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George Mason University – Graduate Council Graduate Course Approval Form All courses numbered 500 or above must be submitted to the Graduate Council for final approval after approval by the sponsoring College, School or Institute. Graduate Council requires submission of this form for a new course or any change to existing courses. For a new course, please attach a copy of the syllabus and catalog description (with catalog credit format, e.g. 3:2:1). The designated representative of the College, School or Institute should forward the form along with the syllabus and catalog description, if required, as an email attachment (in one file) to the secretary of the Graduate Council. A printed copy of the form with signatures and the attachments should be brought to the Graduate Council meeting. Please complete the Graduate Course Coordinator Form if the proposed changes will affect other units. Note: Colleges, Schools or Institutes are responsible for submitting new or modified catalog descriptions (35 words or less, using catalog format) to Creative Services by deadlines outlined in the yearly Catalog production calendar. Please indicate: New___X____ Modify_______ Delete_______ Department/Unit: _______Computer Science____ Course Subject/Number: CS780 / INFS 780_______________ Submitted by:_______Jessica Lin___________ Ext:__3-4693____ Email:__ [email protected]_________ Course Title:___ Data Mining in Multimedia Databases ________________________________________________ Effective Term (New/Modified Courses only): __Fall 08_____ Credit Hours: (Fixed) ___3_ (Var.) ______ to ______ Final Term (deleted courses only):____________ Grade Type (check one): __x___ _____ _____ Regular graduate (A, B, C, etc.) Satisfactory/No Credit only Special graduate (A, B, C, etc. + IP) Repeat Status*(check one): _X__ NR-Not repeatable ___ RD-Repeatable within degree ____ RT-Repeatable within term *Note: Used only for special topics, independent study, or internships courses Total Number of Hours Allowed: _______ Schedule Type Code(s): 1._LEC_ LEC=Lecture SEM=Seminar STU=Studio INT=Internship IND=Independent Study 2.____ LAB=Lab RCT=Recitation (second code used only for courses with Lab or Rct component) Prereq _X_ Coreq ___ (Check one):___INFS 755 or CS 750 or permission of instructor______________________ __________________________________________________________________________________________ Note: Modified courses - review prereq or coreq for necessary changes; Deleted courses - review other courses to correct prereqs that list the deleted course. Description of Modification (for modified courses):____________________________________________________________________ Special Instructions (major/college/class code restrictions, if needed):__________________________________________ Department/Unit Approval Signature:_________________________________________ Date: _____________ College/School Committee Approval Signature:__________________________________ Date:_____________ Graduate Council Approval Date:____________ Provost Office Signature:_________________________________ George Mason University Graduate Course Coordination Form Approval from other units: Please list those units outside of your own who may be affected by this new, modified, or deleted course. Each of these units must approve this change prior to its being submitted to the Graduate Council for approval. Unit: Head of Unit’s Signature: Date: Unit: Head of Unit’s Signature: Date: Unit: Head of Unit’s Signature: Date: Unit: Head of Unit’s Signature: Date: Unit: Head of Units Signature: Date: Graduate Council approval: ______________________________________________ Date: ____________ Graduate Council representative: __________________________________________ Date: ____________ Provost Office representative: ____________________________________________ Date: ____________ COURSE PROPOSAL BY THE DEPARTMENT OF COMPUTER SCIENCE PROPOSAL DESIGNATION New Course Proposal I. CATALOG DESCRIPTION A. CS 780 (cross-listed INFS 780) – Data Mining in Multimedia Databases (3:3:0) B. Prerequisite: INFS 755 or CS 750 or permission of instructor. This course covers advanced algorithms for data management, learning, and mining large, multimedia databases. Issues related to handling such data including feature selection, high dimensional indexing, interactive search and information retrieval, pattern discovery, and scalability to large datasets are discussed. Mining techniques and data types to be covered include texts/web, images, videos, DNA, temporal, spatial, spatiotemporal databases, graph mining, stream mining, and data visualization. II. JUSTIFICATION A. Desirability of adding this course: This course provides an overview on the advanced database and mining techniques for multimedia databases. While some of the topics discussed here might be broad enough to deserve a course of its own, this course serves to give students a general picture of database and data mining techniques on specialized, real-world data types for which conventional techniques are not suitable nor sufficient. Students will learn practical knowledge that will help them relate data mining concepts to real world scenarios. B. Relation to other courses: This course can be considered a companion course to INFS 785, INFS 623, CS 682 and CS 782. III. SCHEDULING A. This course has already been offered (with modifications) as a special topics course in Spring 2007 and should be offered in the Fall semester 2008 and every academic year thereafter in the Fall semester. B. The current instructor is Dr. Jessica Lin. Other possible instructors are Drs. Daniel Barbara and Carlotta Domeniconi. IV. SAMPLE SYLLABUS Syllabus: CS 780/INFS 780 – Data Mining in Multimedia Databases Course Objectives: This course covers advanced algorithms for data management, learning, and mining large, multimedia databases. Issues related to handling such data including feature selection, high dimensional indexing, interactive search and information retrieval, pattern discovery, and scalability to large datasets are discussed. Mining techniques and data types to be covered include texts/web, images, videos, DNA, temporal, spatial, spatiotemporal databases, graph mining, stream mining, and data visualization. Topics Covered: The course will address the following topics: Advanced database management concepts: hashing and multi-key access methods, for main-memory and disk-based data. Text/web mining Video clips/surveillance Image mining Audio Temporal/Spatial/Spatiotemporal databases DNA analysis Stream Mining Data Visualization Grading Policy: The grade will be determined by grades obtained in individual assignments, research paper review and report, a group research project, and class participation. Individual Assignments: 30% Individual Research Paper Review: 20% Project and Presentation: 25% Class Participation/Quizzes: 10% Final Exam: 15% Sample Schedule: Class 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Textbooks: Topics Introduction Advanced database indexing techniques Text/Web Mining I Text/Web Mining II Image Mining I Image Mining II Video Mining Audio Temporal/Sequence Data Temporal/Sequence Data Spatiotemporal Data Data Streams Data Streams Data Visualization Final Exam Required reading: Christos Faloutsos, Searching Multimedia Databases by Content, Kluwer Academic Press. Optional reading: Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2000. Course Description CS780/INFS780 Data Mining in Multimedia Databases (3:3:0) Prerequisite: INFS 755 or CS 750 or permission of instructor. This course covers advanced algorithms for data management, learning, and mining large, multimedia databases. Issues related to handling such data including feature selection, high dimensional indexing, interactive search and information retrieval, pattern discovery, and scalability to large datasets are discussed. Mining techniques and data types to be covered include texts/web, images, videos, DNA, temporal, spatial, spatiotemporal databases, graph mining, stream mining, and data visualization.