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Peter Fox

Xinformatics – ITEC 6961/CSCI 6960/ERTH-6963-01

Week 12, May 3, 2011

XInformatics course summary

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

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

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==

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

THE PHYSICS OF INFORMATIONTHE PHYSICS OF INFORMATION

© 2005 EvREsearch LTD© 2005 EvREsearch LTD

EvREsearch©EvREsearch©

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?

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

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

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

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

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

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

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

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

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

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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