Babes-Bolyai University of Cluj-Napoca
Faculty of Mathematics and Computer Science
Study Cycle: Master

SUBJECT

Code
Subject
MIH1006 Advanced Database Topics
Section
Semester
Hours: C+S+L
Category
Type
Database
4
2+0+2
speciality
optional
Distributive Systems in Internet
4
2+0+2
speciality
optional
Teaching Staff in Charge
Lect. CÂMPAN Alina, Ph.D.,  alinacs.ubbcluj.ro
Lect. SABAU Andreea, Ph.D.,  deiushcs.ubbcluj.ro
Aims
The course assembles together several advanced database topics, as a supplement to other topics previously studied by the students (object-oriented databases, distributed databases, data mining). It aims:
- to introduce and familiarize students with some special database types (spatial, temporal, spatio-temporal, private and anonymous data), originated from particular application problems;
- to present these domains as important research and development areas in the database field;
- to help students to understand these domains by studying and developing practical relevant projects / applications.
Content
1. Introduction
- Application domains that requested special data organization and management
2. Spatial databases - data modeling
- Spatial data types (points, segments, regions) and space representation (raster and vectorial)
- Spatial data modeling (simplicial complexes, realm)
3. Spatial databases - spatial relationships and operations
- Spatial relationships (topological, directional and metric)
- Spatial operations; implementing spatial operations by using computational geometry algorithms
4. Querying spatial databases
- Access methods - indexes
- Query languages
5. Temporal databases - data modeling; temporal relationships
- Temporal concepts
- Temporal data conceptual modeling
- Temporal data logical modeling. Temporal normal forms
- Temporal relationships (topological and metric)
6. Querying temporal databases
- Temporal access methods - indexes
- Query languages
7. Spatio-temporal databases
- Spatio-temporal conceptual models
- Spatio-temporal logical models
- Querying spatio-temporal data (query types, spatio-temporal access methods)
8. Data security and privacy
- Protecting data secrecy and privacy
- Techniques for enforcing data secrecy and privacy (statistical db vs. data anonymity)
9. Statistical databases
- Inference channels and statistical queries
10. Data privacy and anonimity
- Basic concepts and techniques in data anonymity
- Information loss vs. data privacy loss
- Data anonymity models and anonymization algorithms
i. K-anonymity model
ii. P-sensitive k-anonymity model
iii. L-Diversity model
iv. (a,k)-anonymity model
v. Extended p-sensitive k-anonymity model
vi. Personalized privacy preservation
References
1. R. H. Guting, An Introduction to Spatial Database Systems, VLDB Journal,vol. 3, pp. 357-399 H. Samet, The Design and Analysis of Spatial Data Structures, Addison-Wesley, Reading, MA, 1990
2. C. S. Jensen, Temporal Database Management, http://www.cs.aau.dk/~csj/Thesis/
3. H. Gregersen, C. S. Jensen, Temporal Entity-Relationship Models - a Survey B. Salzberg, V. J. Tsotras, Comparison of Access Methods for Time-Evolving Data, ACM Comput. Surv., 31(2), 158-221, 1999
4. N. Pelekis, et al - Literature Review of Spatio-Temporal Database Models, The Knowledge Engineering Review Journal, 19(3), 235-274, 2005
5. Mohamed F. Mokbel, Thanaa M. Ghanem, Walid G. Aref, Spatio-temporal Access Methods, 2003, disponibil la http://citeseer.ist.psu.edu/mokbel03spatiotemporal.html
6. Samarati P. - Protecting Respondents Identities in Microdata Release, IEEE Transactions on Knowledge and Data Eng., Vol. 13, No. 6, 2001, 1010-1027
7. Sweeney L. - k-Anonymity: A Model for Protecting Privacy, Intl. Journal on Uncertainty, Fuzziness, and Knowledge-based Systems, Vol. 10, No. 5, 2002, 557 - 570
8. Sweeney L. - Achieving k-Anonymity Privacy Protection Using Generalization and Suppression, Intl. Journal on Uncertainty, Fuzziness, and Knowledge-based Systems, Vol. 10, No. 5, 2002, 571 - 588
9. Truta, T.M., Bindu, V. - Privacy Protection: p-Sensitive k-Anonymity Property, Workshop on Privacy Data Management, 22th IEEE Intl. Conf. of Data Eng., 2006
10. Campan, A., Truta, T.M. - Extended p-Sensitive k-Anonymity for Privacy Protection, Studia Universitatis Babes-Bolyai, Informatica, Vol. LI(2), pp. 19-30, 2006
Assessment
The activity ends with a written exam (grade E). During the semester, the students will prepare and present a theoretical report (grade R) and several practical / lab projects (grade P). The final grade is a weighted mean of the three grades mentioned above: Final Grade = 40%E + 25%R + 35%P. The students who will show considerable research abilities, involving into projects development and research results publication will be granted additional 10% score to the final grade. In order to successfully pass the exam, the final grade has to be at least 5.
Links: Syllabus for all subjects
Romanian version for this subject
Rtf format for this subject