Myths and Challenges in Knowledge Extraction and Big Data Analysis

For centuries, science (in German “Wissenschaft”) has aimed to create (“schaften”) new knowledge (“Wissen”) from the observation of physical phenomena, their modelling, and empirical validation.

Recently, a new source of knowledge has emerged: not (only) the physical world any more, but the virtual world, namely the Web with its ever-growing stream of data materialized in the form of social network chattering, content produced on demand by crowds of people, messages exchanged among interlinked devices in the Internet of Things. The knowledge we may find there can be dispersed, informal, contradicting, unsubstantiated and ephemeral today, while already tomorrow it may be commonly accepted.

Picture2The challenge is once again to capture and create consolidated knowledge that is new, has not been formalized yet in existing knowledge bases, and is buried inside a big, moving target (the live stream of online data).

The myth is that existing tools (spanning fields like semantic web, machine learning, statistics, NLP, and so on) suffice to the objective. While this may still be far from true, some existing approaches are actually addressing the problem and provide preliminary insights into the possibilities that successful attempts may lead to.

I gave a few keynote speeches on this matter (at ICEIS, KDWEB,…), and I also use this argument as a motivating class in academic courses for letting students understand how crucial is to focus on the problems related to big data modeling and analysis. The talk, reported in the slides below, explores through real industrial use cases, the mixed realistic-utopian domain of data analysis and knowledge extraction and reports on some tools and cases where digital and physical world have brought together for better understanding our society.

The presentation is available on SlideShare and are reported here below:

Model-driven Development of User Interfaces for IoT via Domain-specific Components & Patterns

This is the summary of a joint contribution with Eric Umuhoza to ICEIS 2017 on Model-driven Development of User Interfaces for IoT via Domain-specific Components & Patterns.
Internet of Things technologies and applications are evolving and continuously gaining traction in all fields and environments, including homes, cities, services, industry and commercial enterprises. However, still many problems need to be addressed.
For instance, the IoT vision is mainly focused on the technological and infrastructure aspect, and on the management and analysis of the huge amount of generated data, while so far the development of front-end and user interfaces for IoT has not played a relevant role in research.
On the contrary, we believe that user interfaces in the IoT ecosystem they can play a key role in the acceptance of solutions by final adopters.
In this paper we present a model-driven approach to the design of IoT interfaces, by defining a specific visual design language and design patterns for IoT applications, and we show them at work. The language we propose is defined as an extension of the OMG standard language called IFML.

The slides of this talk are available online on Slideshare as usual: