Advanced Data Models and Databases2014HT
Doctoral students in computer science.
The course was last given
The increase of variation in modern data applications and in data sets
available on the Internet puts higher and higher requirements on technology for
information retrieval and storage. The aim of this course is to gain
theoretical and practical knowledge about principles for storage and retrieval
in text, semi-structured and structured data. The course also discusses
alternative data models for databases, and representation of semantic
information. After the completion of the course you should be able to:
- explain differences between text, semi-structured, data models and knowledge-based data; further, given a given data set state advantages and disadvantages of search and storage techniques
- describe different algorithms for information retrieval in text
- describe the properties of semi-structured data and how it differs from text and traditional data models
- represent a given semi-structured data set using XML or RDF
- design, implement and use XML schema and the query language XQuery
- explain key notions and algorithms related to noSQL databases
- describe the main principles of knowledge bases
- design, implement and use a knowledge base represented using OWL
- represent knowledge using description logics
- explain key notions related to the Semantic web
- describe methods and difficulties for data integration
Programming, course on relational databases.
Information retrieval. Semi-structured data. XML databases. NoSQL databases. Semantic Web and ontologies. Data integration.
The course consists of lectures and laboratory work. Lectures are devoted to theory and methodology, and give practical examples. During the laboratory work students work with a number of exercises that illustrate principles for the data models, algorithms and database models that are discussed during the lectures.
Professor Patrick Lambrix
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Last updated: 2012-05-03