CS2125 Paper Review Form - Winter 2018

Reviewer: Or Aharoni

Paper Title: Mega-modeling for Big Data Analytics

Author(s): Stefano Ceri, Emanuele Della Valle, Dino Pedreschi, Roberto Trasarti

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

2) Originality
 [ ] Very good (very novel, trailblazing work)
 [x] Good 
 [ ] 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
 [x] Significant
 [ ] 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 authors of the paper presented an approach of creating models that can dealing 
with today's challenges of dealing with mass data, ex data gathered via social media, 
economic changes, and health industry, while providing real time information and 
patterns.  The solution that the paper has provided is Mega-modeling, which provides 
a combination of comprehensive theory and model flexibility via model construction, 
model search, model fitness evaluation, and model reuse. In overall, the paper has 
presented the Mega-modeling and patterns that could be taken while developing it. 

Mega-modeling dose provide the possibility to provide simple solution of providing 
real-time answers to a place to eat, via a rating and location, or the fastest 
directions from my location to complex solutions that involves data and teams from 
around the globe, that look at what - if analyses. The authors have stated that 
aim to make the first steps looking at Mega-modeling. They have provided descriptions
of Big Data Processing of gathering information, analysis of data, and evaluating the 
data.


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

S1 - The paper does come to solve a real issue. As the demand for and availability of 
information increases, there is a need for a way to go through the information and 
group the data in a way that we could understand.  

S2 - The authors have provided a good start for approaching the issue. Providing 
definitions and thought process.

S3 - Pisa carpool Mega-modeling application example has show that Mega-modeling 
is a useful analytical tool. It showed that 67% would benefit from carpooling in the 
city.  


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

W1 - I would have like to see how Mega-modeling could handle "bad" information. As the 
restaurant application, how would the modeling check if the review is from someone 
working in the restaurant, a competing restaurant, or multiple review if same visit. 

W2 - Would have liked to see more in-depth interaction between micro-macro decomposition 
and borders computation. This could help with understanding the scalability of Mega-modeling.