I’ve been invited to give a keynote talk at the WISE 2022 Conference. Thinking about it, I decided to focus on my idea of a bi-verse. To me, the bi-verse is the duality between the physical and digital worlds.
On one side, the Web and social media are the environments where people post their content, opinions, activities, and resources. Therefore, a considerable amount of user-generated content is produced every day for a wide variety of purposes.
On the other side, people live their everyday life immersed in the physical world, where society, economy, politics, and personal relations continuously evolve. These two opposite and complementary environments are today fully integrated: they reflect each other and they interact with each other in a stronger and stronger way.
Exploring and studying content and data coming from both environments offers a great opportunity to understand the ever-evolving modern society, in terms of topics of interest, events, relations, and behavior.
This slidedeck summarizes my contribution:
In my speech, I discuss business cases and socio-political scenarios, to show how we can extract insights and understand reality by combining and analyzing data from the digital and physical world, so as to reach a better overall picture of reality itself. Along this path, we need to keep into account that reality is complex and varies in time, space, and many other dimensions, including societal and economic variables. The speech highlights the main challenges that need to be addressed and outlines some data science strategies that can be applied to tackle these specific challenges.
Modern User Interfaces (UIs) are becoming complex software artifacts themselves, through integration of AI-enhanced software components that enable even more natural interactions, including the possibility to use Natural Language Processing (NLP) via chatbots or voicebots (aka., Conversational User Interfaces or CUIs).
Some times, several types of UIs are combined as part of the same application (e.g. a chatbot in a web page), what it is known as Multiexperience User Interface. These multiexperience UIs may be built together by using a Multiexperience Development Platform (MXDP).
“Multiexperience development involves ensuring a consistent user experience across web, mobile, wearable, conversational and immersive touchpoints”. [Gartner]
A typical scenario of multiexperience user interaction could unroll as follows (see image below too). Suppose that a customer on a Sunday morning wants to buy a new technical product (a cell phone or a home theater system). He first interacts with his home assistant (like Alexa or Google assistant) to ask it to find the best nearby tech store open on Sunday. With this information in mind, he looks at the store web site on his PC and, being satisfied with the kind of store, he asks the web site chatbot to find the type of products he is looking for. After browsing the various alternatives, he finds one item he likes, and sets the place and the product as preferences on his mobile phone. He reads the details of the product on the phone while walking to his car. When he reaches the car, he transfers the information about the place to the car navigation system and drives there. Finally, in the stores he looks around, tries various items, reads the reviews about them on a dedicated mobile app, and finally picks up the product and pays for it.
This kind of dynamic and seamless interaction demands a variety of complex design and implementation mechanisms to be put in place. Clearly, also very critical integration, evolution, and maintenance challenges need to be faced for these CUIs. Developers need to handle the coordination of the cognitive services to build multiexperience UIs, integrate them with external services, and worry about extensibility, scalability, and maintenance.
We believe a model-driven approach for MXDP could be an important first step towards facilitating the specification of rich UIs able to coordinate and collaborate to provide the best experience for end-users. Indeed, most non-trivial systems adhere to some kind of model-based philosophy, where software design models (including GUI models) are transformed into the production code the system executes at run-time. This transformation can be (semi)automated in some cases.
Our recent research tackles the application of model-driven techniques to the development of software applications embedding a multiexperience UI.
The research has been published in our recent paper Towards a Model-Driven Approach for Multiexperience AI-based User Interfaces, co-authored by Elena Planas, Gwendal Daniel, Marco Brambilla and Jordi Cabot, recently published in the International Journal on Software and Systems Modeling (SoSyM) available online here (open access).
The paper contribution is twofold:
we raise the abstraction level used in the definition of this new kind of conversational and smart interfaces.
we show how these CUI models can be used in conjunction with more “traditional” GUI models to combine the benefits of all these different types of interfaces in a multiexperience development project.
In practice, we propose a new Domain Specific Language (DSL), that generalizes the one defined by the Xatkit model to cover all types of CUIs, and we show how this seamlessly integrates with appropriate extensions of the IFML model to design comprehensive multi-experience interfaces.
IFML model integrating traditional navigation of a web interface and a chatbot component.
You can refer to the full paper here for covering the details. The paper reference is:
Planas, E., Daniel, G., Brambilla, M., Cabot, J. Towards a model-driven approach for multiexperience AI-based user interfaces. Software and System Modeling (SoSyM)20, 997–1009 (2021). https://doi.org/10.1007/s10270-021-00904-y
Over one billion cars interact with each other on the road every day. Each driver has his own driving style, which could impact safety, fuel economy and road congestion. Knowledge about the driving style of the driver could be used to encourage “better” driving behaviour through immediate feedback while driving, or by scaling auto insurance rates based on the aggressiveness of the driving style.
In this work we report on our study of driving behaviour profiling based on unsupervised data mining methods. The main goal is to detect the different driving behaviours, and thus to cluster drivers with similar behaviour. This paves the way to new business models related to the driving sector, such as Pay-How-You-Drive insurance policies and car rentals. Here is the presentation I gave on this topic:
Driver behavioral characteristics are studied by collecting information from GPS sensors on the cars and by applying three different analysis approaches (DP-means, Hidden Markov Models, and Behavioural Topic Extraction) to the contextual scene detection problems on car trips, in order to detect different behaviour along each trip. Subsequently, drivers are clustered in similar profiles based on that and the results are compared with a human-defined ground-truth on drivers classification.
The proposed framework is tested on a real dataset containing sampled car signals. While the different approaches show relevant differences in trip segment classification, the coherence of the final driver clustering results is surprisingly high.
This work has been published at the 4th IEEE Big Data Conference, held in Boston in December 2017. The full paper can be cited as:
M. Brambilla, P. Mascetti and A. Mauri, “Comparison of different driving style analysis approaches based on trip segmentation over GPS information,” 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, 2017, pp. 3784-3791.
doi: 10.1109/BigData.2017.8258379
You can download the full paper PDF from the IEEE Explore Library, at this url:
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.
The 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.
We organize a crash-course on how the science of urban data can be applied to solve metropolitan issues.
The course is a 2 days face-to-face event with teaching sessions, workshops, case study discussions and hands-on activities for non-IT professionals in the field of city management. It is issued in two editions along the year:
in Milan, Italy, on November 8th-9th, 2017
in Amsterdam, The Netherlands, on November 30th-December 1st, 2017.
Ideal participants include: Civil servants, Professionals, Students, Urban planners, and managers of city utilities and services. No previous experience in data science or computer science is required. Attendees should have experience in areas such as economic affairs, urban development, management support, strategy & innovation, health & care, public order & safety.
Data is the catalyst needed to make the smart city vision a reality in a transparent and evidence-based (i.e. data-driven) manner. The skills required for data-driven urban analysis and design activities are diverse, and range from data collection (field work, crowdsensing, physical sensor processing, etc.); data processing by employing established big data technology frameworks; data exploration to find patterns and outliers in spatio-temporal data streams; and data visualization conveying the right information in the right manner.
The CrowdInsights professional school “Urban Data Science Bootcamp” provides a no-frills, hands-on introduction to the science of urban data; from data creation, to data analysis, data visualization and sense-making, the bootcamp will introduce more than 10 real-world application uses cases that exemplifies how urban data can be applied to solve metropolitan issues. Attendees will explore the challenges and opportunities that come from the adoption of novel types of urban data source, including social media, mobile phone data, IoT networks, etc.
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:
The 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).
Marco Brambilla 