Technology Symposium (September 2012, Imperial College, London). The goal of this presentation was to explore with the audience use cases at the intersection of SOA, Big Data and Fast Data. If you are working with both SOA and Big Data I would would be very interested to hear about your projects.
Response needed 1The paper is well placed on the issues of the.docxaudeleypearl
The document discusses the need for the data integration (DI) community to devote more effort to building DI systems in order to advance the field. It argues that rather than building isolated DI systems, the community should extend the existing PyData ecosystem of Python packages for data science. Specifically, it proposes developing Python packages that solve specific DI problems, fostering an ecosystem of DI packages called PyDI under PyData, and extending PyDI to cloud and collaborative settings. This would enable an integrated agenda of DI research, system building, education, and outreach to make practical impacts and position the community as a key player in data science.
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- How to streamline operations with automated policy checks on container images
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My slides at Nordic Testing Days 6.6.2024
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Towards the Integration of Research Group Website into the Web of Data
1. Towards the Integration of a Research Group
Website into the Web of Data
Mikel Emaldi David Buj´n Diego L´pez de Ipi˜a
a o n
{m.emaldi, dbujan, dipina}@deusto.es
Deusto Institute of Technology - DeustoTech
November 2011
2. Motivation Our Solution Linked Data Extension Conclusions Future Work
1 Motivation
2 Our Solution
First Approach
Solution Overview
Data Extraction
System Architecture
3 Linked Data Extension
4 Conclusions
5 Future Work
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
3. Table of Contents
1 Motivation
2 Our Solution
First Approach
Solution Overview
Data Extraction
System Architecture
3 Linked Data Extension
4 Conclusions
5 Future Work
4. Motivation Our Solution Linked Data Extension Conclusions Future Work
Motivation
The desire of offering our research group website’s
(http://www.morelab.deusto.es) data as Linked Data
Our web is supported by Joomla! CMS
The data is unstructured
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
5. Motivation Our Solution Linked Data Extension Conclusions Future Work
Motivation
The desire of offering our research group website’s
(http://www.morelab.deusto.es) data as Linked Data
Our web is supported by Joomla! CMS
The data is unstructured
We chose our publications section as first attempt
Almost 100 publications
Possibility to link them to external datasets
We saw the oportunity of centralize group’s FOAF files
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
6. Table of Contents
1 Motivation
2 Our Solution
First Approach
Solution Overview
Data Extraction
System Architecture
3 Linked Data Extension
4 Conclusions
5 Future Work
7. Motivation Our Solution Linked Data Extension Conclusions Future Work
First Approach
First Approach
A solution based on Python web-script (mod python)
The core code of Joomla! was to be modified
Here there was a major problem:
When a security update was installed, Joomla! used to destroy
our custom code
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
8. Motivation Our Solution Linked Data Extension Conclusions Future Work
Solution Overview
Joomla! Extension
A solution based on an Extension for Joomla!
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
9. Motivation Our Solution Linked Data Extension Conclusions Future Work
Solution Overview
Joomla! Extension
A solution based on an Extension for Joomla!
Component
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
10. Motivation Our Solution Linked Data Extension Conclusions Future Work
Solution Overview
Joomla! Extension
A solution based on an Extension for Joomla!
Plugin
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
11. Motivation Our Solution Linked Data Extension Conclusions Future Work
Solution Overview
Joomla! Extension
A solution based on an Extension for Joomla!
It offers a feasible solution for analyze published publications
and to generate correspondent Linked Data
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
12. Motivation Our Solution Linked Data Extension Conclusions Future Work
Data Extraction
Joomla! Content Example
TALISMAN+: Intelligent System for Follow-Up and
Promotion of Personal Autonomy
o n e ´
David Aus´ Diego L´pez-de-Ipi˜a, Jos´ Bravo, Miguel Angel Valero, Francisco Fl´rez. TALISMAN+:
ın, o
Intelligent System for Follow-Up and Promotion of Personal Autonomy. III International Workshop on
Ambient Assisted Living - IWAAL 2011. M´laga, Spain. June 2011.
a
The TALISMAN+ project, financed by the Spanish Ministry of Science and Innovation, aims to research
and demonstrate innovative solutions transferable to society which offer services and products based on
information and communication technologies in order to promote personal autonomy in prevention and
monitoring scenarios. It will solve critical interoperability problems among systems and emerging
technologies in a context where heterogeneity brings about accessibility barriers not yet overcome and
demanded by the scientific, technological or social-health settings.
Download
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
13. Motivation Our Solution Linked Data Extension Conclusions Future Work
Data Extraction
Overview
Data is extracted throught three ways:
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
14. Motivation Our Solution Linked Data Extension Conclusions Future Work
Data Extraction
Overview
Data is extracted throught three ways:
User defined Regular Expression
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
15. Motivation Our Solution Linked Data Extension Conclusions Future Work
Data Extraction
Overview
Data is extracted throught three ways:
User defined Regular Expression
DBLP SPARQL Endpoint
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
16. Motivation Our Solution Linked Data Extension Conclusions Future Work
Data Extraction
Overview
Data is extracted throught three ways:
User defined Regular Expression
DBLP SPARQL Endpoint
Google Scholar search engine
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
17. Motivation Our Solution Linked Data Extension Conclusions Future Work
Data Extraction
Regex I
User defines a regular expression to parse its content
User has to define used ontologies and their prefixes into the
admin control panel
The regex tags are clearly understandable
The ontology properties to be mapped are tagged between {}
Every delimiter (also the {}) is identified by a
The term {dummy } can be used to ignore content
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
18. Motivation Our Solution Linked Data Extension Conclusions Future Work
Data Extraction
Regex II
o n e ´
David Aus´ Diego L´pez-de-Ipi˜a, Jos´ Bravo, Miguel Angel Valero, Francisco Fl´rez. TALISMAN+:
ın, o
Intelligent System for Follow-Up and Promotion of Personal Autonomy. III International Workshop on
Ambient Assisted Living - IWAAL 2011. M´laga, Spain. June 2011.
a
The TALISMAN+ project, financed by the Spanish Ministry of Science and Innovation, aims to research
and demonstrate innovative solutions transferable to society which offer services and products based on
information and communication technologies in order to promote personal autonomy in prevention and
monitoring scenarios. It will solve critical interoperability problems among systems and emerging
technologies in a context where heterogeneity brings about accessibility barriers not yet overcome and
demanded by the scientific, technological or social-health settings.
Download
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
19. Motivation Our Solution Linked Data Extension Conclusions Future Work
Data Extraction
Regex II
o n e ´
David Aus´ Diego L´pez-de-Ipi˜a, Jos´ Bravo, Miguel Angel Valero, Francisco Fl´rez. TALISMAN+:
ın, o
Intelligent System for Follow-Up and Promotion of Personal Autonomy. III International Workshop on
Ambient Assisted Living - IWAAL 2011. M´laga, Spain. June 2011.
a
The TALISMAN+ project, financed by the Spanish Ministry of Science and Innovation, aims to research
and demonstrate innovative solutions transferable to society which offer services and products based on
information and communication technologies in order to promote personal autonomy in prevention and
monitoring scenarios. It will solve critical interoperability problems among systems and emerging
technologies in a context where heterogeneity brings about accessibility barriers not yet overcome and
demanded by the scientific, technological or social-health settings.
Download
{dc : c r e a t o r , s e p ( , ) } . {dc : t i t l e }.
{ s w r c : s e r i e s }. { s w r c : l o c a t i o n }.
{dc : d a t e }. { b i b o : a b s t r a c t } Download$
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
20. Motivation Our Solution Linked Data Extension Conclusions Future Work
Data Extraction
DBLP I
Digital Bibliography & Library Project
> 1.3 million articles
SPARQL endpoint at:
http://dblp.l3s.de/d2r/sparql/
http://dblp.l3s.de/d2r/snorql/
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
21. Motivation Our Solution Linked Data Extension Conclusions Future Work
Data Extraction
DBLP II
DBLP SPARQL endpoint is used to search data about
publications
SELECT DISTINCT ?uri ?p ?o WHERE {?uri dc:title
“title-of-article”ˆˆ<http://www.w3.org/2001/XMLSchema#string>}
Data is enriched with our own data and saved into the RDF
store
We also link members FOAF’s to DBLP authors data
<http://www.morelab.deusto.es/resource/dipina> owl:sameAs
<http://dblp.l3s.de/d2r/resource/authors/Diego L´pez-de-Ipi˜a> ;
o n
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
22. Motivation Our Solution Linked Data Extension Conclusions Future Work
Data Extraction
Google Scholar I
A simple way to broadly search for scholarly literature
http://scholar.google.com
It exports data in diferent formats
BibTeX
EndNote
RefMan
RefWorks
WenXiangWang
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
23. Motivation Our Solution Linked Data Extension Conclusions Future Work
Data Extraction
Google Scholar II
The data from GS is extracted via BibTeX scrapping
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
24. Motivation Our Solution Linked Data Extension Conclusions Future Work
Data Extraction
Google Scholar II
The data from GS is extracted via BibTeX scrapping
An HTTP request using an specific cookie to retrieve BibTeX
data
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
25. Motivation Our Solution Linked Data Extension Conclusions Future Work
Data Extraction
Google Scholar II
The data from GS is extracted via BibTeX scrapping
BibTeX data is retrieved
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
26. Motivation Our Solution Linked Data Extension Conclusions Future Work
Data Extraction
Google Scholar II
The data from GS is extracted via BibTeX scrapping
Mapping from BibTeX data to RDF
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
27. Motivation Our Solution Linked Data Extension Conclusions Future Work
Data Extraction
FOAF
Every member of our group has its own FOAF file
http://www.morelab.deusto.es/resource/member-alias
Every publication is linked to its author’s URI
<http://www.morelab.deusto.es/resource/imhotep-an-approach-to-user-and-device-conscious-
mobile-applications> dc:creator
<http://www.morelab.deusto.es/resource/dipina>
This is done automatically looking for author’s nicknames
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
28. Motivation Our Solution Linked Data Extension Conclusions Future Work
Data Extraction
Flowchart
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
29. Motivation Our Solution Linked Data Extension Conclusions Future Work
System Architecture
Overview
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
30. Motivation Our Solution Linked Data Extension Conclusions Future Work
System Architecture
Overview
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
31. Motivation Our Solution Linked Data Extension Conclusions Future Work
System Architecture
Overview
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
32. Motivation Our Solution Linked Data Extension Conclusions Future Work
System Architecture
Overview
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
33. Motivation Our Solution Linked Data Extension Conclusions Future Work
System Architecture
Overview
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
34. Motivation Our Solution Linked Data Extension Conclusions Future Work
System Architecture
Overview
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
35. Motivation Our Solution Linked Data Extension Conclusions Future Work
System Architecture
Joseki + SDB
Joseki
A SPARQL server for Jena
Storage into RDF files and relational databases
It allows SPARQL Updates
It is private for our system
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
36. Motivation Our Solution Linked Data Extension Conclusions Future Work
System Architecture
Joseki + SDB
Joseki
A SPARQL server for Jena
Storage into RDF files and relational databases
It allows SPARQL Updates
It is private for our system
SDB
A component of Jena
It provides:
Scalable storage
Query of RDF datasets using conventional SQL databases
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
37. Motivation Our Solution Linked Data Extension Conclusions Future Work
System Architecture
Pubby
Pubby adds Linked Data interfaces to SPARQL endpoints
It allows content negotiation among these formats:
HTML
RDF/XML
N3
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
38. Motivation Our Solution Linked Data Extension Conclusions Future Work
System Architecture
Snorql
An AJAXy front-end for exploring RDF SPARQL endpoints
More usable than Joseki
It is MoreLab’s public SPARQL endpoint
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
39. Table of Contents
1 Motivation
2 Our Solution
First Approach
Solution Overview
Data Extraction
System Architecture
3 Linked Data Extension
4 Conclusions
5 Future Work
40. Motivation Our Solution Linked Data Extension Conclusions Future Work
Admin Overview
Dataset Creation:
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
41. Motivation Our Solution Linked Data Extension Conclusions Future Work
Admin Overview
Ontology Prefix Definition:
Regex Definition:
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
42. Motivation Our Solution Linked Data Extension Conclusions Future Work
User Overview
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
43. Table of Contents
1 Motivation
2 Our Solution
First Approach
Solution Overview
Data Extraction
System Architecture
3 Linked Data Extension
4 Conclusions
5 Future Work
44. Motivation Our Solution Linked Data Extension Conclusions Future Work
Conclusions
This solution integrates our data into Web of Data easily
Provides a reusable solution
Opens the door to more extendable solutions
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
45. Table of Contents
1 Motivation
2 Our Solution
First Approach
Solution Overview
Data Extraction
System Architecture
3 Linked Data Extension
4 Conclusions
5 Future Work
46. Motivation Our Solution Linked Data Extension Conclusions Future Work
Future Work
Link our datasets with more external datasets
DBPedia
Geonames
RDF and SPARQL search form
Externalize linked data sources
Building the Extension modularly
Mikel Emaldi, David Buj´n, Diego L´pez de Ipi˜a
a o n DeustoTech - Internet
Towards the Integration of a Research Group Website into the Web of Data
47. Towards the Integration of a Research Group
Website into the Web of Data
Mikel Emaldi David Buj´n Diego L´pez de Ipi˜a
a o n
{m.emaldi, dbujan, dipina}@deusto.es
Deusto Institute of Technology - DeustoTech
November 2011