Learn how Ocean Protocol can be used to further scientific research. A presentation by Ocean's Lead Data Scientist Marcus Jones at Blockchain for Science Conference in Berlin on November 3, 2019.
1. The document discusses the need for a "Web3 Data Economy" using blockchain and tokens to open up data access and incentivize data sharing, similar to how cryptocurrencies have opened up and incentivized money transfers.
2. It proposes that Ocean Protocol can serve as the foundational platform and token for this new data economy, fulfilling similar roles that Ethereum plays in the cryptocurrency economy like being the unit of exchange and hosting applications.
3. The goal is to connect data owners with AI companies and researchers to provide more open access to data and tools while protecting privacy and properly incentivizing all parties, helping to shift power away from current large data platforms.
Ocean Protocol: New Powers for Data ScientistsTrent McConaghy
Summary of benefits: more data, AI data/compute provenance, new income opportunities.
This talk was presented at WorldSummitAI in Amsterdam, October, 2018.
Opportunities for Genetic Programming Researchers in BlockchainTrent McConaghy
Genetic programming can be used with blockchains to evolve code like Solidity, EVM bytecode, and WASM bytecode. This opens opportunities for massive distributed genetic programming runs to evolve smart contracts and agent-based systems. Blockchains also allow for the possibility of genetic programming that runs continuously and modifies its own code to improve over time.
AI for Good is starting to be demonstrated, in addressing impact problems like the UN Sustainable Development Goals. But how can we scale it? This talk describes how an AI Commons manifested as a blockchain public utility network -- Ocean Protocol -- can be a key part of the solution.
This talk was a keynote at DutchChain Odyssey conference, Den Bosch, Feb 4, 2019.
Data Science Courses - BigData VS Data ScienceDataMites
Go through the slides to know what is Big Data and what is Data Science and Know the difference between Big Data and Data Science.
DataMites is a global institute, providing industry-aligned courses in Data Science, Machine Learning, and
Artificial Intelligence.
The Certified Data Scientist certification offered by DataMites covers all the important aspects of data science knowledge. The course is designed based on the accepted standards which demonstrates the quality of knowledge of a data science professional.
For more details please visit: https://datamites.com/data-science-course-training-chennai/
This talk introduces Ocean protocol. It describes:
-how data drives AI (artificial intelligence)
-the gap between data-haves and AI-haves
-the data silo crisis
-how Ocean addresses these issues by creating a substrate to catalyze a flowering of data marketplaces
-the Ocean structured approach to token design, from values to stakeholders to software stack.
Video: https://www.youtube.com/watch?v=fMDD0aTVt4s
This talk was presented at "9984 Summit - Blockchain Futures for Developers, Enterprises, and Society" hosted by IPDB & BigchainDB.
Learn how Ocean Protocol can be used to further scientific research. A presentation by Ocean's Lead Data Scientist Marcus Jones at Blockchain for Science Conference in Berlin on November 3, 2019.
1. The document discusses the need for a "Web3 Data Economy" using blockchain and tokens to open up data access and incentivize data sharing, similar to how cryptocurrencies have opened up and incentivized money transfers.
2. It proposes that Ocean Protocol can serve as the foundational platform and token for this new data economy, fulfilling similar roles that Ethereum plays in the cryptocurrency economy like being the unit of exchange and hosting applications.
3. The goal is to connect data owners with AI companies and researchers to provide more open access to data and tools while protecting privacy and properly incentivizing all parties, helping to shift power away from current large data platforms.
Ocean Protocol: New Powers for Data ScientistsTrent McConaghy
Summary of benefits: more data, AI data/compute provenance, new income opportunities.
This talk was presented at WorldSummitAI in Amsterdam, October, 2018.
Opportunities for Genetic Programming Researchers in BlockchainTrent McConaghy
Genetic programming can be used with blockchains to evolve code like Solidity, EVM bytecode, and WASM bytecode. This opens opportunities for massive distributed genetic programming runs to evolve smart contracts and agent-based systems. Blockchains also allow for the possibility of genetic programming that runs continuously and modifies its own code to improve over time.
AI for Good is starting to be demonstrated, in addressing impact problems like the UN Sustainable Development Goals. But how can we scale it? This talk describes how an AI Commons manifested as a blockchain public utility network -- Ocean Protocol -- can be a key part of the solution.
This talk was a keynote at DutchChain Odyssey conference, Den Bosch, Feb 4, 2019.
Data Science Courses - BigData VS Data ScienceDataMites
Go through the slides to know what is Big Data and what is Data Science and Know the difference between Big Data and Data Science.
DataMites is a global institute, providing industry-aligned courses in Data Science, Machine Learning, and
Artificial Intelligence.
The Certified Data Scientist certification offered by DataMites covers all the important aspects of data science knowledge. The course is designed based on the accepted standards which demonstrates the quality of knowledge of a data science professional.
For more details please visit: https://datamites.com/data-science-course-training-chennai/
This talk introduces Ocean protocol. It describes:
-how data drives AI (artificial intelligence)
-the gap between data-haves and AI-haves
-the data silo crisis
-how Ocean addresses these issues by creating a substrate to catalyze a flowering of data marketplaces
-the Ocean structured approach to token design, from values to stakeholders to software stack.
Video: https://www.youtube.com/watch?v=fMDD0aTVt4s
This talk was presented at "9984 Summit - Blockchain Futures for Developers, Enterprises, and Society" hosted by IPDB & BigchainDB.
Energy Data Access Management with Ocean ProtocolTrent McConaghy
Ocean Protocol is a decentralized network for data and compute access management that aims to solve two key issues: 1) connecting problem owners with problem solvers and 2) unlocking the benefits of more data while maintaining privacy and control. The Ocean network allows private data to remain behind firewalls while running algorithms and modeling to gain insights. This enables businesses to gain value from their private data without compromising privacy or control. Ocean launched its mainnet in March 2019 and its open source code and thorough documentation allow any organization, like the EWF ecosystem, to use Ocean for energy data access management across offices or collaborators on either private or public networks.
This document discusses big data in agriculture. It defines big data as large volumes of data that require automation to process rather than individual humans. It notes that data comes from people through surveys and sensors, as well as systems like communication networks. While some technologies aim to marginally increase yields, most big data solutions will need to generate revenue by serving the agricultural value chain through traders, processors, and other stakeholders rather than smallholder farmers directly. Success requires understanding both the technology costs and dimensions as well as the agricultural revenue targets and dimensions.
this presentation will let you know the in and out of bigdata growing trends... market potential , solutions provided by bigdata, advantages and disadvantages.
The Evolution of Blue Ocean Databases, from SQL to BlockchainTrent McConaghy
1. The evolution of blue ocean databases, from Oracle to MySQL to MongoDB to BigchainDB
2. Decentralized software stacks, including decentralized file systems, decentralized databases, and decentralized processing (smart contracts)
[This was presented at a BigchainDB Hackfest, Feb 2017 in Berlin]
Europe needs a clear strategy for leveraging Big Data
Economy in Europe. Our objectives are work at technical, business and policy levels, shaping the future through the positioning of Big Data in Horizon 2020. Bringing the necessary stakeholders into a sustainable industry-led initiative, which will greatly contribute to enhance the EU competitiveness taking full advantage of Big Data technologies.
Big data refers to the large volumes of data that are constantly being generated. This document discusses how big data is being generated from various sources like stock exchanges, aircraft sensors, phone calls, and online banking. It then discusses using public cloud infrastructure and services like Amazon Web Services, Microsoft Azure, and Google Cloud Platform to analyze and manage big data using tools and frameworks like Hadoop. The cloud provides scalable and cost-effective options for organizations to build big data solutions without having to make large up-front investments in hardware.
This document discusses big data, including its definition, characteristics of volume, velocity, and variety. It describes sources of big data like administrative data, transactions, public data, sensor data, and social media. It discusses processing big data using techniques like Hadoop MapReduce. It outlines benefits like real-time decision making but also drawbacks like security, privacy, and performance issues. It provides some facts about the size of data generated daily by companies and potential impacts and future growth of the big data industry and job market.
Big data characteristics, value chain and challengesMusfiqur Rahman
Abstract—Recently the world is experiencing an deluge of
data from different domains such as telecom, healthcare
and supply chain systems. This growth of data has led to
an explosion, coining the term Big Data. In addition to the
growth in volume, Big Data also exhibits other unique
characteristics, such as velocity and variety. This large
volume, rapidly increasing and verities of data is becoming
the key basis of completion, underpinning new waves of
productivity growth, innovation and customer surplus. Big
Data is about to offer tremendous insight to the
organizations, but the traditional data analysis
architecture is not capable to handle Big Data. Therefore,
it calls for a sophisticated value chain and proper analytics
to unearth the opportunity it holds. This research
identifies the characteristics of Big Data and presents a
sophisticated Big Data value chain as finding of this
research. It also describes the typical challenges of Big
Data, which are required to be solved. As a part of this
research twenty experts from different industries and
academies of Finland were interviewed.
This document discusses big data, defining it as data that is too large and complex for traditional data processing systems due to its volume, variety and velocity. It outlines the 3Vs of big data - volume, referring to the large amount of data being generated daily; variety, referring to different data formats; and velocity, referring to the speed at which data is generated and needs to be processed. The document also discusses characteristics of big data like structured, semi-structured and unstructured data, benefits of big data, challenges of capturing, storing, analyzing and presenting big data, and technologies like Hadoop and MapReduce used for big data solutions.
NeosIT provides full-service support for big data projects, from initial analysis and solution development to ongoing integration, operation and support. With exponential growth in data creation, most companies' legacy systems are unable to handle the increasing demands for real-time analytics insights. NeosIT's Vertica data platform is designed to deliver these insights quickly using high-performance infrastructure and tools integrated with customers' existing IT environments. NeosIT aims to take care of all aspects of customers' big data initiatives so they can simply start their analytics journey.
This document defines big data and discusses its importance. It describes big data as large datasets that are difficult to manage with traditional tools due to their volume, variety, and velocity. Examples are provided of how governments and private sector organizations like Walmart and Facebook generate and use big data. The phases of big data analysis and challenges like heterogeneity are outlined. Technologies used for big data from companies like Oracle, Microsoft and IBM are listed. Opportunities big data provides for driving revenue, understanding customers, and improving operations are discussed. The document concludes with other aspects of big data like its impact on knowledge and transparency.
This document provides a charter and roadmap for a Computing, Data, and Informatics Working Group. It discusses their vision of enabling data, computing, and identity services at unlimited scale. It highlights how information technology has been critical but also a source of tension in large projects like the Human Genome Project. The document outlines current enabling technologies like machine learning, blockchain, and DevOps practices. It identifies key challenge areas the working group will focus on, including identity and authorization, information security and privacy, and issues around data storage in multi-cloud environments. The working group members are then listed.
Big data refers to very large data sets that are analyzed computationally to reveal patterns, trends, and relationships. It is characterized by 3Vs - volume, velocity, and variety. Big data has many applications in recent scenarios including politics, weather, medicine, media, and manufacturing. It is used in politics to analyze voter data beyond basic demographics. In weather, sensor data from devices is used to create more detailed weather maps and forecasts. Medicine uses big data to identify patterns in symptoms that can help predict and prevent diseases like heart failure. Media analyzes data on user behaviors to tailor content instead of relying on traditional formats. Manufacturing leverages big data for increased transparency and insights into performance issues.
The document discusses emerging technologies including quantum computing, artificial intelligence, machine learning, blockchains, the internet of things, cloud computing, edge computing, and data analytics. While these technologies show promise, the document cautions against hype and notes that many technologies do not yet work as described or have practical applications. The author offers experience supporting genomics and building IT infrastructure and is available for consulting work.
Big data - Key Enablers, Drivers & ChallengesShilpi Sharma
Big data is characterized by the 3 Vs - volume, velocity, and variety. The document discusses how big data is growing exponentially due to factors like the internet of things. Key enablers of big data include data storage, computation capacity, and data availability. Addressing big data requires technologies, techniques, and talent across the value chain of aggregating, analyzing, and consuming data to derive value. However, big data also presents management challenges around decision making, change management, technology clashes, and skills shortages. The document provides an example of how big data could help sales professionals better prepare for client meetings.
This document lists 7 facts about big data: 1) The amount of data generated in two days now exceeds all data up until 2003. 2) The big data analytics industry is currently worth $3 billion but is expected to grow to $20 billion in 5 years. 3) Harnessing big data could reduce healthcare costs by 8%. It then encourages following their social media accounts to learn more about big data.
This document discusses issues around big data and intellectual property in agriculture. It notes that the effects of data sharing are unclear, leading to uncertainty for farmers around who owns their data, privacy, potential monopolies, lock-ins, and business models. The document proposes creating an open software ecosystem called DATA-FAIR to help address these issues. It suggests preventing data monopolies by sharing data between companies, defining joint data ownership, and designing governance and business models for agricultural data exchange facilities.
International Journal on Cloud Computing: Services and Architecture (IJCCSA)ijccsa
Cloud computing helps enterprises transform business and technology. Companies have begun to look for solutions that would help reduce their infrastructures costs and improve profitability. Cloud computing is becoming a foundation for benefits well beyond IT cost savings. Yet, many business leaders are concerned about cloud security, privacy, availability, and data protection. To discuss and address these issues, we invite researches who focus on cloud computing to shed more light on this emerging field. This peer-reviewed open access Journal aims to bring together researchers and practitioners in all security aspects of cloud-centric and outsourced computing, including (but not limited to):
In AI, it's all about the data. But it's hard to get the data, and to get *good* data with provenance. This talk shows how blockchains can help, with real-world examples including:
-a data exchange for self-driving car data (with Toyota Research and others)
-pooling designs for 3d printing fraud detection (with Innogy and others)
-and AI DAOs - AIs that can accumulate wealth
This was given as an invited talk at Consensus 2017, May 22 in NYC.
This document discusses 3 business technology trends: 1) APIs will continue growing in importance as more systems become programmable and embedded in clients' processes, 2) SaaS and cloud computing will drive new ways of sharing information across enterprises through on-demand services, and 3) "Big Data" approaches can deliver value if companies make better use of the large amounts of data they collect, such as by sharing supplier transaction data to improve inventory management.
"Benefits of utilizing open source in contemporary startups" by Jarkko MoilanenMindtrek
Track | the Future of Open Source Business
Jarkko Moilanen, PhD, API & Data Economy Entrepreneur, Data Product Business
Mindtrek Conference
15th of November 2022.
Tampere, Finland
www.mindtrek.org
Energy Data Access Management with Ocean ProtocolTrent McConaghy
Ocean Protocol is a decentralized network for data and compute access management that aims to solve two key issues: 1) connecting problem owners with problem solvers and 2) unlocking the benefits of more data while maintaining privacy and control. The Ocean network allows private data to remain behind firewalls while running algorithms and modeling to gain insights. This enables businesses to gain value from their private data without compromising privacy or control. Ocean launched its mainnet in March 2019 and its open source code and thorough documentation allow any organization, like the EWF ecosystem, to use Ocean for energy data access management across offices or collaborators on either private or public networks.
This document discusses big data in agriculture. It defines big data as large volumes of data that require automation to process rather than individual humans. It notes that data comes from people through surveys and sensors, as well as systems like communication networks. While some technologies aim to marginally increase yields, most big data solutions will need to generate revenue by serving the agricultural value chain through traders, processors, and other stakeholders rather than smallholder farmers directly. Success requires understanding both the technology costs and dimensions as well as the agricultural revenue targets and dimensions.
this presentation will let you know the in and out of bigdata growing trends... market potential , solutions provided by bigdata, advantages and disadvantages.
The Evolution of Blue Ocean Databases, from SQL to BlockchainTrent McConaghy
1. The evolution of blue ocean databases, from Oracle to MySQL to MongoDB to BigchainDB
2. Decentralized software stacks, including decentralized file systems, decentralized databases, and decentralized processing (smart contracts)
[This was presented at a BigchainDB Hackfest, Feb 2017 in Berlin]
Europe needs a clear strategy for leveraging Big Data
Economy in Europe. Our objectives are work at technical, business and policy levels, shaping the future through the positioning of Big Data in Horizon 2020. Bringing the necessary stakeholders into a sustainable industry-led initiative, which will greatly contribute to enhance the EU competitiveness taking full advantage of Big Data technologies.
Big data refers to the large volumes of data that are constantly being generated. This document discusses how big data is being generated from various sources like stock exchanges, aircraft sensors, phone calls, and online banking. It then discusses using public cloud infrastructure and services like Amazon Web Services, Microsoft Azure, and Google Cloud Platform to analyze and manage big data using tools and frameworks like Hadoop. The cloud provides scalable and cost-effective options for organizations to build big data solutions without having to make large up-front investments in hardware.
This document discusses big data, including its definition, characteristics of volume, velocity, and variety. It describes sources of big data like administrative data, transactions, public data, sensor data, and social media. It discusses processing big data using techniques like Hadoop MapReduce. It outlines benefits like real-time decision making but also drawbacks like security, privacy, and performance issues. It provides some facts about the size of data generated daily by companies and potential impacts and future growth of the big data industry and job market.
Big data characteristics, value chain and challengesMusfiqur Rahman
Abstract—Recently the world is experiencing an deluge of
data from different domains such as telecom, healthcare
and supply chain systems. This growth of data has led to
an explosion, coining the term Big Data. In addition to the
growth in volume, Big Data also exhibits other unique
characteristics, such as velocity and variety. This large
volume, rapidly increasing and verities of data is becoming
the key basis of completion, underpinning new waves of
productivity growth, innovation and customer surplus. Big
Data is about to offer tremendous insight to the
organizations, but the traditional data analysis
architecture is not capable to handle Big Data. Therefore,
it calls for a sophisticated value chain and proper analytics
to unearth the opportunity it holds. This research
identifies the characteristics of Big Data and presents a
sophisticated Big Data value chain as finding of this
research. It also describes the typical challenges of Big
Data, which are required to be solved. As a part of this
research twenty experts from different industries and
academies of Finland were interviewed.
This document discusses big data, defining it as data that is too large and complex for traditional data processing systems due to its volume, variety and velocity. It outlines the 3Vs of big data - volume, referring to the large amount of data being generated daily; variety, referring to different data formats; and velocity, referring to the speed at which data is generated and needs to be processed. The document also discusses characteristics of big data like structured, semi-structured and unstructured data, benefits of big data, challenges of capturing, storing, analyzing and presenting big data, and technologies like Hadoop and MapReduce used for big data solutions.
NeosIT provides full-service support for big data projects, from initial analysis and solution development to ongoing integration, operation and support. With exponential growth in data creation, most companies' legacy systems are unable to handle the increasing demands for real-time analytics insights. NeosIT's Vertica data platform is designed to deliver these insights quickly using high-performance infrastructure and tools integrated with customers' existing IT environments. NeosIT aims to take care of all aspects of customers' big data initiatives so they can simply start their analytics journey.
This document defines big data and discusses its importance. It describes big data as large datasets that are difficult to manage with traditional tools due to their volume, variety, and velocity. Examples are provided of how governments and private sector organizations like Walmart and Facebook generate and use big data. The phases of big data analysis and challenges like heterogeneity are outlined. Technologies used for big data from companies like Oracle, Microsoft and IBM are listed. Opportunities big data provides for driving revenue, understanding customers, and improving operations are discussed. The document concludes with other aspects of big data like its impact on knowledge and transparency.
This document provides a charter and roadmap for a Computing, Data, and Informatics Working Group. It discusses their vision of enabling data, computing, and identity services at unlimited scale. It highlights how information technology has been critical but also a source of tension in large projects like the Human Genome Project. The document outlines current enabling technologies like machine learning, blockchain, and DevOps practices. It identifies key challenge areas the working group will focus on, including identity and authorization, information security and privacy, and issues around data storage in multi-cloud environments. The working group members are then listed.
Big data refers to very large data sets that are analyzed computationally to reveal patterns, trends, and relationships. It is characterized by 3Vs - volume, velocity, and variety. Big data has many applications in recent scenarios including politics, weather, medicine, media, and manufacturing. It is used in politics to analyze voter data beyond basic demographics. In weather, sensor data from devices is used to create more detailed weather maps and forecasts. Medicine uses big data to identify patterns in symptoms that can help predict and prevent diseases like heart failure. Media analyzes data on user behaviors to tailor content instead of relying on traditional formats. Manufacturing leverages big data for increased transparency and insights into performance issues.
The document discusses emerging technologies including quantum computing, artificial intelligence, machine learning, blockchains, the internet of things, cloud computing, edge computing, and data analytics. While these technologies show promise, the document cautions against hype and notes that many technologies do not yet work as described or have practical applications. The author offers experience supporting genomics and building IT infrastructure and is available for consulting work.
Big data - Key Enablers, Drivers & ChallengesShilpi Sharma
Big data is characterized by the 3 Vs - volume, velocity, and variety. The document discusses how big data is growing exponentially due to factors like the internet of things. Key enablers of big data include data storage, computation capacity, and data availability. Addressing big data requires technologies, techniques, and talent across the value chain of aggregating, analyzing, and consuming data to derive value. However, big data also presents management challenges around decision making, change management, technology clashes, and skills shortages. The document provides an example of how big data could help sales professionals better prepare for client meetings.
This document lists 7 facts about big data: 1) The amount of data generated in two days now exceeds all data up until 2003. 2) The big data analytics industry is currently worth $3 billion but is expected to grow to $20 billion in 5 years. 3) Harnessing big data could reduce healthcare costs by 8%. It then encourages following their social media accounts to learn more about big data.
This document discusses issues around big data and intellectual property in agriculture. It notes that the effects of data sharing are unclear, leading to uncertainty for farmers around who owns their data, privacy, potential monopolies, lock-ins, and business models. The document proposes creating an open software ecosystem called DATA-FAIR to help address these issues. It suggests preventing data monopolies by sharing data between companies, defining joint data ownership, and designing governance and business models for agricultural data exchange facilities.
International Journal on Cloud Computing: Services and Architecture (IJCCSA)ijccsa
Cloud computing helps enterprises transform business and technology. Companies have begun to look for solutions that would help reduce their infrastructures costs and improve profitability. Cloud computing is becoming a foundation for benefits well beyond IT cost savings. Yet, many business leaders are concerned about cloud security, privacy, availability, and data protection. To discuss and address these issues, we invite researches who focus on cloud computing to shed more light on this emerging field. This peer-reviewed open access Journal aims to bring together researchers and practitioners in all security aspects of cloud-centric and outsourced computing, including (but not limited to):
In AI, it's all about the data. But it's hard to get the data, and to get *good* data with provenance. This talk shows how blockchains can help, with real-world examples including:
-a data exchange for self-driving car data (with Toyota Research and others)
-pooling designs for 3d printing fraud detection (with Innogy and others)
-and AI DAOs - AIs that can accumulate wealth
This was given as an invited talk at Consensus 2017, May 22 in NYC.
This document discusses 3 business technology trends: 1) APIs will continue growing in importance as more systems become programmable and embedded in clients' processes, 2) SaaS and cloud computing will drive new ways of sharing information across enterprises through on-demand services, and 3) "Big Data" approaches can deliver value if companies make better use of the large amounts of data they collect, such as by sharing supplier transaction data to improve inventory management.
"Benefits of utilizing open source in contemporary startups" by Jarkko MoilanenMindtrek
Track | the Future of Open Source Business
Jarkko Moilanen, PhD, API & Data Economy Entrepreneur, Data Product Business
Mindtrek Conference
15th of November 2022.
Tampere, Finland
www.mindtrek.org
Alleantia - internet of things for enterprises - enabling data-driven organiz...Antonio Conati Barbaro
Overview of Alleantia strategy and solutions enabling Internet-of-Things capabilities for data-driven Enterprise business processes, for Equipment manufacturers' and any other corporate. Alleantia has developed an innovative method ('mapping of the DNA of Things) for creating software objects of any device, existing and new, and a IoT Application Platform for creating devices and applications.
Presentation shown in Q4 2013 at Pioneers', Webit, Slush, IVF and EVS.
The document discusses how the rise of the Internet of Things (IoT) will change product roadmaps. It notes that IoT will result in more smart, connected devices generating large amounts of fragmented data from multiple sources. This will require applications to become smarter by incorporating predictive and prescriptive capabilities to leverage IoT data. Challenges also need to be overcome, such as handling big data, privacy, and security issues. However, properly leveraging IoT data through smarter applications can provide significant financial benefits and opportunities for innovation across industries.
Databricks CEO Ali Ghodsi introduces Databricks Delta, a new data management system that combines the scale and cost-efficiency of a data lake, the performance and reliability of a data warehouse, and the low latency of streaming.
1) IOT and big data focus on real-time processing which can create autonomous control to reduce cost, time, energy and resources. When trillions of devices are connected through IP addresses, they will produce huge amounts of data which is challenging to control.
2) IOT and big data have applications in smart homes, cities, transportation and industry to make human life easier with less energy, money and time. Data from IOT devices is stored in big data for analysis using techniques like heterogeneous, nonlinear, high-dimensional and distributed parallel processing.
3) Key challenges for IOT, big data and their engineers include securing and authenticating the huge amounts of data from diverse sources, dealing with unreliable data and
The document discusses trends driving the need for a new content management approach, including the growth of digital content, mobility, cloud computing and more interconnected digital experiences. It notes the limitations of legacy content management systems in addressing these trends. The presentation outlines Alfresco's approach to a digital platform, including smart process applications, RESTful APIs, scalability, and cloud-native design. This enables flexible content and process management to drive digital transformation.
The document summarizes a presentation given by Ed Franklin of RiverMeadow Software on cloud computing trends, business drivers, and career opportunities. Some key points include:
- Cloud computing delivers computing resources as a utility over the internet.
- It allows for pay-as-you-go access to shared hardware, software, and data.
- Major trends driving cloud adoption include the growth of internet usage, demands for efficiency and sustainability, and business models requiring flexible computing resources.
- Jobs in areas like cloud services, big data analytics, and mobile applications are expected to grow significantly in the coming years.
On 22 September 2016 we presented the 'art of data science' at Lord's Cricket Ground. See here a collection of the slides presented.
Many thanks to our partners: Insight, Automated Intelligence and CORETX.
See more data science here: https://redpixie.com/data-science/
Wireless Global Congress: 2020 is not that far awayRob Van Den Dam
1) The document discusses how emerging technologies like cognitive computing, blockchain, and the internet of things are transforming industries by 2020. It notes that 30 billion devices will be connected and 85% of data will be unstructured.
2) Most data from the internet of things is invisible like sensor data and video, requiring new technologies to analyze it and extract insights. Cognitive systems that can understand, reason, and learn from this data are entering a new era of computing.
3) Blockchain technology will transform transactions in the same way the internet transformed information, providing benefits like reduced costs, risks, and increased trust through shared recordkeeping. A cognitive business can turn data into knowledge to adapt to customer needs.
Guest Lecture: Introduction to Big Data at Indian Institute of TechnologyNishant Gandhi
This document provides an introduction to big data, including definitions of big data and why it is important. It discusses characteristics of big data like volume, velocity, variety and veracity. It provides examples of big data applications in various industries like GE, Boeing, social media, finance, CERN, journalism, politics and more. It also introduces NoSQL and the CAP theorem, and concludes that big data is changing business and technology by enabling new insights from data to reduce costs and optimize operations.
1. The document discusses cloud computing and its applications in libraries. It defines cloud computing and provides examples of infrastructure, platform, software and data services.
2. Advantages of cloud computing for libraries include scalability, flexibility, and cost savings, though security, privacy and loss of control are potential disadvantages.
3. When considering cloud services, libraries must understand requirements, costs, legal issues, and have a plan for exiting cloud-based systems. Comprehensive service level agreements are also important.
The document discusses the growth of the Internet of Things and its implications. It notes that IoT will significantly impact both individuals and enterprises whether they actively engage with it or not. High tech companies will be key beneficiaries as IoT requires new hardware, software, and systems. Successfully taking advantage of IoT will require different strategic thinking and developing new capabilities.
A Connected Data Landscape: Virtualization and the Internet of ThingsInside Analysis
The Briefing Room with Dr. Robin Bloor and Cisco
Live Webcast March 3, 2015
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=a75f0f379405de155800a37b2bf104db
Data at rest, data in motion - regardless of its trajectory, data remains the lifeblood of today's information economy. But finding a way to bridge old systems with new opportunities requires an innovative data strategy, one that takes advantage of multiple processing technologies. With the optimal architecture in place, companies can harness years of work in traditional information systems, while opening the door to the flood of new data sources available.
Register for this episode of The Briefing Room to learn from veteran Analyst Dr. Robin Bloor, as he explains how data virtualization and other data technologies fundamentally change what's possible with data access, movement and analysis. He'll be briefed by David Besemer of Cisco, who will discuss how this new kind of data strategy can enable the integration of legacy systems, Cloud computing and the Internet of Things. He'll also answer questions about how Big Data and the IoT are helping to redefine the practice of data management.
Visis InsideAnalysis.com for more information.
IoT Architecture - are traditional architectures good enough?Guido Schmutz
Independent of the source of data, the integration of event streams into an Enterprise Architecture gets more and more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analysed, often with many consumers or systems interested in all or part of the events. Dependent on the size and quantity of such events, this can quickly be in the range of Big Data. How can we efficiently collect and transmit these events? How can we make sure that we can always report over historical events? How can these new events be integrated into traditional infrastructure and application landscape?
Starting with a product and technology neutral reference architecture, we will then present different solutions using Open Source frameworks and the Oracle Stack both for on premises as well as the cloud.
Open Data Open Innovation and The Cloud gayler berlin nov12Mark Gayler
Open data, open innovation, and cloud computing can provide significant benefits but expectations are changing. Citizens and workers expect personalized services, engagement, tools for collaboration and work style, and respect for privacy and security. Leaders expect to consult constituents anywhere, gain insights and manage performance. These trends can be exploited by opening government data through the cloud, which provides a low-cost way to build applications, access data easily, and stimulate public innovation. Microsoft's open government data initiative leverages the cloud to provide open APIs, scale, and reliability for open data portals.
Saavuta hankinnan, maksamisen ja myymisen 100% digitalisaatio webinaari 02 2017OpusCapita
This document summarizes a webinar about achieving 100% digitalization of procurement, payment, and sales. It discusses moving from business silos to digital business ecosystems. An example project called DBE Core was presented, which aims to automate order to pay processes electronically. Research found it could generate significant cost savings. Blockchain technology was briefly mentioned as a potential future way to link digital systems and apps more securely. The webinar concluded with an open discussion portion.
Similar to Ocean Protocol - Diffusion 2019 Workshop (20)
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
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Ocean Protocol - Diffusion 2019 Workshop
1. Ocean ProtocolA decentralized data exchange protocol to unlock data for AI
Ocean Protocol – October 2019
Diffusion 2019 Workshop
Ocean Protocol buying and selling datasets
Sebastian Gerske
2. Part 1: Ocean Protocol Overview
Part 2: #BUIDL on Ocean
4. Ocean Protocol solves data sharing
for all Stakeholders
▪ a decentralized data exchange protocol to unlock
data for AI
▪ uses blockchain technology that allows data to be
shared and transferred in a safe, secure and
transparent manner
▪ enables a decentralized platform and network
connecting providers and consumers of valuable
data, and providing open access for developers to
build services
13. ocean :: nile
Nile
● The Ocean Beta Network
● Launched in April 2019!
Try it out (RPC):
https://nile.dev-ocean.com/
Explorer:
https://submarine.nile.dev-ocean.com/
Documentation:
https://docs.oceanprotocol.com/tutorials/connect-t
o-networks/#connect-to-the-nile-testnet
14. ocean :: squid
Squid
● Single point of integration to ocean
● Abstraction layer over web3
Repos:
JavaScript:
https://github.com/oceanprotocol/squid-js
Python:
https://github.com/oceanprotocol/squid-py
Java:
https://github.com/oceanprotocol/squid-java
Documentation:
https://docs.oceanprotocol.com/concepts/compon
ents/#squid-libraries
https://docs.oceanprotocol.com/references/introdu
ction/
15. ocean :: common
Commons
● Marketplace for commons data sets
● Running on Ocean Pacific mainnet
● More than 1400 assets already registered
● Can be used as boilerplate to bootstrap new
marketplaces
Repo:
https://github.com/oceanprotocol/commons
Try it out:
https://commons.oceanprotocol.com/