Tag: data science

Modeling and Analyzing Engagement in Social Network Challenges

by Marco Brambilla, posted in social network, social network analysis

Within a completely new line of research, we are exploring the power of modeling for human behaviour analysis, especially within social networks and/or in occasion of large scale live events. Participation to challenges within social networks is a very effective instrument for promoting a brand or event and therefore it is regarded as an excellent marketing tool.
Our first reasearch has been published in November 2016 at WISE Conference, covering the analysis of user engagement within social network challenges.
In this paper, we take the challenge organizer’s perspective, and we study how to raise the
engagement of players in challenges where the players are stimulated to
create and evaluate content, thereby indirectly raising the awareness about the brand or event itself. Slides are available on slideshare:

We illustrate a comprehensive model of the actions and strategies that can be exploited for progressively boosting the social engagement during the challenge evolution. The model studies the organizer-driven management of interactions among players, and evaluates
the effectiveness of each action in light of several other factors (time, repetition, third party actions, interplay between different social networks, and so on).
We evaluate the model through a set of experiment upon a real case, the YourExpo2015 challenge. Overall, our experiments lasted 9 weeks and engaged around 800,000  users on two different social platforms; our quantitative analysis assesses the validity of the model.

The paper is published by Springer here.

cross-platform_pdf

 

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How Mature is of Model-driven Engineering as an Engineering Discipline? – Panel with Manfred Broy, Paola Inverardi and Lionel Briand

by Marco Brambilla, posted in modeling

Within ModelsWard 2016, just after the opening speech I gave on February 19 in Rome, the opening panel has been about the current maturity of model-driven engineering. I also hosted a poll on twitter on this matter (results are available in this other post).  

I’m happy the panelists raised several issues I pointed out myself in the introduction to the conference: as software modelling scientists, we are facing big challenges nowadays, as the focus of modelling is shifting, due to the fact that now software is more and more pervasive, in fields like IoT, social network and social media, personal and wearable devices, and so on.

Panel included the keynote speakers of the conference: Manfred Broy, Paola Inverardi and Lionel Briand, three well known names in the Software Engineering and Modeling community.

Manfred Broy highlighted:

  • there is a different between scientific maturity and practical maturity. Sometimes, the latter in companies is far beyond the former.
  • a truck company in Germany has been practicing modelling for years, and now has this take on the world: whatever is not in the models, doesn’t exist
  • The current challenges are about how to model cyber-physical systems
  • The flow of model must be clarified: traceability, refinement, model integration are crucial. You must grant syntactic and semantic coherence
  • You also need a coherent infrastructure of tools and artefacts, that grants logic integration. You cannot obtain coherence of models without coherence of tools.
  • You need a lot of automation, otherwise you won’t get practical maturity. This doesn’t mean to have end-to-end, or round-trip complete model transformations, but you need to push automaton as much as possible

Lionel Briand clarified that:

  • by definition, engineering underpins deep mathematical background as a foundation and implies application of the scientific method to solving problems
  • maturity can be evaluated in terms of: how much math underpinning is foundational, how many standards and tools exist and are used, whether the scientific approach is used
  •  Tools, methods, engineers, and scale of MDE are increasing (aka. MDE is increasingly more difficult to avoid)
Paola Inverardi recalled a position by Jean Bezivin:
  • we need to split Domain Engineering (where the problem is) and Support Engineering (where the solution will be)
  • MDE is the application of modelling principles and tools to any engineering field
  • So: is actually SOFTWARE the main field of interest of model-driven engineering?
  • In the modern interpretation of life, covering from smart cities to embedded, wearable, and cyber-physical systems, is the border between the environment and the system still relevant?
  • In the future we will need to rely less and less on the “creativity” of engineers when building models, and more and more on the scientific/ quantitative/ empirical methods for building models

The debate obviously stirred around this aspects, starting from Bran Selic who asked a very simple question:

Isn’t it the case that the real problem is about the word “modeling”? In any other fields (architecture, mechanics, physics) modelling is implicit and obvious. Why not in our community? At the end, what we want to achieve is to raise abstraction and increase automation, nothing else.

Other issues have been raised too:

  • why is there so much difference in attitude towards modelling between Europe and US?
  • what’s the role of notations and standards in the success / failure of MDE?

What’s your take on this issue?
Feel free to share your thoughts here or on Twitter, mentioning me  (@MarcoBrambi).
AND:
Respond to my poll on twitter!

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