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Databases, Data Mining, Information Retrieval Systems

Databases, data mining and information retrieval systems focus on how to organize, access, search, and discover information, with emphasis on the systems, interfaces, and algorithms necessary for both structured and unstructured data.

Research Faculty

James Caverlee

  • Professor, Computer Science & Engineering
  • Presidential Impact Fellow
James Caverlee

Richard Furuta

  • Professor, Computer Science & Engineering
  • Undergraduate Advisor
Richard Furuta

Dmitri Loguinov

  • Professor, Computer Science & Engineering
Dmitri Loguinov

Frank M. Shipman

  • Professor, Computer Science & Engineering
Frank M. Shipman

Courtesy Appointments

 

Nick Duffield

  • Professor, Electrical & Computer Engineering
  • Royce E. Wisenbaker Professor I
  • Director, Texas A&M Institute of Data Science
Nick Duffield

Hye-Chung Kum

  • Adjunct Faculty, Computer Science & Engineering and Industrial & Systems Engineering
  • Associate Professor, School of Public Health, Department of Health Policy and Management
  • Director, Population Informatics Lab
Hye-Chung Kum

Courses Offered

CSCE 603. Database Systems and Applications. Credits 3. 3 Lecture Hours

Introduction to the concepts and design methodologies of database systems for non-computer science majors; emphasis on E. F. Codd's relational model with hands-on design application. No credit will be given for both CSCE 310 and CSCE 603. 

Prerequisite: CSCE 601; graduate classification.

CSCE 608. Database Systems. Credits 3. 3 Lecture Hours

Database modeling techniques; expressiveness in query languages including knowledge representation; manipulation languages data models; physical data organization; relational database design theory; query processing; transaction management and recovery; distributed data management. 

Prerequisite: CSCE 310 or CSCE 603.

CSCE 666. Pattern Analysis. Credits 3. 3 Lecture Hours

Introduction to methods for the analysis, classification and clustering of high dimensional data in Computer Science applications. Course contents include density and parameter estimation, linear feature extraction, feature subset selection, clustering, Bayesian and geometric classifiers, non-linear dimensionality reduction methods from statistical learning theory and spectral graph theory, Hidden Markov models, and ensemble learning. 

Prerequisite: MATH 222, MATH 411 (or equivalent) and graduate classification.

CSCE 670. Information Storage and Retrieval. Credits 3. 3 Lecture Hours

Representation, storage, and access to very large multimedia document collections; fundamental data structures and algorithms of information storage and retrieval systems; techniques to design and evaluate complete retrieval systems, including cover of algorithms for indexing, compressing, and querying very large collections. 

Prerequisite: CSCE 310 or CSCE 603 or approval of instructor; graduate classification.

CSCE 675. Digital Libraries. Credits 3. 3 Lecture Hours

Surveys current research and practice in Digital Libraries, which seek to provide intellectual access to large-scale, distributed digital information repositories; current readings from the research literature which covers the breadth of this interdisciplinary area of study.

Prerequisite: Graduate classification in computer science.