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© Copyright 2016‐2018 Anna Fensel
Semantic Web Services
SS 2018
Semantic Services as a Part of the
Future Internet and Big Data Technology
Anna Fensel
14.05.2018
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Where are we?
# Title
1 Introduction
2 Web Science + Cathy O’Neil’s talk: “Weapons of Math Destruction”
3 Service Science
4 Web services
5 Web2.0 services
6 Semantic Web + ONLIM APIs (separate slideset)
7 Semantic Web Service Stack (WSMO, WSML, WSMX)
8 OWL-S and the others
9 Semantic Services as a Part of the Future Internet and Big Data Technology
10 Lightweight Annotations
11 Linked Services
12 Applications
13 Mobile Services

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Outline
• Motivation
• Big Data, Smart Data, Linked (Open) Data
– Semantic Web Evolution in One Slide
– What is Big Data?
– Public Open Data
– Linked (Open) Data
– Data Economy & Valorization
• Future Internet – FI-WARE
– Definitions, EU Initiative
– Technical Examples from FI-WARE
• Converged Participatory Services
– Definitions
– Technical Examples
• Summary
• References
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MOTIVATION
SLIDES TAKEN FROM PRESENTATION OF L. NIXON: “LIMITATIONS
OF THE CURRENT INTERNET FOR THE FUTURE INTERNET OF
SERVICES”, HTTP://WWW.SLIDESHARE.NET/MBASTI2/SOFI-
SERVICEARCHITECTURES300910

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BIG DATA, SMART DATA,
LINKED (OPEN) DATA
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• Going mainstream and broad
• Linked Open Data cloud
counts 25 billion triples
• Open government initiatives
• BBC, Facebook, Google,
Yahoo, etc. use semantics
• SPARQL becomes W3C
recommendation
• Life science and other
scientific communities use
ontologies
• RDF, OWL become W3C
recommedations
• Research field on ontologies
and semantics appears
• Term „Semantic Web“ has
been „seeded“, Scientific
American article, Tim Berners-
Lee et al.
Semantic Web Evolution in One Slide
2008
2001
2010
2004 Source: Open Knowledge Foundatio

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From Semantic Web to Semantic World:
Data Challenges
• Large volumes of raw data to smaller volumes of
„processed“ data
– Streaming, new data acquisition infrastructures
– Data modeling, mining, analysis, processing, distribution
– Complex event processing (e.g. in-house behaviour identification)
• Data which is neither „free“ nor „open“
– How to store, discover and link it
– How to sell it
– How to define and communicate its quality / provenance
– How to get the stekeholders in the game, create marketplaces
• Establishment of radically new B2B and B2C services
– „Tomorrow, your carton of milk will be on the Internet“ – J. da Silva,
referring to Internet of Things
– But how would the services look like?
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What is Big Data?
• “Big data” is a loosely-defined term
• used to describe data sets so large and
complex that they become awkward to
work with using on-hand database
management tools.
– White, Tom. Hadoop: The Definitive Guide.
2009. 1st Edition. O'Reilly Media. Pg 3.
– MIKE2.0, Big Data Definition
http://mike2.openmethodology.org/wiki/Big_D
ata_Definition
Infromation Explosion in data
and real world events (IBM)

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Big Data Application Areas
Picture taken from http://www-01.ibm.com/software/data/bigdata/industry.html
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Use case : Climate Research
• Eiscat and Eiscat 3D are multimillion reserch projects doing
environmental research as well as evaluation of the built infrastructures.
– Observation of climate: sun, troposphere, etc.
– Simulations, e.g. Creation of artificial Nothern light
– Run by European Incoherent Scatter Association
• 1,5 Petabytes of data are generated daily (1,5 Million Gigabytes).
– Processing of this data would require 1K petaFLOPS performance
– Or 1 billion Euro electricity costs p.a.

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Large Scale Reasoning
• Performing deductive inference with a given set of axioms at the Web
scale is practically impossible
– Too manyRDF triples to process
– Too much processing power is needed
– Too much time is needed
• LarKC aimed at contributing to an ‘infinitely scalable’ Semantic Web
reasoning platform by
– Giving up on 100% correctness and completeness (trading quality for size)
– Include heuristic search and logic reasoning into a new process
– Massive parallelization (cluster computing)
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Volumes of Data Exceed the Availale Storage
Volume Globally
There is a need
to throw the data
away due to
the limited storage
space.
Before throwing the
data away some
processing can be
done at run-time
• Processing
streams of
data as they
happen

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Data Stream Processing for Big Data
• Logical reasoning in real time on multiple, heterogeneous, gigantic and
inevitably noisy data streams in order to support the decision process…
-- S. Ceri, E. Della Valle, F. van Harmelen and H. Stuckenschmidt, 2010
window
Extremely large
input streams
streams of answer
Registered
Continuous
Query
Picture taken from Emanuele Della Valle “Challenges, Approaches, and Solutions in Stream Reasoning”, Semantic Days
2012
Query engine
takes stream
subsets for query
answering
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Public Open Data - Data.gv.at

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Data.gv.at (Vienna)
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Open Data Vienna Challenge Contest
50 apps with OGD Vienna - now nearly 80 (March 2013)
https://www.newschallenge.org/open/open-government/submission/open-government-city-of-vienna/

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Public Open Data
• Openess:
Open Data is about changing behaviour
• Heterogenity:
Different vocabularies are used
• Interlinkage:
Need to link these data sets to prevent data silos
•  Linked Open Data
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Motivation: From a Web of Documents to a Web of
Data
• Web of Documents • Fundamental elements:
1. Names (URIs)
2. Documents (Resources)
described by HTML, XML, etc.
3. Interactions via HTTP
4. (Hyper)Links between
documents or anchors in
these documents
• Shortcomings:
– Untyped links
– Web search engines fail on
complex queries
“Documents”
Hyperlinks

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Motivation: From a Web of Documents to a Web of
Data
• Web of Documents • Web of Data
“Documents”
“Things”
Hyperlinks
Typed Links
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Motivation: From a Web of Documents to a Web of
Data
• Characteristics:
– Links between arbitrary things
(e.g., persons, locations,
events, buildings)
– Structure of data on Web
pages is made explicit
– Things described on Web
pages are named and get
URIs
– Links between things are
made explicit and are typed
• Web of Data
“Things”
Typed Links

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Google Knowledge Graph
• “A huge knowledge graph of interconnected entities and their
attributes”.
Amit Singhal, Senior Vice President at Google
• “A knowledge based used by Google to enhance its search engine’s
results with semantic-search information gathered from a wide
variety of sources”
http://en.wikipedia.org/wiki/Knowledge_Graph
• Based on information derived from
many sources including Freebase,
CIA World Factbook, Wikipedia
• Contains about 3.5 billion facts
about 500 million objects
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Semantic Web: knowledge graph & rich snippets

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Linked Data – a definition and principles
• Linked Data is about the use of Semantic Web technologies to
publish structured data on the Web and set links between data
sources.
Figure from C. Bizer
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5-star Linked OPEN Data
★ Available on the web (whatever
format) but with an open licence,
to be Open Data
★★ Available as machine-
readable structured data (e.g.
excel instead of image scan of a
table)
★★★ as (2) plus non-proprietary
format (e.g. CSV instead of excel)
★★★★ All the above plus, Use
open standards from W3C (URIs,
RDF and SPARQL) to identify
things, so that people can point at
your stuff
★★★★★ All the above, plus: Link
your data to other people’s data to
provide context

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Linked Open Data – silver bullet for data
integration
• Linked Open Data can be seen as a global data integration platform
– Heterogeneous data items from different data sets are linked to each other following the
Linked Data principles
– Widely deployed vocabularies (e.g. FOAF) provide the predicates to specify links between
data items
• Data integration with LOD requires:
1. Access to Linked Data
• HTTP, SPARQL endpoints, RDF dumps
• Crawling and caching
2. Normalize vocabularies – data sets that overlap in content use different vocabularies
• Use schema mapping techniques based on rules (e.g. RIF, SWRL) or query languages (e.g. SPARQL
Construct, etc.)
3. Resolve identifies – data sets that overlap in content use different URIs for the same real
world entities
• Use manual merging or approaches such as SILK (part of Linked Data Integration Framework) or
LIMES
4. Filter data
• Use SIVE ((part of Linked Data Integration Framework)
See: http://www4.wiwiss.fu-berlin.de/bizer/ldif/
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What is Data Economy?
• Non tangible assets (i.e. data) play a significant role in
the creation of economic value
• Data is nowadays more important than, for example,
search or advertisement
• The value of the data, its potential to be used to create
new products and services, is more important than the
data itself
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Why a Data Economy?
• New businesses can be built on the back of these data: Data are an
essential raw material for a wide range of new information products and
services which build on new possibilities to analyse and visualise data
from different sources. Facilitating re-use of these raw data will create
jobs and thus stimulate growth.
• More Transparency: Open data is a powerful tool to increase the
transparency of public administration, improving the visibility of
previously inaccessible information, informing citizens and business
about policies, public spending and outcomes.
• Evidence-based policy making and administrative efficiency: The
availability of solid EU-wide public data will lead to better evidence-
based policy making at all levels of government, resulting in better
public services and more efficient public spending.
See:
http://europa.eu/rapid/pressReleasesAction.do?reference=MEMO11/891&format=HTML&aged=0&language=EN&guiLanguage=en
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Combining Open Data and Services – Tourist
Map Austria
• Use LOD to integrate and lookup
data about
– places and routes
– time-tables for public transport
– hiking trails
– ski slopes
– points-of-interest

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Combining Open Data and Services – Tourist
Map Austria
LOD data sets
• Open Streetmap
• Google Places
• Databases of government
– TIRIS
– DVT
• Tourism & Ticketing association
• IVB (busses and trams)
• OEBB (trains)
• Ärztekammer
• Supermarket chains: listing of products
• Hofer and similar: weekly offers
• ASFINAG: Traffic/Congestion data
• Herold (yellow pages)
• City archive
• Museums/Zoo
• News sources like TT (Tyrol's major daily
newspaper)
• Statistik Austria
• Innsbruck Airport (travel times, airline
schedules)
• ZAMG (Weather)
• University of Innsbruck (Curricula,
student statistics, study possibilities)
• IKB (electricity, water consumption)
• Entertainment facilities (Stadtcafe,
Cinema...)
• Special offers (Groupon)
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Combining Open Data and Services – Tourist
Map Austria
• Data and services from
destination sites integrated for
recommendation and booking of
– Hotels
– Restaurants
– Cultural and entertainment events
– Sightseeing
– Shops

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• Web scraping integration
• Create wrappers for current web sites and extract
data automatically
• Many Web scraping tools available on the market
Combining Open Data and Services – Tourist
Map Austria
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“There's No Money in Linked (Open) Data”
http://knoesis.wright.edu/faculty/pascal/pub/nomoneylod.pdf
• It turns out that using LOD datasets in realistic settings is not always easy.
– Surprisingly, in many cases the underlying issues are not technical but
legal barriers erected by the LD data publishers.
– Generally, mostly non-technical but socio-economical barriers hamper
the reuse of date (do patents and IPR protections hamper or facilitate
knowledge reuse?).
– Business intelligence
– Dynamic Data
– On the fly generation of data

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FUTURE INTERNET – FI-WARE
FOR THIS PART, FOLLOW PRESENTATION OF F.-M. FACCA:
“FIWARE PRIMER - LEARN FIWARE IN 60 MINUTES”,
HTTP://WWW.SLIDESHARE.NET/CHICCO785/FIWARE-PRIMER-
LEARN-FIWARE-IN-60-MINUTES
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CONVERGED PARTICIPATORY
SERVICES

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Research Aim
Aims:
• Enabling efficient participation vs. current social network silos and groups
– More possible roles for an individual
– More roles at a time for an individual
– More matching and satisfying roles for an individual
=> Motivation, added value and revenue increase
Technologically that means:
• Benefiting from data and services reuse at the maximum
• Enabling participators to establish added value new and converged
services on top of the data
– commercially re-applying them across platforms
=>There is a need to „understand“ and interlink content and objects coming
from heterogeneous numerous sources
Converged Semantic Services
For Empowering Participation
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Young People‘s Participation
• Psychology perspective:
„Child-Adult“
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Participation in Terms of Social Media
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90-9-1 Rule for Participation Inequality
• Web use follows a Zipf distribution
• Also applicable to social media
• Also to working groups?
• Is that wrong?
– In some cases (e.g. inappropriate
match), yes.
– In many cases (e.g.
dissemination effect), no.
Jakob Nielsen,
http://www.useit.com/alertbox/participation_inequality.html

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Participation is Linked to Value
• Participation level relates to the value one gets from participation
• Participation also has a value in itself
Lurkers‘
Perspective
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Participation is Linked to a Role
1 person: gatherer or hunter
2 persons: gatherer and hunter?
– Problem with the role choice starts from
the moment where there is a choice.
Having more persons implies:
• fine-grained devision of labor and
service economy,
• community as a regulator on which
roles are appropriate and which not,
as well as their values.

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Impact of Roles/Relations and their Weights on
Ontology Evolution Dynamics
• People and relations are inherently associated with / connected to / can
be decomposed into concepts and properties.
– See also: Peter Mika, „Ontologies are Us: A Unified Model of Social Networks and
Semantics”. International Semantic Web Conference 2005: 522-536.
• Changing the roles drive social, ontology and market evolution.
• One of the important drive factors are the quantity of concepts/people
relating to another concept/person via a specific property (hub vs.
stub), e.g. a property spouse is stronger than friend. Thus, the networks
are self-restructuring depending on the roles and weights put on them.
– See also: Zhdanova, A.V., Predoiu, L., Pellegrini, T., Fensel, D. "A Social Networking
Model of a Web Community". In Proceedings of the 10th International Symposium on
Social Communication, 22-26 January 2007, Santiago de Cuba, Cuba, ISBN: 959-
7174-08-1, pp. 537-541 (2007).
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Convergence
• “Telecommunications convergence, network convergence or
simply convergence are broad terms used to describe emerging
telecommunications technologies, and network architecture used to
migrate multiple communications services into a single network.[1]
Specifically this involves the converging of previously distinct media
such as telephony and data communications into common interfaces on
single devices.”
– Wikipedia
• Convergent technologies/services include:
– IP Multimedia Subsystem
– Session Initiation Protocol
– IPTV
– Voice over IP
– Voice call continuity
– Digital video broadcasting - handheld
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Link to Value - Mobile Operators‘ Use Case -
Business Potential of Openness and Collaboration
Forecasts from the start of decade (by ATOS)
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Increasing Participation –
From Static Social Network Silos to Pervasive Social Spaces
...where everyone
benefits.
Semantic
technologies
take you there.

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Mobile Ontology
Villalonga, C., Strohbach, M., Snoeck, N., Sutterer, M., Belaunde, M., Kovacs, E., Zhdanova,
A.V., Goix, L.W., Droegehorn, O. "Mobile Ontology: Towards a Standardized Semantic Model for the Mobile
Domain". In Proceedings of the 1st International Workshop on Telecom Service Oriented Architectures
(TSOA 2007) at the 5th International Conference on Service-Oriented Computing, 17 September 2007,
Vienna, Austria (2007).
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high
medium
low
Relevance
On market
Product concept
Applied Research
Basic Research
smart metering
EU 2050 nearly-zero goal
large-scale &
stream data processing
(semantic) service
description, discovery,
composiion
Internet of Things
M2M services
energy control & monotoring
demand-response management
consumer „manipulation“
empowering renewable
energy „prosumers“
Web-Grid
convergence
raising consumer
awareness
CIM, OPC & other models
data-intensive
services
automatisation
New Directions Example: Smart Grids
Technology Radar

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Project Examples for Participatory Converged
Services
2 FFG COIN Projects
 SESAME – Semantic Smart Metering,
Enablers for Energy Efficiency (9’09-11’10,
800k Euro)
– Prototype, proof of concepts, feasibility
study
 SESAME-S – Services for Energy
Efficiency (4’11-9’11, 770k Euro)
– setting up usable smart home
hardware, a portal and repository
– organizing a test installation in real
buildings: in a school (Kirchdorf,
Austria) and a factory (Chernogolovka,
Russia)
– developing specialized UIs and
designing mobile apps for the school
use case
 Consortium partner network of 6
organizations
Data Acquisition

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Data Acquisition – Extended, SESAME-S
Data Acquisition

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Extension to More Buildings
 Research challenge: moving logics components, such as
building automation settings, user preferences.
 Ministries
 Provincial councils and centers
 Energy efficiency bodies
 Energy companies
 Municipalities
 Construction companies and Investors
 Home-automation market holders
 Home-appliance market holders
 Tourism companies: hotels, tourism settlements
 Telecommunication companies
 Cloud service providers
 …
Many Stakeholders - Same Data

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Smart Home End User Service Interfaces –
Increasing Participation
© FTW 2011
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• Over 50 users were interviewed f2f plus over a 100 online
• Some outcomes
– „Saving costs“ is the strongest motivator, “reputation“ is the weakest
– Main system cost expectation is 200 Euro per installation, plus up to 5
Euro as a monthly fee, with energy savings of 20%
– Preference to delegate unobtrusive tasks (e.g. stand by device
management vs. lights control)
– Every 4th user will choose the „fanciest“ and not the „easiest to use“
interface
– 2/3rds of users are „absolutely sure“ or „sure“ they‘d use such or a
similar system in the future
– 2/3rds of users would also share their home settings with „friends“
Energy Efficient Buildings –
User Trials
• Fensel, A., Tomic, S., Kumar, V., Stefanovic, M., Aleshin, S., Novikov, D. "SESAME-S: Semantic Smart Home System for Energy
Efficiency". In Proceedings of D-A-CH Energieinformatik 2012, 5-6 July 2012, Oldenburg, Germany.
• Schwanzer, M., Fensel, A. "Energy Consumption Information Services for Smart Home Inhabitants". In Proceedings of the 3rd
Future Internet Symposium (FIS'10), 20-22 September 2010, Berlin, Germany; Springer Verlag, LNCS 6369, pp. 78-87.

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End User Attitudes
© FTW 2011
End User Expectations
© FTW 2010

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Smart Home Installation
School, Kirchdorf - AT
 Several Smart Meters
 Sensors (e.g. light,
temperature, humidity)
 Smart plugs, for individual
sockets
 Shutdown services for PCs
 User interfaces and apps:
Web, tablet, smartphone
(Android)
Factory, Chernogolovka - RU
 Heating system regulation
and monitoring extension
Services Addressing Users
@ School
 Energy awareness,
monitoring
 Remote control - manual and
programmed - e.g. scheduled
activities and triggering rules
 How do we get the users?
– By having workshops with pupils:
introduction to energy efficiency,
building analysis, explaining the
system and services

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Demand Management
@ Smart Building
Millions of triples collected
in the semantic repository
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SUMMARY
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Big, Smart, Linked (Open) Data: Conclusions
• Semantics and big data application domains are currently diverse
– Embracing a big data processing strategy can have a significant impact
– Some application domains are pioneers, some lagging behind
• (Big) data on Web scale suffers from an inherent heterogeneity and different levels of
expressiveness
– Complexity is more than just size! Web of things will be on the rise.
– Think of integrating drastically new items, such as hardware and human brain.
• Introducing the technology at the standards / best practice level is important.
• Open Data can be used to enrich on-line presence of e.g. of touristic destination.
• Addressing both “elephants” and “rabbits” (larger and smaller industry: For example,
allow “rabbits” to build services on top of the data the “elephants” have anyway.
• Valorization is important. Having “no money” in ecosystem is not sustainable.
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Conclusions
• Semantic technology as an enabler for the individuals and
organisations to participate productively
– By getting new roles.
– By changing existing roles easier.
• Trends and examples have been shown:
– FI-WARE
– End users taking part in energy efficiency
in smart buildings
Possible future research aspects include data analytics e.g. for:
• Scenarios involving heterogeneous multiple stakeholders.
• Changing/steering behavior, engagement of users/customers.
• Enabling participation vs. yield management / resilience.
– “Resilience is the ability to provide and maintain an acceptable level of service in the
face of faults and challenges to normal operation.”, “A superset of survivability.” -
Wikipedia

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REFERENCES
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http://fiware.org
http://lab.fiware.org
References
Big, Smart, Linked (Open) Data:
Cavanillas, J. M., Curry, E., & Wahlster, W.
New Horizons for a Data-Driven Economy. Spinger, 2016.
http://link.springer.com/book/10.1007/978-3-319-21569-3
Book is in open access!!

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References: participatory converged services
– energy efficiency in smart buildings
• Fensel, A., Kumar, V., Tomic, S.D.K. "End User Interfaces for Energy
Efficient Semantically-Enabled Smart Homes". Energy Efficiency,
Volume 7, Issue 4, pp. 655–675, Springer, 2014. ISSN: 1570-646X.
• Fensel, A., Tomic, S., Kumar, V., Stefanovic, M., Aleshin, S., Novikov,
D. "SESAME-S: Semantic Smart Home System for Energy Efficiency",
Informatik-Spektrum, Volume 36, Issue 1, pp. 46-57, Springer, January
2013.
• Schwanzer, M., Fensel, A. "Energy Consumption Information Services
for Smart Home Inhabitants". In Proceedings of the 3rd Future Internet
Symposium (FIS'10), 20-22 September 2010, Berlin, Germany;
Springer Verlag, LNCS 6369, pp. 78-87 (2010).
• Ongoing EU project in Energy:
http://entropy-project.eu
2015-2018
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Next Lecture
# Title
1 Introduction
2 Web Science + Cathy O’Neil’s talk: “Weapons of Math Destruction”
3 Service Science
4 Web services
5 Web2.0 services
6 Semantic Web + ONLIM APIs (separate slideset)
7 Semantic Web Service Stack (WSMO, WSML, WSMX)
8 OWL-S and the others
9 Semantic Services as a Part of the Future Internet and Big Data Technology
10 Lightweight Annotations
11 Linked Services
12 Applications
13 Mobile Services

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Questions?