1. Query evaluation in database systems (DBMS): query evaluation plan, IO cost, CPU cost, physical and logical plans: physical design patterns, query tuning, performance measurement.
2. Random and sequential operations in memory/disk, classification of disks, RAID.
3. Physical implementation of DBMS: heap table/clustered table, indices: B-tree, hash table, bitmap index.
4. Performance of the data layer in an information systems.
5. Data paging in the data layer, row/column store, compression in DBMS. Special data types in DBMS.
6. Extension of DBMS for storage and querying of text documents and graphs.
7. Extension of DBMS for storage and querying of spatial data.
8. Extension of DBMS for storage and querying of images, videos, and streams.
9. Criticism of ACID, distributed DBMS, NoSQL: CAP theorem, eventually consistency.
10. Main representatives of NoSQL DBMS: key-value, document, and graph DBMS.
11. In-memory DBMS: features, representatives.
12. Join algorithms: nested loop join, hash join, merge join.
13. Data structures for single-dimensional point and range queries.
14. Data structures for multi-dimensional point and range queries.
Practices:
Practices follow topics of lectures, students will work on tasks defined for individual topics.
2. Random and sequential operations in memory/disk, classification of disks, RAID.
3. Physical implementation of DBMS: heap table/clustered table, indices: B-tree, hash table, bitmap index.
4. Performance of the data layer in an information systems.
5. Data paging in the data layer, row/column store, compression in DBMS. Special data types in DBMS.
6. Extension of DBMS for storage and querying of text documents and graphs.
7. Extension of DBMS for storage and querying of spatial data.
8. Extension of DBMS for storage and querying of images, videos, and streams.
9. Criticism of ACID, distributed DBMS, NoSQL: CAP theorem, eventually consistency.
10. Main representatives of NoSQL DBMS: key-value, document, and graph DBMS.
11. In-memory DBMS: features, representatives.
12. Join algorithms: nested loop join, hash join, merge join.
13. Data structures for single-dimensional point and range queries.
14. Data structures for multi-dimensional point and range queries.
Practices:
Practices follow topics of lectures, students will work on tasks defined for individual topics.