TY  - JOUR
A1  - Jiang, Lan
A1  - Naumann, Felix
T1  - Holistic primary key and foreign key detection
JF  - Journal of intelligent information systems : JIIS
N2  - Primary keys (PKs) and foreign keys (FKs) are important elements of relational schemata in various applications, such as query optimization and data integration. However, in many cases, these constraints are unknown or not documented. Detecting them manually is time-consuming and even infeasible in large-scale datasets. We study the problem of discovering primary keys and foreign keys automatically and propose an algorithm to detect both, namely Holistic Primary Key and Foreign Key Detection (HoPF). PKs and FKs are subsets of the sets of unique column combinations (UCCs) and inclusion dependencies (INDs), respectively, for which efficient discovery algorithms are known. Using score functions, our approach is able to effectively extract the true PKs and FKs from the vast sets of valid UCCs and INDs. Several pruning rules are employed to speed up the procedure. We evaluate precision and recall on three benchmarks and two real-world datasets. The results show that our method is able to retrieve on average 88% of all primary keys, and 91% of all foreign keys. We compare the performance of HoPF with two baseline approaches that both assume the existence of primary keys.
KW  - Data profiling application
KW  - Primary key
KW  - Foreign key
KW  - Database
KW  - management
Y1  - 2019
U6  - https://doi.org/10.1007/s10844-019-00562-z
SN  - 0925-9902
SN  - 1573-7675
VL  - 54
IS  - 3
SP  - 439
EP  - 461
PB  - Springer
CY  - Dordrecht
ER  - 
TY  - GEN
A1  - Kruse, Sebastian
A1  - Kaoudi, Zoi
A1  - Contreras-Rojas, Bertty
A1  - Chawla, Sanjay
A1  - Naumann, Felix
A1  - Quiané-Ruiz, Jorge-Arnulfo
T1  - RHEEMix in the data jungle
BT  - a cost-based optimizer for cross-platform systems
T2  - Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät
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.
T3  - Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät - 22 
KW  - cross-platform
KW  - polystore
KW  - query optimization
KW  - data processing
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-519443
IS  - 6
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  -