Our era is undergoing profound and unexpected changes in the economic and social level. On the one hand, we talk about “knowledge society”, pointing out that the knowledge and domain expertise, know-how and human competencies are increasingly becoming the key to the growth of the economy and, more generally, of society. On the other hand, we talk about “digital society”, underlining the fact that virtually every complex human activity employs, directly or indirectly, infrastructure and IT services. An important consequence of this is that a huge amount of data, digitally available, is routinely generated and stored in the most disparate fields of application.
These “big data” implicitly provide the picture of the complex reality on which decisions are based. Of course, an individual cannot administer such a large amount of data, while modern computer systems can manage and analyze large amounts of data. On the other hand, these same actors, at present, are not able to take advantage of the complex knowledge, experience, and insight of the domain experts who are at the heart of decision-making. One, therefore, needs to face the dichotomy between human decisions based on knowledge but not data, and automatic and efficient analysis of data, but often irrelevant for decision-making.
Technology transfer aims to overcome the dichotomy highlighted above, enabling the public and private sectors to benefit concretely from the advanced skills developed by researchers to solve the complex problems of intelligent data management that are faced in the industry.
Unfortunately, technology transfer is not easy to accomplish: the various actors not only speak different “languages”, but also have different goals, and different ways to work. Formal methods, as well as software engineering research, have particular fields in which industry applies the identified results with ease, but in many cases processes that accompany innovation, namely Open Innovation, are needed. Moreover, many companies, especially small and medium enterprises, need further support to adopt the research results developed in academia.
Promoting a close cooperation among the actors involved, technology transfer activities also aim to foster the co-design and co-development (including the design and participatory development) of IT applications focused on the intelligent use of the data, whose innovativeness and complexity does not allow the independent realization by the various actors.
To this end, in this workshop, we intend to provide a shared panel that discusses technology transfer activities, to create an ecosystem of research and industrial partners to support the entire life-cycle of technology transfer.
Both field of Formal Methods and Software Engineering can benefit from a discussion of how to alleviate the obstacles between efficient and effective technology transfer activities between the various actors in the marketplace and in the research landscape.
Call for papers
The 1st workshoP On Technology transfEr iN sofTware engIneering And formaL methods (POTENTIAL) will be held in Trento, Italy, on September 4th, 2017. The workshop is co-located with SEFM 2017.
Our era is undergoing profound and unexpected changes in the economic and social level: we talk about “knowledge society”, pointing out that the knowledge and domain expertise, know-how, and human competencies are increasingly becoming the key to the growth of the economy and, more generally, of society. Furthermore, we talk about “digital society”, underlining the fact that, virtually, every complex human activity employs, directly or indirectly, infrastructure and services based on big data.
Data implicitly provide the picture of the complex reality on which decisions are based. Unfortunately, an individual cannot administer the large amount of data needed, and at present, is not able to take advantage of the complex knowledge, experience, and insight of the domain experts who are at the heart of decision-making. One, therefore, faces the dichotomy between human decisions based on knowledge but not data, and automatic, efficient data analysis techniques, but often irrelevant for decision-making.
Technology transfer of formal methods and software engineering can overcome the dichotomy highlighted above, and, in this workshop, we discuss technology transfer activities in the form of case studies, experience reports, failure and success stories, studies about the needs of the industry and research institutions to engage in productive technology transfer activities.
– Brainstorm and evaluate existing technology transfer activities
– Identify strengths, weaknesses, opportunities, and threats of technology transfer activities
– Develop best practices and a research agenda of how to overcome technology transfer obstacles.
Researchers, practitioners, tool developers and users, and technology transfer experts are welcome.
Topics of interest include (but not limited to)
16 June 2017 (Extended) July 31, 2017
07 July 2017 (Extended) August 4, 2017
Post-proceedings camera-ready version:
28 July 2017 (Extended) August 13, 2017
Workshop date: September 4th, 2017
We seek for both full and short papers. Full papers will be formatted using the Springer LNCS proceedings format with a page limit of 15 pages. Short paper should be limited to 8 pages. All accepted papers will be published in a joint LNCS
Barbara Russo, Free University of Bozen-Bolzano
Diego Calvanese, Free University of Bozen-Bolzano
Juan Antonio Rodriguez, Artificial Intelligence Research Institute (IIIA-CSIC)
Lissette Lemus, Artificial Intelligence Research Institute (IIIA-CSIC)
Matthew Yee-King, Goldsmiths College, University of London
Pablo Almajano, Narada Robotics S.L. and University of Barcelona
Patrick Ohnewein, IDM Südtirol
Peter Hopfgartner, Free University of Bozen-Bolzano
Tarek R. Besold, Digital Media Lab, TZI, University of Bremen
Wang Xiaofeng, Free University of Bozen-Bolzano