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:

Spark-based Big Data Analysis of Semantic IFML Models and Web Logs for Enhanced User Behavior Analytics

I’d like to report on our demonstration paper at WWW 2017, focusing on Spark-based Big Data Analysis of  Semantic IFML Models and Web Logs  for Enhanced User Behavior Analytics.

The motivation of the work is that  no approaches exist for merging web log analysis and statistics with information about the Web application structure, content and semantics. Indeed, basic Web analytics tools are widespread and provide statistics about Web site navigation at the syntactic level only: they analyze the user interaction at page level in terms of page views, entry and landing page, page views per visit, and so on. Unfortunately, those tools do not provide precise statistics neither about the content and semantics of the visited pages, nor about the actual reactions of the users to the actual content (instances) he is shown.

With our work we demonstrate the advantages of combining Web application models with runtime navigation logs, at the purpose of deepening the understanding of users behaviour.

We propose a model-driven approach that combines user interaction modeling (based on the IFML standard), full code generation of the designed application, user tracking at runtime through logging of runtime component execution and user activities, integration with page content details, generation of integrated schema-less data streams, and application of large-scale analytics and visualization tools for big data, by applying data visualization techniques that build direct representation of statistics on the IFML visual models of the Web application.

The paper describing the approach is available in the WWW 2017 proceedings.

The video of the demo is available on YouTube:

Social Media Behaviour during Live Events: the Milano Fashion Week #MFW case

Social media are getting more and more  important in the context of live events, such as fairs, exhibits, festivals, concerts, and so on,  as they play an essential role in communicating them to  fans, interest groups, and the general population. These kinds of events are geo-localized within a city or territory and are scheduled within a public calendar.

Together with the people in the Fashion in Process group of Politecnico di Milano, we studied the impact on social media of a specific scenario, the Milano Fashion Week (MFW), which is an important event in Milano for the whole fashion business.

We presented this work at the Location and the Web workshop co-located with the WWW 2017 Conference in Perth, Australia.

We focus our attention on the spreading of social content  in space, measuring the spreading of the event propagation in space. We build different clusters of fashion brands, we characterize several features of propagation in space and we correlate them to the popularity of the brand and temporal propagation.

We show that the clusters along space, time and popularity dimensions are loosely correlated, and therefore trying to  understand the dynamics of the events only based on popularity  aspects would not be appropriate.

The paper PDF is available as open access PDF online on the WWW 2017 Conference web site. You can download it here.

The PowerPoint presentation is available on SlideShare.

When a Smart City gets Personal

When people talk about smart cities, the tendency is to think about them in a technology-oriented or sociology-oriented manner.

However, smart cities are the places where we leave and work everyday now.

Here is a very broad perspective (in Italian) about the experience of big data analysis and smart city instrumentation for the town of Como, in Italy: an experience on how phone calls, mobility data, social media, people counters can contribute to take and evaluate decisions.


You can read it on my Medium channel.

View story at

The role of Big Data in Banks

I was listening at R. Martin Chavez, Goldman Sachs deputy CFO just last month in Harvard at the ComputeFest 2017 event, more precisely, the SYMPOSIUM ON THE FUTURE OF COMPUTATION IN SCIENCE AND ENGINEERING on “Data, Dollars, and Algorithms: The Computational Economy” held in Harvard on Thursday, January 19, 2017.

His claim was that

Banks are essentially API providers.

The entire structure and infrastructure of Goldman Sachs is being restructured for that. His case is that you should not compare a bank with a shop or store, you should compare it with Google. Just imagine that every time you want to search on Google you need to get in touch (i.e., make a phone call or submit a request) to some Google employee, who at some points comes back to you with the result. Non sense, right?  Well, but this is what actually happens with banks. It was happening with consumer-oriented banks before online banking, and it’s still largely happening for business banks.

But this is going to change. Amount of data and speed and volume of financial transaction doesn’t allow that any more.

Banks are actually among the richest (not [just] in terms of money, but in data ownership). But they are also craving for further “less official” big data sources.

Juri Marcucci: Importance of Big Data for Central (National) Banks.

Today at the ISTAT National Big Data Committee meeting in Rome, Juri Marcucci from Bank of Italy discussed their research activity in integration of Google Trends information in their financial predictive analytics.

Google Trends provide insights of user interests in general, as the probability that a random user is going to search for a particular keyword (normalized and scaled, also with geographical detail down to city level).

Bank of Italy is using Google Trends data for complementing their prediction of unemployment rates in short and mid term. It’s definitely a big challenge, but preliminary results are promising in terms of confidence on the obtained models. More details are available in this paper.

Paolo Giudici from University of Pavia showed how one can correlate the risk of bank defaults with their exposition on Twitter:

Paolo Giudici: bank risk contagion based (also) on Twitter data.

Obviously, all this must take into account the bias of the sources and the quality of the data collected. This was pointed out also by Paolo Giudici from University of Pavia. Assessment of “trustability” of online sources is crucial. In their research, they defined the T-index on Twitter accounts in a very similar way academics define the h-index for relevance of publications, as reported in the photographed slide below.

Paolo Giudici: T-index describing the quality of Twitter authors in finance.

It’s very interesting to see how creative the use of (non-traditional, web based) big data is becoming, in very diverse fields, including very traditional ones like macroeconomy and finance.

And once again, I think the biggest challenges and opportunities come from the fusion of multiple data sources together: mobile phones, financial tracks, web searches, online news, social networks, and official statistics.

This is also the path that ISTAT (the official institute for Italian statistics) is pursuing. For instance, in the calculation of official national inflation rates, web scraping techniques (for ecommerce prices) upon more than 40.000 product prices are integrated in the process too.



The Dawn of a new Digital Renaissance in Cultural Heritage

Fluxedo joined forces with the Observatory of Digital Innovation in Arts & Culture Heritage (Osservatorio per l’innovazione digitale nei beni e attività culturali) by the School of Management (MIP) of Politecnico di Milano, for covering the social media analytics of Italian and international museums.

The results of the work have been presented during an event on January 19th, 2017 hosted by Piccolo Teatro di Milano, which was very successful.

The live dashboard of the SocialOmeters analysis on the museums is available here:


A summary of the event through social media content of the event as generated via Storify is available here.


The official hashtag of the event #OBAC17 has become Twitter trend  in Italy, with 579 tweets, 187 users, around 600 likes and retweets, and a potential audience of 2.2 million users.

The event had a huge visibility on the national media, as reported in this press review:

1.      La rivoluzione dei musei online. Il primato di Triennale e Pinacoteca – read Il Corriere della Sera Milano

2.      L’innovazione prolifera (ma fatica) – read Il Sole 24 Ore Nòva

3.      I musei italiani e la digitalizzazione: il punto del Politecnico di Milano – read Advertiser

4.      Osservatorio Politecnico, musei social ma con pochi servizi digitali – read Arte Magazine

5.      Arte & Innovazione. Musei italiani sempre più social (52%) e virtuali (20%) – read
Corriere del Web

6.      Boom di visitatori nei musei ma è flop dei servizi digitali – read Il Sole 24 Ore Blog

7.      Capitolini, comunali e Maxxi di Roma tra i musei più popolari sui social network – read La Repubblica Roma

8.      La pagina Facebook della Reggia di Venaria è la più apprezzata d’Italia con oltre 166 mila “like” – read La Stampa Torino

9.      Il 52% dei musei italiani è social ma i servizi digitali per la fruizione delle opere sono limitati. Un’analisi dell’Osservatorio Innovazione Digitale nei Beni e Attività Culturali – read Lombard Street
10.  Musei sempre più social, ecco i più cliccati  – read TTG Italia

11.  Tanta cultura, poco digitale: solo il 52% dei musei italiani è sui social e il 43% non ha ancora un sito – read Vodafone News

12.  Tra Twitter e Instagram, 52% musei italiani punta sui social media – read ADNKronos

13.  Musei strizzano occhi a social, ma strada è lunga – read ANSA ViaggiArt

14.  Oltre la metà dei musei italiani è online e sui social, ma i servizi digitali evoluti e quelli on site sono ancora scarsi – read Brand News

15.  Il 52% dei musei italiani è social ma i servizi digitali per la fruizione delle opere sono limitati – read DailyNet

16.  Musei italiani sempre più social, ma i servizi digitali sono limitati – read Diario Innovazione

17.  Musei e social network – read Inside Art

18.  Musei Vaticani e Maxxi tra i più social d’Italia – read Il Messaggero

19.  I musei si fanno spazio sui social – read Italia Oggi

20.  Musei italiani social, ma non troppo – read La Repubblica

21.  Capitolini e Maxxi da record sui social – read La Repubblica Roma

22.  Musei lucani poco social e poco visitabili in web – read La Siritide

23.  Venaria regina dei social – read La Stampa Torino

24.  In calo nel 2016 il numero degli ingressi nei luoghi di cultura in Basilicata – read Oltre

25.  Musei sempre più social, ma poco interattivi – read QN ILGIORNO – il Resto del Carlino – LA NAZIONE

26.  Dal marketing alle guide per disabili. Cultura, boom dell’industria digitale – read QN ILGIORNO – il Resto del Carlino – LA NAZIONE

27.  Musei romani sempre più social – read Radio Colonna

28.  Social network: il Maxxi tra i musei più popolari – read Roma2Oggi

29.  Musei italiani sempre più social, ma la strada è ancora lunga – read Travel No Stop

30.  Musei italiani sempre più social e virtuali – read Uomini & Donne della Comunicazione

31.  Tra Twitter e Instagram, un museo su due in Italia scommette sui …  – read Italia per Me

32.  Beni culturali, musei lucani poco social – read La Nuova del Sud

33.  Fb, Instagram e Twitter: i musei italiani puntano sui social ma non basta – read  La Repubblica

34.  Tra Twitter e Instagram, un museo su due in Italia scommette sui social media – read La Stampa

35.  Il 52% dei musei italiani è social ma i servizi digitali per la fruizione delle opere sono limitati – read Sesto Potere

36.  Il 52% dei musei italiani è social, ma la fruizione delle opere digital è limitata – read Il Sole 24 Ore

The Harvard-Politecnico Joint Program on Data Science in full bloom

After months of preparation, here we are.

This week we kicked off the second edition of the DataShack program on Data Science that brings together interdisciplinary teams of data science, software engineering & computer science, and design students from Harvard (Institute of Applied Computational Science) and Politecnico di Milano (faculties of Engineering and Design).

The students will address big data extraction, analysis, and visualization problems provided by two real-world stakeholders in Italy: the Como city municipality and Moleskine.

logo-moleskineThe Moleskine Data-Shack project will explore the popularity and success of different Moleskine products co-branded with other famous brands (also known as special editions) and launched in specific periods in time. The main field of analysis is the impact that different products have on social media channels. Social media analysis then will be correlated with product distribution and sales performance data, along multiple dimensions (temporal, geographical, etc.) and product features.

logo-comoThe project consists of collecting and analyzing data about the city and the way people live and move within it, by integrating multiple and diverse data sources. The problems to be addressed may include providing estimates of human density and movements within the city, predicting the impact of hypothetical future events, determining the best allocation of sensors in the streets, and defining optimal user experience and interaction for exploring the city data.

The kickoff meeting of the DataShack 2017 projects, in Harvard. Faculties Pavlos Protopapas, Stefano Ceri, Paola Bertola, Paolo Ciuccarelli and myself (Marco Brambilla) are involved in the program.

The teams have been formed, and the problems assigned. I really look forward to advising the groups in the next months and seeing the results that will come out. The students have shown already commitment and engagement. I’m confident that they will be excellent and innovative this year!

For further activities on data science within our group you can refer to the DataScience Lab site, Socialometers, and Urbanscope.


Modeling and Analyzing Engagement in Social Network Challenges

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.


Advanced Software Engineering past exam solutions

Here you find the solutions of a couple of past exams

Exam of 02-09-2013: a modeling language for defining user interfaces of mobile applications.

Description: advanced-software-eng-exam-2013-09-02
Solution 1: Solution (1) (by Candan Eylül Kilsedar)
Solution 2: Solution (2) (by Ahmet Erdem)

Exam of 17-09-2013: language for high level models of computer hardware architectures.

Description: advanced-software-eng-exam-2013-09-17
Solution: Solution (by Riccardo Delegà)

The Co-evolution problem

After a few discussions on the importance and complexity of co-evolution in software engineering, during the exam session I asked to describe and explain the problem, with specific reference to model co-evolution (and metamodel – model coevolution). The crucial point of the problem is that if a model evolves to a newer version, the related models should reflect this evolution too. Analogously, if a metamodel describing a modeling language evolves, the models that conform to the previous version of the metamodel will not conform to the new version. Therefore, versions converters should be provided together with the new metamodel.

model - metamodel coevolution
model – metamodel coevolution

However, some people have a very tranchant (meaning, all black and white) vision of the world. Here is a lapidary response by a student:

The problem of co-evolution of models is when done getting back to the previous model is impossible.

Well, … your turn. (no, it’s impossible)