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Advanced Data Models and Databases, 6 credits
Datamodeller och databaser, avancerad kurs, 6 hp
TDDD43
Main field of study
Information Technology Computer Science and Engineering Computer ScienceCourse level
Second cycleCourse type
Programme courseExaminer
Patrick LambrixDirector of studies or equivalent
Patrick LambrixEducation components
Preliminary scheduled hours: 42 hRecommended self-study hours: 118 h
Available for exchange students
YesECV = Elective / Compulsory / Voluntary
Main field of study
Information Technology, Computer Science and Engineering, Computer ScienceCourse level
Second cycleAdvancement level
A1XCourse offered for
- Computer Science, Master's Programme
- Industrial Engineering and Management - International, M Sc in Engineering
- Industrial Engineering and Management, M Sc in Engineering
- Computer Science and Software Engineering, M Sc in Engineering
- Computer Science and Engineering, M Sc in Engineering
- Information Technology, M Sc in Engineering
- Computer Science, Master's programme
Entry requirements
Note: Admission requirements for non-programme students usually also include admission requirements for the programme and threshold requirements for progression within the programme, or corresponding.
Prerequisites
Programming, Databases.Intended learning outcomes
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
Course 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.
Teaching and working methods
The course consists of lectures, laboratory work and a project. 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.
Examination
| UPG1 | Voluntary assignment | 0 credits | U, G |
| LAB1 | Laboratory work | 3 credits | U, G |
| TEN1 | Written examination | 3 credits | U, 3, 4, 5 |
Grades
Four-grade scale, LiU, U, 3, 4, 5Department
Institutionen för datavetenskapDirector of Studies or equivalent
Patrick LambrixExaminer
Patrick LambrixCourse website and other links
http://www.ida.liu.se/~TDDD43/index.en.shtmlEducation components
Preliminary scheduled hours: 42 hRecommended self-study hours: 118 h
Course literature
Additional literature
Articles
Articles
Other
| Code | Name | Scope | Grading scale |
|---|---|---|---|
| UPG1 | Voluntary assignment | 0 credits | U, G |
| LAB1 | Laboratory work | 3 credits | U, G |
| TEN1 | Written examination | 3 credits | U, 3, 4, 5 |
Regulations (apply to LiU in its entirety)
The university is a government agency whose operations are regulated by legislation and ordinances, which include the Higher Education Act and the Higher Education Ordinance. In addition to legislation and ordinances, operations are subject to several policy documents. The Linköping University rule book collects currently valid decisions of a regulatory nature taken by the university board, the vice-chancellor and faculty/department boards.
LiU’s rule book for education at first-cycle and second-cycle levels is available at http://styrdokument.liu.se/Regelsamling/Innehall/Utbildning_pa_grund-_och_avancerad_niva.
Additional literature
Articles
Articles
Other
Note: The course matrix might contain more information in Swedish.
I = Introduce, U = Teach, A = Utilize
| I | U | A | Modules | Comment | ||
|---|---|---|---|---|---|---|
| 1. DISCIPLINARY KNOWLEDGE AND REASONING | ||||||
| 1.1 Knowledge of underlying mathematics and science (G1X level) |
|
|
X
|
|||
| 1.2 Fundamental engineering knowledge (G1X level) |
|
X
|
X
|
TEN1
|
||
| 1.3 Further knowledge, methods, and tools in one or several subjects in engineering or natural science (G2X level) |
|
X
|
X
|
LAB1
TEN1
|
||
| 1.4 Advanced knowledge, methods, and tools in one or several subjects in engineering or natural sciences (A1X level) |
|
|
|
|||
| 1.5 Insight into current research and development work |
|
|
|
|||
| 2. PERSONAL AND PROFESSIONAL SKILLS AND ATTRIBUTES | ||||||
| 2.1 Analytical reasoning and problem solving |
|
X
|
X
|
LAB1
TEN1
|
||
| 2.2 Experimentation, investigation, and knowledge discovery |
|
X
|
X
|
LAB1
|
||
| 2.3 System thinking |
|
X
|
X
|
LAB1
TEN1
|
||
| 2.4 Attitudes, thought, and learning |
|
X
|
X
|
LAB1
TEN1
|
||
| 2.5 Ethics, equity, and other responsibilities |
X
|
|
|
LAB1
|
||
| 3. INTERPERSONAL SKILLS: TEAMWORK AND COMMUNICATION | ||||||
| 3.1 Teamwork |
|
|
X
|
LAB1
|
||
| 3.2 Communications |
|
|
X
|
LAB1
|
||
| 3.3 Communication in foreign languages |
|
|
|
|||
| 4. CONCEIVING, DESIGNING, IMPLEMENTING AND OPERATING SYSTEMS IN THE ENTERPRISE, SOCIETAL AND ENVIRONMENTAL CONTEXT | ||||||
| 4.1 External, societal, and environmental context |
|
|
|
|||
| 4.2 Enterprise and business context |
|
|
|
|||
| 4.3 Conceiving, system engineering and management |
|
X
|
X
|
LAB1
TEN1
|
||
| 4.4 Designing |
|
X
|
X
|
LAB1
TEN1
|
||
| 4.5 Implementing |
|
X
|
X
|
LAB1
|
||
| 4.6 Operating |
|
|
|
|||
| 5. PLANNING, EXECUTION AND PRESENTATION OF RESEARCH DEVELOPMENT PROJECTS WITH RESPECT TO SCIENTIFIC AND SOCIETAL NEEDS AND REQUIREMENTS | ||||||
| 5.1 Societal conditions, including economic, social, and ecological aspects of sustainable development for knowledge development |
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| 5.2 Economic conditions for knowledge development |
|
|
|
|||
| 5.3 Identification of needs, structuring and planning of research or development projects |
|
|
|
|||
| 5.4 Execution of research or development projects |
X
|
|
|
LAB1
|
||
| 5.5 Presentation and evaluation of research or development projects |
X
|
|
|
LAB1
|
||
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