Data compression: methods and theory
Data compression: methods and theory
Query evaluation techniques for large databases
ACM Computing Surveys (CSUR)
Database abstractions: aggregation and generalization
ACM Transactions on Database Systems (TODS)
A relational model of data for large shared data banks
Communications of the ACM
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Total
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
DBMSs on a Modern Processor: Where Does Time Go?
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
C-store: a column-oriented DBMS
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Generic database cost models for hierarchical memory systems
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Adaptive aggregation on chip multiprocessors
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
High Performance Parallel Database Processing and Grid Databases
High Performance Parallel Database Processing and Grid Databases
A common database approach for OLTP and OLAP using an in-memory column database
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
HYRISE: a main memory hybrid storage engine
Proceedings of the VLDB Endowment
Proceedings of the 21st ACM international conference on Information and knowledge management
Lazy data structure maintenance for main-memory analytics over sliding windows
Proceedings of the sixteenth international workshop on Data warehousing and OLAP
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Precise prediction of query execution performance is the basis for various database optimization strategies. With columnar in-memory databases, cost modeling changes in two dimensions: First, models for disk-based databases are not well-suited as the new bottleneck is main memory access. Second, the possibility to execute mixed workloads creates new challenges. For transactional and analytical queries with aggregation operations, memory access patterns and thus execution times vary significantly. This paper discusses the influences of data characteristics on aggregation operations and elevates not considered factors by existing cost model approaches. Further, we present benchmarks implemented and executed on a columnar in-memory research database to underline our assumptions.