PICASSO Big Data Expert Group Sören Auer © Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS The three Big Data „V“ – Variety is often neglected Quelle: Gesellschaft für Informatik © Fraunhofer · Seite 2 Sören Auer 2 Semantic Web Layer Cake 2001 • Monolithic based on XML • Focus on heavyweight Semantic (Ontologies, Logic, Reasoning) http://www.w3.org/2001/10/03-sww-1/slide7-0.html © Fraunhofer · Seite 3 • Lingua Franca of Data integration with many technology interfaces (XML, HTML, JSON, CSV, RDB,…) SWRL Regeln • Focus on lightweight vocabularies, rules, thesauri etc. SKOS Thesauri Logik Ontologien Vocabularies • Less “invasive” SPARQL RDF RDF Data Shapes RDF-Schema RDF/XML JSON-LD CSV2RDF R2RML RDFa XML JSON CSV RDB HTML Unicode © Fraunhofer · Seite 4 URIs (Access control), Signatur, Encryption (HTTPS/CERT/DANE), The Semantic Web Layer Cake 2015 – “A Little Semantics Goes a Long Way” INTEGRATING BIG DATA & LINKED DATA © Fraunhofer – Seite 5 Blueprint of the Data Aggregator Platform Follows typical Lambda Architecture Batch Layer Batch View Input data Domain-specific BDE apps Spatial Big Data Analytics Real-time data & message passing Social Statistical Temporal Transactiona l Imagery Applications & Showcases Real-time dashboards Stream BDE Platform & Intelligence Data Storage message passing In-stream Mining Speed Layer Real-time View Transactions … Integrated on top of existing Big Data distribution + Semantic Layer (Retaining Semantics using LD approach ) © Fraunhofer · Seite 6 6 Adding a Semantic Layer to Data Lakes Accounting Outbound and Consumption Management Accounting Regulatory Reporting Frontend to Access Relationship and KPI Definition / Documentation Risk Frontend to Access (ad hoc) Reports Treasury Outbound Data Delivery to Target Systems Knowledge Graph for Relationship Definition and Meta Data 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 XML2RDF JSON-LD CSVW R2RML Data Lake (order of magnitude cheaper scalable data store) Inbound Raw Data Store Data Sources © Fraunhofer · Seite 7 [1] Wrobel, Voss, Köhler, Beyer, Auer: Big Data, Big Opportunities - Anwendungssituation und Forschungsbedarf.7 Informa [2] Debattista, Lange, Scerri, Auer: Linked 'Big' Data. IEEE/ACM Big Data Computing BDC 2015: 92-98 INDUSTRIAL DATA SPACE © Fraunhofer · Seite 8 Vocabulary-based Integration facilitates Data-driven Businesses Vocabulary © Fraunhofer · Seite 9 Die Arbeiten zum Industrial Data Space sind komplementär verzahnt mit der Plattform Industrie 4.0 Versicherung Handel 4.0 4.0 Industrie 4.0 Fokus auf die produzierende Industrie Bank 4.0 … Smart Services Industrial Data Space Fokus auf Daten Daten Übertragung, Netzwerke Echtzeitsysteme … © Fraunhofer ·· Seite 10 The Industrial Data Space Initiative Community of >30 large German and European Companies Pre-competitive, publicly funded innovation project involving 11 Fraunhofer institutes for developing IDS reference architecture Current signatories of the MoU to support the Industrial Data Space Association © Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS Semantic Data Linking for Enterprise Data Value Chains Data Lake Industrial Data Space Pure Internet centralized, monopolistic federated, secure, „trusted“, standard-based completely dezentral, open, unsecure Central Repository Decentral Decentral Central Decentral Decentral Data Linking Single provider Federated, on demand Missing Data Security Bilateral Certified system Bilateral Central Provider Role system Unstructured Internet Internet Internet Data management Data Ownership Market structure Transport infrastructure Bilder: ©Fotolia Francesco De Paoli, Nmedia, hakandogu © Fraunhofer · Seite 12 Basic principles of the Industrial Data Space On Demand Vernetzung Interlinking Bilder: © Fotolia 77260795 ∙ 73040142 58947296 ∙ 68898041 © Fraunhofer · Seite 13 Linked Light Semantics Security with Industrial Data Container Certified Roles Industrial Data Space: On Demand Interlinking All Data stays with its Ownern and are controlled and secured. Only on request for a service data will be shared. No central platform. Service F Enterprise 6 Enterprise 5 Service G Service A Enterprise 1 Enterprise 4 Service B Service E Service C Bildquellen: Istockphoto © Fraunhofer · Seite 14 Enterprise 2 Enterprise 3 Service D Linked Light Semantics A lighweight approach for Data Interlinking Classical Enterprise systems Linked Light Semantics Internet / WWW Fixed Data schema Reference vocabularies Web pages Globale Enforcement Bridge between local Representations Only Links Closed Intelligent and structured interlinked Completely open Manuel Transformation Automatic translation/mapping Lack of standardization High cost Leight-weight No structure Q: istockphoto.com © Fraunhofer · Seite 15 --- VERTRAULICH --- IDS Architecture Overview Clearing Vocabulary Apps Index Industrial Data Space App Store Industrial Data Space Registry Industrial Data Space Broker Download Third Party Upload Internal IDS Connector Upload / Download / Search External IDS Connector External IDS Internet Connector Upload / Download Company A Internal IDS Connector © Fraunhofer © Fraunhofer · Seite 16 Company B --- VERTRAULICH --- Cloud Provider Industry 4.0 Semantic Models as Bridge between Shop & Office Floor © Fraunhofer · Seite 17 Semantic Administrative Shell & Reference Architecture for Industry 4.0 (RAMI4.0) Administrative Shell (Verwaltungsschale) provides a digital identity for arbitrary Industry 4.0 components (e.g. sensors, actors/robots) exposing data covering the whole life-cycle Reference Architecture for Industry 4.0 (RAMI4.0) provides a conceptual framework for implementing comprehensive Industry 4.0 scenarios We have implemented both concepts along with a number of IEC and ISO standards in a comprehensive information model ready to be implemented in productive environments © Fraunhofer · Seite 18 Summary Challenges and Opportunities - Interoperability and Standardization • Adding a semantic layer to Big Data technology • Integrating Linked Data and Big Data technology • Towards Enterprise Knowledge Graphs and Data Spaces • Applications e.g. in Manufacturing, Cultural Heritage, Finance © Fraunhofer · Seite 19
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