Luca Chiarandini


Luca Chiarandini received a PhD at the Universitat Pompeu Fabra in collaboration with Yahoo! Research Barcelona. He currently works as a Software Engineer at YouTube in Zurich. His main research interests are in the field of social media, data mining, machine learning and human-computer interaction.

Luca Chiarandini


Luca Chiarandini received a PhD at the Universitat Pompeu Fabra in collaboration with Yahoo! Research Barcelona. He currently works as a Software Engineer at Google in Zurich. His main research interests are in the field of social media, data mining, machine learning and human-computer interaction.

Relocation Assistant


The general goal of this project is to help people or business finding the neighbourhood that best fits their needs, given the information about their current location.

The main idea is to use the current location as a statement of preference. Indeed, if a person or business resides somewhere for a period of time, it can be assumed that the location satisfies one’s needs and requirements. When moving to another location, one may want to live in a place that may satisfy the needs in a similar way.

How it works

To start, tell us where you live. This will allow Relocation Assistant to understand your current neighbourhood. Next, tell us where you want to go.

This is all the information we need. At this point, Relocation Assistant will start looking for your perfect match. We analyze a large sample of customer transaction to understand the most similar neighbourhood. In moments, Relocation Assistant will show its suggestion and allow you to explore the recommendations.

You can go in depth and see the data yoursef. You can explore simple and clear visualizations of the characteristics as well as comparing the neighbourhood with your home or with the average in the city.


Relocation Assistant works in Spain (Barcelona and Madrid) and Mexico (Mexico City, Guadalajara, and Monterrey).



Bike Watch is an app for your smart watch that tells you where the closest bike sharing station is.

How it works

Simple, it just points to it in your smartwatch! Bike Watch keeps the up to date status of the nearby stations. It tells you where you can find an available bike or a free spot, depending on your need.

Being on your smart watch, you can use Bike Watch while you are biking, running, talking, driving, shopping, or even having a coffee.


Bike Watch works on more than 27 cities across the world: Amiens - Barcelona - Besancon - Bruxelles - Cergy-Pontoise - Creteil - Goteborg - Kazan - Lillestrom - Ljubljana - Luxembourg - Lyon - Marseille - Mulhouse - Namur - Nancy - Nantes - New York - Paris - Rouen - Santander - Seville - Stockholm - Toulouse - Toyama - Valence - Vilnius.

Qkly with Michele Trevisiol


How many times did you get stuck in the queue at a supermarket or at a grocery store? While standing in the queue, you may have wondered if there is a way in which you could have avoided it. And, as Murphy’s law teaches us, when you are done thinking and you can finally pay at the cashier, looking back will only reveal that the queue is now much shorter than when you came.

This situation is quite common especially when living in large cities as Madrid or Barcelona. During the sales period, for example, large crowds of people wander around shops. In the touristic areas, hordes of tourists are visiting churches, monuments, museums or theatres. The question we may think of is: can we avoid queues? Our answer is yes, or at least reduce them. There are ways to optimize our schedule not to waste hours in queues. And best, how about having somebody else that can do this for us?

Qkly is our solution to this problem. It is a computer application that automatically produces optimized schedules of all the business, attractions, or places based on your preferences. Using historical data provided by the BBVA Open API, Qkly is able to estimate how crowded a place is at a certain time. Based on this information, it helps you to pick the time in which less people are present, so as to save time and avoid stress. Qkly comes with a web app that can be accessed from any device as well as, a publicly available mobile app, that provides additional features, such as the possibility of adding reminders to the calendar.

By allocating the people visiting shops more evenly in time, Qkly can help to reduce the period of time in which shops are saturated with people, without reducing the overall number of potential customers. Shop owners and dealers can therefore benefit from a more fluent business and provide better experience to their customers.

Qkly places customers in the middle, by helping them get the best from the available time. We see a great opportunity to allow customers to experience business in a more relaxed and enjoyable way. People will no longer have to avoid shopping because of stress or the desire to leave as soon as possible.

With Qkly you save time, keep the stress away and enjoy your day more.

Quartz with Michele Trevisiol


Similarly to OnLine Analytical Processing (OLAP) tools, Quartz allows users to pivot among dimensions and look at the data from the perspective they prefer.

We applied Quartz to a dataset consisting in customer’s credit card transactions for the months of November 2012 to April 2013. The data has been collected from the BBVA Innova Challenge API. The dimensions of the data are:

  • zipcode: the location where the transaction occurred;
  • gender: the gender of the customer;
  • category: the category of business of the shop

How it works

The Interface

Quartz allows you to explore how people spend their money. The pie charts on the map represent the amount of money spent by people at a particular location, split by customer gender or business category.

The Filtering

The legend on the bottom right corner allows you to filter out genders or categories you are not interested in.

The Point of View

You can decide whether you want to focus your exploration on genders or categories.

You can easily switch between the two by clicking on the Focus button in the legend.

The Quartz Wheel

If you want more information, you can click on a pie chart to open the Quartz Wheel.

If you move the mouse on a segment of the wheel, you can see the number of payments in the center. Also, you can see additional segments on the wheel representing how the payments in the category distribute in sub-segments.

If you move from a segment to one of its sub-segment, you will see the percentage of payments in the segment which refer to it, as well as the percentage on the total amount for the location.




  • Quartz, simple yet powerful tool to explore credit card transaction by customer gender and business category.
  • Quartz 3D, allows the user to explore three dimensions: customer gender, customer age, and business category.



eyeTime with Eduardo Graells and Diego Saez-Trumper


The project proposes an application that consists on two parts: a client part where we provide an extension for browsers to aid children in web browsing, and a server part where we classify content and analyze children behavior. The server provides a dashboard where teachers can control how children are aid by the client application and how content is processed. Also, the dashboard presents reports about usage of the network. The client performs web site modifications to adapt content to children and provides suggestions on what children should or should not do. The look and feel of these suggestions is specially designed for children.




Timebook with Eduardo Graells


Did you ever wondered how would the world have been different if social media always existed? People spending ages on Facebook, tweeting during the battle of Troy, tagging in portrait paintings, ... Sure it would be interesting to take a look at the Facebook profiles of famous people or heroes of history, to find out how they actually cope with daily life.

This is the purpose for which Notebook has been created. Notebook is an aggregator of information coming from different sources concerning personalities of the past (in particular composers, painters, writes or artist in general). It formats the information in the form of a Facebook-like profile. In addition, Notebook creates a social network of artist by looking at the influences in each other's works.

Since the Facebook-like interface is well known by people all around the world, the learning time (i.e. time needed to understand how the system works and start using it) is quite short. In addition, the exploration of the social network of artist turned out to be interesting and may create engagement from the user's perspective.

Applications of the system that go beyond joking could be found in education, promotion and exploration of digital libraries.

How it works

Timebook builds the profiles of personalities of the past by aggregating multiple sources:

  • DBPedia: DBPedia is a collection of structured information extracted from Wikipedia. It gives access to the basic information about historical people such as family, artistic influences, place of birth, etc.
  • Europeana: Europeana is an European project aimed at creating an access point to the media material of many institutions. From here we may extract the paintings, the music tracks and the books created in the past
  • WikiQuote: WikiQuote is a huge collection of quotations of artist and personalities of the past. It is used to populate the profiles of the users with their most known words
  • EchoNest: used to extract audio sample of the artists
  • SEEVL: integrating the DBPedia data related to musical artists with additional accurate information (facts about their life, concerts, ...)

Polysound Master Thesis at Politecnico di Milano


The objective of the project is to explore and improve the capabilities of music recommendation systems and to develop a software able to act as an automatic DJ. We would like to develop a general framework for music feature-based recommendation systems. We will then apply it to some specific applications. The project is based on the fact that the audio features are extracted directly from the audio content. Users can interact by expressing preferences on them, without passing through labeling or audio meta-data. Moreover, the interaction between the user and the recommendation framework is performed in real-time, meaning that the effect of the actions occur after a short time.

At startup, the system reads the data from an audio database and randomly picks up a song and starts playing it. The playback is scheduled for a small amount of time (around 20 seconds) at the end of which the system lets the user decide how to continue the live performance. By analysing the audio features extracted from the audio data (e.g. the tempo of the song, its harmony, its brightness...), the system searches the database for songs that fit well with the one currently played and shows them on the screen.

The user may:

  • continue with the same song
  • change to a song among the songs proposed by the system. In this case the system performs the transition by adjusting the BPM and synchronizing the beats of the two audio segments

In addition to this, the user can specify a set values of the audio features he or she the system to follow.

The user may also manually choose the next song to be played. By doingf so, the program will try to find the best way to cross over to the chosen song.

The system also keeps track of the choices of the user over time, and uses these information to rank the music according to the user's taste.

To provide user with more information about the current track, APIs are used to retrieve online and in real time the meta-data for the song, and is then used in combination with Musixmatch to include the lyrics in the infos for the song, trying to combine all the aspects of listening to music in one single program.

Purewings Final project at Alta Scuola Politecnica


The search for innovative solution in the energy production boosted the Fuel Cells development, and Hydrogen appears as the ideal element to work with for its energetic capability and environmental attitude having a practical zero emissions. On the traction side, direct drive electric motors ensure high efficiency, compactness, and reliability, key issues in the aeronautic application, and are also particularly suited to the propeller aircraft.

Power electronics nowadays allows to control the torque and the speed of the propeller by the electric motor with high dynamics. All of these is needed for the quality and the safety of the flight, together with a fast electrochemical accumulator (a Lithium Polymer battery) as "hot reserve".

The balance and control of the on board energy are managed by the power electronics through digital control. In this scenario Pure Wing has the goal to develop a computer simulation with these main tasks: design of the strategies for on board energy management; evaluate the expected performance of the aircraft; design the strategies in the fault management for the on-board and off-board safety; design the pilot interface and the cockpit appearance in the respect of the new ergonomics and the flight qualities for this new aircraft. Simulation models will be validated in the different phases of the aircraft development.

Ground logistics, safety, and the effect on the airport handling procedures have to be evaluated too in relation with this new and innovative propulsion technology for the general aviation field.


Luca Chiarandini, Ricardo Baeza-Yates, Alejandro Jaimes, Characterizing and Modeling Web Sessions with Applications, PhD Thesis at Universitat Pompeu Fabra PDF

This thesis focuses on the analysis and modeling of web sessions, groups of requests made by a single user for a single navigation purpose. Understanding how people browse through websites is important, helping us to improve interfaces and provide to better content.
After first conducting a statistical analysis of web sessions, we go on to present algorithms to summarize and model web sessions. Finally, we describe applications that use novel browsing methods, in particular parallel browsing.
We observe that people tend to browse images in a sequences and that those sequences can be considered as units of content in their own right. The session summarization algorithm presented in this thesis tackles a novel pattern mining problem, and this algorithm can also be applied to other fields, such as information propagation. From the statistical analysis and the models presented, we show that contextual information, such as the referrer domain and the time of day, plays a major role in the evolution of sessions. To understand browsing one should therefore take into account the context in which it takes place.

Michele Trevisiol, Luca Chiarandini, Ricardo Baeza-Yates, Buon Appetito - Recommending Personalized Menus, Hypertext 2014 PDF

This paper deals with the problem of menu recommendation, namely recommending menus that a person is likely to consume at a particular restaurant. We mine restaurant reviews to extract food words, we use sentiment analysis applied to each sentence in order to compute the individual food preferences. Then we extract frequent combination of dishes using a variation of the Apriori algorithm. Finally, we propose several recommender systems to provide suggestions of food items or entire menus, i.e. sets of dishes.


Lucrezia Macchia, Francesco Gullo, Francesco Bonchi, Luca Chiarandini, Mining Summaries of Propagations, Yahoo! Techpulse 2013

Silviu Maniu, Neil O'Hare, Luca Aiello, Luca Chiarandini, Alejandro Jaimes, Search Behaviour on Photo Sharing Platforms, Yahoo! Techpulse 2013

Luca Chiarandini, Luca Maria Aiello, Neil O'Hare, Alejandro Jaimes, Metro: Exploring Participation in Public Events, SocInfo 2013 PDF

The structure of a social network is time-dependent, as relationships between entities change in time. In large networks, static or animated visualizations are often insufficient to capture all the information about the interactions between people over time, which could be captured better by interactive interfaces. We propose a novel system for exploring the interactions of entities over time, and support it with an application that displays interactions of public figures at events.

Lucrezia Macchia, Francesco Gullo, Francesco Bonchi, Luca Chiarandini, Mining Summaries of Propagations, ICDM 2013 PDF

Analyzing the traces left by a meme of information propagating through a social network or by a user browsing a website can help to unveil the structure and dynamics of such complex networks. This may in turn open the door to concrete applications, such as finding influential users for a topic in a social network, or detecting the typical structure of a web browsing session that leads to a product purchase.
In this paper we define the problem of mining summaries of propagations as a constrained pattern-mining problem. A propagation is a dag where an entity (e.g., information exchanged in a social network, or a user browsing a website) flows following the underlying hierarchical structure of the nodes. A summary is a set of propagations that (i) involve a similar population of nodes, and (ii) exhibit a coherent hierarchical structure when merged altogether to form a single graph. The first constraint is defined based on the Jaccard coefficient, while the definition of the second one relies on the graph-theoretic concept of "agony" of a graph. It turns out that both constraints satisfy the downward-closure property, thus enabling Apriori-like algorithms. However, motivated by the fact that computing agony is much more expensive than computing Jaccard, we devise two algorithms that explore the search space differently. The first algorithm is an Apriori-like, bottom-up method that checks both the constraints level-by-level. The second algorithm consists of a first phase where the search space is pruned as much as possible by exploiting the Jaccard constraint only, while involving the second constraint only afterwards, in a subsequent phase.
We test our algorithms on four real-world datasets. Quantitative results reveal that the choice of the most efficient algorithm depends on the selectivity of the two constraints. Qualitative results show the relevance of the extracted summaries in a number of real-world scenarios.

Silviu Maniu, Neil O'Hare, Luca Aiello, Luca Chiarandini, Alejandro Jaimes, Search Behaviour on Photo Sharing Platforms, ICME 2013 PDF

The behavior, goals, and intentions of users while searching for images in large scale online collections are not well understood, with image search log analysis providing limited insights, due to the fact they tend only to have access to user search and result click information. In this paper we study user search behavior in a large photo-sharing platform, analyzing all user actions during search sessions (i.e. including post results pageviews). We show that search accounts for a significant part of user interactions with such platforms, and we that the queries issued on such platforms differs from general image search. We show that user behavior is influenced by the query type, and also dependent on the user. Finally, we provide an analysis on how users behave when they reformulate their queries, and develop URL class prediction models for image search, showing that query-specific model significantly outperform query-agnostic models. The insights provided in this paper are intended as a launching point for the design of better interfaces and ranking models for image search.

Luca Chiarandini, Przemyslaw Grabowicz, Michele Trevisiol, Alejandro Jaimes, Leveraging Browsing Patterns for Topic Discovery and Photostream Recommendation, ICWSM 2013 PDF (PosterPOSTER)

Photographs, particularly those residing in photo hosting and sharing services, are most often viewed as a sequence (i.e., users often view an enlarged version of a photo, followed by other photos). Those sequences of photos, as opposed to just individual images, can therefore be considered to be very important content units in their own right. In spite of their importance, however, those sequences (which we call photostreams}) have received little attention even though they are at the core of how people consume image content. In this paper, we focus on photostreams. First, we perform an analysis of a large dataset of user logs containing over 100 million pageviews, examining navigation patterns between photostreams. Based on observations from the analysis we build a co-view graph (e.g., users who viewed stream x also viewed stream y), create a stream transition graph to analyze common stream topic transitions (e.g., users often view "landscape" streams followed by "portrait" streams), implement a stream recommendation system based on co-views, and report on the results of user study involving 33 participants to investigate the advantages of adding stream recommendations to the Flickr interface. Our analysis yields interesting insights into how people navigate between photostreams, while the results of the user study provide useful feedback for future work.


Peter Haider, Luca Chiarandini, Ulf Brefeld, Discriminative Clustering for Market Segmentation, KDD 2012 PDF

We study discriminative clustering for market segmentation tasks. The underlying problem setting resembles discriminative clustering, however, existing approaches focus on the prediction of univariate cluster labels. By contrast, market segments encode complex (future) behavior of the individuals which cannot be represented by a single variable. In this paper, we generalize discriminative clustering to structured and complex output variables that can be represented as graphical models. We devise two novel methods to jointly learn the classifier and the clustering using alternating optimization and collapsed inference, respectively. The two approaches jointly learn a discriminative segmentation of the input space and a generative output prediction model for each segment. We evaluate our methods on segmenting user navigation sequences from Yahoo! News. The proposed collapsed algorithm is observed to outperform baseline approaches such as mixture of experts. We showcase exemplary projections of the resulting segments to display the interpretability of the solutions.

Michele Trevisiol, Luca Chiarandini, Luca Aiello, Alejandro Jaimes, Image Ranking Based on User Browsing Behavior, Yahoo! Techpulse 2012

Przemyslaw Grabowicz, Luca Chiarandini, Michele Trevisiol, Alejandro Jaimes, Browsing and Recommendation of Photostreams, Yahoo! Techpulse 2012

Peter Haider, Luca Chiarandini, Ulf Brefeld, Alejandro Jaimes, Contextual Models for User Interaction on the Web, I-Pat: Mining and exploiting interpretable local patterns @ ECML-PKDD 2012 PDF

Accurately modeling user sessions on the web is important because such models can be used, on one hand to predict a user's actions, and on the other hand to inform design and content decisions. This includes predicting what links a user will click on, deciding where webpage components should be placed, and what content to provide. Often it is either undesirable or not possible to build personalized models, and even when available, such models suffer from the cold start problem, or are unable to deal with context-dependent variations in user behavior. In this paper, we present a probabilistic framework for session modeling that creates clusters of similar sessions and uses contextual session information (time, referrer domain, link locations). Sessions are probabilistically assigned to the clusters by conditioning on the context. The framework addresses wide variations in user behavior that are due to context by explicitly incorporating it in the model, while specifically leveraging periodicity (weekly and daily behavioral regularities). We evaluate the framework on a set of logs from Yahoo! News.

Pancho Tolchinsky, Luca Chiarandini, Alejandro Jaimes, PRiSMA: Searching Images in Parallel, ACM Multimedia 2012 PDF

PRiSMA is an image search application intended to facilitate and promote searching images in parallel. With an intuitive user interface, users can branch their queries into multiple horizontal sliding strips to explore simultaneously different perspectives of the image collections (e.g., colors, geographical location or topic). Strips can easily be created, further tailored, merged and removed, allowing users to effectively perform and manage the results of multiple queries in a dynamic and orderly fashion. With PRiSMA we aim to explore the potential and limitations of parallel image search from a user perspective.

Luca Chiarandini, Alejandro Jaimes, Browsing-Based Content Discovery, DIS 2012 PDF

In this demo we present a novel web-based photo navigation and discovery application that leverages page-view patterns of thousands of users to generate interactive graphs. The graphs allow users to easily navigate large collections in non-traditional ways, by following anonymous, aggregated browsing patterns of past users. Our implementation uses website user navigation logs: in this particular demonstration we use Flickr logs and demonstrate our approach for image discovery.

Michele Trevisiol, Luca Chiarandini, Luca Maria Aiello, Alejandro Jaimes, Image Ranking Based on User Browsing Behavior, SIGIR 2012 PDF

General ranking of images in photo hosting Web services is traditionally based on factors describing the single pictures such as their content, metadata, and user implicit and explicit feedback. However, the plethora of importance and interestingness parameters that apply to rich media prevent the emergence of a widely accepted metric for this task. We shed light on this issue by comparing rankings based on different implicit and explicit sources of information in terms of an extensive set of evaluation metrics including popularity, diversity of content, interest aroused in Web surfers, and visual appearance of the image. Among the other ranking methods, we propose to apply a graph-centrality metric, namely the BrowseRank, on the complex network emerging from the user browsing activity. Experiments on Flickr data show significant differences between different rankings. In particular, our proposed method is able to provide the best balance between quality, variety, and impact of ranked pictures on the Web outside the Flickr boundaries.


Luca Chiarandini, Michele Trevisiol, Alejandro Jaimes, Discovering Social Photo Navigation Patterns, ICME 2012 PDF

Understanding user navigation patterns is important in many websites. With the growth of social networks and of many different kinds of web services (e.g., aggregators, specialized sites, vertical search, etc.), the web can be viewed more as an ecosystem in which the referral domains may have an important impact on user behavior. In this paper, we part from the hypothesis that visitors to social sites such as Flickr behave differently depending on where they come to the site from. For this purpose, we analyze a large sample of Flickr user logs and present techniques to discover social photo navigation patterns. More specifically, we classify pages into different categories (e.g., "add a friend page", "single photo page", etc.), and by clustering sessions discover important differences in social photo navigation. Our work is useful in that it can contribute to a better understanding of how people use photo services like Flickr, and it can be used to inform the design of user modeling and recommendation algorithms, among others.

Luca Chiarandini, Exploration and Discovery of User-generated Content in Large Information Spaces, WSDM 2012 - Doctoral Consortium PDF

The accumulation of large collections of social media data poses new challenges for the design of exploratory experiences, such as when a user browses through a collection to discover content (e.g. exploring photo collections, network of friends, etc). Cardinality and characteristics of the set, together with volatility of the information, resulting from fast and continuous creation, deletion and updating of entries, trigger novel research questions.
In this context, we plan to investigate and contribute to the data analysis, and user interface design of exploratory experiences. The proposed approach is an iterative process where analysis and design phases are performed in cycles. The long-term vision is to understand the underlying reasoning in order to be able to automatically replicate it.

Luca Chiarandini, Human-centered Exploration and Discovery of Content in Large Information Spaces, PhD Proposal at Universitat Pompeu Fabra, Barcelona PDF

The accumulation of large collections of social media data poses new challenges for the design of exploratory experiences, such as when a user browses through a collection to discover content (e.g. exploring photo collections, network of friends, etc). Cardinality and characteristics of the set, together with volatility of the information, resulting from fast and continuous creation, deletion and updating of entries, trigger novel research questions.
In this context, we plan to investigate and contribute to the data analysis, and user interface design of exploratory experiences. The proposed approach is an iterative process where analysis and design phases are performed in cycles. The long-term vision is to understand the underlying reasoning in order to be able to automatically replicate it.

Peter Haider, Luca Chiarandini, Ulf Brefeld, Behavioral User Models for Yahoo! News, Yahoo! TechPulse 2011

Alejandro Jaimes, Brian Theodore, Meekal Bajaj, Luca Chiarandini, Eduardo Graells, Pancho Tolchinsky, Yahoo! Clues Visualization Widgets, Yahoo! TechPulse 2011


Luca Chiarandini, Massimiliano Zanoni, Augusto Sarti, A System for Dynamic Playlist Generation Driven by Multimodal Control Signals and Descriptors, MMSP 2011 PDF

This work describes a general approach to multimedia playlist generation and description and an application of the approach to music information retrieval. The example of system that we implemented updates a musical playlist on the fly based on prior information (musical preferences); current descriptors of the song that is being played; and fine-grained and semantically rich descriptors (descriptors of user’s gestures, of environment conditions, etc.). The system incorporates a learning system that infers the user’s preferences. Subjective tests have been conducted on usability and quality of the recommendation system.

Luca Chiarandini, Massimiliano Zanoni, Augusto Sarti, A System for Dynamic Playlist Generation Driven by Multimodal Control Signals and Descriptors, Master of Science Thesis at Politecnico di Milano PDF

The challenge of modern recommendation systems is how to process this huge amount of data in order to extract useful descriptors of the musical content, i.e. how to perform automatic tagging, catalog, indexing media material. This information may be used for many purposes: media search, media classification, market suggestions, media similarity measurements, etc. Until now, the traditional approach to this problem has been audio labelling. This operation consists in the definition of symbolic descriptors that can be used for generating the playlist. Examples of this sort are playlists based on music genre or artist name. This approach has some strong limitations: first of all, since labels are usually considered as descriptors of the whole musical piece, they cannot capture mood or genre changes inside the same song. Moreover, the label classification sometimes results in heterogeneous classes (e.g. music belonging to the same genre can be very different one from each other).
This thesis gets into this context and it consists in the study and development of a music recommendation framework that allows the user to interact by means of more precise descriptors. The system intelligently recommends items of an audio database on the basis of the preferences of the user. Music Information Retrieval techniques are used in order to extract significant features from the audio signal and allow the user to interact with the system by means of high level interfaces such as musical tempo or timbric features. During the description of the system, we will prove the generality of the approach by describing some of the many applications that could be derived from the framework: an automatic DJ system, a tabletop interaction system, a playlist generation system based on runner's step frequency and training-based recommendation system.
The goal of this project is not only the development of a technically valid product but also an exploration of the artistic applications. The system is addressed to a wide public of performers (DJs, contemporary music executors, ...), composers and amateurs.


Luca Chiarandini, Leonardo Felician, Prevenzione ed Emissione di Polizze Assicurative Online, Bachelor Thesis at Università degli Studi di Trieste PDF

Si vuole sviluppare un sistema informativo assicurativo generalizzato. Con questa definizione si intende mettere in evidenza che il sistema in questione non viene ottimizzato per uno specifico prodotto bensì permette la gestione di molteplici prodotti assicurativi, anche con caratteristiche e ambiti totalmente diversi. Sarà quindi possibile gestire, con la stessa interfaccia, polizze auto, casa, vita, etc. ottenendo un’ottima integrazione fra i diversi processi dell’azienda.
L’interfaccia utente (front end) deve permettere al cliente di inserire, in un form HTML, i dati necessari alla stipula della polizza, fornendo poi il premio assicurativo a seconda del loro valore. L’utente potrà quindi decidere se acquistare o no la proposta e, in caso affermativo, il sistema si occuperà di dialogare con il Legacy System aziendale e notificare l’acquisto. Oltre a ciò, deve essere integrato un sistema di identificazione utente (login), che permetta al sistema di riconoscere che la polizza viene eseguita tramite un determinato account e concedere, eventualmente, privilegi di accesso a determinate sezioni.

Here are some songs I wrote. Feel free to listen to them. While listening, you can move the earphones around the stage to focus on a particular instrument.


Luca Chiarandini

@Google Inc.

Brandschenkestrasse 110

8002 Zürich, Switzerland

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