A Biological Internet: Building Eywa from a Social Web of Things with a Little Fog, Stream processing and Linked Data.
Keynote at the Web Science Summer School 2017.
http://www.webscience.org/2017/04/19/shenzhen-web-science-summer-school-2017/
Dr. Frank Wuerthwein from the University of California at San Diego presentation at International Super Computing Conference on Big Data, 2013, US Until recently, the large CERN experiments, ATLAS and CMS, owned and controlled the computing infrastructure they operated on in the US, and accessed data only when it was locally available on the hardware they operated. However, Würthwein explains, with data-taking rates set to increase dramatically by the end of LS1 in 2015, the current operational model is no longer viable to satisfy peak processing needs. Instead, he argues, large-scale processing centers need to be created dynamically to cope with spikes in demand. To this end, Würthwein and colleagues carried out a successful proof-of-concept study, in which the Gordon Supercomputer at the San Diego Supercomputer Center was dynamically and seamlessly integrated into the CMS production system to process a 125-terabyte data set.
Accelerating data-intensive science by outsourcing the mundaneIan Foster
Talk at eResearch New Zealand Conference, June 2011 (given remotely from Italy, unfortunately!)
Abstract: Whitehead observed that "civilization advances by extending the number of important operations which we can perform without thinking of them." I propose that cloud computing can allow us to accelerate dramatically the pace of discovery by removing a range of mundane but timeconsuming research data management tasks from our consciousness. I describe the Globus Online system that we are developing to explore these possibilities, and propose milestones for evaluating progress towards smarter science.
Digital Science: Reproducibility and Visibility in AstronomyJose Enrique Ruiz
The science done in Astronomy is digital science, from observing proposals to final publication, to data and software used: each of the elements and actions involved in scientific output could be recorded in electronic form. This fact does not prevent the final outcome of an experiment is still difficult to reproduce. This procedure can be long, tedious, not easily accessible or understandable, even to the author. At the same time, we have a rich infrastructure of files, observational data and publications. This could be used more efficiently if we reach greater visibility of the scientific production, which avoids duplication of effort and reinvention.
Reproducibility is a cornerstone in scientific method, and extraction of relevant information in the current and future data flood is key in Astronomy. The AMIGA group (Analysis of the interstellar Medium of Isolated GAlaxies, IAA-CSIC, http://amiga.iaa.es) faces these two challenges in the European project "Wf4Ever: Advanced technologies for enhanced preservation workflow Science" to enable the preservation of the methodology in scalable semantic repositories to facilitate their discovery, access, inspection, exploitation and distribution. These repositories store the experiments on "Research Objects" whose main constituents are digital scientific workflows. These provide a comprehensive view and clear scientific interpretation of the experiment as well as the automation of the method, going beyond the usual pipelines that normally end up in data processing.
The quantitative leap in volume and complexity of the next generation of archives will need analysis and data mining tasks to live closer to the data, in computing and distributed storage environments, but they should also be modular enough to allow customization from scientists and be easily accessible to foster their dissemination among the community. Astronomy is a collaborative science, but it has also become highly specialized, as many other disciplines. Sharing, preservation, discovery and a much simplified access to resources in the composition of scientific workflows will enable astronomers to greatly benefit from each other’s highly specialized knowhow, they constitute a way to push Astronomy to share and publish not only results and data, but also processes and methodologies.
We will show how the use of scientific workflows can help to improve the reproducibility of the experiment and a more efficient exploitation of astronomical archives, as well as the visibility of the scientific methodology and its reuse.
Data Tribology: Overcoming Data Friction with Cloud AutomationIan Foster
A talk at the CODATA/RDA meeting in Gaborone, Botswana. I made the case that the biggest barriers to effective data sharing and reuse are often those associated with "data friction" and that cloud automation can be used to overcome those barriers.
The image on the first slide shows a few of the more than 20,000 active Globus endpoints.
A talk at the RPI-NSF Workshop on Multiscale Modeling of Complex Data, September 12, 2011, Troy NY, USA.
We have made much progress over the past decade toward effectively
harnessing the collective power of IT resources distributed across the
globe. In fields such as high-energy physics, astronomy, and climate,
thousands benefit daily from tools that manage and analyze large
quantities of data produced and consumed by large collaborative teams.
But we now face a far greater challenge: Exploding data volumes and powerful simulation tools mean that far more--ultimately
most?--researchers will soon require capabilities not so different from those used by these big-science teams. How is the general population of researchers and institutions to meet these needs? Must every lab be filled
with computers loaded with sophisticated software, and every researcher become an information technology (IT) specialist? Can we possibly afford to equip our labs in this way, and where would we find the experts to operate them?
Consumers and businesses face similar challenges, and industry has
responded by moving IT out of homes and offices to so-called cloud providers (e.g., GMail, Google Docs, Salesforce), slashing costs and complexity. I suggest that by similarly moving research IT out of the lab, we can realize comparable economies of scale and reductions in complexity. More importantly, we can free researchers from the burden of managing IT, giving them back their time to focus on research and empowering them to go beyond the scope of what was previously possible.
I describe work we are doing at the Computation Institute to realize this approach, focusing initially on research data lifecycle management. I present promising results obtained to date and suggest a path towards
large-scale delivery of these capabilities.
Dr. Frank Wuerthwein from the University of California at San Diego presentation at International Super Computing Conference on Big Data, 2013, US Until recently, the large CERN experiments, ATLAS and CMS, owned and controlled the computing infrastructure they operated on in the US, and accessed data only when it was locally available on the hardware they operated. However, Würthwein explains, with data-taking rates set to increase dramatically by the end of LS1 in 2015, the current operational model is no longer viable to satisfy peak processing needs. Instead, he argues, large-scale processing centers need to be created dynamically to cope with spikes in demand. To this end, Würthwein and colleagues carried out a successful proof-of-concept study, in which the Gordon Supercomputer at the San Diego Supercomputer Center was dynamically and seamlessly integrated into the CMS production system to process a 125-terabyte data set.
Accelerating data-intensive science by outsourcing the mundaneIan Foster
Talk at eResearch New Zealand Conference, June 2011 (given remotely from Italy, unfortunately!)
Abstract: Whitehead observed that "civilization advances by extending the number of important operations which we can perform without thinking of them." I propose that cloud computing can allow us to accelerate dramatically the pace of discovery by removing a range of mundane but timeconsuming research data management tasks from our consciousness. I describe the Globus Online system that we are developing to explore these possibilities, and propose milestones for evaluating progress towards smarter science.
Digital Science: Reproducibility and Visibility in AstronomyJose Enrique Ruiz
The science done in Astronomy is digital science, from observing proposals to final publication, to data and software used: each of the elements and actions involved in scientific output could be recorded in electronic form. This fact does not prevent the final outcome of an experiment is still difficult to reproduce. This procedure can be long, tedious, not easily accessible or understandable, even to the author. At the same time, we have a rich infrastructure of files, observational data and publications. This could be used more efficiently if we reach greater visibility of the scientific production, which avoids duplication of effort and reinvention.
Reproducibility is a cornerstone in scientific method, and extraction of relevant information in the current and future data flood is key in Astronomy. The AMIGA group (Analysis of the interstellar Medium of Isolated GAlaxies, IAA-CSIC, http://amiga.iaa.es) faces these two challenges in the European project "Wf4Ever: Advanced technologies for enhanced preservation workflow Science" to enable the preservation of the methodology in scalable semantic repositories to facilitate their discovery, access, inspection, exploitation and distribution. These repositories store the experiments on "Research Objects" whose main constituents are digital scientific workflows. These provide a comprehensive view and clear scientific interpretation of the experiment as well as the automation of the method, going beyond the usual pipelines that normally end up in data processing.
The quantitative leap in volume and complexity of the next generation of archives will need analysis and data mining tasks to live closer to the data, in computing and distributed storage environments, but they should also be modular enough to allow customization from scientists and be easily accessible to foster their dissemination among the community. Astronomy is a collaborative science, but it has also become highly specialized, as many other disciplines. Sharing, preservation, discovery and a much simplified access to resources in the composition of scientific workflows will enable astronomers to greatly benefit from each other’s highly specialized knowhow, they constitute a way to push Astronomy to share and publish not only results and data, but also processes and methodologies.
We will show how the use of scientific workflows can help to improve the reproducibility of the experiment and a more efficient exploitation of astronomical archives, as well as the visibility of the scientific methodology and its reuse.
Data Tribology: Overcoming Data Friction with Cloud AutomationIan Foster
A talk at the CODATA/RDA meeting in Gaborone, Botswana. I made the case that the biggest barriers to effective data sharing and reuse are often those associated with "data friction" and that cloud automation can be used to overcome those barriers.
The image on the first slide shows a few of the more than 20,000 active Globus endpoints.
A talk at the RPI-NSF Workshop on Multiscale Modeling of Complex Data, September 12, 2011, Troy NY, USA.
We have made much progress over the past decade toward effectively
harnessing the collective power of IT resources distributed across the
globe. In fields such as high-energy physics, astronomy, and climate,
thousands benefit daily from tools that manage and analyze large
quantities of data produced and consumed by large collaborative teams.
But we now face a far greater challenge: Exploding data volumes and powerful simulation tools mean that far more--ultimately
most?--researchers will soon require capabilities not so different from those used by these big-science teams. How is the general population of researchers and institutions to meet these needs? Must every lab be filled
with computers loaded with sophisticated software, and every researcher become an information technology (IT) specialist? Can we possibly afford to equip our labs in this way, and where would we find the experts to operate them?
Consumers and businesses face similar challenges, and industry has
responded by moving IT out of homes and offices to so-called cloud providers (e.g., GMail, Google Docs, Salesforce), slashing costs and complexity. I suggest that by similarly moving research IT out of the lab, we can realize comparable economies of scale and reductions in complexity. More importantly, we can free researchers from the burden of managing IT, giving them back their time to focus on research and empowering them to go beyond the scope of what was previously possible.
I describe work we are doing at the Computation Institute to realize this approach, focusing initially on research data lifecycle management. I present promising results obtained to date and suggest a path towards
large-scale delivery of these capabilities.
The Discovery Cloud: Accelerating Science via Outsourcing and AutomationIan Foster
Director's Colloquium at Los Alamos National Laboratory, September 18, 2014.
We have made much progress over the past decade toward harnessing the collective power of IT resources distributed across the globe. In high-energy physics, astronomy, and climate, thousands work daily within virtual computing systems with global scope. But we now face a far greater challenge: Exploding data volumes and powerful simulation tools mean that many more--ultimately most?--researchers will soon require capabilities not so different from those used by such big-science teams. How are we to meet these needs? Must every lab be filled with computers and every researcher become an IT specialist? Perhaps the solution is rather to move research IT out of the lab entirely: to leverage the “cloud” (whether private or public) to achieve economies of scale and reduce cognitive load. In this talk, I explore the past, current, and potential future of large-scale outsourcing and automation for science.
The Pacific Research Platform (PRP) aims to achieve transparent and rapid data access among collaborating scientists at multiple institutions through an integrated implementation of data-focused networking that extends the university campus Science DMZ model to a regional, national, and, eventually, a global scale.
PRP researchers are routinely achieving high-performance end-to-end networking from their labs to their collaborators’ labs and data centers, traversing multiple, heterogeneous Science DMZs and wide-area networks connecting multiple campus gateways, enabling researchers across the partnership to transfer data over dedicated optical lightpaths at speeds from 10Gb/s to 100Gb/s.
My talk at the Winter School on Big Data in Tarragona, Spain.
Abstract: We have made much progress over the past decade toward harnessing the collective power of IT resources distributed across the globe. In high-energy physics, astronomy, and climate, thousands work daily within virtual computing systems with global scope. But we now face a far greater challenge: Exploding data volumes and powerful simulation tools mean that many more--ultimately most?--researchers will soon require capabilities not so different from those used by such big-science teams. How are we to meet these needs? Must every lab be filled with computers and every researcher become an IT specialist? Perhaps the solution is rather to move research IT out of the lab entirely: to leverage the “cloud” (whether private or public) to achieve economies of scale and reduce cognitive load. I explore the past, current, and potential future of large-scale outsourcing and automation for science, and suggest opportunities and challenges for today’s researchers.
The science performed in Astronomy is digital science, from observing proposals to final publication, including data and software used: each of the elements and actions involved in the scientific output could be recorded in electronic form.
This fact does not prevent the final outcome of an experiment is still difficult to reproduce. An exhaustive process of documentation can be long, tedious, where access to all the resources must be granted, and after all, the repeatability of results is not even guaranteed. At the same time, we have access to a wealth of files, observational data and publications that could be used more efficiently with a better visibility of the scientific production, avoiding duplication of effort and reinvention.
These are the slides from a plenary panel that I participated in at IEEE Cloud 2011 on July 5, 2011 in Washington, D.C. I discussed the Open Science Data Cloud and concluded the talk by three research questions
Accelerating Discovery via Science ServicesIan Foster
[A talk presented at Oak Ridge National Laboratory on October 15, 2015]
We have made much progress over the past decade toward harnessing the collective power of IT resources distributed across the globe. In big-science projects in high-energy physics, astronomy, and climate, thousands work daily within virtual computing systems with global scope. But we now face a far greater challenge: Exploding data volumes and powerful simulation tools mean that many more--ultimately most?--researchers will soon require capabilities not so different from those used by such big-science teams. How are we to meet these needs? Must every lab be filled with computers and every researcher become an IT specialist? Perhaps the solution is rather to move research IT out of the lab entirely: to develop suites of science services to which researchers can dispatch mundane but time-consuming tasks, and thus to achieve economies of scale and reduce cognitive load. I explore the past, current, and potential future of large-scale outsourcing and automation for science, and suggest opportunities and challenges for today’s researchers. I use examples from Globus and other projects to demonstrate what can be achieved.
New learning technologies seem likely to transform much of science, as they are already doing for many areas of industry and society. We can expect these technologies to be used, for example, to obtain new insights from massive scientific data and to automate research processes. However, success in such endeavors will require new learning systems: scientific computing platforms, methods, and software that enable the large-scale application of learning technologies. These systems will need to enable learning from extremely large quantities of data; the management of large and complex data, models, and workflows; and the delivery of learning capabilities to many thousands of scientists. In this talk, I review these challenges and opportunities and describe systems that my colleagues and I are developing to enable the application of learning throughout the research process, from data acquisition to analysis.
Astronomy is a collaborative science, but it has also become highly specialized, as many other disciplines. Improvement of sharing, discovery and access to resources will enable astronomers to greatly benefit from each other’s highly specialized knowhow. Some initiatives led by scientists and publishers, complement traditional paper publishing with assets published in more interactive digital formats. Among the main goals of these efforts are improving the reproducibility and clarity of the scientific outcome, going beyond the static PDF file, and fostering re-use, which turns into a more efficient exploitation of available digital resources.
Using the Open Science Data Cloud for Data Science ResearchRobert Grossman
The Open Science Data Cloud is a petabyte scale science cloud for managing, analyzing, and sharing large datasets. We give an overview of the Open Science Data Cloud and how it can be used for data science research.
Los IPython Notebooks nos han proporcionado una sustancial mejora en la documentación del scripts, así como su inspección y una mayor re-utilización. Los IPython Notebooks también permiten acceder a distintos lenguajes de programación (Fortran, IDL, R, Shell,..) en un mismo script, lo que unido a su modo de acceso Web les hace ser un elemento ideal para el trabajo colaborativo (multi-lenguaje, multi-usuario, multi-plataforma, etc..) Os contaré qué tipo de cosas pueden hacerse con IPython Notebooks, desde desarrollo colaborativo de código multi-lenguaje, pasando por la reutilización de tutoriales, visualización interactiva de resultados, hasta la distribución de código más modular, y la publicación final de un experimento digital verificable y reproducible: el preámbulo de los papers ejecutables.
Large Scale On-Demand Image Processing For Disaster ReliefRobert Grossman
This is a status update (as of Feb 22, 2010) of a new Open Cloud Consortium project that will provide on-demand, large scale image processing to assist with disaster relief efforts.
Scaling collaborative data science with Globus and JupyterIan Foster
The Globus service simplifies the utilization of large and distributed data on the Jupyter platform. Ian Foster explains how to use Globus and Jupyter to seamlessly access notebooks using existing institutional credentials, connect notebooks with data residing on disparate storage systems, and make data securely available to business partners and research collaborators.
Challenges and Issues of Next Cloud Computing PlatformsFrederic Desprez
Cloud computing has now crossed the frontiers of research to reach industry. It is used every day , whether to exchange emails or make
reservations on web sites. However, many research works remain to be done to improve the performance and functionality of these platforms of tomorrow. In this talk, I will do an overview of some these theoretical and appliead researches done at INRIA and particularly around Clouds distribution, energy monitoring and management, massive data processing and exchange, and resource management.
The Discovery Cloud: Accelerating Science via Outsourcing and AutomationIan Foster
Director's Colloquium at Los Alamos National Laboratory, September 18, 2014.
We have made much progress over the past decade toward harnessing the collective power of IT resources distributed across the globe. In high-energy physics, astronomy, and climate, thousands work daily within virtual computing systems with global scope. But we now face a far greater challenge: Exploding data volumes and powerful simulation tools mean that many more--ultimately most?--researchers will soon require capabilities not so different from those used by such big-science teams. How are we to meet these needs? Must every lab be filled with computers and every researcher become an IT specialist? Perhaps the solution is rather to move research IT out of the lab entirely: to leverage the “cloud” (whether private or public) to achieve economies of scale and reduce cognitive load. In this talk, I explore the past, current, and potential future of large-scale outsourcing and automation for science.
The Pacific Research Platform (PRP) aims to achieve transparent and rapid data access among collaborating scientists at multiple institutions through an integrated implementation of data-focused networking that extends the university campus Science DMZ model to a regional, national, and, eventually, a global scale.
PRP researchers are routinely achieving high-performance end-to-end networking from their labs to their collaborators’ labs and data centers, traversing multiple, heterogeneous Science DMZs and wide-area networks connecting multiple campus gateways, enabling researchers across the partnership to transfer data over dedicated optical lightpaths at speeds from 10Gb/s to 100Gb/s.
My talk at the Winter School on Big Data in Tarragona, Spain.
Abstract: We have made much progress over the past decade toward harnessing the collective power of IT resources distributed across the globe. In high-energy physics, astronomy, and climate, thousands work daily within virtual computing systems with global scope. But we now face a far greater challenge: Exploding data volumes and powerful simulation tools mean that many more--ultimately most?--researchers will soon require capabilities not so different from those used by such big-science teams. How are we to meet these needs? Must every lab be filled with computers and every researcher become an IT specialist? Perhaps the solution is rather to move research IT out of the lab entirely: to leverage the “cloud” (whether private or public) to achieve economies of scale and reduce cognitive load. I explore the past, current, and potential future of large-scale outsourcing and automation for science, and suggest opportunities and challenges for today’s researchers.
The science performed in Astronomy is digital science, from observing proposals to final publication, including data and software used: each of the elements and actions involved in the scientific output could be recorded in electronic form.
This fact does not prevent the final outcome of an experiment is still difficult to reproduce. An exhaustive process of documentation can be long, tedious, where access to all the resources must be granted, and after all, the repeatability of results is not even guaranteed. At the same time, we have access to a wealth of files, observational data and publications that could be used more efficiently with a better visibility of the scientific production, avoiding duplication of effort and reinvention.
These are the slides from a plenary panel that I participated in at IEEE Cloud 2011 on July 5, 2011 in Washington, D.C. I discussed the Open Science Data Cloud and concluded the talk by three research questions
Accelerating Discovery via Science ServicesIan Foster
[A talk presented at Oak Ridge National Laboratory on October 15, 2015]
We have made much progress over the past decade toward harnessing the collective power of IT resources distributed across the globe. In big-science projects in high-energy physics, astronomy, and climate, thousands work daily within virtual computing systems with global scope. But we now face a far greater challenge: Exploding data volumes and powerful simulation tools mean that many more--ultimately most?--researchers will soon require capabilities not so different from those used by such big-science teams. How are we to meet these needs? Must every lab be filled with computers and every researcher become an IT specialist? Perhaps the solution is rather to move research IT out of the lab entirely: to develop suites of science services to which researchers can dispatch mundane but time-consuming tasks, and thus to achieve economies of scale and reduce cognitive load. I explore the past, current, and potential future of large-scale outsourcing and automation for science, and suggest opportunities and challenges for today’s researchers. I use examples from Globus and other projects to demonstrate what can be achieved.
New learning technologies seem likely to transform much of science, as they are already doing for many areas of industry and society. We can expect these technologies to be used, for example, to obtain new insights from massive scientific data and to automate research processes. However, success in such endeavors will require new learning systems: scientific computing platforms, methods, and software that enable the large-scale application of learning technologies. These systems will need to enable learning from extremely large quantities of data; the management of large and complex data, models, and workflows; and the delivery of learning capabilities to many thousands of scientists. In this talk, I review these challenges and opportunities and describe systems that my colleagues and I are developing to enable the application of learning throughout the research process, from data acquisition to analysis.
Astronomy is a collaborative science, but it has also become highly specialized, as many other disciplines. Improvement of sharing, discovery and access to resources will enable astronomers to greatly benefit from each other’s highly specialized knowhow. Some initiatives led by scientists and publishers, complement traditional paper publishing with assets published in more interactive digital formats. Among the main goals of these efforts are improving the reproducibility and clarity of the scientific outcome, going beyond the static PDF file, and fostering re-use, which turns into a more efficient exploitation of available digital resources.
Using the Open Science Data Cloud for Data Science ResearchRobert Grossman
The Open Science Data Cloud is a petabyte scale science cloud for managing, analyzing, and sharing large datasets. We give an overview of the Open Science Data Cloud and how it can be used for data science research.
Los IPython Notebooks nos han proporcionado una sustancial mejora en la documentación del scripts, así como su inspección y una mayor re-utilización. Los IPython Notebooks también permiten acceder a distintos lenguajes de programación (Fortran, IDL, R, Shell,..) en un mismo script, lo que unido a su modo de acceso Web les hace ser un elemento ideal para el trabajo colaborativo (multi-lenguaje, multi-usuario, multi-plataforma, etc..) Os contaré qué tipo de cosas pueden hacerse con IPython Notebooks, desde desarrollo colaborativo de código multi-lenguaje, pasando por la reutilización de tutoriales, visualización interactiva de resultados, hasta la distribución de código más modular, y la publicación final de un experimento digital verificable y reproducible: el preámbulo de los papers ejecutables.
Large Scale On-Demand Image Processing For Disaster ReliefRobert Grossman
This is a status update (as of Feb 22, 2010) of a new Open Cloud Consortium project that will provide on-demand, large scale image processing to assist with disaster relief efforts.
Scaling collaborative data science with Globus and JupyterIan Foster
The Globus service simplifies the utilization of large and distributed data on the Jupyter platform. Ian Foster explains how to use Globus and Jupyter to seamlessly access notebooks using existing institutional credentials, connect notebooks with data residing on disparate storage systems, and make data securely available to business partners and research collaborators.
Challenges and Issues of Next Cloud Computing PlatformsFrederic Desprez
Cloud computing has now crossed the frontiers of research to reach industry. It is used every day , whether to exchange emails or make
reservations on web sites. However, many research works remain to be done to improve the performance and functionality of these platforms of tomorrow. In this talk, I will do an overview of some these theoretical and appliead researches done at INRIA and particularly around Clouds distribution, energy monitoring and management, massive data processing and exchange, and resource management.
La résolution de problèmes à l'aide de graphesData2B
- Science des Réseaux
- Réseaux géographiques
- Réseaux temporels
- Le Big Data et la Science des Réseaux
- Les réseaux en Intelligence Analytique
- Réseaux de données sociales et analyse communautaire
- Réseaux de données agroalimentaires et analyse stratégique
- Intelligence émotionnelle
- Intelligence analytique et réseaux de neurones
- De l’apprentissage automatique (machine learning) au raisonnement automatique.
Recent trends of Big Data and the Internet of Things pose challenges to our current computational paradigms such as event processing systems. While three dimensions of Big Data are identified including Volume, Variety and Velocity, we think that more attention shall be given to the Variety aspect within distributed and event based systems, as it can have profound challenges to our computational systems. Event-based systems follow an interaction model based on three decoupling dimensions: space, time, and synchronization. However, event producers and consumers are tightly coupled by event semantics: types, attributes, and values. That limits scalability in large scale heterogeneous environments such as the Internet of Things (IoT) due to difficulties to establish semantic agreements on such scales. This tutorial studies this problem from various perspectives and investigates the suitability of semantic models such as vector space models for tackling the issue.
The title of this talk is a crass attempt to be catchy and topical, by referring to the recent victory of Watson in Jeopardy.
My point (perhaps confusingly) is not that new computer capabilities are a bad thing. On the contrary, these capabilities represent a tremendous opportunity for science. The challenge that I speak to is how we leverage these capabilities without computers and computation overwhelming the research community in terms of both human and financial resources. The solution, I suggest, is to get computation out of the lab—to outsource it to third party providers.
Abstract follows:
We have made much progress over the past decade toward effective distributed cyberinfrastructure. In big-science fields such as high energy physics, astronomy, and climate, thousands benefit daily from tools that enable the distributed management and analysis of vast quantities of data. But we now face a far greater challenge. Exploding data volumes and new research methodologies mean that many more--ultimately most?--researchers will soon require similar capabilities. How can we possible supply information technology (IT) at this scale, given constrained budgets? Must every lab become filled with computers, and every researcher an IT specialist?
I propose that the answer is to take a leaf from industry, which is slashing both the costs and complexity of consumer and business IT by moving it out of homes and offices to so-called cloud providers. I suggest that by similarly moving research IT out of the lab, we can realize comparable economies of scale and reductions in complexity, empowering investigators with new capabilities and freeing them to focus on their research.
I describe work we are doing to realize this approach, focusing initially on research data lifecycle management. I present promising results obtained to date, and suggest a path towards large-scale delivery of these capabilities. I also suggest that these developments are part of a larger "revolution in scientific affairs," as profound in its implications as the much-discussed "revolution in military affairs" resulting from more capable, low-cost IT. I conclude with some thoughts on how researchers, educators, and institutions may want to prepare for this revolution.
This talk will examine issues of workflow execution, in particular using the Pegasus Workflow Management System, on distributed resources and how these resources can be provisioned ahead of the workflow execution. Pegasus was designed, implemented and supported to provide abstractions that enable scientists to focus on structuring their computations without worrying about the details of the target cyberinfrastructure. To support these workflow abstractions Pegasus provides automation capabilities that seamlessly map workflows onto target resources, sparing scientists the overhead of managing the data flow, job scheduling, fault recovery and adaptation of their applications. In some cases, it is beneficial to provision the resources ahead of the workflow execution, enabling the re-use of resources across workflow tasks. The talk will examine the benefits of resource provisioning for workflow execution.
Big Data to SMART Data : Process scenario
Scenario of an implementation of a transformation process of the Data towards exploitable data and representative with treatments of the streaming, the distributed systems, the messages, the storage in an NoSQL environment, a management with an ecosystem Big Data graphic visualization of the data with the technologies:
Apache Storm, Apache Zookeeper, Apache Kafka, Apache Cassandra, Apache Spark and Data-Driven Document.
PIOTRe: Personal Internet of Things RepositoryEugene Siow
Personal IoT Repository based on sparql2sql, sparql2stream and sparql2fed technologies. The name PIOTRe is also derived from the name Peter meaning "stone" or "rock" and is the foundation for applications built on interoperable and efficient database technology on lightweight IoT devices.
Presentation at ISWC2016 in Kobe, Japan.
Lightning talk on various projects and technologies from my work that contribute towards building Fog and Edge Computing networks, Social Web of Things and the necessary infrastructure.
SPARQL-to-SQL on Internet of Things Databases and StreamsEugene Siow
To realise a semantic Web of Things, the challenge of achieving efficient Resource Description Format (RDF) storage and SPARQL query performance on Internet of Things (IoT) devices with limited resources has to be addressed. State-of-the-art SPARQL-to-SQL engines have been shown to outperform RDF stores on some benchmarks. In this paper, we describe an optimisation to the SPARQL-to-SQL approach, based on a study of time-series IoT data structures, that employs metadata abstraction and efficient translation by reusing existing SPARQL engines to produce Linked Data `just-in-time'. We evaluate our approach against RDF stores, state-of-the-art SPARQL-to-SQL engines and streaming SPARQL engines, in the context of IoT data and scenarios. We show that storage efficiency, with succinct row storage, and query performance can be improved from 2 times to 3 orders of magnitude.
Presentation at International Semantic Web Conference 2016
Explains the process described in the core specification for OpenID Connect 1.0 which is a simple identity layer on top of the OAuth 2.0 protocol. It allows Clients to verify the identity of the End-User based on the authentication performed by an Authorization Server, as well as to obtain basic profile information about the End-User in an interoperable and REST-like manner.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
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Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
2. Eywa is like a huge biological internet; the trees being computer servers that
store information and sensors being neural-connected flora and fauna
JAMES CAMERON’S
3.
4. “The Internet of Things is currently beset by product silos.”
W3C Web of Things Interest Group
5. Siow, E., Tiropanis, T. and Hall, W. (2017) A Decentralised Social Web of Things.
6. Siow, E., Tiropanis, T. and Hall, W. (2017) A Decentralised Social Web of Things.
Crowd Sourcing
Web 2.0/Mobile
ChatbotsMessaging Client Neural Representation
Collaborative Editing
Strong AIAlgorithmicRule-based Learned
Friend/Follow
Trustless
Networks
Edge PredictionPolicy Game Theory
7.
8. Fog Computing utilises the space between the
“Ground” and “Cloud”
Irrigation Application
Soil Moisture
Analytics
Lightweight
Computer Hub
Data Stream
Environmental
Sensors
National Disaster Monitoring Application
Weather
Data
State Inclement
Weather Planning
Application
Distributed Queries
9. Building ”Pillars” to support EYWAS
Fog RDF Stream
Processing
Personal IoT
Repository
Faster Queries,
Stream
Processing
eugenesiow.github.io/iot
14. produces
located
unit
13.0 93.0 10.52016-01-01 06:00:00
Siow, E., Tiropanis, T. and Hall, W. (2016) SPARQL-to-SQL on internet of things databases and streams. ISWC2016: The 15th International Semantic Web Conference
15. produces
located
unit
13.0 93.0 10.52016-01-01 06:00:00
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16. produces
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17. produces
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18. produces
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24. ~20,000 Stations
100 – 300k triples
Wind, Rainfall, etc.
10 SRBench Queries
Zhang, Y, et al. (2012) "SRBench: a streaming RDF/SPARQL
benchmark.”The 11th International Semantic Web Conference.
Siow, E., Tiropanis, T., Hall, W. (2016). "Interoperable and Efficient:
Linked Data for the Internet of Things." The 3rd International
Conference on Internet Science.
3 months, 1 home
~30k triples
Motion, energy, environment
4 Analytics Queries
GraphDB (OWLIM)
Ontop
Our Approach (S2S)
TDB
Morph
26. Get the rainfall observed in a particular
hour from all stations
Q01 with an optional clause
on unit of measure
x5
x3
x13
x4k
x2
x4
x4
x5k
27. Detect if a hurricane has been observed
Get the average wind speed at the stations
where the air temperature is >32
Join between wind observation and temperature
observation subtrees time-consuming in low resource
environment (Raspberry Pi)
Detect if a station is observing a blizzard
x3
x6
x6
x88
x3
x3
28. Get the stations with extremely low visibility
Detect stations that are recently broken
Get the daily minimal and maximal air
temperature observed by the sensor at a
given location
x2
x14
x4
x6
x6
x5
x2
29. Get the daily average wind force and direction
observed by the sensor at a given location
Get the locations where a heavy snowfall has
been observed
Our Approach (s2s) is shown to be faster on all queries
in the Distributed Meteorological System with SRBench
Join between wind force and wind direction observation
subtrees is time-consuming in low resource
environment (Raspberry Pi)
x3
x3k
x2
x7
30. Temperature aggregated by hour on a
specified day
Minimum and maximum temperature
each day for a particular month
x7
x29
x3
x9
31. Energy Usage Per Room By Day
Diagnose unattended appliances consuming
energy with no motion in room
Our Approach (s2s) is shown, once again, to be faster on
all queries for Smart Home Analytics
Involves motion and meter data (much larger set), with
space-time aggregations and joins between motion and
meter tables/subgraphs.
Involves meter data (larger set), with space-time
aggregations.
x69
x13
x4
32. sparql2stream
Same engine and
mappings but translates
to EPL instead of SQL
2
Stream Window
SPARQL query specifying
stream window size
1
Stream Sockets
Supports multiple
platforms and streams
with ZeroMQ
3
Real-time analytics
4
34. 1 2 3 4 5 7 8 9 10
294 261
306
277k 3243k 5245
426
280k
98
Le-Phuoc, D., et al. (2011) "A native and adaptive approach for unified processing of
linked streams and linked data.” The 10th International Semantic Web Conference.
Performance Improvement
For IoT Data Over
196
21
167
xImprovement
Query
35. >99% <1ms latency increasing from 1 to 1000 rows/ms
33.5million rows, projected ~2.5 billion triples!
<1ms 10-100ms
1
2
5
10
100
1000
99% 100%
Rateinrows/ms
Percentage Latency in ms Bands
36. Siow, E., Tiropanis, T. and Hall, W. (2016) PIOTRe: Personal Internet of Things Repository: The 15th International Semantic Web Conference P&D
github.com/eugenesiow/piotresparql2streamsparql2sql github.com/eugenesiow/sparql2sql
Apps
sparql2stream
sparql2sql
Metadata
37. Siow, E., Tiropanis, T. and Hall, W. (2017) A Fog Computing Framework for RDF Stream Processing.
Sensors
Node
Data Stream
Broker
Subscribe(URI_1)
Client
Publish ([Query_p1,Q_p2])𝞹
Push (Select_Stream),
Access Control,
Bandwidth Control
Query Broadcast, Nodes manage distributed processing
No single point of failure. Any RPi can serve
as a broker. ‘Best effort’ for source nodes
ResultSet
38. “It’s really hard to stimulate your brain with no [sensation of] light. It’s blanking
me. I can feel my brain just not wanting to do anything.” Adam Bloom, sensory
deprivation subject in BBC documentary “Total Isolation” (2008)
“"Who's Eywa? Only their deity! Their goddess, made up of all living things.
Everything they know!” Norm explaining Eywa to Jake in Avatar (2009)
39. @eugene_siow
“It's a long road, it's a long and narrow way. If I can't work up to you,
you'll surely have to work down to me someday.”
Narrow Way by Bob Dylan
EUGENE SIOW
THANASSIS TIROPANIS
WENDY HALL