TY  - JOUR
A1  - Koßmann, Jan
A1  - Papenbrock, Thorsten
A1  - Naumann, Felix
T1  - Data dependencies for query optimization
BT  - a survey
JF  - The VLDB journal : the international journal on very large data bases / publ. on behalf of the VLDB Endowment
N2  - Effective query optimization is a core feature of any database management system. While most query optimization techniques make use of simple metadata, such as cardinalities and other basic statistics, other optimization techniques are based on more advanced metadata including data dependencies, such as functional, uniqueness, order, or inclusion dependencies. This survey provides an overview, intuitive descriptions, and classifications of query optimization and execution strategies that are enabled by data dependencies. We consider the most popular types of data dependencies and focus on optimization strategies that target the optimization of relational database queries. The survey supports database vendors to identify optimization opportunities as well as DBMS researchers to find related work and open research questions.
KW  - Query optimization
KW  - Query execution
KW  - Data dependencies
KW  - Data profiling
KW  - Unique column combinations
KW  - Functional dependencies
KW  - Order dependencies
KW  - Inclusion dependencies
KW  - Relational data
KW  - SQL
Y1  - 2021
U6  - https://doi.org/10.1007/s00778-021-00676-3
SN  - 1066-8888
SN  - 0949-877X
VL  - 31
IS  - 1
SP  - 1
EP  - 22
PB  - Springer
CY  - Berlin ; Heidelberg ; New York
ER  - 
TY  - JOUR
A1  - Kruse, Sebastian
A1  - Kaoudi, Zoi
A1  - Contreras-Rojas, Bertty
A1  - Chawla, Sanjay
A1  - Naumann, Felix
A1  - Quiane-Ruiz, Jorge-Arnulfo
T1  - RHEEMix in the data jungle
BT  - a cost-based optimizer for cross-platform systems
JF  - The VLDB Journal
N2  - Data analytics are moving beyond the limits of a single platform. In this paper, we present the cost-based optimizer of Rheem, an open-source cross-platform system that copes with these new requirements. The optimizer allocates the subtasks of data analytic tasks to the most suitable platforms. Our main contributions are: (i) a mechanism based on graph transformations to explore alternative execution strategies; (ii) a novel graph-based approach to determine efficient data movement plans among subtasks and platforms; and (iii) an efficient plan enumeration algorithm, based on a novel enumeration algebra. We extensively evaluate our optimizer under diverse real tasks. We show that our optimizer can perform tasks more than one order of magnitude faster when using multiple platforms than when using a single platform.
KW  - Cross-platform
KW  - Polystore
KW  - Query optimization
KW  - Data processing
Y1  - 2020
U6  - https://doi.org/10.1007/s00778-020-00612-x
SN  - 1066-8888
SN  - 0949-877X
VL  - 29
IS  - 6
SP  - 1287
EP  - 1310
PB  - Springer
CY  - Berlin
ER  -