1 Peter Fox Xinformatics 4400/6400 Week 12, May 27, 2015 XInformatics course summary.
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Transcript of 1 Peter Fox Xinformatics – ITEC 6961/CSCI 6960/ERTH-6963-01 Week 12, May 3, 2011 XInformatics...
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1
Peter Fox
Xinformatics – ITEC 6961/CSCI 6960/ERTH-6963-01
Week 12, May 3, 2011
XInformatics course summary
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Contents
• Summary of this course
• What you needed to learn/ objectives
• Last class
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The key is:• As the volume, complexity and heterogeneity
increases…– Suddenly information looks more like a continuum– All known methods, algorithms do not scale
(except for very simple operations)– And because it is information, humans are part of
the loop
• Thus - understand and apply theoretical foundations
• All to date are developed in an analog world, not a digital one!! 3
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Abduction• method of logical inference
(Peirce) • prior to induction and
deduction i.e. "hunch”• starts with a set of
(seemingly unrelated) facts + intuition (some connection) and brought together – via abductive reasoning
• abduction is the process of inference that produces a hypothesis as its end result
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A Use Case
• … is a collection of possible sequences of interactions between the information system under discussion/ design and its actors, relating to a particular goal
• … consists of a prose description of an information system's behavior when interacting with the actors
• … is a technique for capturing functional requirements of an information system
• … captures non-functional requirements
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Ultimately: Wetware
• ‘Before you make the software interoperable, you need to make the people interoperable’: Ian Jackson,
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Developed for NASA TIWG
E.g. Table of Contents• ==Plain Language Description==• ===Short Definition===• ===Purpose===• ===Describe a scenario of expected use===• ===Definition of Success===• ==Formal Use Case Description==• === Use Case Identification===• ===Revision Information===• ===Definition===• ===Successful Outcomes===• ===Failure Outcomes===• ==General Diagrams==• ===Schematic of Use case===• ==Use Case Elaboration==• ===Actors===• ====Primary Actors====• ====Other Actors====• ===Preconditions===• ===Postconditions===• ===Normal Flow (Process Model)===• ===Alternative Flows===
• ===Special Functional Requirements===• ===Extension Points===• ==Diagrams==• ===Use Case Diagram===• ===State Diagram===• ===Activity Diagram===• ===Other Diagrams===• ==Non-Functional Requirements==• ===Performance===• ===Reliability===• ===Scalability===• ===Usability===• ===Security===• ===Other Non-functional Requirements===• ==Selected Technology==• ===Overall Technical Approach===• ===Architecture===• ===Technology A===• ====Description====• ====Benefits====• ====Limitations====• ===Technology B===• ====Description====• ====Benefits====• ====Limitations====• ==References==
• ===Special Functional Requirements===• ===Extension Points===• ==Diagrams==• ===Use Case Diagram===• ===State Diagram===• ===Activity Diagram===• ===Other Diagrams===• ==Non-Functional Requirements==• ===Performance===• ===Reliability===• ===Scalability===• ===Usability===• ===Security===• ===Other Non-functional Requirements===• ==Selected Technology==• ===Overall Technical Approach===• ===Architecture===• ===Technology A===• ====Description====• ====Benefits====• ====Limitations====• ===Technology B===• ====Description====• ====Benefits====• ====Limitations====• ==References==
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Information theory
• Semiotics - study of sign processes or signification and communication, signs and symbols, into three branches:– Syntax: Relation of signs to each other in formal
structures– Semantics: Relation between signs and the
things to which they refer - meaning– Pragmatics: Relation of signs to their impacts on
those who use them8
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THE PHYSICS OF INFORMATIONTHE PHYSICS OF INFORMATION
© 2005 EvREsearch LTD© 2005 EvREsearch LTD
EvREsearch©EvREsearch©
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Physics of information = uncertainty/ integrity
• Information of a random variable is defined as the Sum of p x log p, where p=probability. It represents the uncertainty of the variable
• Mutual information of two variables = how much information one variable contains about the other – i.e. the decrease of the uncertainty of one
variable by knowing the other
• In probabilistic terms, the entropy decreases by conditioning on the distribution
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Noise
• Uncertainty, especially any that is introduced is a source of noise, or more accurately – bias in the use or interpretation of the information– is context and structure dependent– Noise/ bias contamination is rampant in
information systems
• Quality assessment, control and verification is less developed for information sources
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Mode of noise introduction
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From Shannon and Weaver (1949)
Information Source
Web Content, Structure
Noise source
Web browser?
HTML page, user
Msg? Signal? Recvd? Msg?
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Intersecting disciplines:
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Library ScienceOrganizes- Cataloging and classificationPreservation - ‘maintaining or restoring access to artifacts’
Cognitive Science
mental representation,the nature of expertise, and intuition
Social Science
CollaborationCultural normsRewards
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Presentation• Separation of content from presentation!!
• The theory here is empirical or semi-empirical
• Is developed based on an understanding of minimizing information uncertainty beginning with content, context and structural considerations and cognitive and social factors to reduce uncertainty
• Physiology for humans, color, …
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Organization• Organizations - producers v/s consumers
• Organization of information presentation, e.g. layout on a web page
• Yes - content, context and structure
• How to organize– What have you seen?– Needed?– Not had resolved?
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Mental Representation
• Thinking = representational structures + procedures that operate on those structures
• Did you make progress?
• Methodological consequence: what have you learned about the study how we think about information systems?
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Behind this: Information Models
• Conceptual models, domain models, explore domain concepts
• High-level conceptual models are created as part of initial requirements envisioning efforts - to explore the high-level static business or science or medicine structures and concepts.
• Conceptual models are created as the precursor to logical models or as alternatives to them
• MUST be followed by logical and physical models
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(Information) Architectures
• Definition:
– “is the art of expressing a model or concept of information used in activities that require explicit details of complex systems” (wikipedia)
– “… as in the creating of systemic, structural, and orderly principles to make something work - the thoughtful making of either artifact, or idea, or policy that informs because it is clear.” Wuman
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Architectures• Building on content,
context, and structure, think of information architectures as “in front of the interface” and “behind the interface”
• What’s the proportion – is it just like an iceberg? I.e. the majority of information architecture work is out of sight, "below the water.” 19
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Reference architectures• “provides a proven template solution for an
architecture for a particular domain. It also provides a common vocabulary with which to discuss implementations, often with the aim to stress commonality.
• A reference architecture often consists of a list of functions and some indication of their interfaces (or APIs) and interactions with each other and with functions located outside of the scope of the reference architecture.” (wikipedia)
• Have you seen a reference architecture?20
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Design?• “In the context of information systems design,
information architecture refers to the analysis and design of the data stored by information systems, concentrating on entities, their attributes, and their interrelationships.
• It refers to the modeling of data for an individual database and to the corporate data models an enterprise uses to coordinate the definition of data in several distinct databases.
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Design theory• Elements
– Form– Value– Texture– Lines– Shapes– Direction– Size– Color
• Relation to signs and relations between them22
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Principles of design
• Balance
• Gradation
• Repetition
• Contrast
• Harmony
• Dominance
• Unity
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Broad life-cycle elements• Acquisition: Process of recording or
generating a concrete artefact from the concept (the act of transduction)
• Curation: The activity of managing the use of data from its point of creation to ensure it is available for discovery and re-use in the future
• Preservation: Process of retaining usability of data in some source form for intended and unintended use
• Stewardship: Process of maintaining integrity across acquisition, curation and preservation 24
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Acquisition• What do you know about
the developer of the means of acquisition– Documents -not be easy to
find/ read/ understand
– Remember bias!!!
• Have a checklist (the Management list) and review it often
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Curation• From Producers to Consumers!
• Organization and presentation!
• Documentation!
• Provenance!
• So sorry for all the !!!!!!!!!!!!!!!
• Technology-neutrality?
• Add metainformation
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Preservation• Archiving is but one component
• Intent is that ‘you can open it any time in the future’ and that ‘it will be there’
• Steps not be conventionally thought of
• Think far into the future …. history gives some guide to future considerations
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Information Management• Creation of logical collections
• Physical handling
• Interoperability support
• Security support
• Ownership
• Metadata collection, management and access.
• Persistence
• Knowledge and information discovery
• Dissemination and publication 28
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Information Workflow• Series of tasks performed to produce a final
outcome
• Information workflow = “analysis pipeline”– Automate tedious jobs that users traditionally
performed by hand for each dataset– Process large volumes of data/ information faster
than one could do by hand
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Information integration• Involves: combining information residing in
different sources and providing users with a unified view of them
• Recall the domain examples:– Geo?– Medical/ health?– Others?
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Discovery?• Discussion
– What is the reality?– Information architecture considerations?– Facilitation of Xinformatics?
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Visualization?• Reducing the amount of data, quantization
– Patterns– Features– Events– Trends– Irregularities– Exit points for analysis
• Also presentation of data
• Cognitive science and the mental representation
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Factors that count!
• Quality– Is in the eyes of the beholder – why this is so important?
• Uncertainty– has aspects of accuracy (how the real world situation is assessed,
it also includes bias) and precision (down to how many digits)
• Bias– Systematic error resulting in the distortion of measurement data
caused by prejudice or faulty measurement technique
– A vested interest, or strongly held paradigm or condition that may skew the results of sampling, measuring, or reporting the findings of a quality assessment:
• Psychological: for example, when data providers audit their own data, they usually have a bias to overstate its quality.
• Sampling: Sampling procedures that result in a sample that is not truly representative of the population sampled. (Larry English) 33
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In one slide?• Use case – you have to know the goal (+more)• Conceptual and logical models -> information
models• Understand information flows and uncertainties
(sign systems), the life cycle and manage them• Apply information, library, cognitive, social science,
and design elements to developing a design of an architecture
• Think the design through (e.g. get closer to the physical model (workflow?)) and assess the presentation, organization, content, context, structure, syntax, semantic and pragmatics 34
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What would your slide include?
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Objectives• To instruct future information architects how to
sustainably generate information models, designs and architectures
• To instruct future technologists how to understand and support essential data and information needs of a wide variety of producers and consumers
• For both to know tools, and requirements to properly handle data and information
• Will learn and be evaluated on the underpinnings of informatics, including theoretical methods, technologies and best practices.
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Learning Objectives• Through class lectures, practical sessions,
written and oral presentation assignments and projects, students should:– Understand and develop skill in Development
and Management of multi-skilled teams in the application of Informatics
– Understand and know how to develop Conceptual and Information Models and Explain them to non-experts
– Knowledge and application of Informatics Standards
– Skill in Informatics Tool Use and Evaluation37
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Discussion• All of the material?
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Reading for this week
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What is next
• Week 13 – Project presentations (May 10)
• DO NOT BE LATE for class, start promptly at 9:05
• And, be prepared to be asked (and answer) questions
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