Selecting Ontologies and Publishing Data of Electrical Appliances: A Refrigerator Example
19
OnTheMove Conferences, Meta4eS workshop, 28 October 2014 SELECTING ONTOLOGIES AND PUBLISHING DATA OF ELECTRICAL APPLIANCES: A REFRIGERATOR EXAMPLE Anna Fensel, Fabian Gasser, Christian Mayr, Lukas Ott, Christina Sarigianni Semantic Technology Institute (STI) Innsbruck, University of Innsbruck, Austria Contact: [email protected]
-
Upload
anna-fensel -
Category
Data & Analytics
-
view
183 -
download
0
description
Application scenarios for the data generated from the Internet of Things are on the rise. For example, given the appliances’ energy consumption data, energy measurement tools now make it possible to save energy whilst efficiently controlling the consumption of different household devices. Yet, when the precise structured data describing appliance models is missing, it is difficult for such application scenarios to be realized. The developed OpenFridge ontology defines a basic vocabulary for the domain of measuring a refrigerator’s energy consumption, showing that the needed ontology schemata are already in place, but need to be identified and skillfully applied. Further, the ontology has been populated from the Web using data scraping, and the created dataset semantically describing the specifics of 1032 refrigerator models with 18665 triples, make these valuable assets for the development of further applications.
Transcript of Selecting Ontologies and Publishing Data of Electrical Appliances: A Refrigerator Example
OnTheMove Conferences, Meta4eS workshop, 28 October 2014
SELECTING ONTOLOGIES AND PUBLISHING DATA OF ELECTRICAL APPLIANCES: A REFRIGERATOR EXAMPLE
Anna Fensel, Fabian Gasser, Christian Mayr, Lukas Ott, Christina Sarigianni
Semantic Technology Institute (STI) Innsbruck, University of Innsbruck, Austria
Contact: [email protected]
OnTheMove Conferences, Meta4eS workshop, 28 October 2014
Smart Grid is a Showcase for Data Economy
Smart Grid
OperationEnergy Markets
Synchro
Phasers
Renewables
Parks
Compliance
Smart Buildings
Electro
Mobility
Smart Cities
Smart
Appliances
Smart
Metering
Plant
Automation
Business
DSM
Compliance
Price Signals
Demand
Response
Capacity
Management
Prosumers
From general project presentation: http://www.slideshare.net/slotomic/big-data
OnTheMove Conferences, Meta4eS workshop, 28 October 2014
What is energy efficiency?
– Using less energy to provide equivalentservice.
– A life-cycle characteristic of home appliances.
Economy for Energy Efficiency Data (Knowledge)?
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.htmlFrom general project presentation: http://www.slideshare.net/slotomic/big-data
OnTheMove Conferences, Meta4eS workshop, 28 October 2014
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
A Value-chain for Energy Efficiency Data
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, technologyimprovements
From general project presentation: http://www.slideshare.net/slotomic/big-data
OnTheMove Conferences, Meta4eS workshop, 28 October 2014
Developing a crowdsourcing platform for data collection
Exploring the concept of context-dependent energy efficiency
Combining (big) data and semantics for add-value services
OpenFridge : Opening and Processing Appliances Data for Energy Efficiency
Improved
labeling
Improved
technology
and CRM
Better
decisions
about
replacement
and use
Home Users
Labeling Institutions
Manufacturers
Energy
Efficiency
Data
Building an ecosystem around data
From general project presentation: http://www.slideshare.net/slotomic/big-data
OnTheMove Conferences, Meta4eS workshop, 28 October 2014
Usage profile avg. consumption, cooling cycle,defrost cycle,…
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
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, …
From Context to Recommendations
Measurementspower level (5s)timestamp
From general project presentation: http://www.slideshare.net/slotomic/big-data
OnTheMove Conferences, Meta4eS workshop, 28 October 2014
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
Platform Enablers
From general project presentation: http://www.slideshare.net/slotomic/big-data
OnTheMove Conferences, Meta4eS workshop, 28 October 2014
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 portal-based analytics
Challenges
From general project presentation: http://www.slideshare.net/slotomic/big-data
OnTheMove Conferences, Meta4eS workshop, 28 October 2014
Community & Content Management
Big Data Infrastructure
Data AcquisitionWeb Service
Drupal Portal &Web Service Client
Recommendations &Visualizations
Appliance ProfileMeasurements Profile
Appliance ProfileMeasurements ProfileMeasurements
Business IntelligenceServices
Users
ManufacturersLabeling Organisations
OpenFridge Architecture
SemanticKnowledg
eBase
AnalyticsSPARQL: Data-as-a-Service
Usage Profile
Volume?Variety?Velocity?Veracity?Value?
From general project presentation: http://www.slideshare.net/slotomic/big-data
OnTheMove Conferences, Meta4eS workshop, 28 October 2014
OpenFridge Ontology – Main Classes
OnTheMove Conferences, Meta4eS workshop, 28 October 2014
Semantic Annotation Process Overview
OnTheMove Conferences, Meta4eS workshop, 28 October 2014
Tools for Data Fetching
OnTheMove Conferences, Meta4eS workshop, 28 October 2014
Sources for Fridge Models Data
OnTheMove Conferences, Meta4eS workshop, 28 October 2014
Results for Data Extraction
OnTheMove Conferences, Meta4eS workshop, 28 October 2014
Tool: Python
• Importation process
• Restructure process
• Creation of the ontology-file
Result:
• OpenFridge ontology published at: http://purl.org/opdm/refrigerator
• 1032 refrigerator models with 18665 triples
• OpenRDF-Workbench at www.openfridge.net
Data Mapping – Implementation & Results
OnTheMove Conferences, Meta4eS workshop, 28 October 2014
Technical:
● How to design an ontology 100% reusing other schemes?
● How to fetch Data from different HTML Websources?
● Screen scraping tools
● Creation of readable instances in protege
● How to get this data into a format that is readalbe for a tool like
protege?
○ How to develop?
○ Challenges
Organizational:
● Managing project (devide tasks)
● Meetings (how to communicate)
● Engagement
Lessons Learned
OnTheMove Conferences, Meta4eS workshop, 28 October 2014
Actions- Interactions with the users- Instrumentation @Home- Privacy & data quality
Data (Big Data) - Efficient storage- Analytic processing, data structures
Semantic Processing- Ontology Design- Integration of external data from structured and
non-structured sources- Development and optimisation of queries
(SPARQL) for added value servies
User Tests- Project partner internal (spring 2014)- With test users & external (ongoing)
Current Actions and Next Steps
OpenFridge@WFF, Oct 2013
From general project presentation: http://www.slideshare.net/slotomic/big-data
OnTheMove Conferences, Meta4eS workshop, 28 October 2014
Experiment in progress – take part in user trials! 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
Summary and Outlook
From general project presentation: http://www.slideshare.net/slotomic/big-data
OnTheMove Conferences, Meta4eS workshop, 28 October 2014
Join via: www.openfridge.net
Thank you for your attention!Questions?
References:• Fensel, A., Gasser, F., Mayr, C., Ott, L., & Sarigianni, C. (2014). Selecting
Ontologies and Publishing Data of Electrical Appliances: A Refrigerator Example. In On the Move to Meaningful Internet Systems: OTM 2014 Workshops (pp. 494-503). Springer.• Tomic, S., & Fensel, A. (2013, October). OpenFridge: A platform for data
economy for energy efficiency data. In IEEE International Conference on Big Data (pp. 43-47). IEEE.
