COMMUNITY. Data Acquisition and Usage Value Chain.
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Transcript of COMMUNITY. Data Acquisition and Usage Value Chain.
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COMMUNITY
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Data Acquisition and Usage Value Chain
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Data Processing Value Chain
Monitor StoreData 1
Monitor StoreData 2
Monitor StoreData n
Monitor StoreData m
IntData1
IntDatan
IntData2 Virtual Int. Data
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Information Processing Value Chain (Taylor, 1975)
Informing Knowledge
ActionProductive Knowledge
InformationData
OrganizingGrouping
Classifying Formatting Displaying
Analyzing
SeparatingEvaluating Interpreting
Synthesizing
Judging Options Quality
Advantages Disadvantages
Deciding Matching goals, Compromising
Bargaining Deciding
• Forces to Move Data• one-shot to reusable form
• External force – contracts
• Internal – humanitarian, benefits
Resistances to Move Data • Mechanical
• Personal
• Institutional
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Assigning maintenance responsibility (Wiederhold)
a. Source data quality – supplier database, files, or web pages
b. Interface to the source – wrapper, supplier or vendor for supplier
c. Source selection – expert specialist in mediator
d. Source quality assess. – customer input to mediator
e. Semantic interoperation – specialist group input to the mediator
f. Consistency & metadata – mediator service operation or warehouse
g. Informal, integration – client services with customer input
h. User presentation – client services with customer input
Services
Sources
Customers
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Standard Data Support System
• Data management systems, DBMS
• Data processing end exploration tools
• Presentation tools
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PM/Haze Data Flow in Support of AQ Management
• There are numerous organizations in need of data relevant to PM/Haze
• Most interested parties (stakeholders) are both producers and consumers of PM and haze data
• There is a general willingness to share data but there are many physical and organizational resistances to data flow and processing
RPO
RPO
RPO
Regional Planning Orgs
FLM
FLM
FLM
Federal Land Managers
EPA
EPA
EPA
EPA Regul. & Research
Industry
AcademicNARSTO
Other: Private, Academic
SuperSite
Shared PM/Haze
Data
• PM and haze data are used for may parts of AQ management, mostly in form of Reports
• The variety of pertinent (ambient, emission) data come from many different sources
• To produce relevant reports, the data need to be ‘processed’ (integrated, filtered aggregated)
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PM/Haze Data Flow in Support of AQ Management
• PM and haze data are used for may parts of AQ management, mostly in form of Reports
• The variety of pertinent (ambient, emission) data come from many different sources
• To produce relevant reports, the data need to be ‘processed’ (integrated, filtered aggregated)
Data from multiple measurements are shared by their providers or custodians
Data are integrated, filtered, aggregated and fused in the process of analysis
Reports use processed data for Status and Trends; Exposure Assessment; Compliance
•
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Data Re-Use and Synergy
• Data producers maintain their own workspace and resources (data, reports, comments).
• Part of the resources are shared by creating a common virtual resources.
• Web-based integration of the resources can be across several dimensions:Spatial scale: Local – global data sharing
Data content: Combination of data generated internally and externally
• The main benefits of sharing are data re-use, data complementing and synergy.
• The goal of the system is to have the benefits of sharing outweigh the costs.
Content
Content
User
User
User
LocalLocal
GlobalGlobal
Virtual Shared Resources
Data, KnowledgeTools, Methods
User
User
Shared part of resources
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Integration for Global-Local Activities
Global Activity Local Benefit
Global data, tools Improved local productivity
Global data analysis Spatial context; initial analysis
Analysis guidance Standardized analysis, reporting
Local Activity Global Benefit
Local data, tools Improved global productivity
Local data analysis Elucidate, expand initial analysis
Identify relevant issues Responsive, relevant global analysis
Global and local activities are both needed – e.g. ‘think global, act local’
‘Global’ and ‘Local’ here refers to relative, not absolute spatial scale
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Content Integration for Multiple Uses (Reports)
Data from multiple measurements are shared by their providers or custodiansData are integrated, filtered, aggregated and fused in the process of analysisReports use processed data for Status and Trends; Exposure Assessment; Compliance
The creation of the needed reports requires data sharing and integration from multiple sources.
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DATAFED Rationale
• As much as possible, data should reside in their respective home environment. ‘Uprooted’ data in centralized databases need updated and maintained.
• Data Providers would need to ‘open up’ their SQL data servers for limited data subsets and queries, in accordance with a ‘contract’. However, the data structures of the Providers will not need to be changed.
• Retrieval of uniform data from the data warehouse facilitates integration and comparison along the key dimensions (space, time, parameter, method)
• The open architecture of DATAFED and the use of web-standards promotes the building tools by and for the community: Data Viewers, Data Transformers and Integrators, Report Generators, Renderers etc..