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Resilient store: a heuristic-based data format selector for intermediate results

Author
Munir, R.; Romero, O.; Abello, A.; Bilalli, B.; Thiele, M.; Lehner, W.
Type of activity
Presentation of work at congresses
Name of edition
6th International Conference on Model and Data Engineering
Date of publication
2016
Presentation's date
2016-09
Book of congress proceedings
Model and Data Engineering - 6th International Conference, MEDI 2016, Proceedings
First page
42
Last page
56
DOI
https://doi.org/10.1007/978-3-319-45547-1_4 Open in new window
Repository
http://hdl.handle.net/2117/103258 Open in new window
URL
http://www.springer.com/gp/book/9783319455464 Open in new window
Abstract
Large-scale data analysis is an important activity in many organizations that typically requires the deployment of data-intensive workflows. As data is processed these workflows generate large intermediate results, which are typically pipelined from one operator to the following. However, if materialized, these results become reusable, hence, subsequent workflows need not recompute them. There are already many solutions that materialize intermediate results but all of them assume a fixed data fo...
Citation
Munir, R., Romero, O., Abello, A., Bilalli, B., Thiele, M., Lehner, W. "Resilient store: a heuristic-based data format selector for intermediate results". Almeria: 2016.
Keywords
Access patterns - Data format - Data fragmentation - HDFS - Intermediate results – Largescale data analysis - Rule-based heuristics - Work-flows, Digital storage
Group of research
DTIM - Database Technologies and lnformation Management Group
IMP - Information Modelling and Processing
inLab FIB
inSSIDE - integrated Software, Service, Information and Data Engineering

Participants

Attachments