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., alina@cs.ubbcluj.ro
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)
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
	
Bibliography	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.