@article{DBLP:journals/corr/abs-1910-03118,
  added-at = {2021-02-19T12:10:28.000+0100},
  archiveprefix = {arXiv},
  author = {Mami, Mohamed Nadjib and Graux, Damien and Thakkar, Harsh and Scerri, Simon and Auer, S{\"{o}}ren and Lehmann, Jens},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://www.bibsonomy.org/bibtex/2304338a32a034b324e950fd208281ee9/soeren},
  eprint = {1910.03118},
  interhash = {0170504fdc71e3151f12c572082335f9},
  intrahash = {304338a32a034b324e950fd208281ee9},
  journal = {CoRR},
  keywords = {imported myown},
  timestamp = {2021-02-19T12:10:28.000+0100},
  title = {The Query Translation Landscape: a Survey},
  url = {http://arxiv.org/abs/1910.03118},
  volume = {abs/1910.03118},
  year = 2019
}

@article{journals/corr/abs-1910-03118,
  added-at = {2021-01-23T00:00:00.000+0100},
  author = {Mami, Mohamed Nadjib and Graux, Damien and Thakkar, Harsh and Scerri, Simon and Auer, Sören and Lehmann, Jens},
  biburl = {https://www.bibsonomy.org/bibtex/22ce35076a4298b0bca7c857686397873/dblp},
  ee = {http://arxiv.org/abs/1910.03118},
  interhash = {0170504fdc71e3151f12c572082335f9},
  intrahash = {2ce35076a4298b0bca7c857686397873},
  journal = {CoRR},
  keywords = {dblp},
  timestamp = {2024-04-08T23:15:24.000+0200},
  title = {The Query Translation Landscape: a Survey.},
  url = {http://dblp.uni-trier.de/db/journals/corr/corr1910.html#abs-1910-03118},
  volume = {abs/1910.03118},
  year = 2019
}

@misc{mami2019query,
  abstract = {Whereas the availability of data has seen a manyfold increase in past years,
its value can be only shown if the data variety is effectively tackled ---one
of the prominent Big Data challenges. The lack of data interoperability limits
the potential of its collective use for novel applications. Achieving
interoperability through the full transformation and integration of diverse
data structures remains an ideal that is hard, if not impossible, to achieve.
Instead, methods that can simultaneously interpret different types of data
available in different data structures and formats have been explored. On the
other hand, many query languages have been designed to enable users to interact
with the data, from relational, to object-oriented, to hierarchical, to the
multitude emerging NoSQL languages. Therefore, the interoperability issue could
be solved not by enforcing physical data transformation, but by looking at
techniques that are able to query heterogeneous sources using one uniform
language. Both industry and research communities have been keen to develop such
techniques, which require the translation of a chosen 'universal' query
language to the various data model specific query languages that make the
underlying data accessible. In this article, we survey more than forty query
translation methods and tools for popular query languages, and classify them
according to eight criteria. In particular, we study which query language is a
most suitable candidate for that 'universal' query language. Further, the
results enable us to discover the weakly addressed and unexplored translation
paths, to discover gaps and to learn lessons that can benefit future research
in the area.},
  added-at = {2020-01-23T15:13:08.000+0100},
  author = {Mami, Mohamed Nadjib and Graux, Damien and Thakkar, Harsh and Scerri, Simon and Auer, Sören and Lehmann, Jens},
  biburl = {https://www.bibsonomy.org/bibtex/24219b3f3fc1ecd36d85a3cb6a123c5a6/ch},
  interhash = {0170504fdc71e3151f12c572082335f9},
  intrahash = {4219b3f3fc1ecd36d85a3cb6a123c5a6},
  keywords = {},
  note = {cite arxiv:1910.03118Comment: 25 pages, 5 tables, 92 references},
  timestamp = {2020-01-23T15:13:08.000+0100},
  title = {The Query Translation Landscape: a Survey},
  url = {http://arxiv.org/abs/1910.03118},
  year = 2019
}