Databases

Content

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1. Architecture of database systems , conceptual data modeling with the Entity-Relationship (ER) modeling language ( pdf , pptx )
2. The relational data model:
   Referential integrity, keys, foreign keys, functional dependencies (FDs), canonical mapping of entity types and relationships in the relational model, update, insertion and deletion anomalies ( pdf , pptx )
3. Relational schema design theory:
   Relational algebra as a query language, determination of candidate keys, FD closure, attribute closure, determination of relational schemas from FDs, canonical cover ( pdf , pptx )
4. Normal forms:
   Boyce-Codd normal form, lossless and dependency-preserving decomposition of relation schemas ( pdf , pptx )
5. Third normal form, synthesis of relational schemas, inclusion dependencies, multiple-value dependencies ( pdf , pptx )
6. SQL: Practical query languages and integrity conditions without consideration of the conceptual data model
   1. Part 1 ( pdf , pptx )
   2. Part 2 ( pdf , pptx )
7. Storage structures and database architecture:
   Characteristics of storage media, I/O complexity, DBMS architecture: external storage manager, pages, buffer manager, file manager, record layout on a page (row-wise, column-wise, mixed ( pdf , pptx )
8. Indexing techniques:
   ISAM index, B+-tree index ( pdf , pptx )
9. Query processing:
   Sort operator: two-way merging, block-by-block processing, selection trees, execution plans, compound operator: nested loops, block-by-block compound, index-based join, merge join, other operators: grouping and duplicate elimination, selection, projection ( pdf , pptx )
10. Query optimization:
    Cost metrics, estimation of result size and selectivity of operators, join optimization, physical plan properties, query rewriting ( pdf , pptx )
11. Transactions:
    ACID, anomalies, serializability, locks, 2-phase commit protocol, concurrency in index structures ( pdf , pptx )
12. Recovery:
    Shadow pages, write-ahead log, snapshot backups ( pdf , pptx )
13. Vector databases:
    Retrieval augmented generation (RAG) data, query language, multidimensional indexing, indexing for high-dimensional data ( pdf , pptx )