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Scientific Data Management (HT2015)

Data exploration has been proclaimed as a new scientific paradigm. With rapid growth in the volume and variety of scientific data, scientists often have to overcome challenges in handling and integrating large data sets before the scientific questions can be addressed. There is also rapid growth in the volume and variety of data management solutions to choose from.

This course provides an overview of different ways to model data, different database management systems, and emerging technologies that support integration, exploration and sharing of scientific data.

Learning outcomes

After the course you are able to

  • compare different data models
  • discuss advantages and disadvantages of various non-relational data management solutions
  • describe Semantic Web technologies such as RDF, RDFS and ontologies.

Course main content

 

  • data models and data modelling, schemas, data independence, data and metadata
  • relational design choices, object-oriented databases, NoSQL databases (such as key-value databases, document databases, column-family stores, graph databases), federated systems
  • Semantic Web, Resource Description Framework (RDF), RDF Schema (RDFS), SPARQL, ontologies and tools

 

Pre-requisites

Basic knowledge of relational databases and the SQL query language is assumed. Even though these are not central to the present course, relational database management systems provide a point of reference for discussing non-relational data models and systems. There will be an opportunity to gain this basic knowledge during the preparatory week.

Literature

  • Lecture notes
  • Links to relevant research articles will be added later

Course schedule

Dates: 26-30 October 2015. Starting after lunch on Monday, and ending before lunch on Friday.

Location: Chalmers Technology University

More information

For more information and timetable please consult http://www.cse.chalmers.se/~kemp/graduate_courses/sdm2015/

Contact

Graham Kemp