Download - IoT 2014 Value Creation Workshop: SDIL
KIT – University of the State of Baden-Wuerttemberg and National Research Center of the Helmholtz Association www.kit.edu
Dr. Till Riedel, Prof. Dr.-Ing. Michael Beigl – Department of Informatics
can data be smart?Accelerating Data driven Innovation with a Smart Data Innovation Lab (SDIL)
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Karlsruhe Institute of Technology
Employees
364Professors
Students
789Annual Budget in Million Euros
9,261 23,836A merger of the University of Karlsruhe (est. 1825) and the Karlsruhe Research Center
3Nobel Laureates
3
Computer Science at KIT
Scientific Staff
41
Professors gained statusfrom KIT‘s CS faculty
Students
19721st CS department in Germany
250 2700Computer Science and applied CS
#1Computer Science Ratings, DFG Funds
190
Professors
> 900 PhDs
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Why Big Data Matters:Dimension of Convergence
Location Uncertainty vs. GPS Sampling RatePaek, J., Kim, J., Govindan, R.: Energy-efficient rate-adaptive GPS-based positioning for smartphones. In: Proc. of the 8th Intl. Conf. on Mobile systems applications and services MobiSys 10, MobiSys ’10, p. 299ff. ACM, ACM Press (2010)
Krijger, J. M., van Weele, M., Aben, I., and Frey, R.: Technical Note: The effect of sensor resolution on the number of cloud-free observations from space, Atmos. Chem. Phys., 7, 2881-2891
Information
Reality
TimeFrequencyDelay
Location
Perception# PhenomenaResolutionError
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Internet of Things and Services
/Services
CRM PLMSRM
/Things
SCM EHMPLMERPLeg-acy
A2A
People
collaboration
B2B
roles embeddedanalytics
alerts
roles
SmartItems
Middleware
Internetof Things
MeaningfulPresentation
Information
Reality
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Smart Economy: Data, the “new oil”
What are the ramifications for Germany and Europe? Traditionally home of engineers.Engineering needs to be extended to also accommodate “Digital Data Engineering”.
real-time processing of large data quantities (“Big Data”). Structuring Big Data results in information (“Smart Data”)
leads to knowledge advantages can be used to support decision-making processes.
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The Smart Data Innovation Lab(throwing oil onto the fire)
Data SourcesIndustrial, Research,
Government, Open Data
Smart Data Innovation Lab
Knowledge and Innovation(Analysis results, prognosis,Online-Analysis Systems)
(Fast) Research and
Development
Systems,Analytics,Researchbased on
open, proprietary
SDIL internalKnowledge
Generated Data
Code Artefacts
SelectedResults
„new oil“
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Our Goals
A Smart Data Centre and a Smart Data CommunityPrompt Co-Working on valuable Problems with valuable „Big Data“ Requires Structure (SDIL Orga) and Trust (SDIL Contract)Knowledge Transfer from Research to Industry and vice versaExploration of
Smart Data Potential / Innovation PotentialBest Practice and Standards
Research in Analytics, Systems, Application Domain specificSmart Data Tools, Support, Management, Privacy, Legal and CurationHCI and Usability
First focus: short term high impact projects
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Smart Data: From Data to Wisdom
Information makes data meaningful for audiences because it requires the creation of relationships and patterns between data.
Source: Nathan Shedroff, nathan.com
Transforming data into information is accomplished by organizing it into a meaningful form, presenting it in meaningful and appropriate ways, and communicating the context around it.
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Context Sensitive Systems w/ Predictive AnalyticsPlanning, Control, Real Time Reaction
Massive Ad-hoc Deployment of CPS-Systems,
Recognition of Process related
parameters
Automatic Correlation to
find novel insights
Use of the data for planning, controlling processes
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Meaningful Data in Context: Realtime Smart Data in Maintenance
L1A10 Temp
L1B22 Temp
L1B20 Temp
L3A10 Temp
L1A10 Temp
teco.kit.edu
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SDIL Project Phases
Phase: Information• Industry and research partners search partners and their competence focus
through the SDIL database
Phase: Project Definition• Data Access / Result Model Selection, Funding & Contract Model• Research and Innovation Question and Results (Data, Algorithm, System)• Technical Details including Data and Service Interfaces
Project Proposal Phase• Proposal handed into SDIL Community• In case of go: Preparing Operation
Phase: Analysis• Prepare Data, Compute, Interpret Data and/or develop analytical method or
system• Publish Results according to Data Model
Continuation Phase• Prepare for follow up actions or development of a run time system• Care of Data Life Cycle according to contract
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3 Data Access / Data Results Models (planned)
Open DataOpen to the public
SDIL community open dataOpen to organisations who signed the contract and agreed to the terms and conditions of the contractRequired for restricted access data (IP-Knowledge, data protection)
SDIL P2P dataOpen to two or more partners in SDIL bound to a general NDA which includes handling rules for ethics and privacy
Mixed dataFair share: Same amount of input (e.g. percentage of Open Data Input) and output (e.g. same amount of Open Data Output)
Same approach for algorithms
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Governance & Communities
Strategy Board
Operational Board
SDIL-Coordinator and Deputy (Michael Beigl, Laure Le Bars)
Innovation CommunitiesP
rojects
Repo
sito
ry
Central Storage
RemoteData Source Catalogue, Broker
HANATerra-cotta Azure ? 9000
…HAL
Generic Tools, Data CurationSupp
ort
Data Curation(Lead:DFKI, FhG) SDIL Platform
Industry 4.0 (Lead: Bosch, DFKI)
Energy (Lead:EnBW, KIT)
Smart Cities (Lead:Siemens, FhG)
Medicine (Lead:Bayer, FZJ)
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Further Activities and Services of SDIL (soon)
Community ForumsWorkshops, CompetitionConsulting & SupportToolsEducation
Contact us:http://www.sdil.de/en/contact/Prof. Michael Beigl ([email protected]) (SDIL Coordinator)
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Bytecode
Till Riedel, Big With Data, Paris, 2014-06-12
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