Advanced Data Models and Databases

Aim

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, XML and NoSQL databases and representation of semantic information, e.g. knowledge bases. After the completion of the course you should be able to:

• explain differences between text, semi-structured and structured data, data models and knowledge-based data; further, given a 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
• represent a given semi-structured data set using an object-oriented data model
• describe the main principles of NoSQL databases
• describe the main principles of knowledge bases
• design, implement and use a knowledge base represented using OWL
• describe methods and difficulties for data integration

Content

• Information retrieval for text; Models, evaluation, query languages, query operations, text operations, indexing and search.
• Semi-structured data: representation of semi-structured data using XML and RDF.
• Data models: NoSQL and XML databases, XQuery, XML Schema, RDF.
• Knowledge bases: ontologies, description logics, OWL, query languages and knowledge deduction for knowledge bases.
• Data integration.