Presentation by Cedric Tedeshi, Associate Professor of Computer Science at the University of Rennes 1 (France), at the FogGuru Workshop on research methods in January 2019.

1. How to carry out
a bibliographic study
Cédric Tedeschi

2. Related work?
In a broad sense: « Works solving problems close to yours »
• Close by the context
– Deploying application on hierarchical Clouds optimally with machine learning
• Close by the model
– Cutting Sparse bipartite graphs: recent breakthroughs and applications
• Close by the technique
– A local greedy heuristic for data partitionning in distributed database
A local greedy heuristic to cut sparse bipartite graphs
for application deployment on a hierarchical Cloud

3. Related work?
« Great, let us scholar-google the keywords! »
• Deployment hierarchical clouds -> 1245 papers
• Sparse bipartite graphs -> 721 papers
• Local greedy heuristics -> 7654 papers (plus those not referenced)
A local greedy heuristic to cut sparse bipartite graphs
for application deployment on a hierarchical Cloud

4. Narrowing the relevant literature (1)
Do not open the PDF at first
– Check the title
• Keywords are meant to make you find the paper
• There are a lot of papers with very similar titles
• Keywords are not everything
– Check the venue
• conference or journal rank, consideration within the community
• IEEE / ACM
• Well identified community workshop
– Check the papers cited
• Some you already know?
• Their authors, their venue, etc.
– Check size /year
• Longer / newer versions?
• Superseded work? By the same authors, other authors?
– Read the abstract
• Same model?
• Same technique?
• Same context?

5. Narrowing the relevant literature (2)
• Discuss with your advisor to
– Pinpoint exactly what is your related work and
what is not
– Point relevant authors, relevant venues,
relevant…
– … and discard a lot of papers (9234 -> 24)

6. Now you can navigate…
and re-enlarge the scope
• Read your 24 remaining papers (or less)
– Start with recent papers
• They can help you discard other papers in their own RW
• They are more likely to represent the « state of the art »
– Trace back to fundamentals when needed
• Some works supersede others
• Some works are variations of more generic works
• You need to understand both
– the state of the art
– The fundamentals on which it relies
– Move forward
• What more recent papers cite the current paper?
• Start positioning your own work
– What has been already done
– What remains to be done

7. Reading papers…
• … is much more than reading
• Estimate / check / assess the paper’s
– Novelty
– Technical soundess and precision
– Validation
– Ease of reading and understanding
• Some very good ideas can be very badly presented
• Yet the idea is the main point… up to a certain point

8. Assessing novelty
• Do not get too fast at discarding papers
– Yes, other researchers did similar stuff…
– But we’ve been doing scheduling for 40 years
– Are you sure there is nothing new?
• Same assumptions as that other paper you read?
• Same technical approach?
• Same… everything?
• Every paper has its own contribution
– Even if not the one claimed by its authors
– Even if the contribution is that this already proposed idea is
actually a good one
– There are many ways to solve the same problems

9. Assessing technical soundness
• Is the modeling relevant?
– Are the assumptions realistic?
– Does it match some kind of reality?
– Does it actually model the problem described
informally?
• For (kind of) formal papers
– Is the algorithm formally and correctly presented
– Are the proofs provided correct and complete?
– In sufficient details so the contribution is perfectly
understood
– You can check the details if needed… or you can trust

10. Assessing validation
• Reproducibility?
– Where is the code?
– What platform was used?
– Is the experimental setup precise enough?
• Results
– Do they actually prove / show / suggest something
about the technical contribution?
– Do the authors comments help understanding the
results?
– Are the authors’ claims really backed by the curves
exhibited?

11. Positioning the read paper against
your own research (1)
• If you think they solve the same problem
– Same method?
– Same assumptions?
– A subproblem?
– A more global problem?
– In the same context?
– On the same platform?

12. Positioning the read paper against
your own research (2)
• If you really think they solve the same
problem
– Check their future work
– What remains to be done?

13. This training material is part of the FogGuru project that has received funding from the European
Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant
agreement No 765452. The information and views set out in this material are those of the author(s)
and do not necessarily reflect the official opinion of the European Union. Neither the European Union
institutions and bodies nor any person acting on their behalf may be held responsible for the use
which may be made of the information contained therein.