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Big Data Provenance: Challenges and Implications for Benchmarking · Study

WorkBig Data Provenance: Challenges and Implications for Benchmarking

Boris Glavic2013

Work2013

Big Data Provenance: Challenges and Implications for Benchmarking

78 citations·0/0 files stored·Lecture notes in computer science

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Scientific Computing and Data Management Cloud Computing and Resource Management Advanced Database Systems and Queries

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2012Challenges and opportunities with big data 2011Putting lipstick on pig 2011Provenance for Generalized Map and Reduce Workflows

Cited by

2021Big data quality framework: a holistic approach to continuous quality management 2015Big Data Pre-processing: A Quality Framework 2016Big Data Storage 2016An Approach to Standalone Provenance Systems for Big Social Provenance Data 2016An Hybrid Approach to Quality Evaluation across Big Data Value Chain 2021Ethical Issues in Democratizing Digital Phenotypes and Machine Learning in the Next Generation of Digital Health Technologies 2017Exploring complex and big data 2020DMN4DQ: When data quality meets DMN

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2015Big data provenance: Challenges, state of the art and opportunitiesother · 2 shared references 2015Titian: Data Provenance Support in Spark.other · 2 shared references 2018A systematic review of provenance systemsreview · 2 shared references 2013Scalable lineage capture for debugging DISC analyticsother · 2 shared references 2013SubZero: A fine-grained lineage system for scientific databasesother · 2 shared references 2021LIMA: Fine-grained Lineage Tracing and Reuse in Machine Learning Systemsother · 2 shared references

Details

Year2013

Typeother

Published2013-12-18

VenueLecture notes in computer science

ISSN-L0302-9743

Languageen

Citations78

Files0 public / 0

ShelfScientific Computing and Data Management

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doi10.1007/978-3-642-53974-9_7

openalex_workW1543225912

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