This document outlines Austria's strategy for conquering data through research and technology initiatives (RTI). It discusses:
1) The role of the Austrian Federal Ministry for Transport, Innovation and Technology (bmvit) in funding RTI, including a €450M annual budget and focus areas like ICT, production, energy and mobility.
2) Studies conducted on topics like the technology roadmap for conquering data through intelligent systems.
3) Actions already taken and planned, including developing lead technologies for data integration and fusion, increasing algorithm efficiency, and establishing a data-services ecosystem in Austria.
4) The need for coordination across stakeholders, developing the necessary legal framework, and ensuring resources and competencies
Presentation: Study: #Big Data in #Austria, Mario Meir-Huber, Big Data Leader Eastern Europe, Teradata GmbH & Martin Köhler, Austrian Institute of Technology, AIT (AT), at the European Data Economy Workshop taking place back to back to SEMANTiCS2015 on 15 September 2015 in Vienna.
From Data Platforms to Dataspaces: Enabling Data Ecosystems for Intelligent S...Edward Curry
The Real-time Linked Dataspace (RLD) is an enabling platform for data management for intelligent systems within smart environments that combines the pay-as-you-go paradigm of dataspaces, linked data, and knowledge graphs with entity-centric real-time query capabilities.
The RLD contains all the relevant information within a data ecosystem including things, sensors, and data sources and has the responsibility for managing the relationships among these participants.
It manages sources without presuming a pre-existing semantic integration among them using specialised dataspace support services for loose administrative proximity and semantic integration for event and stream systems. Support services leverage approximate and best-effort techniques and operate under a 5 star model for “pay-as-you-go” incremental data management.
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.
EDF2014: BIG - NESSI Networking Session: Edward Curry, National University of...European Data Forum
BIG - NESSI Networking Session, Talk by Edward Curry, National University of Ireland Galway at the European Data Forum 2014, 20 March 2014 in Athens, Greece: The Big Data Value Chain.
An introductory but highly practical talk on starting a Data Science career and life. It touches upon all the main aspects of the path towards becoming a Data scientist, also seen through a personal development perspective. Moreover, we talk about the role that a data scientist ultimately fulfills - as an individual or as a team - in the technology innovation life cycle and the product life-cycle.
Presentation: Study: #Big Data in #Austria, Mario Meir-Huber, Big Data Leader Eastern Europe, Teradata GmbH & Martin Köhler, Austrian Institute of Technology, AIT (AT), at the European Data Economy Workshop taking place back to back to SEMANTiCS2015 on 15 September 2015 in Vienna.
From Data Platforms to Dataspaces: Enabling Data Ecosystems for Intelligent S...Edward Curry
The Real-time Linked Dataspace (RLD) is an enabling platform for data management for intelligent systems within smart environments that combines the pay-as-you-go paradigm of dataspaces, linked data, and knowledge graphs with entity-centric real-time query capabilities.
The RLD contains all the relevant information within a data ecosystem including things, sensors, and data sources and has the responsibility for managing the relationships among these participants.
It manages sources without presuming a pre-existing semantic integration among them using specialised dataspace support services for loose administrative proximity and semantic integration for event and stream systems. Support services leverage approximate and best-effort techniques and operate under a 5 star model for “pay-as-you-go” incremental data management.
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.
EDF2014: BIG - NESSI Networking Session: Edward Curry, National University of...European Data Forum
BIG - NESSI Networking Session, Talk by Edward Curry, National University of Ireland Galway at the European Data Forum 2014, 20 March 2014 in Athens, Greece: The Big Data Value Chain.
An introductory but highly practical talk on starting a Data Science career and life. It touches upon all the main aspects of the path towards becoming a Data scientist, also seen through a personal development perspective. Moreover, we talk about the role that a data scientist ultimately fulfills - as an individual or as a team - in the technology innovation life cycle and the product life-cycle.
Annual Big Data Landscape prepared by FIrstMark. Check out full blog post: "Is Big Data Still a Thing"? at http://mattturck.com/2016/02/01/big-data-landscape/
In their webinar "Big Data Fabric 2.0 Drives Data Democratization" Ben Szekley, Cambridge Semantics’ SVP of Field Operations, and guest speaker, Forrester’s Noel Yuhanna, author of the Forrester report: “Big Data Fabric 2.0 Drives Data Democratization”, explored why data-driven businesses are making a big data fabric part of their data strategy to minimize data complexity, integrate siloed data, deliver real-time trusted insights, and to create new business opportunities. These are the slides from that webinar.
Big Data Fabric: A Necessity For Any Successful Big Data InitiativeDenodo
Watch this webinar in full here: https://buff.ly/2IxM8Iy
Watch all webinars from the Denodo Packed Lunch webinar series here: https://buff.ly/2IR3q6w
While big data initiatives have become necessary for any business to generate actionable insights, big data fabric has become a necessity for any successful big data initiative. The best of breed big data fabrics should deliver actionable insights to the business users with minimal effort, provide end-to-end security to the entire enterprise data platform and provide real-time data integration, while delivering self-service data platform to business users.
Attend this session to learn how big data fabric enabled by data virtualization:
• Provides lightning fast self-service data access to business users
• Centralizes data security, governance and data privacy
• Fulfills the promise of data lakes to provide actionable insights
Data Services and the Modern Data Ecosystem (Middle East)Denodo
Watch full webinar here: https://bit.ly/3xdSTIU
Digital Transformation has changed IT the way information services are delivered. The pace of business engagement, the rise of Digital IT (formerly known as “Shadow IT), has also increased demands on IT, especially in the area of Data Management. Data Services exploits widely adopted interoperability standards, providing a strong framework for information exchange but also has enabled a growth of robust systems of engagement that can now exploit information that was normally locked away in some internal silo.
Join us for our upcoming Middle East Webinar series episode, “Data Services and the Modern Data Ecosystem,” presented by Chief Evangelist MEA, Alexey Sidorov. Tune-in as we explore how a business can easily support and manage a Data Service ecosystem, providing a more flexible approach for information sharing supporting an ever diverse community of consumers.
Watch on-demand this webinar to learn:
- Why Data Services are a critical part of a modern data ecosystem
- How IT teams can manage Data Services and the increasing demand by businesses
- How Digital IT can benefit from Data Services and how this can support the need for rapid prototyping allowing businesses to experiment with data and fail fast where necessary.
- How a good Data Virtualization platform can encourage a culture of Data amongst business consumers (internally and externally)
A Seminar Presentation on Big Data for Students.
Big data refers to a process that is used when traditional data mining and handling techniques cannot uncover the insights and meaning of the underlying data. Data that is unstructured or time sensitive or simply very large cannot be processed by relational database engines. This type of data requires a different processing approach called big data, which uses massive parallelism on readily-available hardware.
Graph technology has truly burst onto the scene with diverse new products and services, proving that graph is relevant and that not all graph use cases are equal. Previously relegated to niche implementations and science projects, graph now finds itself deployed as the foundational technology for enterprise analytics solutions and enterprise Data Fabric strategies. It is no surprise that many are calling 2018 “The Year of the Graph”.
State of the State: What’s Happening in the Database Market?Neo4j
Speaker: Lance Walter, CMO, Neo4j
Abstract: The data management landscape continues to evolve rapidly. More and more organizations are waking up to the value of connections and relationships in data, and that’s why Gartner recently named Graph databases one of their Top 10 Technology Trends for 2019.
This session will provide an overview of graph technology and talk about the past, present, and future of graphs and data management. Multiple use cases and customer examples will be covered, including examples of where graph databases can assist and accelerate machine learning and AI projects.
Lewis Crawford's presentation from the BI Boss event in Leeds, focussing on our perspective on Big Data, Big Data projects, what to avoid, and how to make it work for you.
Agile Data Management with Enterprise Data Fabric (Middle East)Denodo
Watch full webinar here: https://bit.ly/3td9ICb
In a world where machine learning and artificial intelligence are changing our everyday lives, digital transformation tops the strategic agenda in many private and government organizations. Data is becoming the lifeblood of a company, flowing seamlessly through it to enable deep business insights, create new opportunities, and optimize operations.
Chief Data Officers and Data Architects are under continuous pressure to find the best ways to manage the overwhelming volumes of the data that tend to become more and more distributed and diverse.
Moving data physically to a single location for reporting and analytics is not an option anymore – this is the fact accepted by the majority of the data professionals.
Join us for this webinar to know about the modern virtual data landscapes including:
- Virtual Data Fabric
- Data Mesh
- Multi-Cloud Hybrid architecture
and to learn how to leverage Denodo Data Virtualization platform to implement these modern data architectures.
Transforming Data Management and Time to Insight with Anzo Smart Data Lake®Cambridge Semantics
This webinar is targeted to Federal Government CIOs and
staff that are researching enterprise data management and
mining tools to help them understand how Smart Data Lakes
enable a viable mechanism for addressing their top priorities.
Agile Data Management with Enterprise Data Fabric (ASEAN)Denodo
Watch full webinar here: https://bit.ly/3juxqaw
In a world where machine learning and artificial intelligence are changing our everyday lives, digital transformation tops the strategic agenda in many private and government organizations. Data is becoming the lifeblood of a company, flowing seamlessly through it to enable deep business insights, create new opportunities, and optimize operations.
Chief Data Officers and Data Architects are under continuous pressure to find the best ways to manage the overwhelming volumes of the data that tend to become more and more distributed and diverse.
Moving data physically to a single location for reporting and analytics is not an option anymore – this is the fact accepted by the majority of the data professionals.
Join us for this webinar to know about the modern virtual data landscapes including:
- Virtual Data Fabric
- Data Mesh
- Multi-Cloud Hybrid architecture
- and to learn how to leverage the Denodo Data Virtualization platform to implement these modern data architectures.
Big Data - The 5 Vs Everyone Must KnowBernard Marr
This slide deck, by Big Data guru Bernard Marr, outlines the 5 Vs of big data. It describes in simple language what big data is, in terms of Volume, Velocity, Variety, Veracity and Value.
Using a Semantic and Graph-based Data Catalog in a Modern Data FabricCambridge Semantics
Watch this webinar to learn about the benefits of using semantic and graph database technology to create a Data Catalog of all of an enterprise's data, regardless of source or format, as part of a modern IT or data management stack and an important step toward building an Enterprise Data Fabric.
EDF2014: Allan Hanbury, Senior Researcher, Vienna University of Technology, A...European Data Forum
Selected Talk by Allan Hanbury, Senior Researcher, Vienna University of Technology, Austria at the European Data Forum 2014, 19 March 2014 in Athens, Greece: Conquering Data in Austria: a technology roadmap
Annual Big Data Landscape prepared by FIrstMark. Check out full blog post: "Is Big Data Still a Thing"? at http://mattturck.com/2016/02/01/big-data-landscape/
In their webinar "Big Data Fabric 2.0 Drives Data Democratization" Ben Szekley, Cambridge Semantics’ SVP of Field Operations, and guest speaker, Forrester’s Noel Yuhanna, author of the Forrester report: “Big Data Fabric 2.0 Drives Data Democratization”, explored why data-driven businesses are making a big data fabric part of their data strategy to minimize data complexity, integrate siloed data, deliver real-time trusted insights, and to create new business opportunities. These are the slides from that webinar.
Big Data Fabric: A Necessity For Any Successful Big Data InitiativeDenodo
Watch this webinar in full here: https://buff.ly/2IxM8Iy
Watch all webinars from the Denodo Packed Lunch webinar series here: https://buff.ly/2IR3q6w
While big data initiatives have become necessary for any business to generate actionable insights, big data fabric has become a necessity for any successful big data initiative. The best of breed big data fabrics should deliver actionable insights to the business users with minimal effort, provide end-to-end security to the entire enterprise data platform and provide real-time data integration, while delivering self-service data platform to business users.
Attend this session to learn how big data fabric enabled by data virtualization:
• Provides lightning fast self-service data access to business users
• Centralizes data security, governance and data privacy
• Fulfills the promise of data lakes to provide actionable insights
Data Services and the Modern Data Ecosystem (Middle East)Denodo
Watch full webinar here: https://bit.ly/3xdSTIU
Digital Transformation has changed IT the way information services are delivered. The pace of business engagement, the rise of Digital IT (formerly known as “Shadow IT), has also increased demands on IT, especially in the area of Data Management. Data Services exploits widely adopted interoperability standards, providing a strong framework for information exchange but also has enabled a growth of robust systems of engagement that can now exploit information that was normally locked away in some internal silo.
Join us for our upcoming Middle East Webinar series episode, “Data Services and the Modern Data Ecosystem,” presented by Chief Evangelist MEA, Alexey Sidorov. Tune-in as we explore how a business can easily support and manage a Data Service ecosystem, providing a more flexible approach for information sharing supporting an ever diverse community of consumers.
Watch on-demand this webinar to learn:
- Why Data Services are a critical part of a modern data ecosystem
- How IT teams can manage Data Services and the increasing demand by businesses
- How Digital IT can benefit from Data Services and how this can support the need for rapid prototyping allowing businesses to experiment with data and fail fast where necessary.
- How a good Data Virtualization platform can encourage a culture of Data amongst business consumers (internally and externally)
A Seminar Presentation on Big Data for Students.
Big data refers to a process that is used when traditional data mining and handling techniques cannot uncover the insights and meaning of the underlying data. Data that is unstructured or time sensitive or simply very large cannot be processed by relational database engines. This type of data requires a different processing approach called big data, which uses massive parallelism on readily-available hardware.
Graph technology has truly burst onto the scene with diverse new products and services, proving that graph is relevant and that not all graph use cases are equal. Previously relegated to niche implementations and science projects, graph now finds itself deployed as the foundational technology for enterprise analytics solutions and enterprise Data Fabric strategies. It is no surprise that many are calling 2018 “The Year of the Graph”.
State of the State: What’s Happening in the Database Market?Neo4j
Speaker: Lance Walter, CMO, Neo4j
Abstract: The data management landscape continues to evolve rapidly. More and more organizations are waking up to the value of connections and relationships in data, and that’s why Gartner recently named Graph databases one of their Top 10 Technology Trends for 2019.
This session will provide an overview of graph technology and talk about the past, present, and future of graphs and data management. Multiple use cases and customer examples will be covered, including examples of where graph databases can assist and accelerate machine learning and AI projects.
Lewis Crawford's presentation from the BI Boss event in Leeds, focussing on our perspective on Big Data, Big Data projects, what to avoid, and how to make it work for you.
Agile Data Management with Enterprise Data Fabric (Middle East)Denodo
Watch full webinar here: https://bit.ly/3td9ICb
In a world where machine learning and artificial intelligence are changing our everyday lives, digital transformation tops the strategic agenda in many private and government organizations. Data is becoming the lifeblood of a company, flowing seamlessly through it to enable deep business insights, create new opportunities, and optimize operations.
Chief Data Officers and Data Architects are under continuous pressure to find the best ways to manage the overwhelming volumes of the data that tend to become more and more distributed and diverse.
Moving data physically to a single location for reporting and analytics is not an option anymore – this is the fact accepted by the majority of the data professionals.
Join us for this webinar to know about the modern virtual data landscapes including:
- Virtual Data Fabric
- Data Mesh
- Multi-Cloud Hybrid architecture
and to learn how to leverage Denodo Data Virtualization platform to implement these modern data architectures.
Transforming Data Management and Time to Insight with Anzo Smart Data Lake®Cambridge Semantics
This webinar is targeted to Federal Government CIOs and
staff that are researching enterprise data management and
mining tools to help them understand how Smart Data Lakes
enable a viable mechanism for addressing their top priorities.
Agile Data Management with Enterprise Data Fabric (ASEAN)Denodo
Watch full webinar here: https://bit.ly/3juxqaw
In a world where machine learning and artificial intelligence are changing our everyday lives, digital transformation tops the strategic agenda in many private and government organizations. Data is becoming the lifeblood of a company, flowing seamlessly through it to enable deep business insights, create new opportunities, and optimize operations.
Chief Data Officers and Data Architects are under continuous pressure to find the best ways to manage the overwhelming volumes of the data that tend to become more and more distributed and diverse.
Moving data physically to a single location for reporting and analytics is not an option anymore – this is the fact accepted by the majority of the data professionals.
Join us for this webinar to know about the modern virtual data landscapes including:
- Virtual Data Fabric
- Data Mesh
- Multi-Cloud Hybrid architecture
- and to learn how to leverage the Denodo Data Virtualization platform to implement these modern data architectures.
Big Data - The 5 Vs Everyone Must KnowBernard Marr
This slide deck, by Big Data guru Bernard Marr, outlines the 5 Vs of big data. It describes in simple language what big data is, in terms of Volume, Velocity, Variety, Veracity and Value.
Using a Semantic and Graph-based Data Catalog in a Modern Data FabricCambridge Semantics
Watch this webinar to learn about the benefits of using semantic and graph database technology to create a Data Catalog of all of an enterprise's data, regardless of source or format, as part of a modern IT or data management stack and an important step toward building an Enterprise Data Fabric.
EDF2014: Allan Hanbury, Senior Researcher, Vienna University of Technology, A...European Data Forum
Selected Talk by Allan Hanbury, Senior Researcher, Vienna University of Technology, Austria at the European Data Forum 2014, 19 March 2014 in Athens, Greece: Conquering Data in Austria: a technology roadmap
Towards a BIG Data Public Private PartnershipEdward Curry
Building an industrial community around Big Data in Europe is the priority of the BIG: Big Data Public Private Forum project. In this workshop we will present the work of the project including analysis of foundational Big Data research technologies, technology and strategy roadmaps to enable business to understand the potential of Big Data technologies, and the necessary collaboration and dissemination infrastructure to link technology suppliers, integrators and leading user organizations. BIG is working towards the definition and implementation of a clear strategy that tackles the necessary efforts in terms of Big Data research and innovation, while also providing a major boost for technology adoption and supporting actions for the successful implementation of the Big Data economy.
New Horizons for a Data-Driven Economy – A Roadmap for Big Data in Europe inside-BigData.com
In this video from the ISC Big Data'14 Conference, Edward Curry from the NUI Galway & Nuria de Lama Sanchez from Atos present: New Horizons for a Data-Driven Economy – A Roadmap for Big Data in Europe.
"In this talk we summarize the results of the BIG project including analysis of foundational Big Data research technologies, technology and strategy roadmaps to enable business to understand the potential of Big Data technologies across different sectors, together with the necessary collaboration and dissemination infrastructure to link technology suppliers, integrators and leading user organizations."
Learn more:
http://www.isc-events.com/bigdata14/schedule.html
and
http://big-project.eu/
Watch the video presentation: http://wp.me/p3RLEV-37G
BigDataPilotDemoDays - I BiDaaS Application to the Manufacturing Sector WebinarBig Data Value Association
The new data-driven industrial revolution highlights the need for big data technologies to unlock the potential in various application domains. To this end, BDV PPP projects I-BiDaaS, BigDataStack, Track & Know and Policy Cloud deliver innovative technologies to address the emerging needs of data operations and applications. To fully exploit the sustainability and take full advantage of the developed technologies, the projects onboarded pilots that exhibit their applicability in a wide variety of sectors. In the Big Data Pilot Demo Days, the projects will showcase the developed and implemented technologies to interested end-users from the industry as well as technology providers, for further adoption.
Sotiris is currently working as Research Director with the Institute of Computer Science at the Foundation for Research and Technology - Hellas, where his research interests include systems, networks, and security. He is also a member of the European Union Agency for Network and Information Security (ENISA) Permanent Stakeholders Group! During Data Science Conference, Sotiris will talk about how data sharing between private companies and research facilities may lead to monetization.
2nd BIG IoT Webinar
The webinar will provide a general overview of the BIG IoT project, the technical solution and the application form of the 1st Open call.
1st BIG IoT Webinar
The webinar will provide a general overview of the BIG IoT project, the technical solution and the application form of the 1st Open call.
One of the main goals of the I-BiDaaS project is to provide a Big Data as a self-service solution that will empower the actual employees of European companies in targeted sectors (banking, manufacturing, telecom), i.e., the true decision-makers, with the insights and tools they need in order to make the right decisions in an agile way. In this big data pilot webinar, we will demonstrate in a step by step fashion the I-BiDaaS self-service solution and its application to the banking sector. In more detail, we will present an overview of the I-BiDaaS project focusing on the requirements of the CaixaBank pilot study, the I-BiDaaS architecture with its core technologies, and a step by step demo of the I-BiDaaS solution. Last but not least, we will show through CaixaBank's success story how I-BiDaaS can resolve data availability, data sharing, and breaking silos challenges in the banking domain.
Project Description of the Linked Open Data (LOD) PILOT Austria - presented at the PiLOD event at VU Amsterdam (Netherlands) on 29.01. 2014 (see: http://www.pilod.nl/) by Martin Kaltenböck of Semantic Web Company.
EDF2014: Marta Nagy-Rothengass, Head of Unit Data Value Chain, Directorate Ge...European Data Forum
PPP on Data & Executive Panel on Big Data, Introduction by Marta Nagy-Rothengass, Head of Unit Data Value Chain, Directorate General for Communications Networks, Content and Technology at the European Data Forum 2014, 20 March 2014 in Athens, Greece: Towards a Data Value Chain Partership in Europe.
How Enterprise Architecture & Knowledge Graph Technologies Can Scale Business...Semantic Web Company
Organising data, for most of us, means Excel spreadsheets and folders upon folders. Knowledge graph technology, however, organises data in ways similar to the brain – through context and relations. By connecting your data, you (and also machines) are able to gain context within your knowledge, helping you to make informed decisions based on all of the information you already have.
So, how can enterprises benefit from this and scale?
PwC Sr. Research Fellow for Emerging Tech, Alan Morrison, and Sebastian Gabler, Head of Sales of Semantic Web Company tackle the importance of Enterprise Knowledge Graphs and how these technologies scale business efficiency.
Learn about:
• Application-centric development to data-centric approaches
• How enterprise architects learn how to benefit from knowledge graphs: use cases
• Learn which use cases fit well to which type of graph, and which technologies are involved
• Understand how RDF helps with data integration.
• What is AI-assisted entity linking?
• Understand data virtualisation vs. materialisation
- Learn to understand what knowledge graphs are for
- Understand the structure of knowledge graphs (and how it relates to taxonomies and ontologies)
- Understand how knowledge graphs can be created using manual, semi-automatic, and fully automatic methods.
- Understand knowledge graphs as a basis for data integration in companies
- Understand knowledge graphs as tools for data governance and data quality management
- Implement and further develop knowledge graphs in companies
- Query and visualize knowledge graphs (including SPARQL and SHACL crash course)
- Use knowledge graphs and machine learning to enable information retrieval, text mining and document classification with the highest precision
- Develop digital assistants and question and answer systems based on semantic knowledge graphs
- Understand how knowledge graphs can be combined with text mining and machine learning techniques
- Apply knowledge graphs in practice: Case studies and demo applications
Deep Text Analytics - How to extract hidden information and aboutness from textSemantic Web Company
- Deep Text Analytics (DTA) is an application of Semantic AI
- DTA fuses methods and algorithms taken from language modeling, corpus linguistics, machine learning, knowledge representation and the semantic web result into Deep Text Analytics methods
- Main areas of use cases for DTA are Information retrieval, NLU, Question answering, and Recommender Systems
Leveraging Knowledge Graphs in your Enterprise Knowledge Management SystemSemantic Web Company
Knowledge graphs and graph-based data in general are becoming increasingly important for addressing various data management challenges in industries such as financial services, life sciences, healthcare or energy.
At the core of this challenge is the comprehensive management of graph-based data, ranging from taxonomy to ontology management to the administration of comprehensive data graphs along with a defined governance framework. Various data sources are integrated and linked (semi) automatically using NLP and machine learning algorithms. Tools for securing high data quality and consistency are an integral part of such a platform.
PoolParty 7.0 can now handle a full range of enterprise data management tasks. Based on agile data integration, machine learning and text mining, or ontology-based data analysis, applications are developed that allow knowledge workers, marketers, analysts or researchers a comprehensive and in-depth view of previously unlinked data assets.
At the heart of the new release is the PoolParty GraphEditor, which complements the Taxonomy, Thesaurus, and Ontology Manager components that have been around for some time. All in all, data engineers and subject matter experts can now administrate and analyze enterprise-wide and heterogeneous data stocks with comfortable means, or link them with the help of artificial intelligence.
Unified views of business-critical information across all customer-facing processes and HR-related tasks are most relevant for decision makers.
In this talk we present a SharePoint extension that supports the automatic linking of unstructured content like Word documents with structured information from other databases, such as statistical data. As a result, decision makers have knowledge portals based on linked data at their fingertips.
While the importance of managed metadata and Term Store is clear to most SharePoint architects, the significance of a semantic layer outside of the content silos has not yet been explored systematically.
We will present a four-layered content architecture and will take a close look on some of the aspects of the semantic layer and its integration with SharePoint:
- Keeping Term Store and the semantic layer in sync
- Automatic tagging of SharePoint content
- Use of graph databases to store tags
- Entity-centric search & analytics applications
Metadata is most often stored per data source, and therefore it is meaningless outside of the silo. In this presentation, we will give a live demo of a SharePoint extension that makes use of an explicit semantic layer based on standards. This approach builds the basis to start linking data across the silos in a most agile way.
The resulting knowledge graph can start on a small scale, to develop continuously and to grow with the requirements. In this presentation we will give an example to illustrate how initially disconnected HR-related data (CVs in SharePoint; statistical data from labour market; skills and competencies taxonomies; salary spreadsheets) gets linked automatically, and is then made available through an extensive search & analytics application.
Slides based on a workshop held at SEMANTiCS 2018 in Vienna. Introduces a methodology for knowledge graph management based on Semantic Web standards, ranging from taxonomies over ontologies, mappings, graph and entity linking. Further topics covered: Semantic AI and machine learning, text mining, and semantic search.
Semantic Artificial Intelligence is the fusion of various types of AI, incl. symbolic AI, reasoning, and machine learning techniques like deep learning. At the same time, Semantic AI has a strong focus on data management and data governance. With the 'wedding' of various AI techniques new promises are made, but also fundamental approaches like 'Explainable AI (XAI)', knowledge graphs, or Linked Data are more strongly focused.
Bringing Machine Learning and Knowledge Graphs Together
Six Core Aspects of Semantic AI:
- Hybrid Approach
- Data Quality
- Data as a Service
- Structured Data Meets Text
- No Black-box
- Towards Self-optimizing Machines
The PoolParty Semantic Classifier is a component of the Semantic Suite, which makes use of machine learning in combination with Knowledge Graphs.
We discuss the potential of the fusion of machine learning, neuronal networks, and knowledge graphs based on use cases and this concrete technology offering.
We introduce the term 'Semantic AI' that refers to the combined usage of various AI methods.
Machines learn better with Semantics!
See how taxonomy management and the maintenance of knowledge graphs benefit from machine learning and corpus analysis, and how, in return, machine learning gets improved when using semantic knowledge models for further enrichment.
A quick introduction to taxonomies, and how they relate to ontologies and knowledge graph. See how they can serve as part of a semantic layer in your information architecture. Learn which use cases can be developed based on this.
PoolParty GraphSearch - The Fusion of Search, Recommendation and AnalyticsSemantic Web Company
See how Cognitive Search works when based on Semantic Knowledge Graphs.
We showcase the latest developments and new features of PoolParty GraphSearch:
- Navigate a semantic knowledge graph
- Ontology-based data access (OBDA)
- Search over various search spaces: Ontology-driven facets including hierarchies
- Sophisticated autocomplete including context information
- Custom views on entity-centric and document-centric search results
- Linked data: put various tagging services such as TRIT or PoolParty Extractor in series and benefit from comprehensive semantic enrichment
- Statistical charts to explain results from unified data repositories quickly
- Plug-in system for various recommendation and matchmaking algorithms
This talk discusses how companies can apply semantic technologies to build cognitive applications. It examines the role of semantic technologies within the larger Artificial Intelligence (AI) technology ecosystem, with the aim of raising awareness of different solution approaches.
To succeed in a digital and increasingly self-service-oriented business environment, companies can no longer rely solely on IT professionals. Solutions like the PoolParty Semantic Suite utilize domain experts and business users to shape the cognitive intelligence of knowledge-driven applications.
Cognitive solutions essentially mimic how the human brain works. The search for cognitive solutions has challenged computer scientists for more than six decades. The research has matured to the extent that it has moved out of the laboratory and is now being applied in a range of knowledge-intensive industries.
There is no such thing as a single, all-encompassing “AI technology.” Rather, the large global professional technology community and software vendors are continuously developing a broad set of methods and tools for natural language processing and advanced data analytics. They are creating a growing library of machine learning algorithms to enhance the automated learning capabilities of computer systems. These emerging technologies need to be customized or combined with complementary solutions as semantic knowledge graphs, depending on the use case.
A hybrid approach to cognitive computing, employing both the statistical and knowledge base models, will have a critical influence on the development of applications. Highly automated data processing based on sophisticated machine-learning algorithms must give end user the option to independently modify the functioning of smart applications in order to overcome the disadvantages associated with ‘black-box’ approaches.
This talk will give an overview over state-of-the-art smart applications, which are becoming a fusion of search, recommendation, and question-answer machines. We will cover specific use cases in focused knowledge domains, and we will discuss how this approach allows for AI-enabled use cases and application scenarios that are currently highly prioritized by corporate and digital business players.
In this engaging, 1-hour webinar (hosted by http://www.poolparty.biz and http://www.mekon.com), you will learn how to tailor information chunks to readers’ unique needs. We will talk about:
- Benefits and principles of granular structured content, and how to start preparing your own content for this new architecture.
- Best practices for linking structured content to standards-based taxonomies, and some pitfalls to avoid
- The underlying semantic architecture that you can work toward for a truly mature and scalable approach to linking content and data
- Key use cases that you can apply to your own organization
See how you can configure your linked data eco-system based on PoolParty's semantic middleware configurator. Benefit from Shadow Concept Extraction by making implicit knowledge visible. Combine knowledge graphs with machine learning and integrate semantics into your enterprise information systems.
Technical Deep Dive: Learn more about the most complete Semantic Middleware on the market. See how to integrate semantic services into your Enterprise Information Systems.
Taxonomies and Ontologies – The Yin and Yang of Knowledge ModellingSemantic Web Company
See how ontologies and taxonomies can play together to reach the ultimate goal, which is the cost-efficient creation and maintenance of an enterprise knowledge graph. The knowledge modelling methodology is supported by approaches taken from NLP, data science, and machine learning.
This talk addresses two questions: “How can the quality of taxonomies be defined?” and “How can it be measured?” See how quality criteria vary depending on how a taxonomy is applied, such as automatic content classification in ecommerce or a knowledge graph for data integration in enterprises. Distinguish between formal quality, structural properties, content coverage, and network topology. Investigate the advantages of standards-based and machine-processable SKOS taxonomies to be able to measure the quality of taxonomies automatically, as well as several tools and techniques for quality assessment.
Consistency is crucial to a good user experience. Designers go to great lengths to create and test consistent visual designs. The structural design of an information environment, which is of equal importance to a good user experience, is too often ignored. Blumauer presents a “four-layered content architecture” for making sense of any information environment by clearly distinguishing between the content, metadata, and semantic layers and the navigation logic. He discusses several use cases for a taxonomy-driven user experience such as personalization or dynamically created topic pages.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
4. RTI – the role of bmvit
- Budget: 450 M € p.a.
- Main Areas:
• ICT
• Intelligent Production
• New Energy
• Mobility
- Cooperative research
- International cooperations
- RTI Programme „ICT of the future“
• 2012 – 2020
• Budget: 25 M € p.a.
- ICT-fields:
• Complex Systems
• Intelligent Systems: Conquering Data
• Trusted Systems
• Interoperability of Systems
Präsentationstitel Name, Abteilung4
5. RTI – the role of bmvit
- Budget: 450 M € p.a.
- Main Areas:
• ICT
• Intelligent Production
• New Energy
• Mobility
- Cooperative research
- International cooperations
- RTI Programme „ICT of the future“
• 2012 – 2020
• Budget: 20 M € p.a.
- ICT-fields:
• Complex Systems
• Intelligent Systems: Conquering Data
• Trusted Systems
• Interoperability of Systems
Präsentationstitel Name, Abteilung5
6. RTI – the role of bmvit
- Budget: 450 M € p.a.
- Main Areas:
• ICT
• Intelligent Production
• New Energy
• Mobility
- Cooperative research
- International cooperations
- RTI Programme „ICT of the future“
• 2012 – 2020
• Budget: 20 M € p.a.
- ICT-fields:
• Complex Systems
• Intelligent Systems: Conquering Data
• Trusted Systems
• Interoperability of Systems
Präsentationstitel Name, Abteilung6
Technology
Roadmap
7. Conquering Data in Austria – Our Source
- Cooperative RTI (since 2002)
• Former programme: Semantic Systems
• Austrian Partners in FP7-Projects
• Competence Centers (e.g. Know-Center)
- Digital Networked Data –platform (DND): the aim of the DND is unlocking the
value-creating potential of the future digital data markets for Austrian industry,
research and users (http://networkeddata.at/en/home.html )
- Studies:
• Technology-Roadmap: Conquering Data – Intelligent Systems
• Big Data in Austria
• Innovative Public Procurement: ICT
IKT der Zukunft Mosnik, III/i57
Industry
Start-Ups
Academia
RTOs
Data centers
Public sector
National
Library
Open Data
…
International
8. Conquering Data in Austria – Our Source
- Cooperative RTI (since 2002)
• Former programme: Semantic Systems
• Austrian Partners in FP7-Projects
• Competence Centers (e.g. Know-Center)
- Digital Networked Data –platform (DND): the aim of the DND is unlocking the
value-creating potential of the future digital data markets for Austrian industry,
research and users (http://networkeddata.at/en/home.html )
- Studies:
• Technology-Roadmap: Conquering Data – Intelligent Systems
• Big Data in Austria
• Innovative Public Procurement: ICT
IKT der Zukunft Mosnik, III/i58
Austrian Data
Forum, 4.11.,
Museumsquartier
„Internet of Things
Day“
9. Open Data in Austria
- Open Government Data
• Federal Chancellery
• data.gv.at
• Principals: Transparency, participation, collaboration
- Open Private Data
• Opendataportal.at since July 2014
• central portal for data from industry, culture, NGO/NPO, research and society
• Wikimedia Österreich, der Open Knowledge Foundation Österreich und der
Cooperation OGD Österreich.
10. Open Government Data in Österreich - data.gv.at
IST Zustand
http://bigdataaustria.wordpress.com10
Also bmvit since June 2015
11. Open Innovation
- Working Group of bmvit „From Open Data to Open Innovation“
• Internal Open Data Strategy
• Open Data Strategy for funded RTI-Projects (GFF, ZSI, AIT)
• Cooperation with Federal Chancellery
- Strategy on „Open Innovation“
• Elaboration launched August 2015
• www.openinnovation.at
Mosnik11
12. Technology Roadmap Conquering Data in Austria
- Point of view: Technology
- Adressing Data
• Open Data, Closed Data, Small Data, Big Data,…
IKT der Zukunft Mosnik, III/i512
13. 1. Develop lead
technologies
4. Produce highly
qualified personnel
2. Achieve lead
positions in
competitive markets
3. Establish and extend
a lead position as
location for research
Objectives of
ICT of the Future
short term
(up to 2015)
mid term
(up to 2020)
long term
(up to 2025)
14. 4. Produce highly
qualified personnel
1. Develop lead
technologies
short term
(up to 2015)
mid term
(up to 2020)
long term
(up to 2025)
2. Achieve lead
positions in
competitive markets
3. Establish and extend
a lead position as
location for research
Build
Data-Services Ecosystem 5a: Concept completed
5b: Data-Services Ecosystem materialized
5c: Selected applications implemented
Objectives of
ICT of the Future
Develop
Legal Framework 6: Common legal framework developed
Network
Stakeholders
7a: National & int’l stakeholder
networking initiatives installed
7b: Future Data
study completed
15. 4. Produce highly
qualified personnel
1. Develop lead
technologies
short term
(up to 2015)
mid term
(up to 2020)
long term
(up to 2025)
2. Achieve lead
positions in
competitive markets
3. Establish and extend
a lead position as
location for research
Build
Data-Services Ecosystem 5a: Concept completed
5b: Data-Services Ecosystem materialized
5c: Selected applications implemented
Objectives of
ICT of the Future
Develop
Legal Framework 6: Common legal framework developed
1: Advanced technologies for Data
Integration & Fusion developed
Advance
Data Integration and Fusion
Network
Stakeholders
7a: National & int’l stakeholder
networking initiatives installed
7b: Future Data
study completed
Increase
Algorithmic Efficiency
2: Efficiency of data analytics
algorithms brought to a new level
Make
Information Actionable
3: Technologies turning data into
actionable information available
Automate
Knowledge Work
4: Intelligent systems for next-
generation decision making developed
16. 4. Produce highly
qualified personnel
1. Develop lead
technologies
short term
(up to 2015)
mid term
(up to 2020)
long term
(up to 2025)
2. Achieve lead
positions in
competitive markets
3. Establish and extend
a lead position as
location for research
Build
Data-Services Ecosystem 5a: Concept completed
5b: Data-Services Ecosystem materialized
5c: Selected applications implemented
Objectives of
ICT of the Future
Develop
Legal Framework 6: Common legal framework developed
1: Advanced technologies for Data
Integration & Fusion developed
Advance
Data Integration and Fusion
Increase
Algorithmic Efficiency
2: Efficiency of data analytics
algorithms brought to a new level
Make
Information Actionable
3: Technologies turning data into
actionable information available
Automate
Knowledge Work
4: Intelligent systems for next-
generation decision making developed
5b: Data-Services Ecosystem materialized
Create
Competencies and Resources
8b: Austrian Data Technologies
Institute established8a: Education
programmes defined
Enforce
Gender & Diversity Measures
9: Measures enforcing gender awareness
in Data Analytics implemented
Network
Stakeholders
7a: National & int’l stakeholder
networking initiatives installed
7b: Future Data
study completed
17. Action Plan
Präsentationstitel Name, Abteilung17
- Coordination
• Network Stakeholders
• Data-Services Ecosystem
• Legal framework
- Technology
• Data Integration and Fusion
• Algorithmic Efficiency
• Actionable Information
• Knowledge work
- Human Resources
• Competences and Resources
• Gender and Diversity
19. Role of Big Data in Austria
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
2012 2013 2014 2015 2016 2017
Wertschöpfung ins Ausland
Wertschöpfung in Österreich
Wertschöpfung durch österreichische
Unternehmen
20. Acceptance of Big Data in Austria
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2013 2012
Wird gerade umgesetzt
Ist in Planungsphase
Wird diskutiert
Derzeit kein Thema
21. What is needed?
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Gesamt
Auswählen geeigneter Datenbanken für die Speicherung der Daten
Anschaffung geeigneter Speichersysteme
Auswählen geeigneter Software für die Analyse von Daten
Aufbau einer IT-Landschaft für Datenspeicherung und Analyse inkl.
Netzwerke und Analysesysteme
Aufbau des Knowhows und Verstehen der Prozesse für ideale
Auswertungen und Repräsentation
22. Towards a data-services ecosystem - Actions 2015
- ICT of the Future - Call Launch in October 2015)
• Fund data technologies: ICT of the future
Manufacturing
Earth observation
• Lighthouse-project „Data-Service-Ecosystem“
- Call for Endowed professorship „Data Science“
• Evaluation in November 2015)
- Open Data & Open Source
• Recommendation to RTI-Projects to use opendataportal.at, FI-WARE
• Recommendation to RTI-Projects to publish Open Data awareness
• Internal Open Data Strategy for bmvit
- Best Practice Guidelines for Big Data-Projects, Public Procurement: Open
Government & Open Services,….
Kick-Off Event
3.11., TechGate
ADF, 4.11.,
Museumsquartier
23. Chances – „Wave 4“
- New opportunities exist (COM)
• in a number of sectors where
application of these methods is still in its infancy and global dominant
players have not yet emerged
• Domains:
smart production,
earth observation,
smart city/smart home
Agriculture
- Working data-service-ecosystem
• Win-Win-Situation for all stakeholders (public sector, LE, SME, Start Ups,
Researchers, Entrepreneurs, civils,…),
• To make services and data accesable and interopberable
24. Links
- Roadmap: Daten durchdringen (www.bmvit.gv.at/ikt)
- Big Data in Austria (http://www.bmvit.gv.at/innovation/publikationen/ikt/index.html)
- Guidelines for Big Data Projects in Austria
- Kommunikation der EC „florierende datengesteuerte Wirtschaft“
(http://ec.europa.eu/transparency/regdoc/rep/1/2014/DE/1-2014-442-DE-F1-1.Pdf)
25. Thank‘s for your attention!
Mag. Lisbeth Mosnik
bmvit – Bundesministerium für Verkehr, Innovation und Technologie
III/i5 – ICT, Industrial Technologies and Space
lisbeth.mosnik@bmvit.gv.at
Präsentationstitel Name, Abteilung25
26. Components of the Data-Services Ecosystem
Präsentationstitel Name, Abteilung26
Data
Services
Ecosystem
Data
Services
Biotopes
Austrian
Open
Cloud
Data
Application
Incubator
Data
Curation
27. Challenges
IKT der Zukunft Mosnik, III/i527
Data Economy and
Open Data
Shared Computing Infrastructure
VSC
ACSC
Austrian Grid I
Austrian Grid IIÖAW ZAMG
Data Curation and
Preservation
30. Elements of Data - Service - Ecosystem
- Data Application Incubator
- Basic infrastructure for data driven economy (Cloud , HPC, 5G,…)
- Lighthouse initiatives for important economic sectors (e.g. energy, manufacturing)
- Data Curation
- Preconditions:
• make sure that the relevant legal framework and the policies, such as on
interoperability, data protection, security and IPR are data-friendly
• Development of skills (multidisciplinary teams with highly skilled specialists),…
Präsentationstitel Name, Abteilung30
Editor's Notes
Transparenz: stärkt das Pflichtbewusstsein und liefert den Bürgerinnen und Bürgern
Informationen darüber, was ihre Regierung und ihre Verwaltung derzeit machen. Die freie
Verfügbarkeit von Daten ist eine wesentliche Grundlage für Transparenz.
Partizipation: verstärkt die Effektivität von Regierung und Verwaltung und verbessert die
Qualität ihrer Entscheidungen, indem das weit verstreute Wissen der Gesellschaft in die
Entscheidungsfindung mit eingebunden wird.
- Kollaboration: bietet innovative Werkzeuge, Methoden und Systeme, um die Zusammenarbeit
über alle Verwaltungsebenen hinweg und mit dem privaten Sektor zu forcieren.
1119 Datensätze
Formate, Governance! - > Projekt Linked Open Data Österreich
Auch: Kaggle und Wikinomics
Durch diese
Zurverfügungstellung der Daten wird die Weiterverwendung in Anwendungen und die Erstellung von
neuen innovativen Applikationen erst ermöglicht
Aus strategischer Sicht beinhaltet eine öffentliche Bereitstellung und Nutzbarmachung von Daten ein
sehr hohes Potenzial für Innovationen in Österreich, sowohl in der Forschung als auch in der
Wirtschaft. Ein wichtiger Schritt hierbei ist aber auch die Verwendung von konsolidierten Formaten,
Beachtung von Datenqualität sowie die Aktualisierung von Daten. Ein weit reichendes Konzept in
Richtung Open Data kann den österreichischen Forschungs- und Wirtschaftsstandort in den
Bereichen IKT, Mobilität und vielen anderen stärken und auch einen internationalen
Wettbewerbsvorteil bewirken.
Ein weiteres, wichtiges Portal ist das "Open Data Portal", welches mit 01. Juli 2014 in Betrieb geht.
Hierbei handelt es sich um das Schwesternportal von "data.gv.at".
Die IDC erwartet, dass sich der weltweite Big Data Markt von 9,8 Milliarden USD im Jahr 2012 auf 32,4 Milliarden USD im Jahr 2017 steigern wird
Das entspricht einer jährlichen Wachstumsrate von 27%