Erin'sThesis

From Datafedwiki

• NOTE: HOw are interop stacks connected through value chain. how are value chains connected in value-networks?

Contents 1 Communication, Sharing, Coordination 2 Working Title: Collaborative Air Quality Decision Support Systems using System of System Approach 2.1 Objective and Scope 2.2 Observations 2.3 SoS Purpose and Tasks - AQ DSS 2.4 Stakeholders - Expertise 2.5 Collaboration - Tools/Methods 2.5.1 SoS Connectivity 2.6 System of Systems 2.7 Support for Thesis 3 Workspaces

[edit] Communication, Sharing, Coordination

Communication is necessary to enable sharing and coordination. Must be alerted before sharing or coordination can occur. Capturing events - new page, resource added, discussion, schedule

Workspace trial/tag Erin Thesis Proposal

Science - Need to capture communication that is going on in e-mail and publish it in an open space. People need to be able to respond through the wiki or the e-mail and be notified both ways. How to engineer this system? Technologies - e-mail, wiki, rss...

Building an ethnography involves extended engagement with the community being researched, collecting field notes, locating and interviewing informants, examining artifacts, and interviewing community members, frequently as a participant observer (Emerson et al., 1995).

Good search Designing System Dualities: Characterizing a Web-Supported Professional Development Community

A Bit More To IT: Scholarly Communication Forums as Socio-Technical Interaction Networks

• Network perspective
• Community perspective
• Resource perspective

• Layered protocol for human-machine interaction

Hurdles users face:

• Can't find
• Can't Access
• Don't know how good it is
• Can't merge with other data

[edit] Working Title: Collaborative Air Quality Decision Support Systems using System of System Approach

Erin's Literature Review
Erin's Memes
The fabric of science is changing, driven by a revolution in digital technologies. These include (1) digital imaging devices for astronomy, (2) microarrays and high-throughput DNA sequencers in genomics, (3) wireless sensor arrays and satellites in geosciences, and (4) powerful computational modeling in meteorology. These technologies generate massive data sets that fuel progress. Technologies for high-speed, high-capacity networked connectivity have changed the nature of collaboration and have also expanded opportunities to participate in science through instant access to rich information resources around the world. While these digital technologies are the engine of this revolution, digital data2 are the fuel.

All elements of the pillars of science – observation, experiment, theory, and modeling – are transformed by the continuous cycle of generation, access, and use of an everincreasing range and volume of digital data. Experiments and observations can be better designed if a rich set of supporting information is easily accessible. A framework of data can provide a strong foundation on which expansive theory can be developed and refined. Data initiate, drive, and produce dynamic modeling and simulation approaches.

Data are not consumed by the ideas and innovations they spark, but are an endless fuel for creativity. A small bit of information, well found, can drive a giant leap of creativity. The power of a data set can be amplified by ingenuity through applications unimagined by the authors and distant from the original field. Re-use and re-purposing of digital scientific data have dramatic benefits.

Interoperability is the ability of two or more systems or components to exchange information and to use the information that has been exchanged (IEEE Standard Computer Dictionary: A Compilation of IEEE Standard Computer Glossaries). The components of interoperability include data, metadata, codes, interfaces, platforms, environments, and networks. Achieving interoperability requires coordination among people, disciplines, and institutions. NITRD Harnessing the Power of the Web

Framework Slides

Slide 3 - Expansion of arrows – the model/obs boxes represent lots of data and models as services which are registered in the GCI (Market Place) this is the end of the data acquisition cycle. Then the services are found and used in DSS (beginning of data usage cycle.

• Ecosystem Services support human well-being
• Earth System Services mapped to Human Well-being

The AQ Domain limits the scope, AQ DSS is the focus of all three major components. AQ Domain/SoSE Domain are informed and inform the other domain. Clearly, each component also extends/applies to other domains (This is VERY similar to Objectives but longer).

• AQ DSS in the center defines the complex problem.
• SoS Approach identifies individual tasks needed to solve the problem and which stakeholders can perform task
  • The breakdown of the AQ DSS problems is already established using a value chain from data provider, processor, analyst, policy analyst
• Stakeholders are identified (Stakeholders include the systems that they operate - not just people.)
• Each stakeholder is supported by an interoperability stack. Enhancing interoperability stack improves performance of stakeholder, allows easier collaboration among peers and consequently the performance of the DSS improves because the individual systems work better.
• Similar stakeholders collaborate through communities of practice (i.e. data provider CoP may work on standard interface WCS/WMS etc) in order to improve particular field (data provider, developer…)
• When connecting multiple stakeholders to create Value chain SoS – you develop communities of interest formed because all have relationship to dss.
• Collaboration through SoS needs technical support tools/methods/best practices (standard interfaces, RSS feeds, human-human connections, etc)

[edit] Objective and Scope

The linearity and unidirectional nature of the production line, the process of taking raw materials, subjecting them to various processes and manufacturing a particular product which is then distributed in the marketplace for consumption by ‘end users’ has been superseded by processes which are more flexible, multidirectional, reliant on knowledge and creativity, and collaborative in nature. In such processes, the figure of the (largely passive) consumer or end user is rapidly being replaced by a new form of user who acts, in collaboration with other peers, as an active producer of content in the very act of consumption. Far from the professional consumer alluded to by the term ‘prosumer’, this new producer-user is best described as a ‘produser’, an active and collaborative participant in the distributed production of new ideas and knowledge (see Bruns 2004, 2005). Wikis in teaching and assessment: the M/Cyclopedia project

The general objective is to improve AQ decision support systems by:

• Clearly identify the AQ DSS (i.e. HTAP/Exceptional Event)
• Break AQ DSS into tasks
• Identifying stakeholder with expertise in each task (Funder, Developer/researcher, Tester, Trainer, User, Sustainer - Tasks are beyond just the key task needed to produce the report, tasks should include developing, testing, using, sustaining)
• Determining what tools and methods each stakeholder currently uses and then how to better ENABLE them by improving tools and methods (How are stakeholder enabled by SoS? (vertical interop stack) human-system connection.)
• Determine how tasks are ALIGNED and CONNECTED in order to solve AQ Event problem. Identify ways to improve connectivity of SoS. (How are stakeholders connected? What is transferred? Who controls flow? What is flowing through connections? Data-> Information)
  • Alignment is how the tasks are ordered and value is produced
  • Connections is the physical linking, info flow

By improving the tools and methods at each task and improving the connectivity of systems, the system of systems is improved overall. The application of general research about system of systems applied to the AQ Event Analysis DSS will bring the inquiry/technical aspects of SoS research together. The human-technical combination with SoS approach allows complex problems to be solved.

[edit] Observations

• In air quality analysis/decision support systems
• In sustainable aq class

The two are very similar b/c both process originally had a linear approach from production to consumption In the class we turn the students into producers and consumers of knowledge.... Data consumers are now also producers of higher grade knowledge, iterative cycles not just a single push economy.

[edit] SoS Purpose and Tasks - AQ DSS

• Clearly identifying the AQ DSS
• Breaking it into tasks

AQ DSS Problems have a general breakdown of the problem into tasks.

However, the AQ DSS may improve if a holistic view was taken (Soft System Methodology is an approach to inquiry into problem situation does not force a particular solution.), instead of current approach to just connect systems without concern for the application.

[edit] Stakeholders - Expertise

• Identifying with expertise in each task (Funder, Developer/researcher, Tester, Trainer, User, Sustainer - Tasks are beyond just the key task needed to produce the report, tasks should include developing, testing, using, sustaining)
  • How do stakeholder COMMUNITIES of PRACTICE benefit the SoS (e.g. EE analysis needs data - data provider CoPs enable standard data access, registration into GEOSS Clearinghouse, deliver data fast; software dev may have another community, analysts form another clear community - how is analysis better done in community?)
• Determining what tools and methods they currently use and then how to better enable them by improving tools and methods (How are stakeholder enabled by SoS? (vertical interop stack) human-system connection.)

Key Stakeholder Classes:

• users, i.e. the people who benefit from system, - multiple users (bureaucratic layering of end users: Federal, Regional and State EPA)
• developers who construct the indiv system, (AND a developer who constructs the SoS)
• acquirers who contract and pay for the system, (pay for excess for connectivity in SoS, individual sys paid for)
• testers who evaluate system for suitability,
• sustainers who keep the system up to date, (SoS ensure connections between systems are intact and that new systems can be added as they become available)
• trainers who insure that the users know how to use it and
• researchers who provide the next generation of ideas.

The first figure describes the types of stakeholders in the value chain. At each node however, there is a network of people. Shows value chain as refinery. Doesn't include funder/mainter of system stakeholders. Every system has stakeholder classes, therefore in SoS there is multiple instances of each class. What is in the arrow being transferred between users?

[edit] Collaboration - Tools/Methods

Each stakeholder has hardware, software and humanware that supports them. What are the tools/methods that are part of each stakeholder's interop stack?

Collaboration also includes the connectivity of stakeholders to produce SoS

[edit] SoS Connectivity

• Determine how tasks are connected in order to solve AQ problem. Identify ways to improve connectivity of SoS. (How are stakeholders connected? What is transferred? Who controls flow? What is flowing through connections? Data-> Information)
• Stakeholders Value-Chain needed to align to perform tasks to create societal benefit (overall value). Some stakeholders not included in value chain.
• Stakeholder value-chain also creates a community of interest

This figure shows that while data flows from the provider to the user, the provider has very little control over what data is used. The user determines from the "market place" which data is applicable and pulls the data. Also at the end of the value chain closest to the "end user" the amount of raw data is small compared to near the provider.

The stakeholders need to interoperate on all of the levels from hardware to humans. However the key connection may not always be the human-human connection. At the data provider to analyst connection may be machine-machine and at the end user between policy analyst and decision maker it may be primarily human-human. However, as mentioned above there is a network of similar stakeholders at each point in the value chain and they also need to interoperate and may benefit from humanware/software/human-human connection enhancement.

Metadata workspaces are one way to connect multiple stakeholders.

[edit] System of Systems

SoS:

• Integrates independent systems
• Generates capabilities beyond any one system
• Emergent behavior exists in SoS
• Ability to engage multiple perspectives
• Complex and uncertain
• Can't predetermine or design the SoS it evolves to fit the needs.

[edit] Support for Thesis

From Decadal Survey:

• Particularly in the case of human health, the critical importance of innovative coupling between in situ and remote observations requires fundamental restructuring of the Earth sciences in service to society.
• Space-based observations are most effective as inputs to public-health decision making when they are used in concert with other data systems, including ground-based observations of environmental and epidemiological conditions, demographic data, data collected from aircraft, and outputs from numerical models. Investments are needed for the coordination of data collection efforts from multiple sources for specific purposes. Specifically, research on public-health decision support systems needs to address the limitations in how current data systems interface, and the opportunities for coordinating observations.
• Although many ecosystem issues develop slowly, there is also a need for remote sensing to provide decision support during and in the wake of episodic events, including abrupt events such as tropical storms and wildfires, and “slower” events, such as insect outbreaks, harmful algal blooms, and droughts.

From DIA Vision Report:

• Collaborative Analytics - Analytic organizations will therefore make a dramatic shift from traditional emphasis on self-reliance toward more collaborative operations — a shift that will allow the Community as a whole to perform routinely at levels unachievable in the past.

[edit] Workspaces

• Barcelona
• Data Summit
• Ad Hoc Data System
• AIP Scenario
• AIP RFP
• DataFed
• Class
• ESIP AQ Cluster

Resources for Class Analysis: