1. Foundations: information modeling and information architecture
Peter Fox
Xinformatics – ITEC, CSCI, ERTH 4400/6400
Module 6, February 27, 2018

2. Contents Assignment 3 Review of reading Information modeling
Information Architecture
Assignment 4

3. Assignment 3 – Sign System Written Analysis due by March 6, 2018 at 11:59 PM
Assignment: Analysis of cognitive, collection and social/cultural aspects of information systems in signs – discussed and decomposed. The weighting for each question is included in the rubric on LMS. Please use the question numbering (1-4) below for your written responses for this assignment. Choose a signage “system.” Pick an analog or digital information system that utilizes ‘signs’ (icons, indices, symbols). It can be one you like or dislike.
Write min 1-2 sentences on why you made your choice. Include a graphic of your chosen information system
Describe signs in the system you chose and why it is a ‘system’ and use the Class 2 system “properties, attributes, and leverage points” to frame your description. Write 2-3 min sentences per sign for at least 3 signs. Graduate question (6400-level): calculate or estimate the uncertainty in the information content for part or all of the information system.
Semiotic analysis: classify the signs according to categories defined in class, e.g. what is the signifier and what is signified; what is the index and indicate which are icons, or symbols? Min. 1 sentence per sign component. Describe the “code” or paradigm used. Min. 1-2 sentences.
For your chosen signage system what library, cognitive and/or social science principles have been applied in their development? What, if any, attention is given to syntax, semantics, and pragmatics; describe them – min 3-4 sentences
Present in class. Discuss the relevant informatics considerations from questions 1 (why), 2 (system aspects), 3 (semiotics), and 4 (principles applied) Present for ~5 minutes with a few questions to follow

4. Assignment 3 – Sign System Presentation due by Monday March 5, 2018 at 5:00 PM
Assignment: Analysis of cognitive, collection and social/cultural aspects of information systems in signs – discussed and decomposed.
Present in class. Discuss the relevant informatics considerations from questions 1 (why), 2 (system aspects), 3 (semiotics), and 4 (principles applied) Present for ~5 minutes with a few questions to follow
Online Participation Assignment – Select the 5 Sign System Presentations (other than your own) that do the best job of meeting the assignment objectives.

5. Semiotic model

6. How to “know” what to model?

7. Meta-modeling -> Mindmaps

8. Some tools
For use case development – simple graphics tools, e.g. graffle
Mindmaps, e.g. Freemind
For modeling (esp. UML if you like it):
Concept, topic, subject maps (try searching)

9. Information Models – 3 levels (ANSI)
Conceptual models, sometimes called domain models, are typically used to explore domain concepts and often created
as part of initial requirements envisioning efforts as they are used to explore the high-level static business or science or medical or …. structures and concepts
Followed by logical and physical models

10. Example use case 1 The first Global Change Information System use case
Title: Visit data center website of dataset used to generate a report figure
Actor and system: A reader of the National Climate Assessment
Flow of interactions: A reader wishes to identify the source of the data used to produce a particular figure in the NCA. A reference to the paper in which the image contained in this figure was originally published appears in the figure caption. Clicking that reference displays a page of metadata information about the paper, including links to the datasets used in that paper. Pursuing each of those links presents a page of metadata information about the dataset, including a link back to the agency/data center web page describing the dataset in more detail and making the actual data available for order or download.

11. A conceptual model of the 1st use case
Ontology engineering differs from database schema modeling in that ontologies do not pertain to specific use cases
Using “gcis:Publication” instead of “gcis:Paper” in the result of the first use case reflects the former aspect. For the latter, we developed and analyzed more use cases to enrich the ontology 11

12. A Conceptual model of the use case
Logical model, i.e. Classes and properties from the use case
Ontology engineering differs from database schema modeling in that ontologies do not pertain to specific use cases
Using “gcis:Publication” instead of “gcis:Paper” in the result of the first use case reflects the former aspect. For the latter, we developed and analyzed more use cases to enrich the ontology 12

13. An intuitive concept map of the use case
From a conceptual model to a logical model (ontology):
A defined class or property should be meaningful and robust enough to meet the requirements of various use cases
An ontology can be extended by adding classes and properties recognized from new use cases via iteration
Classes and properties recognized from the use case
Ontology engineering differs from database schema modeling in that ontologies do not pertain to specific use cases
Using “gcis:Publication” instead of “gcis:Paper” in the result of the first use case reflects the former aspect. For the latter, we developed and analyzed more use cases to enrich the ontology 13

14. Logical models
A logical entity-relationship model is provable in the mathematics of data science.
Given the current predominance of relational databases, logical models generally conform to relational theory.
Thus a logical model contains only fully normalized entities.
Some of these may represent logical domains rather than potential physical tables.

15. Information models - bad
It's very easy to tell when an information system you're trying to navigate has no underlying Information Model. Tell-tale characteristics:
You can't tell how to get from the home page to the information you're looking for.
You click on a promising link and are unpleasantly surprised at what turns up.
You keep drilling down into the information layer after layer until you realize you're getting farther away from your goal rather than closer.
Every time you try to start over from the home page, you end up in the same wrong place.
You scroll through a long alphabetic list of all the articles ever written on a particular subject with only the title to guide you.

16. Information models – good
Oddly enough, you generally don't notice a well-conceived Information Model because it simply doesn't get in your way….
On the main page, you notice promising links right away.
Two or three clicks get you to exactly what you wanted.
The information seems designed just for you because someone has anticipated your needs.
You can read a little or ask for more - the cross-references are in the right places.
Right away you feel that you're on familiar ground - similar types of information start looking the same.

17. Physical models
A physical model is a single logical model instantiated in a specific information system (e.g., relational database schema, RDF/XML document, etc.) in a specific installation.
The physical model specifies implementation details which may be features of a particular product or version, as well as configuration choices for that instance.

18. Physical models
E.g. for a database, these could include index construction, alternate key declarations, modes of referential integrity (declarative or procedural), constraints, views, and physical storage objects such as tablespaces.
E.g. for RDF/XML, this would include namespaces, declarative relations, etc.

19. Object oriented design
Object-oriented modeling is a formal way of representing something in the real world (draws from traditional set theory and classification theory). Some basics to keep in mind in object-oriented modeling are that:
Instances are things.
Properties are attributes.
Relationships are pairs of attributes.
Classes are types of things.
Subclasses are subtypes of things.

20. Object model
Class: a means of grouping all the objects which share the same set of attributes and methods.
an object must belong to only one class as an instance of that class (instance-of relationship).
a class is similar to an abstract data type.
Class hierarchy and inheritance: derive a new class (subclass) from an existing class (superclass)
subclass inherits all the attributes and methods of the existing class and may have additional attributes and methods
single inheritance (class hierarchy) vs. multiple inheritance (class lattice).

21. Core object models consist of:
object and object identifier: any real world entity is uniformly modeled as an object (associated with a unique id: used to pinpoint an object to retrieve).
attributes and methods: every object has a state (the set of values for the attributes of the object) and a behavior (the set of methods - program code - which operate on the state of the object).
the state and behavior encapsulated in an object are accessed or invoked from outside the object.

22. For example for relational DBs
Feature Conceptual Logical Physical
Entity Names ✓ ✓  
Entity Relationships ✓ ✓  
Attributes   ✓  
Primary Keys   ✓ ✓
Foreign Keys   ✓ ✓
Table Names     ✓
Column Names     ✓
Column Data Types     ✓

23. Information Modeling
Conceptual
Logical
Physical

24. More examples at:

25. Steps in modeling Identify objects (entity) and their types
Identify attributes
Apply naming conventions
Identify relationships
Apply model patterns (if known)
Assign relationships
Normalize to reduce redundancy (this is called refactoring in software engineering)

26. Assignment 4

27. In-Class Exercise
For one section of your Use Case in Assignments 1 and 2:
Identify objects (entity) and their types
Identify attributes
Apply naming conventions
Identify at least one relationship
Assign relationships
Be prepared to present (draw diagram) your “Conceptual Model” or “Logical Model” to the class:

28. Not just an isolated set of models
Most important for handling errors, evolution, extension, restriction, … and where do we do that?
To the physical model? NO
To the logical model? MAYBE
To the conceptual model? YES IF POSSIBLE
You will see why in the next module

29. Not just an isolated set of models
They relate to and/ or integrate with other information models:
General rule – integrate at the highest level you can (i.e. more abstract -> conceptual)
Remember the cognitive aspects! Truth <intersect> Belief ~ Knowledge
Less detail is easier to understand

30. Questions?
Information Modeling and relation to semiotics?

31. Information architectures: theory and practice (Internet, Web, Grid, Cloud), design

32. (Information) Architecture
Definition:
“is the art of expressing a model or concept of information used in activities that require explicit details of complex systems” (wikipedia)
“… I mean architect 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
R.S. Wurman: "Information Architects”

33. More detail to connect us
“The term information architecture describes a specialized skill set which relates to the interpretation of information and expression of distinctions between signs and systems of signs.” (wikipedia, emphasis added)

34. Meaning not deep thought
“Information architecture is the categorization of information into a coherent structure, preferably one that the most people can understand quickly, if not inherently.
It's usually hierarchical, but can have other structures, such as concentric or even chaotic.” (wikipedia)

35. Typical example – learning portal

36. And relation to 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 (perhaps scores or hundreds) of distinct databases.
The "canonical data model" is applied to integration technologies as a definition for specific data passed between the systems of an enterprise.
At a higher level of abstraction it may also refer to the definition of data stores.” (wikipedia)

37. Art or skill?
Form follows function (Sullivan) – who put this into effect in building structures, homes?
Based on two previous foundations classes, information theory and signs, it should be clear that the answer is ‘yes’ (both).
Frank Lloyd Wrighthttp://

38. Is this Architected?

39. Semiotic triangle
When you build an information system (elements, relations, operation), it has “SYMBOLS” to stand for “SOMETHING”
Design of your symbols and how they go together (architecture) enables the “THOUGHT” (or not)

40. Remember this one?
Wikipedia syntax entry

41. Examples

42. So, now some “design”

43. Design theory; elements, principles, color…
Form
Value
Texture
Lines
Shapes
Direction
Size
Color
Relate these to previous class, signs and relations between them
Based on

44. Principles of design Balance Gradation Repetition
Balance in design is similar to balance in physics
Gradation
of size and direction produce linear perspective.
of color from warm to cool and tone from dark to light produce aerial perspective.
can add interest and movement to a shape.
from dark to light will cause the eye to move along a shape.
Repetition
with variation is interesting, without variation repetition can become monotonous. 

45. Balance, gradation, repetition

46. Principles of design Contrast
is the juxtaposition of opposing elements e.g. opposite colors on the color wheel - red / green, blue / orange etc.
in tone or value - light / dark.
in direction - horizontal / vertical.
The major contrast in a painting should be located at the center of interest.
Too much contrast scattered throughout a painting can destroy unity and make a work difficult to look at.
Unless a feeling of chaos and confusion are what you are seeking, it is a good idea to carefully consider where to place your areas of maximum contrast.

47. Contrast

48. Principles of design Harmony Dominance
in painting is the visually satisfying effect of combining similar, related elements. e.g. adjacent colors on the color wheel, similar shapes etc.
Dominance
gives a scene interest, counteracting confusion and monotony
can be applied to one or more of the elements to give emphasis

49. Harmony, Dominance

50. Principles of design Unity
Relating the design elements to the idea being expressed in a rendering reinforces the principal of unity.
E.g. a scene with an active aggressive subject would work better with a dominant oblique direction, course, rough texture, angular lines etc. whereas a quiet passive subject would benefit from horizontal lines, soft texture and less tonal contrast.
in a painting also refers to the visual linking of various elements of the work.

51. Unity

52. Color
Primary Colors - Red, Yellow, Blue - these colors should not be intermingled, they must be brought together in some other form
Secondary Color - Orange, Violet, Green, these colors are created by mixing two primaries.
Intermediate Colors - Red Orange, Yellow Green, Blue Violet, etc.; mixing a primary with a secondary creates these colors.
Complementary Colors - are colors that are opposite each other on the color wheel.  When placed next to each other they look bright and when mixed together they neutralize each other. 

53. Wheels

54. Color applied
Warm colors are on one side of the color wheel and they give the felling of warmth for example red, orange and yellow are the color of fire and feel warm.
Cool colors are on the other side of the color wheel and they give the feeling of coolness for example blue, violet, are the color of water, and green are the color of cool grass.

55. Back to (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)

56. U.S. Federal Enterprise Arch
E.g. The Federal Enterprise Architecture Reference Model Ontology (FEA-RMO) is a domain specific ontology of the Federal Enterprise Architecture reference models.
FEA-RMO directly translates the Performance, Business, Service Component, and Technical reference models into their executable representation in OWL-DL.

57. FEA Domain model

58. http://www. whitehouse

59. Data Description

60. Data Sharing

61. Data Context

62. Architectures – what is seen?
Some illustrate information architecture as an iceberg.
Just like an iceberg, the majority of information architecture work is out of sight, "below the water."
The work includes the creation of plans, controlled-vocabularies, and blueprints all before any user interfaces are created.

63. Architecture paradigms
Model
View
Controller

64. Internet/ Intranet Communications versus information architecture?
See the reading for this week, the role of the Internet Engineering Task Force (IETF) and architecture

65. WWW Design for the web (Tim Berners Lee)
“Principles such as simplicity and modularity are the stuff of software engineering; decentralization and tolerance are the life and breath of Internet. To these we might add the principles of least powerful language, and the test of independent invention when considering evolvable Web technology.”

66. Original design issues
See
Here are the criteria and features to be considered:
Intended uses of the system.
Availability on which platforms?
Navigational techniques and tools: browsing, indexing, maps, resource discovery, etc
Keeping track of previous versions of nodes and their relationships
Multiuser access: protection, editing and locking, annotation.
Notifying readers of new material available
The topology of the web of links
The types of links which can express different relationships between nodes
Tim Berners Lee -

67. Original design issues
These are the three important issues which require agreement between systems which can work together
Naming and Addressing of documents
Protocols
The format in which node content is stored and transferred
Implementation and optimization
Caching , smart browsers, knowbots etc., format conversion, gateways

68. Web architectural elements
URI
HTML
HTTP
Yes, this is the conceptual information model of the Web!

69. Client – Server and multi tier
Interface!

70. Web page/site architecture
Hierarchies, we call them levels:
Top level (the main page)
Second (and further) level (via navigation)
Balancing the levels
Interface
Remember your use case, the actors, the resources, the information model, information entropy, the signs, ...

71. History: Grid (architecture) = think “interface”
“One of the main strategies of Grid computing is to use middleware to divide and apportion pieces of a program among several computers, sometimes up to many thousands.
Grid computing involves computation in a distributed fashion, which may also involve the aggregation of large-scale cluster computing based systems.” (wikipedia)

72. Open Grid Services Architecture

73. Cloud
"a computing capability that provides an abstraction between the computing resource and its underlying technical architecture (e.g., servers, storage, networks), enabling convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction.” (wikipedia)
Logical extension of virtualization
Often tied to the cost model

74. Primary Benefits of Cloud Computing
To deliver a future state architecture that captures the promise of Cloud Computing, architects need to understand the primary benefits of Cloud computing
Decoupling and separation of the business service from the infrastructure needed to run it (virtualization)
Flexibility to choose multiple vendors that provide reliable and scalable business services, development environments, and infrastructure that can be leveraged out of the box and billed on a metered basis—with no long term contracts
Elastic nature of the infrastructure to rapidly allocate and de-allocate massively scalable resources to business services on a demand basis
Cost allocation flexibility for customers wanting to move CapEx into OpEx
Reduced costs due to operational efficiencies, and more rapid deployment of new business services
Oracle

75. Software as a service (SaaS)
A SaaS provider typically hosts and manages a given application in their own data center and makes it available to multiple tenants and users over the Web.
Some SaaS providers run on another cloud provider’s PaaS or IaaS service offerings.
Oracle CRM On Demand, Salesforce.com, and Netsuite are some of the well known SaaS
Oracle

76. Infrastructure as a service (IaaS)
is the delivery of hardware (server, storage and network), and associated software (operating systems virtualization technology, file system), as a service. It is an evolution of traditional hosting that does not require any long term commitment and allows users to provision resources on demand.
Unlike PaaS services, the IaaS provider does very little management other than keep the data center operational and users must deploy and manage the software services themselves--just the way they would in their own data center. Amazon Web Services Elastic Compute Cloud (EC2) and Secure Storage Service (S3) are examples of IaaS offerings.

77. Platform as a service (Paas)
is an application development and deployment platform delivered as a service to developers over the Web.
facilitates development and deployment of applications without the cost and complexity of buying and managing the underlying infrastructure, providing all of the facilities required to support the complete life cycle of building and delivering web applications and services entirely available from the Internet.
consists of infrastructure software, and typically includes a database, middleware and development tools.
A virtualized and clustered grid computing architecture is often the basis for this infrastructure software.

78. Platform as a service (Paas)
Some PaaS offerings have a specific programming language or API.
For example, Google AppEngine is a PaaS offering where developers write in Python or Java.
EngineYard is Ruby on Rails.
Sometimes PaaS providers have proprietary languages like force.com from Salesforce.com and Coghead, now owned by SAP

79. Simple cloud “architectures”

80. Assignments
Assignment 4: Construction of an information model and preliminary information architecture/ discussion of design issues of <your> use case from Assignment 1/ 2.
Due on Mar 27
Project Assignment will be available ~ Mar 20

81. What is next Week 7 – Class presentations for assignment 3
Then ….. Spring break (yay)
Assignment 4 – due March 27