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Breaking the Chains: On Declarative Data Analysis and Data Independence in the Big Data Era

Summary: Argues that big-data systems’ lack of data independence and declarative specifications forces data scientists to hand-tune analyses for data and execution environments, hindering broad adoption. Calls for declarative analytics abstractions to restore database-style usability. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
11054
Venue
VLDB
Year
2014
Pagerank
5.2175099e-05
Overall Rank
9,801 | 32.76%
DOI
10.14778/2733004.2733075

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{markl_vldb14,
        title = {{Breaking the Chains: On Declarative Data Analysis and Data Independence in the Big Data Era}},
        author = {Markl, Volker},
        journal = {PVLDB},
        series = {{VLDB} '14},
        volume = {7},
        number = {13},
        pages = {1730},
        doi = {10.14778/2733004.2733075},
        url = {https://doi.org/10.14778/2733004.2733075},
        year = {2014}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
4,049 Resource Elasticity for Large-Scale Machine Learning 2015 SIGMOD 6.9369379e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
2,196 Spinning Fast Iterative Data Flows 2012 VLDB 8.9704984e-05
2,354 epiC: an Extensible and Scalable System for Processing Big Data 2014 VLDB 8.7060612e-05
4,778 ASTERIX: An Open Source System for "Big Data" Management and Analysis (Demo) 2012 VLDB 6.5133529e-05
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