CS2125 Paper Review Form - Winter 2018

Reviewer: Lobna AbuSerrieh	

Paper Title:Mega Modelling for Big Data Analysis

Author(s): Ceri, S., Della Valle, E., Pedreschi, D., Trasarti, R. 

1) Is the paper technically correct?
 [ ] Yes
 [X] Mostly (minor flaws, but mostly solid)
 [ ] No

2) Originality
 [ ] Very good (very novel, trailblazing work)
 [ ] Good 
 [X] Marginal (very incremental)
 [ ] Poor (little or nothing that is new)

3) Technical Depth
 [ ] Very good (comparable to best conference papers)
 [X] Good (comparable to typical conference papers)
 [ ] Marginal depth
 [ ] Little or no depth

4) Impact/Significance
 [ ] Very significant
 [ ] Significant
 [X] Marginal significance.
 [ ] Little or no significance.

5) Presentation
 [ ] Very well written
 [X] Generally well written
 [ ] Readable
 [ ] Needs considerable work
 [ ] Unacceptably bad

6) Overall Rating
 [ ] Strong accept (award quality)
 [X] Accept (high quality - would argue for acceptance)
 [ ] Weak Accept (borderline, but lean towards acceptance)
 [ ] Weak Reject (not sure why this paper was published)


7) Summary of the paper's main contribution and rationale
   for your recommendation. (1-2 paragraphs)

The paper represents the mega modeling concept and its composition abstractions. It articulates the use of mega modeling in big-data analytical processes. It allow the reader to easily understand the main foundations.  It clearly shows the phases of Mega module computations (data preparation, data analysis, data evaluation), Then how these three phases lead to define two standard inspection points.
The paper brings the light to design a top down recursive approach over big data, it demonstrates the two types of the abstraction compositions, General purpose abstraction composition (Pipeline decomposition, Parallel decomposition, or Map-reduce decomposition), and Specific abstraction composition (what-if control, Drift control, or component-based graph decomposition).
Finally to clarify all these concepts the authors demonstrated some examples for different composition abstractions.

8) List 1-3 strengths of the paper.  (1-2 sentences each,
identified as S1, S2, S3.)

S1: The paper presentation is clear; the ideas are sequential and easily represented.
S2: Examples are effectively used and demonstrated.
S3: Authors assumed that each data to be conformant using GAV mapping and they did not rely on any specific mega schema syntax, adds more accuracy and avoids conflicts, especially with the existence of many data conversion tools.

9) List 1-3 weaknesses of the paper (1-2 sentences each,
identified as W1, W2, W3.)

W1: The authors mentioned that their objective is to raise the interest of using big data processing of model composition and its reuse, while they did not provide any proposal or guideline on how to reuse the composition abstractions.