This is our perspective on the world: it’s all about modeling.
So, why is it that model-driven engineering is not taking over the whole technological and social eco-system?
Let me make the case that it is.
In the occasion of the 25th edition of the Italian Symposium of Database Systems (SEBD 2017) we (Stefano Ceri and I) have been asked to write a retrospective on the last years of database and systems research from our perspective, published in a dedicated volume by Springer. After some brainstorming, we agreed that it all boils down to this: modeling, modeling, modeling.
Long time ago, in the past century, the International DB Research Community used to meet for assessing new research directions, starting the meetings with 2-minutes gong shows to tell each one’s opinion and influencing follow-up discussion. Bruce Lindsay from IBM had just been quoted for his message:
There are 3 important things in data management: performance, performance, performance.
Stefano Ceri had a chance to speak out immediately after and to give a syntactically similar but semantically orthogonal message:
There are 3 important things in data management: modeling, modeling, modeling.
Data management is continuously evolving for serving the needs of an increasingly connected society. New challenges apply not only to systems and technology, but also to the models and abstractions for capturing new application requirements.
In our retrospective paper, we describe several models and abstractions which have been progressively designed to capture new forms of data-centered interactions in the last twenty five years – a period of huge changes due to the spreading of web-based applications and the increasingly relevant role of social interactions.
We initially focus on Web-based applications for individuals, then discuss applications among enterprises, and this is all about WebML and IFML; then we discuss how these applications may include rankings which are computed using services or using crowds, and this is related to our work on crowdsourcing (liquid query and crowdsearcher tool); we conclude with hints to a recent research discussing how social sources can be used for capturing emerging knowledge (the social knowledge extractor perspective and tooling).
All in all, modeling as a cognitive tool is all around us, and is growing in terms of potential impact thanks to formal cognification.
It’s also true that model-driven engineering is not necessarily the tool of choice for this to happen. Why? As technician, we always tend to blame the customer for not understanding our product. But maybe we should look into ourselves and the kind of tools (conceptual and technical) the MDE community is offering. I’m pretty sure we could find plenty of space for improvement.
Any idea on how to do this?