Xinformatics Spring 2011

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Xinformatics.

Instructors: Professor Peter Fox (pfox at cs dot rpi dot edu), TA Stephanie Ardizzone (ardizs at rpi dot edu) contact by email

Meeting times: Tuesday morning 9:00 am - 11:50 am. SAGE 2707;

Office Hours: Monday 3-4pm in Winslow 2120 or by appointment at JSC 2C04

phone: 276-4862

ITEC 4962/6961 53353/53354, ERTH 4963/6963 53355/53356, CSCI 4960/6960 53357/53358

Description

In the last 2-3 years, Informatics has attained greater visibility across a broad range of disciplines, especially in light of great successes in bio- and biomedical-informatics and significant challenges in the explosion of data and information resources. Xinformatics is intended to provide both the common informatics knowledge as well as how it is implemented in specific disciplines, e.g. X=astro, geo, chem, etc. Informatics' theoretical basis arises from information science, cognitive science, social science, library science as well as computer science. As such, it aggregates these studies and adds both the practice of information processing, and the engineering of information systems. This course will introduce informatics, each of its components and ground the material that students will learn in discipline areas by coursework and project assignments.

Syllabus/ Calendar (tentative)

Refer to Reading/ Assignment/ Reference list for each week (see below).

  • Week 1 (Jan. 25): Introduction to informatics - slides
  • Week 2 (Feb. 1): Capturing the problem: Use case development and requirement analysis - slides
  • Week 3 (Feb. 8): State-of-the-Art examples; Astroinformatics - slides, Bioinformatics - slides
  • Week 4 (Feb. 15): Information systems theory - slides
  • (Feb 22): class rescheduled
  • Week 5 (Mar. 1): Foundations; semiotics, library, cognitive and social science and class exercise - information modeling - slides
  • Week 6 (Mar. 8): Information architectures theory and practice (Internet, Web, Grid, Cloud) Class project definitions - Week 6 slides [Download]
  • Mar. 15: no classes - spring break
  • Week 7 (Mar. 22): Class presentations for assignment 3
  • Week 8 (Mar. 29): Class presentations for assignment 3 ctd.
  • Week 9 (Apr. 5): Information Management, Workflow, and Discovery, Project definition check-in/ discussion - Week 9 slides [Download]
  • Apr. 12: no class - Grand Marshall week
  • Week 10 (Apr. 19): Information Integration, Life-cycle and Visualization Week 10 slides [Download]
  • Week 11 (Apr. 26): Quality - project case study as an example... Week 11 slides [Download]
  • Week 12 (May 3): Course material review - what you need to remember - Week 12 slides [Download]
  • Week 13 (May. 10): Final project presentations

Reading/ Assignment/ Reference List

Class 1 Reading Assignment: Xinformatics Applications - State of the Art

Class 2: Reading Assignment: Use case development and requirement analysis

Required:

Optional:

  • http://members.aol.com/acockburn/papers/AltIntro.htm
  • http://alistair.cockburn.us/index.php/Resources_for_writing_use_cases
  • http://alistair.cockburn.us/images/Usecasesintheoryandpractice180.ppt
  • http://alistair.cockburn.us/images/Agileusecases1dy.ppt
  • http://alistair.cockburn.us/index.php/Structuring_use_cases_with_goals
  • http://www.foruse.com/publications/bibliographies/usecases.htm
  • http://en.wikipedia.org/wiki/Use_case
  • http://www.ddj.com/dept/architect/184414701

Note - Assignment 1 due Feb 15

Class 3: Reading Assignment: Information theory, models, tools

Class 4: Reading Assignment:

Assignment 2

Class 5: Reading Assignment:

Next week

Assignment 3

Class 6: Reading Assignment:

Final Assignment Project [Download]

Class 7: Reading Assignment:

  • none

Class 8: Reading Assignment:

  • none

Class 9: Reading Assignment:

Information Discovery

Metadata

  • http://en.wikipedia.org/wiki/Metadata
  • http://www.niso.org/publications/press/UnderstandingMetadata.pdf
  • http://dublincore.org/

Class 10: Reading Assignment:

Information Integration

Information Life Cycle

Information Visualization

Information model development and visualization

Class 11: Reading Assignment:

  • None

Class 12: Reading Assignment:

  • TBD

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.

Course 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 Evaluation

Assessment Criteria

  • Via written assignments with specific percentage of grade allocation provided with each assignment
  • Via oral presentations with specific percentage of grade allocation provided
  • Via group projects and presentations
  • Via participation in class (not to exceed 10% of total)
  • Graduate students are assessed on identified components per assignment. Undergraduates may complete graduate components for extra credit
  • Late submission policy: first time with valid reason – no penalty, otherwise 20% of score deducted each late day

Topics for Xinformatics/ Foundations:

  • Introduction to informatics
  • State-of-the-Art examples; bioinformatics
  • Capturing the problem: Use case development and requirement analysis
  • Information theory, models, tools
  • Foundations; semiotics, library, cognitive and social science
  • Information life-cycle
  • Information architectures (Internet, Web, Grid, Cloud)
  • Information Visualization,
  • Information and Workflow Management
  • Information Discovery, Information Integration

Xinformatics Applications:

  • Geoinformatics
  • Astroinformatics
  • Cheminformatics
  • Bioinformatics
  • Helioinformatics
  • Health informatics
  • Ecoinformatics

Xinformatics Project options (examples):

  • Information Management for collaborative web sites
  • Information Modeling for Health Sciences: Patient Records
  • Information Architecture Analysis Case Study: GEOSS
  • Information Management for Disasters: Earthquakes
  • Information Content, Content and Structure Analysis: Library Information Systems

Academic Integrity

Student-teacher relationships are built on trust. For example, students must trust that teachers have made appropriate decisions about the structure and content of the courses they teach, and teachers must trust that the assignments that students turn in are their own. Acts, which violate this trust, undermine the educational process. The Rensselaer Handbook of Student Rights and Responsibilities defines various forms of Academic Dishonesty and you should make yourself familiar with these. In this class, all assignments that are turned in for a grade must represent the student’s own work. In cases where help was received, or teamwork was allowed, a notation on the assignment should indicate your collaboration. Submission of any assignment that is in violation of this policy will result in a penalty.

If found in violation of the academic dishonesty policy, students may be subject to two types of penalties. The instructor administers an academic (grade) penalty, and the student may also enter the Institute judicial process and be subject to such additional sanctions as: warning, probation, suspension, expulsion, and alternative actions as defined in the current Handbook of Student Rights and Responsibilities. If you have any question concerning this policy before submitting an assignment, please ask for clarification.

Suggested Prerequisites

  • Knowledge such as that gained in a Data Base class (e.g., CSCI-4380)
  • Knowledge such as that gained in a Data Structures class (e.g., CSCI-1200)
  • Knowledge such as that gained in a Data Science class (e.g. ITEC/CSCI/ERTH 6961-01)
  • or permission of the instructor

Attendance Policy

Enrolled students may miss at most one class without permission of the instructor.