BDE SC6-hang out - technology part-SWC - Martin
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BDE SC6 hang out - technology part-SWC - Martin
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BDE SC6-hang out - technology part-SWC - Martin
- 1. BIG DATA EUROPE PILOT SC6: CITIZEN BUDGET ON MUNICIPAL LEVEL EUROPE IN A CHANGING WORLD - INCLUSIVE, INNOVATIVE AND REFLECTIVE SOCIETIES HANG OUT 28 SEPTEMBER 2016 MARTIN KALTENBOECK (CFO, SEMANTIC WEB COMPANY) Integrating Big Data, Software & Communities for Addressing Europe’s Societal ChallengesBDE SC6 Hangout
- 2. Big Data Europe (CSA: 2015-17) Show societal value of Big Data: 7 Domains Lower barrier for using big data technologies o Required effort and resources o Limited data science skills Help establishing cross- lingual/organizational/domain Data Value Chains 26-oct.-16
- 3. Big Data Europe 26-oct.-16 COORDINATION Stakeholder Engagement (Requirements Elicitation) SUPPORT Design, Realise, Evaluate Big Data Aggregator Platform Create and Manage Societal Big Data Interest Groups Cloud-deployment ready Big Data Aggregator Platform CSA Measures Results
- 4. THE BDE PLATFORM ARCHITECTURE & COMPONENTS Integrating Big Data, Software & Communities for Addressing Europe’s Societal Challenges
- 5. The three Big Data „V“ Variety is often neglected
- 6. Current State of Platform Architecture
- 7. Adding a Semantic Layer to Data Lakes Manufacturing Marketing Sales SupportAccounting Semantic Data Lake • central place for model, schema and data historization • Combination of Scale Out (cost reduction) and semantics (increased control & flexibility) • grows incrementally (pay-as-you-go) Inbound Data Sources Outbound and Consumption Inbound Raw Data Store Data Lake (order of magnitude cheaper scalable data store) Knowledge Graph for Relationship Definition and Meta Data Frontend to Access Relationship and KPI Definition / Documentation Frontend to Access (ad hoc) Reports Outbound Data Delivery to Target Systems JSON-LD CSVW R2RMLXML2RDF
- 8. Why to use BDE Technology? Hortonworks Cloudera MapR Bigtop BDE File System HDFS HDFS NFS HDFS HDFS Installation Native Native Native Native lightweight virtualization Plug & play components (no rigid schema) no no no no yes High Availability Single failure recovery (yarn) Single failure recovery (yarn) Self healing, mult. failure rec. Single failure recovery (yarn) Multiple Failure recovery Cost Commercial Commercial Commercial Free Free Scaling Freemium Freemium Freemium Free Free Addition of custom components Not easy No No No Yes Integration testing yes yes yes yes -- Operating systems Linux Linux Linux Linux All Management tool Ambari Cloudera manager MapR Control system - Docker swarm UI+ Custom
- 9. SC6 PILOT CITIZENS BUDGET ON MUNICIPAL LEVEL ARCHITECTURE & COMPONENTS Integrating Big Data, Software & Communities for Addressing Europe’s Societal Challenges
- 10. SC6: Social Sciences 26-oct.-16www.big-data-europe.eu Pilot Architecture & Components
- 11. SC6: Social Sciences 26-oct.-16www.big-data-europe.eu Pilot focus area: Citizens budget spending on municipal level Big Data Focus area: Statistical and research data linking & integration Selected Key Data assets: Detailed budget execution data in city level, statistical data from public data portals and statistical offices, federated social sciences data
- 12. 4 Vs of Big Data in SC6 Pilot Variety: requirement based on the harvesting of budget data and budget execution data from several sources, available in different structures and formats. Volume: requirement regarding the growing amount of open budget data available as well as of budget execution data Velocity: requirements regarding budget execution data that is provided on continuous basis by the publisher (daily, weekly, monthly). Veracity: Veracity refers to the biases, noise and abnormality in data. Even for within the same country there are differences on the published data because often are coming from different systems or public accounting standards are not enforced absolutely uniformly (e.g. different municipal departments) 26-oct.-16www.big-data-europe.eu
- 13. SC6 Pilot - Architecture 26-oct.-16www.big-data-europe.eu
- 14. SC6 Pilot: Technical Components Apache Flume, https://flume.apache.org/ (data ingestion) Apache Kafka, http://kafka.apache.org (messaging service) Apache Spark, http://spark.apache.org (distributed analysis, transformation) Apache HDFS, http://hadoop.apache.org (raw data storage) SWCs’ PoolParty Semantic Suite, http://poolparty.biz (data consolidation, curation, mapping) OpenLink s’ Virtuoso, http://virtuoso.openlinksw.com (triple store – data storage) Apache HTTP, http://httpd.apache.org (linked data serving) Apache Avro, http://avro.apache.org/docs/current/ (intermediate data schema) D3 JS Library, https://d3js.org/ (visualisation of RDF data using SPARQL queries) SWCs’ PoolParty GraphSearch (SPARQL based interface component for filter & faceted search) 26-oct.-16www.big-data-europe.eu
- 15. SC6 Pilot: 1st MockUp / WireFrame 26-oct.-16www.big-data-europe.eu
- 16. SC6 Pilot: Pilot Evaluation Evaluation Approach SC6 Pilot: Invite municipalities to evaluate and use the system Invite community (open data, data community, BDE community, W3C) Evaluate within the 2 participating projects (BDE, YourDataStories) BDE SC6 workshop in Cologne, 5.12.2016 + Overall BDE Tech WS (ApacheCon) Additional evaluation – tests over time with a growing amount of data a growing number of different sources & formats docked onto the system additional analytics in place 26-oct.-16www.big-data-europe.eu
- 17. How to benefit best from BDE 26-oct.-16www.big-data-europe.eu Health 19 October Brussel s Standalone Workshop Food&Agri 30 September 2016 Brussel s Collocated with DG AGRI WP2018-20 stakeholder consultation Energy 20 September 2016 Brussel s Collocated with H2020 Energy InfoDay (19 th ) Transport 16 September 2016 Brussel s Collocated with TM 2.0 Steering Body meeting Climate February 2017 Brussel s Collocated with EC JRC ISPRA Workshop Societies 5 December 2016 Cologn e Collocated with EDDI16- 8th Annual European DDI User Conference: http://bde-sc6-2016.eventbrite.com (40 seats) Security 18 October 2016 Brussel s Standalone Workshop • BDE Workshops& Webinars • Use & expand the BDE Platform • Visit Website: news, events, community, … • Big Data Europe W3C Community
- 18. Contacts: CESSDA, http://cessda.net/ Ivana Ilijasic Versic, ivana.versic@cessda.net Hossein Abroshan, hossein.abroshan@cessda.net NCSR-D, http://www.demokritos.gr/?lang=en Michalis Vafopoulos, vafopoulos@gmail.com Semantic Web Company (SWC), http://www.semantic-web.at Martin Kaltenböck, m.kaltenboeck@semantic-web.at Jürgen Jakobitsch, j.jakobitsch@semantic-web.at 26-oct.-16www.big-data-europe.eu
- 19. Questions & Contacts www.big-data-europe.eu 26-oct.-16 #BigDataEurope Martin Kaltenböck CFO, Semantic Web Company m.kaltenboeck@semantic-web.at http://www.linkedin.com/in/martinkaltenboeck https://twitter.com/kalte2707 http://de.slideshare.net/MartinKaltenboeck http://blog.semantic-web.at
Notes de l'éditeur
- Project obecjtives: Addressing each of the Societal Challenge domains (7), we have a domain representative for each & a pilot instantiation of the BDE platform for each in progress One of the challenges to Big Data opportunities is the lack of skills (data science) – our aim is to provide out of the box technology with not a lot of training required to use and apply BDE technology can be applied in multiple domains and in different phases within Data Value Chains, working with different data providers and addressing multiple objectives (as opposed to current solutions, which tend to be very specific to one data source or domain, and address one objective.
- Project obecjtives: Addressing each of the Societal Challenge domains (7), we have a domain representative for each & a pilot instantiation of the BDE platform for each in progress One of the challenges to Big Data opportunities is the lack of skills (data science) – our aim is to provide out of the box technology with not a lot of training required to use and apply BDE technology can be applied in multiple domains and in different phases within Data Value Chains, working with different data providers and addressing multiple objectives (as opposed to current solutions, which tend to be very specific to one data source or domain, and address one objective.
- http://www.gi.de/nc/service/informatiklexikon/detailansicht/article/big-data.html
- Data Lake is a storage repository for big data scale raw data in original data formats. late binding approach to schema: “Let us decide, when we need it.” scale out architecture on commodity infrastructure, mostly with HFS/Hadoop/Spark, which gives a huge cost advantage – about factor 10 compared to data warehouses. Semantic Data Lake = Data Lake + Knowledge Graph management of structure (vocabularies/schemas, KPIs trees, metadata, …) on top of the Data Lake is performed in a knowledge graph - a complex data fabric representing all kinds of things and how they relate to each other. A knowledge graph is unique regarding flexibility, multiple views and metadata capabilities. Based on the Resource Description Framework (RDF) standard and Linked Data principles.
- Data Lake is a storage repository for big data scale raw data in original data formats. late binding approach to schema: “Let us decide, when we need it.” scale out architecture on commodity infrastructure, mostly with HFS/Hadoop/Spark, which gives a huge cost advantage – about factor 10 compared to data warehouses. Semantic Data Lake = Data Lake + Knowledge Graph management of structure (vocabularies/schemas, KPIs trees, metadata, …) on top of the Data Lake is performed in a knowledge graph - a complex data fabric representing all kinds of things and how they relate to each other. A knowledge graph is unique regarding flexibility, multiple views and metadata capabilities. Based on the Resource Description Framework (RDF) standard and Linked Data principles.