Creating Data Value Chains by Linking Enterprise Data

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1 Creating Data Value Chains by Linking Enterprise Data How the interlinking of distributed and heterogeneous data can facilitate enterprise development, production and services

2 The three Big Data V Variety is often neglected Quelle: Gesellschaft für Informatik Dr. Dirk Hecker Fraunhofer-Allianz Big Data 2

3 Proaktive Maintenance at Rolls Royce New Business Model integrating Sensor Data & Big Data Analytics Mark Hillary Flickr Condition Monitoring, Proaktive Wartung, Power-by-the-hour, as-a-service Business Model Payment by flight hours Quelle: Dr. Dirk Hecker Fraunhofer-Allianz Big Data 3

4 Quelle: GTÜ The rolling Smartphone New Business Models for the Automotive Industry with Data Value Chains Windshield wiper as rain sensors for micro wether prognosis Automotive industry can become data provider for other industries Quelle: Dr. Dirk Hecker Fraunhofer-Allianz Big Data 4

5 Predictive Analytics From Business Intelligence to Big Data Analytics Quelle: BMW Quelle: Quelle: BMW Quelle: Volvo What happened before? What happens now? What will happen soon? What should happen? Business Intelligence Monitoring Predictive Analytics Prescriptive Analytics the last Mile prescriptive analytics suggests decision options on how to take advantage of a future opportunity Dr. Dirk Hecker Fraunhofer-Allianz Big Data 5

6 Expansion of IT companies in manufacturing realms Possible disruption of business models through digitization Dr. Dirk Hecker Fraunhofer-Allianz Big Data 6

7 Semantic Data Linking for Enterprise Data Value Chains Data Lake Enterprise Data Value Chains Pure Internet centralized, monopolistic federated, secure, trusted, standard-based completely dezentral, open, unsecure Data management Central Repository Decentral Decentral Data Ownership Central Decentral Decentral Data Linking Single provider Federated, on demand Missing Data Security Bilateral Certified system Bilateral Market structure Central Provider Role system Unstructured Transport infrastructure Internet Internet Internet Bilder: Fotolia Francesco De Paoli, Nmedia, hakandogu Fraunhofer Seite 7

8 Enterprise Data Value Chains 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 Enterprise 2 Enterprise 3 Service D Bildquellen: Istockphoto Fraunhofer Seite VERTRAULICH ---

9 The Semantic Web Layer Cake 2001 Monolithic based on XML Focus on heavyweight semantic (ontologies, logic, reasoning)

10 (Access control), Signatur, Encryption (HTTPS/CERT/DANE), The Semantic Web Layer Cake 2015 A Little Semantics Goes a Long Way Lingua Franca of Data integration with many technology interfaces (XML, HTML, JSON, CSV, RDB, ) Focus on lightweight vocabularies, rules, thesauri etc. SKOS Thesauri SWRL Regeln Logik Ontologien SPARQL Vocabularies RDF Data Shapes RDF-Schema RDF RDF/XML JSON-LD CSV2RDF R2RML RDFa XML JSON CSV RDB HTML Unicode URIs

11 Linked Data Paradigm as a Basis for I40, Cyber-Physical Systems and Big Data Integration Entities (people, places, organisations etc.) are identified using URIs in a worldwide unique way Data (Resources) describing these entities is made available using the HTTP/HTTPS protocoll when dereferencing the URIs The entity descriptions made available via HTTP/HTTPS are represented according to the W3C Resource Description Format (RDF) Entity descriptions in RDF content Links to related entities / concepts / resources

12 RDF & Linked Data in a Nutshell 1. Graph based RDF data model consisting of SPO statements (facts) Joinup conf:organizes Subject Predicate Object SEMIC2015 conf:starts conf:takesplacein dbpedia:riga 2. Serialisiert in RDF Triple: Joinup conf:organizes SEMIC2015. SEMIC2015 conf:starts ^^xsd:date. SEMIC2015 conf:takesplaceat dbpedia:riga. 3. Publication under URL in Web, Intranet, Extranet

13 Linked (Open) Data: The RDF Data Model DHL full name DHL International GmbH headquarters industry Logistics label Logistik Post Tower located in height m label 物 流 Bonn RDF = Resource Description Framework 13

14 RDF Data Model (a bit more technical) dbp:dhl_international_gmbh foaf:name "DHL International GmbH"^^xsd:string ex:headquarters dbp:post_tower ex:height gn:locatedin dbp:bonn dbo:industry rdf:value ex:unit dbp:logistics unit:meter rdfs:label rdfs:label "162.5"^^xsd:decimal " 物 流 Graph consists of: Resources (identified via URIs) Literals: data values with data type (URI) or language (multilinguality integrated) Attributes of resources are also URI-identified (from vokabularies) Various data sources and vocabularies can be arbitrarily mixed and meshed URIs can be shortened with namespace prefixes; e.g. dbp: 14

15 I40 Example Semantic Description of Sensor Data myd:m rdf:type i40:sensormeasurement. myd:m rdf:hasvalue 40 ^^i40:degreecelsius. myd:m i40:hasmeasuretime T12:38:54:12Z ^^xsd:datetime. myd:m i40:fromsensor myd:sensor # ^ subject ^ predicate ^ object

16 Linked Data vs. XML from the Data Integration Perspective Linked Data X X X X X O Provenance Data integration Evolution Extensibility Reusability Validation XML - O X Beware: This comparison would look very different from a (office) document (hypertext, spreadsheets, presentation) format perspective.

17 Data Value Chains using Linked Data Open Linked Data Map data, parking, gas stations, Points-of-Interest Propagation of sales prognoses in the value chain Golf 7 Aggregation of Emmissions in the Value Chain Golf7_Infotainment Zulieferer hascomponent SMARTi_LU 5T 5kg 90g

18 IDS I40: Semantische Modelle als Brücke zwischen Shop & Office Floor

19 Adding a Linked Data Layer to the Internet Architecture Linked Data Layer can possibly be also integrated in lower levels of the Internet Architecture

20 >100 Billion facts are avaialbale as Linked Open Data (August 2014) Many Domains are well covered, e.g. Geo data, Pharma & life-sciences Great Potential for Linking with internal Enterprise data sources 20

21 Provisioning of all types of Enterprise Data as Linked Data Daten Ground Truth Vokabulare Structure of the Data Metadata Description of the Data Raw data People, Places, Organisations, Sensor data, Production data, Metadata Lizence informationen, Provenance, Versioning, Documentation Vocabularies Definitionen of Class and Property(-hierarchies), typical structures (W3C Data Shapes)

22 Volume, Velocity & Variety

23 Example Mobility Vokabular MobiVoc Supporting the mobility of humans by the mobility of data Interlinking and Integration of Information froma variety of different sources (map data, car data, weather, public transport, events, ) various organizations, actors, formats, Goal is to increase the interlinking and fusion of data through the use of extensible, lightweight vocabularies Adresses weaknesses of XML-based DATEXII standard closedness, lack of extensibility Initiative of ITA Automotive Service Partner e.v. with BMW, Microsoft, Accenture, Fraunhofer, BROX/eccenca Collaborative, agile vocabulary development on GitHub

24 Example Semantic Model for Material, Products and Services Comprehensive taxonomic classification scheme for materials, products and services 9.0 BASIC from comprises Classes: 40,870 Properties: 16,845 Values: 14,365 is based on the ISO ontoml file format, the XML representation of the ISO (PLIB) ontology eclassowl - The Web Ontology for Products and Services an RDF/OWL representation of

25 Summary: Linked Data for semantic interoperability Problem Linked Data Approach Example 1. Unique Identifikation of (data) objects 2. Adressability and Data access URIs (analog Web addresses) for Identification of arbitrary objects Web Protokols HTTP/HTTPS for De- Referencing and access of data https://data.vw.de/car/golf7 3. Semantic Data Representation Triple & Graph-based RDF Data Model Golf7 producedin Wolfsburg 4. Wide Interlinking of Data 5. Domain-specific Data structures 6. Security-by-Design URIs serve as Data Links between distributed Databases Creation of interlinked, modular, reuseable vokabularies Certificates, Encryption, Authentification as Internet Banking FOAF

26 Big Data is not Just Volume and Velocity Variety is the real challenge Inter-organization collaboration & data exchange Usage of Open Data Standardisation on all levels Smart Data (embracing Variety) Dr. Dirk Hecker Data security & privacy - integral part of innovative services, without blocking them

27 DO MORE WITH [BIG LINKED OPEN] DATA! Prof. Dr. Sören Auer, Fraunhofer-Allianz Big Data Fraunhofer IAIS Schloss Birlinghoven Sankt Augustin vege Fotolia Luxembourg, Nov Dr. Dirk Hecker Fraunhofer-Allianz Big Data 27

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