Presentation of the project OpenFridge in the Workshop on Big Data and Society Data Economy, Real-Time Mining and Analytics, Mining Techniques for Online and Customer Service in Big data Era Part of the 2013 IEEE International Conference on Big Data (IEEE Big Data 2013) 6-9 October 2013, Silicon Valley, CA, USA

1. OPEN FRIDGE: A PLATFORM FOR
DATA ECONOMY FOR ENERGY EFFICIENCY DATA
Dr. Dana Tomic, FTW The Telecommunication Research Center Vienna, Austria
Dr. Anna Fensel, University of Innsbruck, Semantic Technology Institute (STI), Austria
Workshop on Big Data and Society Data Economy, Real-Time Mining and Analytics, Mining Techniques
for Online and Customer Service in Big data Era, Part of the 2013 IEEE International Conference on Big
Data (IEEE Big Data 2013), 6-9 October 2013, Silicon Valley, CA, USA

2. Smart Grid is a Showcase for Data Economy
Smart Grid
Operation
Synchro
Phasers
Smart Buildings
Smart
Metering
Smart Cities
Prosumers
Compliance
Energy Markets
Price Signals
Renewables
Parks
Smart
Appliances
Electro
Mobility
Compliance
Plant
Automation
Demand
Response
Business
DSM
Capacity
Management
Workshop on Big Data and Society Data Economy, Real-Time Mining and Analytics, Mining Techniques
for Online and Customer Service in Big data Era, Part of the 2013 IEEE International Conference on Big
Data (IEEE Big Data 2013), 6-9 October 2013, Silicon Valley, CA, USA

3. Economy for Energy Efficiency Data (Knowledge)?
 What is energy efficiency?
– Using less energy to provide equivalent service.
– A life-cycle characteristic of home appliances.

–
–
–
–
How energy efficiency is being assessed?
By measuring and comparison.
EE of Design: Efficiency labels awarded by
verification institutes.
EE of Use: Best practices, comparisons
 How potential for increasing energy efficiency is
being assessed?
– By measuring/comparison  More context needed
More info: http://www.atlete.eu, http://eetd.lbl.gov/ee/ee-1.html
Workshop on Big Data and Society Data Economy, Real-Time Mining and Analytics, Mining Techniques
for Online and Customer Service in Big data Era, Part of the 2013 IEEE International Conference on Big
Data (IEEE Big Data 2013), 6-9 October 2013, Silicon Valley, CA, USA

4. A Value-chain for Energy Efficiency Data
 Metering (Data)
-
A source of big data, two-way exchange
Dynamic tariffs, distributed generation, demand management
Granularity of measurements aggregated vs. appliance level
Provides energy awareness context
 Energy Awareness (Knowledge)
-
Awareness context vs. usage context
Awareness at the energy service level needed.
Smart-plugs for individual measurements
Label is a decision support tool pointing to technological
improvements in energy efficiency of appliances.
 Efficiency Increasing Actions
- Appliance replacement, more efficient use, technology improvements
Workshop on Big Data and Society Data Economy, Real-Time Mining and Analytics, Mining Techniques
for Online and Customer Service in Big data Era, Part of the 2013 IEEE International Conference on Big
Data (IEEE Big Data 2013), 6-9 October 2013, Silicon Valley, CA, USA

5. OpenFridge : Opening and Processing Appliances
Data for Energy Efficiency
 Building an ecosystem around data
Improved
labeling
Better
decisions
about
replacement
and use
Home Users
Labeling Institutions
Energy
Efficiency
Data
Improved
technology
and CRM
Manufacturers
 Developing a crowdsourcing platform for data collection
 Exploring the concept of context-dependent energy efficiency
 Combining big data and semantics for add-value services
Workshop on Big Data and Society Data Economy, Real-Time Mining and Analytics, Mining Techniques
for Online and Customer Service in Big data Era, Part of the 2013 IEEE International Conference on Big
Data (IEEE Big Data 2013), 6-9 October 2013, Silicon Valley, CA, USA

6. Crowdsourcing and Data Market in Action
Community &
Content Management
SPARQL: Dataas-a-Service
Business
Intelligence
Services
Manufacturers
Labeling Orgs.
Semantic
Knowledge
Base
Usage Profile
Big Data
Infrastructure
Appliance Profile
Measurements Profile
Volume?
Variety?
Velocity?
Veracity?
Value?
Analytics
Appliance Profile
Measurements Profile
Measurements
Recommendations &
Visualizations
Users
Drupal Portal &
Web Service Client
Data Acquisition
Web Service
Workshop on Big Data and Society Data Economy, Real-Time Mining and Analytics, Mining Techniques
for Online and Customer Service in Big data Era, Part of the 2013 IEEE International Conference on Big
Data (IEEE Big Data 2013), 6-9 October 2013, Silicon Valley, CA, USA

7. From Context to Recommendations
Appliance profile
type, volume,
producer, efficiency,
year of production,
stand-alone/built-in,
facing south, location:
kitchen / cellar,
city, country,
number of users
Measurement profile
cooling level (1,2,3,..), inside
temperature, room
temperature, level of filling,
doors opening events,
measurement duration
Measurements
power level (5s)
timestamp
Usage profile
avg.
consumption,
cooling cycle,
defrost cycle,…
Comparisons, Recommendations & Analytics Services
Compare different refrigerators, refrigerators of the same type, performance at
different environmental conditions, set-points and loadings, impact of opening the
door, of aging, of installation, …
Workshop on Big Data and Society Data Economy, Real-Time Mining and Analytics, Mining Techniques
for Online and Customer Service in Big data Era, Part of the 2013 IEEE International Conference on Big
Data (IEEE Big Data 2013), 6-9 October 2013, Silicon Valley, CA, USA

8. Platform Enablers
 Hardware & service interfaces for data acquisition
- Currently based on the existing commercial system with web-service
interface
 Big data & analytics for data processing
- Anticipating large user base
 Semantic technology for value-add services
- Easy integration of external data, vocabularies and ontologies from the
ecommerce and energy efficiency domain
- Logic-based reasoning
 Privacy and security protection of data
- Data provenance and veracity
 Community building and crowdsourcing
- Incentives based on high-quality recommendations
Workshop on Big Data and Society Data Economy, Real-Time Mining and Analytics, Mining Techniques
for Online and Customer Service in Big data Era, Part of the 2013 IEEE International Conference on Big
Data (IEEE Big Data 2013), 6-9 October 2013, Silicon Valley, CA, USA

9. Challenges
 Interfaces
-
Attractiveness and usability of User interfaces for data acquisition
Instrumentation for appliances data acquisition
Privacy of user and appliances data
Accuracy of data
 Big Data
- Analytics on raw data: mappers/reducers feed semantic knowledgebase
with model data
 Semantic Layer
-
Ontology engineering
External data integration
Performance of the semantic knowledgebase
Expressiveness of services via SPARQL queries for B2B/B2C portalbased analytics
Workshop on Big Data and Society Data Economy, Real-Time Mining and Analytics, Mining Techniques
for Online and Customer Service in Big data Era, Part of the 2013 IEEE International Conference on Big
Data (IEEE Big Data 2013), 6-9 October 2013, Silicon Valley, CA, USA

10. Summary and Outlook
 Experiment in progress !
 Our Goal: A platform for crowdsourcing of energy efficiency data
and a community for propagation of energy efficiency social values
 Exploring the concept of context-dependent energy efficiency:
- Measurements in a broader context of different usage parameters within a
community of users
- Providing necessary explanations to motivate corresponding users’
actions towards improving the energy efficiency of services.
 Integrating Big Data and semantic technology
- Maintaining large volumes of raw data, analytics to transform raw data into
the parameterized information
- Developing appropriate ontologies to link parameterized energy efficiency
information with the usage context information
 Developing semantic-based delivery of add-value services
- Querying and Reasoning
 Focusing on refrigerators as they are the largest energy consuming
home appliance; the same principles could be further extended
Workshop on Big Data and Society Data Economy, Real-Time Mining and Analytics, Mining Techniques
for Online and Customer Service in Big data Era, Part of the 2013 IEEE International Conference on Big
Data (IEEE Big Data 2013), 6-9 October 2013, Silicon Valley, CA, USA

11. Contact
Dr. S. Dana Kathrin Tomic
Senior Researcher | Networked Services
FTW Forschungszentrum Telekommunikation Wien GmbH
Donau-City-Straße 1/3 | A-1220 Vienna | Austria
tomic@ftw.at | www.ftw.at/~tomic
+43/1/5052830 -54 | fax -99 | +43/6769129023
Thank you for your
attention!
Questions?
Workshop on Big Data and Society Data Economy, Real-Time Mining and Analytics, Mining Techniques
for Online and Customer Service in Big data Era, Part of the 2013 IEEE International Conference on Big
Data (IEEE Big Data 2013), 6-9 October 2013, Silicon Valley, CA, USA