Babes-Bolyai University of Cluj-Napoca
Faculty of Mathematics and Computer Science
Study Cycle: Master
SUBJECT
| MIH1006 | Advanced Database Topics |
| Teaching Staff in Charge |
Lect. SABAU Andreea, Ph.D., deiush cs.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, k-spaghetti, spatial constraint databases, etc.) 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 data 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 + 20%R + 40%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 |