Semantic Services as a Part of the Future Internet and Big ...From Semantic Web to Semantic World: Data Challenges • Large volumes of raw data to smaller volumes of „processed“

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1© Copyright 2016‐2018 Anna Fensel

Semantic Web ServicesSS 2018

Semantic Services as a Part of the Future Internet and Big Data Technology

Anna Fensel14.05.2018

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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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MOTIVATIONSLIDES 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_Data_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 tothe limited storagespace.

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 largeinput streams

streams of answerRegistered Continuous

Query

Picture taken from Emanuele Della Valle “Challenges, Approaches, and Solutions in Stream Reasoning”, Semantic Days 2012

Query enginetakes 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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• Web of Documents • Web of Data

“Documents”“Things”

Hyperlinks

Typed Links

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• 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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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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• 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


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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-WAREFOR 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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highmediumlow

Relevance

On market

Product concept

Applied Research

Basic Research

smart metering

EU 2050 nearly-zero goal

large-scale & stream data processing

(semantic) servicedescription, discovery, composiion

Internet of ThingsM2M services

energy control & monotoring

demand-response management

consumer „manipulation“

empowering renewableenergy „prosumers“

Web-Grid convergence

raising consumer awareness

CIM, OPC & other models

data-intensiveservices

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/10.1007/978-3-319-21569-3Book 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?