- AutorIn
- Kai Herrmann Technische Universität Dresden, Fakultät für Informatik, Institut für Systemarchitektur, Lehrstuhl für Datenbanken
- Hannes VoigtTechnische Universität Dresden, Fakultät für Informatik, Institut für Systemarchitektur, Lehrstuhl für Datenbanken
- Wolfgang LehnerTechnische Universität Dresden, Fakultät für Informatik, Institut für Systemarchitektur, Lehrstuhl für Datenbanken
- Titel
- Online horizontal partitioning of heterogeneous data
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-729230
- Quellenangabe
- Information Technology Erscheinungsort: Berlin
Verlag: Karger
Erscheinungsjahr: 2014
Jahrgang: 56
Heft: 1
Seiten: 4-12
E-ISSN: 2196-7032 - Erstveröffentlichung
- 2014
- Abstract (EN)
- In an increasing number of use cases, databases face the challenge of managing heterogeneous data. Heterogeneous data is characterized by a quickly evolving variety of entities without a common set of attributes. These entities do not show enough regularity to be captured in a traditional database schema. A common solution is to centralize the diverse entities in a universal table. Usually, this leads to a very sparse table. Although today’s techniques allow efficient storage of sparse universal tables, query efficiency is still a problem. Queries that address only a subset of attributes have to read the whole universal table includingmany irrelevant entities. Asolution is to use a partitioning of the table, which allows pruning partitions of irrelevant entities before they are touched. Creating and maintaining such a partitioning manually is very laborious or even infeasible, due to the enormous complexity. Thus an autonomous solution is desirable. In this article, we define the Online Partitioning Problem for heterogeneous data. We sketch how an optimal solution for this problem can be determined based on hypergraph partitioning. Although it leads to the optimal partitioning, the hypergraph approach is inappropriate for an implementation in a database system. We present Cinderella, an autonomous online algorithm for horizontal partitioning of heterogeneous entities in universal tables. Cinderella is designed to keep its overhead low by operating online; it incrementally assigns entities to partition while they are touched anyway duringmodifications. This enables a reasonable physical database design at runtime instead of static modeling.
- Andere Ausgabe
- Link zum Artikel der zuerst in der Zeitschrift 'Information Technology ' erschienen ist
DOI: 10.1515/itit-2014-1015 - Freie Schlagwörter (DE)
- ACM CCS, Informationssysteme, Datenbankentwurf und -modelle, Physikalische Datenmodelle, Autonome Datenbankverwaltung
- Freie Schlagwörter (EN)
- ACM CCS, Information systems, Database design and models, Physical data models, Autonomous database administration
- Klassifikation (DDC)
- 004
- 620
- Verlag
- De Gruyter, Berlin
- Version / Begutachtungsstatus
- publizierte Version / Verlagsversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-729230
- Veröffentlichungsdatum Qucosa
- 30.11.2020
- Dokumenttyp
- Artikel
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
- https://rightsstatements.org/files/buttons/InC.dark-white-interior.svg/