1. Distributed Database Management Systems

2. Reading Textbook: Ch. 4 Textbook: Ch. 4 FarkasCSCE 824 - Spring 20112

3. Design Issues Placing of data and programs (DBMS and application) Placing of data and programs (DBMS and application) Network issues Network issues FarkasCSCE 824 - Spring 20113

4. Level of Sharing No sharing No sharing Data sharing Data sharing Data and program sharing Data and program sharing FarkasCSCE 824 - Spring 20114 Heterogeneous environment!

5. Top-Down Design Global Conceptual schema  distribution Global Conceptual schema  distribution –Fragmentation –Replication –Allocation Figure 3.2 Figure 3.2 FarkasCSCE 824 - Spring 20115

6. Correctness of Fragmentation 1. Completeness: F R ={R 1, …, R n } 2. Reconstruction: R=  R i,  R i  R 3. Disjointness: –Horizontal: does not  d j  R i such that d j  R k where k  i –Vertical: same as horizontal for non- primary key attributes FarkasCSCE 824 - Spring 20116 1&2: Lossless-join (normalization)

7. Data Directory Global vs. local conceptual schemas Global vs. local conceptual schemas –How to search? –Where to store? –Single vs. multiple copies? FarkasCSCE 824 - Spring 20117

8. Current Research Allocation: new requirements, technology, etc. Allocation: new requirements, technology, etc. Where to store the fragments? Where to store the fragments? Dynamic environment Dynamic environment –Usage pattern –Application characteristics –Network changes –Security FarkasCSCE 824 - Spring 20118

9. Bottom-Up Approach Multi-database systems Multi-database systems How to integrate them into 1 database? How to integrate them into 1 database? –Interoperability FarkasCSCE 824 - Spring 20119

10. Database Integration Physical integration Physical integration –Materialized database: data warehouses –Extract-transform-load (ETL) tools Logical integration Logical integration –Virtual (not materialized) integration –Enterprise Information Integration FarkasCSCE 824 - Spring 201110

11. Data Warehouses On-line Analytical Processing (OLAP) applications: On-line Analytical Processing (OLAP) applications: –Decision support systems –Trend analysis and forecasting Complex queries, large databases Complex queries, large databases Materialized view maintanence Materialized view maintanence FarkasCSCE 824 - Spring 201111

12. Logical Integration No materialized global database No materialized global database Virtual integration: data remains at the local (operational) databases Virtual integration: data remains at the local (operational) databases Global conceptual schema may not contain everything from local schemas Global conceptual schema may not contain everything from local schemas Autonomous and heterogeneous local systems Autonomous and heterogeneous local systems FarkasCSCE 824 - Spring 201112

13. Bottom-Up Design Global Conceptual Schema (GCS or mediated schema) Global Conceptual Schema (GCS or mediated schema) –Defined first: local conceptual schemas (LCS) are mapped to GCS –Defined during the integration of the LCSs and develop the corresponding mappings from LCSs to the GCS FarkasCSCE 824 - Spring 201113

14. GCS Defined First Local-as-view (LAV) systems Local-as-view (LAV) systems –Each LCS is treated as a view over the GCS –Query results: constrained to the objects in the local DBs while the GCS definition may be richer –Potential incomplete answers Global-as-view GCS is defined as a set of views over the LCSs Global-as-view GCS is defined as a set of views over the LCSs –View definition defines how to derive elements of the GCS –Query results: constrained to the GCS while the local DBs might be richer FarkasCSCE 824 - Spring 201114

15. Design Tasks Schema translation Schema translation Schema generation Schema generation Figure 4.3 Figure 4.3 FarkasCSCE 824 - Spring 201115

16. Intermediate Canonical Representation Expressive to incorporate all concepts in the local databases Expressive to incorporate all concepts in the local databases Simple, intuitive, practical, etc. Simple, intuitive, practical, etc. Example: E/R model, relational model, graph/tree models, etc. Example: E/R model, relational model, graph/tree models, etc. Tools Tools FarkasCSCE 824 - Spring 201116

17. Schema Generation Schema matching: syntax and semantics Schema matching: syntax and semantics Integration of common schema elements Integration of common schema elements Schema mapping Schema mapping See example 4.1, 4.2 See example 4.1, 4.2 FarkasCSCE 824 - Spring 201117

18. Schema Matching Defined or discovered (e.g., web data) Defined or discovered (e.g., web data) Rules: Rules: –Correspondence between 2 elements –Predicate whether the correspondence holds or not –Similarity value between the 2 elements FarkasCSCE 824 - Spring 201118

19. Finding Correspondence Difficult process due to schema heterogeneity Difficult process due to schema heterogeneity Can be automated? Can be automated? –Insufficient schema and instance information –Unavailability of schema documentation –Subjectivity of matching FarkasCSCE 824 - Spring 201119

20. Matching Algorithm Issues Schema vs. instance matching Schema vs. instance matching –Concept match –Data instance: semantic inconsistencies Element-level vs. structure-level mapping Element-level vs. structure-level mapping –Element name  semantics –Multiple attribute mapping? Matching cardinality Matching cardinality –One-to-one, one-to-many, many-to-many FarkasCSCE 824 - Spring 201120

21. Semantic Schema Heterogeneity Semantic: meaning, interpretation, and intended use of data Semantic: meaning, interpretation, and intended use of data –Synonyms, homonyms, hypernyms –Different ontologies –Imprecise wording FarkasCSCE 824 - Spring 201121

22. Structural Schema Heterogeneity –Type conflict: attribute vs. entity –Dependency conflict: mapping cardinality inconsistencies –Key conflict: different primary keys –Behavioral conflict: modeling assumptions, e.g., referential integrity, deletion, etc. Farkas CSCE 824 - Spring 2011 22

23. Schema Integration Binary Binary N-ary N-ary FarkasCSCE 824 - Spring 201123

24. Schema Mapping How the data from local databases can be mapped to GCS How the data from local databases can be mapped to GCS Mapping creating Mapping creating Mapping maintanence Mapping maintanence FarkasCSCE 824 - Spring 201124

25. Mapping Creation Input: LCS, GCS, M (schema matches) Input: LCS, GCS, M (schema matches) Output: Q={Q 1, …, Q k } such that Output: Q={Q 1, …, Q k } such that –DB GCS =  Q(DB CLS ) FarkasCSCE 824 - Spring 201125

26. Security Objectives Confidentiality Confidentiality Integrity Integrity Availability Availability FarkasCSCE 824 - Spring 201126

27. Question 1 How distributed databases impact the security objectives? How distributed databases impact the security objectives? –Confidentiality in traditional vs. distributed DBs –Integrity in traditional vs. distributed DBs –Availability in traditional vs. distributed DBs FarkasCSCE 824 - Spring 201127

28. Integrity Correctness criteria Correctness criteria –Top-down design –Bottom-up design FarkasCSCE 824 - Spring 201128

29. Availability What are the issues related to availability when dealing with What are the issues related to availability when dealing with –Top-down design –Bottom-up design FarkasCSCE 824 - Spring 201129

30. Confidentiality (will be covered in 2 nd part of semester but…) (will be covered in 2 nd part of semester but…) Centralized vs. distributed security policy Centralized vs. distributed security policy –Top-down design –Bottom-up design FarkasCSCE 824 - Spring 201130

31. FarkasCSCE 824 - Spring 201131 Next Class Semantics-based Database Integration