ADVANCED MODELS FOR DATA MANAGEMENT
- Overview
- Assessment methods
- Learning objectives
- Contents
- Bibliography
- Delivery method
- Teaching methods
- Contacts/Info
The student knows the topics forming a syllabus of a standard, introductory relational database course. Therefore, the student is knowledgeable about the relational data model, the relational algebra, the SQL query language and the conceptual design methodology.
Final written exam: the exam yields a final vote - expressed in 30 points - depending on the knowledge and the comprehension capabilities of the student about the topics of the syllabus. The written exam is formed by 4 questions, 2 related to theoretical topics and 2 exercises.
The student will acquire knowledge about advanced aspect concerning advanced data management models. In particular, extensions to the relational data model will be introduced (the object-relational data model). Other models will be introduced, like the semi-structured data models based on xml or graph-based. Furthermore, the corresponding query languages will be introduced. Therefore, the student will be able to frame a problem related to modeling/querying data, according to what exposed and to solve it. In the end, the student will be able to assess the quality of a database implemented with one the above-mentioned models. Equivalently, the student will be able to design and implement a database using such models and query them with the corresponding query languages, according to the design pre-requisites.
The first part of the course is on the object-relational data model:
1. complex data types in SQL:1999 (3 hr)
2. structured data types and hereditary data types in SQL (3 hr)
3. nested relations (3 hr)
4. collection types (3 hr)
The second part of the course is on the semi-structured data models based on XML.
1. XML data, SGML e HTML (2 hr)
2. DTD (3 hr)
3. XML Schema, simple and complex types (4 hr)
4. Relax NG and Schematron (3 hr)
5. XML Infoset and Document Object Model (3 hr)
6. introduction to the XQuery data model (2 hr)
7. XSLT (2 hr)
8. XPath (2 hr)
9. XQuery (3 hr)
The third part of the course is on graph data models, for example based on RDF.
1. how to describe resources in RDF (2 hr)
2. RDFS, RDF/XML (3 hr)
3. inference rules for RDF/RDFS (2 hr)
4. triplestores (2 hr)
5. Sparql (3 hr)
slides and notes distributed by the teacher, to be found on the university e-learning site.
The targets of the course will be achieved through frontal lectures summing up to 48 hrs.
student meeting upon agreement, via email.
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