Lectures:
1. Relational data processing principles - query plan, plan rewritings, and optimization techniques
2. Cost-based optimization - statistics, cost-model
3. Index selection based on cost optimization
4. Query parameterization and MEMO structure
5. Environment - main-memory/persistent environment, L2 cache, SIMD operation, parallelization
6. Graph databases - shortest distance, centrality index computation
7. Spatial queries - range query and k nearest neighbors query in low dimensions
8. Spatial queries - k nearest neighbors in high dimension, approximate nearest neighbors (ANN)
9. Set merging - full-text, similarity search
10. Semi-structured data - twig query pattern
11. Filters
Exercises will be held in a PC lab. Exercises:
1. Query plans in relational databases - display and operator meaning
2. Influence of statistics on a query plan
3. Change of query plans with respect to physical structure change
4. Test of knowledge related to query plans
5. Basic types of queries in graph databases
6. Graph query processing
7. ANN
8. Inverted list merging
9. StackTree algorithm on a tree data
10. Filter
11. Presentation of a project
1. Relational data processing principles - query plan, plan rewritings, and optimization techniques
2. Cost-based optimization - statistics, cost-model
3. Index selection based on cost optimization
4. Query parameterization and MEMO structure
5. Environment - main-memory/persistent environment, L2 cache, SIMD operation, parallelization
6. Graph databases - shortest distance, centrality index computation
7. Spatial queries - range query and k nearest neighbors query in low dimensions
8. Spatial queries - k nearest neighbors in high dimension, approximate nearest neighbors (ANN)
9. Set merging - full-text, similarity search
10. Semi-structured data - twig query pattern
11. Filters
Exercises will be held in a PC lab. Exercises:
1. Query plans in relational databases - display and operator meaning
2. Influence of statistics on a query plan
3. Change of query plans with respect to physical structure change
4. Test of knowledge related to query plans
5. Basic types of queries in graph databases
6. Graph query processing
7. ANN
8. Inverted list merging
9. StackTree algorithm on a tree data
10. Filter
11. Presentation of a project