Creating Data Value Chains by Linking Enterprise Data

(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

(3)

Proaktive Maintenance at Rolls Royce

New Business Model integrating Sensor Data & Big Data Analytics

Dr. Dirk Hecker

Condition

Monitoring, Proaktive Wartung, „Power-by-the-hour“,

as-a-service Business Model – Payment by flight hours

Quelle: www.springboeck.ch/SR_Technics.htm © Mark Hillary | Flickr

(4)

The rolling Smartphone

New Business Models for the Automotive Industry with Data Value Chains

Dr. Dirk Hecker

Windshield wiper as rain sensors for micro wether prognosis

Automotive industry can become data provider for other industries

Q u el le : G TÜ Q u ell e: w w w .far m in g -si m u lato r.c o m

(5)

Predictive Analytics

Dr. Dirk Hecker

From Business Intelligence to Big Data Analytics

Business Intelligence

Monitoring

Predictive Analytics

What happened

before?

What happens now?

What will happen

soon?

What should

happen?

Prescriptive Analytics

„the last Mile“

“prescriptive analytics suggests decision options on how to take advantage of a

future opportunity”

(6)

Expansion of IT companies in manufacturing realms

Dr. Dirk Hecker

(7)

Francesco De Paoli, Nmedia, hakandogu

Semantic Data Linking for Enterprise Data Value Chains

Data Lake

Pure Internet

centralized, monopolistic

federated, secure, „trusted“,

standard-based

completely dezentral, open,

unsecure

Data management

Central Repository

Decentral

Data Ownership

Central

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

Enterprise Data

Value Chains

(8)

VERTRAULICH

---Bildquellen: Istockphoto

Enterprise Data Value Chains

Service A Service C Service E Service B Service D Service G Service F Enterprise 4 Enterprise 1 Enterprise 6 Enterprise 2 Enterprise 3 Enterprise 5

All Data

stays

with its Ownern

and are controlled and secured. Only on request for a service data

will be shared. No central platform.

(9)

The Semantic Web Layer Cake 2001

http://www.w3.org/2001/10/03-sww-1/slide7-0.html

•

Monolithic based on XML

•

Focus on heavyweight semantic

(ontologies, logic, reasoning)

(10)

The Semantic Web Layer Cake 2015 –

“A Little Semantics Goes a Long Way”

Unicode

URIs

XML

JSON

CSV

RDB

HTML

RDF

RDF/XML

JSON-LD

CSV2RDF

R2RML

RDFa

RDF Data Shapes

RDF-Schema

Vocabularies

Ontologien

SKOS Thesauri

Logik

SWRL Regeln

SPARQL

(Ac

ce

ss

control),

Sig

na

tur

,

Enc

ryption

(HTT

P

S/CERT/DAN

E),

•

Lingua Franca of Data integration

with many technology interfaces

(XML, HTML, JSON, CSV, RDB,…)

•

Focus on lightweight

vocabularies, rules,

thesauri etc.

(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)

SEMIC2015

dbpedia:Riga

05.05.2015

Joinup

conf:organizes

conf:starts

conf:takesPlaceIn

2. Serialisiert in RDF Triple:

Joinup

conf:organizes

SEMIC2015 .

SEMIC2015

conf:starts

“2015-05-05”^^xsd:date .

SEMIC2015

conf:takesPlaceAt

dbpedia:Riga .

3. Publication under URL in Web, Intranet, Extranet

(13)

Linked (Open) Data: The RDF Data Model

RDF = Resource Description Framework

13

located in

label

industry

headquarters

full name

DHL

Post Tower

162.5 m

Bonn

Logistics

Logistik

DHL International GmbH

height

物流

label

(14)

RDF Data Model (a bit more technical)



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: →

http://dbpedia.org/resource/

14

gn:locatedIn

rdfs:label

dbo:industry

ex:headquarters

foaf:name

dbp:DHL_International_GmbH

dbp:Post_Tower

"162.5"^^xsd:decimal

dbp:Bonn

dbp:Logistics

"Logistik"@de

"DHL International GmbH"^^xsd:string

ex:height

"

物流

"@zh

rdfs:label

rdf:value

unit:Meter

ex:unit

(15)

I40 Example –

Semantic Description of Sensor Data

myd:m123245 rdf:type

i40:SensorMeasurement .

myd:m123245 rdf:hasValue

“40”^^i40:DegreeCelsius .

myd:m123245

i40:hasMeasureTime “2015-03-24T12:38:54:12Z”^^xsd:DateTime .

myd:m123245 i40:fromSensor myd:Sensor123 .

...

(16)

Linked Data vs. XML

from the Data Integration Perspective

Linked Data

X

O

XML

-O

-X

Provenance

Data integration

Evolution

Extensibility

Reusability

Validation

Beware: This comparison would look very different from a (office) document

(hypertext, spreadsheets, presentation) format perspective.

(17)

Golf7_Infotainment

Data Value Chains using Linked Data

Golf 7

Zulieferer

70.000

5kg

SMARTi_LU

90g

5T

hasComponent

75.000

500.000

Aggregation of Emmissions in

the Value Chain

Propagation of sales

prognoses in the value chain

Map data, parking,

gas stations,

Points-of-Interest

…

(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



Many Domains are well covered, e.g. Geo data, Pharma & life-sciences



Great Potential for Linking with internal Enterprise data sources

http://lod-cloud.net

(August 2014)

(21)

Provisioning of all types of Enterprise Data

as Linked Data

Meta-data

Description of

the Data

Vokabulare

Structure of the

Data

Daten

Ground Truth

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)

(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,

light-weight 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 eCl@ss

– Semantic Model for Material,

Products and Services

Comprehensive taxonomic classification scheme for

materials, products and services

9.0 BASIC from 2014-12-08 comprises

Classes:

40,870

Properties:

16,845

Values:

14,365

eCl@ssXML is based on the ISO 13584-32 ontoML file

format, the XML representation of the ISO 13584

(PLIB) ontology

eClassOWL - The Web Ontology for Products and

Services an RDF/OWL representation of eCl@ss

(25)

Summary:

Linked Data for semantic interoperability

Problem

Linked Data Approach

Example

1. Unique Identifikation of (data)

objects

URIs (analog Web addresses) for

Identification of arbitrary objects

https://data.vw.de/car/Golf7

2. Adressability and Data access

Web Protokols HTTP/HTTPS for

De-Referencing and access of data

3. Semantic Data Representation

Triple & Graph-based RDF Data Model

Golf7 producedIn

Wolfsburg

4. Wide Interlinking of Data

URIs serve as “Data Links” between

distributed Databases

5. Domain-specific Data structures

Creation of interlinked, modular, reuseable

vokabularies

6. Security-by-Design

Certificates, Encryption, Authentification as

Internet Banking

(26)

Big Data is not Just Volume and Velocity

Variety is the real challenge

Dr. Dirk Hecker

Standardisation

on all levels

Smart Data

(embracing Variety)

Inter-organization

collaboration

& data exchange

Usage of

Open Data

Data security & privacy

-integral part of innovative

services, without blocking

them

(27)

Prof. Dr. Sören Auer, [email protected]

Fraunhofer-Allianz Big Data | Fraunhofer IAIS

Schloss Birlinghoven

53757 Sankt Augustin

www.iais.fraunhofer.de

www.bigdata.fraunhofer.de

„DO MORE WITH [BIG|LINKED|OPEN] DATA!“

Luxembourg, 16-17 Nov 2015

http://www.w3.org/2001/10/03-sww-1/slide7-0.html

http://www.w3.org/DesignIssues/LinkedData.html

http://lod-cloud.net

Dr. Dirk Hecker

www.iais.fraunhofer.de

www.bigdata.fraunhofer.de