Mobile + cloud + internet of things (iot) = nuove opportunità di business

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The business value of Mobile + Cloud + Internet of Things Once again, this year I gave a presentation at SMAU Milano together with WebRatio (with Matteo Sassi as a co-speaker). The purpose this time was to show and inspire on how Mobile, Cloud and IoT are becoming concrete business opportunities. We tried to give a different interpretation to these technology trends: At first glance, the technologies that integrate Cloud, Mobile andInternet of Things (IoT) are attractive only to industry leaders with the ability to invest huge budgets and resources (such as Google Nest or Philips HUE, for example). These technologies are becoming attractive for individual developers who implement solutions thanks to open-source hardware and systems, such as Arduino and Raspberry PI which then inevitably force them to make design compromises. We showed how conceptual solutions like the one proposed by WebRatio offer a code less approach that gives companies the opportunity to enter as main players, without limitations, in this new Mobile, Cloud and IoT market. By using WebRatio Platform, companies can model apps with the IFML language (Interaction Flow Modeling Language) and "rationalize" the interaction with the IoT networks (think of the interaction of the sensors on which the "Internet of Things" technology is based) and put them into operation directly in the Cloud. In this way, companies save on infrastructure costs and reduce the cost and time to design and build applications. It is possible to design new applications in this context caring only to use the correct interfaces and focusing on the opportunities provided by this new technology. [ITA] Mobile, Cloud e IoT sono le tecnologie più potenti che alimentano la “rivoluzione digitale”; ne vediamo tutti i giorni i primi effetti sul nostro modo di vivere, lavorare ed interagire. Grazie a tablet e smartphone (tecnologia Mobile) abbiamo accesso in qualsiasi ora e luogo a dati e informazioni come clienti, dipendenti, consumatori o cittadini. Il Cloud consente una completa virtualizzazione e scalabilità delle infrastrutture e servizi IT. L’Internet delle cose (IoT) consente di connettere alla “rete” potenzialmente qualsiasi tipo di oggetto: automobili, sensori ambientali, telecamere, elettrodomestici, impianti industriali, etichette intelligenti ecc. Per le aziende, l’unione di Mobile, Cloud e IoT costituisce un’incredibile occasione per creare nuove opportunità di business a costo contenuto e con un potenziale enorme. Ad esempio inserire sensori a basso costo collegati a internet nei propri prodotti e offrire ai propri clienti una App mobile per il loro controllo, il tutto supportato da un infrastruttura Cloud scalabile. Oppure vendere i propri servizi attraverso una App mobile in grado di riconoscere chi è il cliente, dove si trova, di cosa ha bisogno.

Business

Agenda
Context: size of the market
Case studies
The global: Google Nest, Philips Hue
The craftman: Arduino, IFTTT
The engineer: Modeling, WebRatio
Business scenarios
Real world examples
2 Mobile + Cloud + IOT

Context – size of the market
Gartner estimates that Internet of Things product and
service suppliers will generate incremental revenue
exceeding $300 billion in 2020.
IDC forecasts that the worldwide market for Internet of
Things solutions will grow from $1.9 trillion in 2013 to
$7.1 trillion in 2020.
Hardware, apps and services for healthb and fitness
may generate up to $5 billion by 2016 (Gartner)
3 Mobile + Cloud + IOT

The global: Philips Hue
Simple devices
No installation
burdens
Control from
web / mobile
4 Mobile + Cloud + IOT

The global: Google nest
Intelligent thermostat?
5 Mobile + Cloud + IOT

Nest ecosystem
Ecosystem of devices and cloud services
Ecosystem of devices and cloud services
data storage, analytics,
machine intelligence
6 Mobile + Cloud + IOT

Nest: sensor or (valuable) data?
7 Mobile + Cloud + IOT

The craftman: Arduino
Opensource hardware platform (from Italy!)
Starts at less than 100$
8 Mobile + Cloud + IOT

The craftman: If-This-Then-That
9 Mobile + Cloud + IOT

The engineer: modeling /abstracting
Modeling languages (BPMN, IFML, UML)
Modeling tools and automatic prototype builders
Abstract from low-level details, everything is a resource
Take a look at WebRatio www.webratio.com
10 Mobile + Cloud + IOT

Business scenarios: Payments
Mobile payment:
Checkout as you go: your bill is ready when done shopping
NFC payments
• Credit card business disruption
• Fight between retail and virtual services (Apple Pay)
Wallet
Beacons e BLE (Bluetooth Low Energy)
NFC-activated restaurant menu
11 Mobile + Cloud + IOT

Scenarios: Health
Food and activity tracking
Sensors in sport apparel
12 Mobile + Cloud + IOT

Health – intelligent cup
13 Mobile + Cloud + IOT

Scenarios: Advertising and awareness
Presence detectors in stores:
Beacon e iBeacon
sensors in membership cards
cameras and sensors in the stores
Ad hoc special offers
14 Mobile + Cloud + IOT

Scenarioa: Sensors and actuators
Consumer
Domotics – home automation
Mobile
Enterprise
Sensors and actuators
Business processes
15 Mobile + Cloud + IOT

IoT + IoP (IoH)
The people! Internet of humans – Internet of people
Human as a sensor
16 Mobile + Cloud + IOT

PROACTIVE project
Infrastructure for public protection from natural
disasters
Heterogeneous sources
Predictive models
Monitoring tools
Response planning
Demonstrators
PeopleOnTheMove: safety and prevention on crowds
SnowWatch: water resources monitoring
LeakWatch: utility improvements (water leaks in distribution network)
19 Mobile + Cloud + IOT

PROACTIVE architecture
20 Mobile + Cloud + IOT

SmartH2O
ICT infrastructure for water consumption measurement
Understand consumers’ behaviour
Forecast consumption based on contingencies
Increase engagement and responsibility of consumers
21 Mobile + Cloud + IOT

SmartH2O infrastructure
22 Mobile + Cloud + IOT

Typical IoT + Cloud + Mobile infrastructure
23 Mobile + Cloud + IOT

Simplified architectural view
User Interface
(HTML5, PhoneGap,…)
Web/Mobile App Logic
(Java, IFML,…)
Web Services
(soap, rest)
Database
(SQL)
“Things”
24 Mobile + Cloud + IOT

Your ideas
Come and share your ideas
We will build a "Proof of Concept" (PoC) with
WebRatio Platform and boost your business
Get in touch!
matteo.sassi@webratio.com
marco.brambilla@polimi.it
Twitter: @marcobrambi
25 Mobile + Cloud + IOT

Contacts Email address:
Email address:
contact@webratio.com
Sales information:
sales@webratio.com
Support:
helpdesk@webratio.com
Customer support:
support@webratio.com
UNITED STATES IT
Minneapolis
12100 Singletree Ln, Suite 109
Eden Prairie, MN - 55344
Phone: +1 (952) 746 1236
Santa Clara
2040 Martin Avenue
Santa Clara, CA - 95050
Phone: +1 (408) 707 6565
ITALY
Milan
Piazza Cadorna, 10
20123 Milano
Phone: (+39) 02 4548 5980
Como
Via Cavour, 2
22074 Lomazzo c/o ComoNExT
Phone: (+39) 02 3671 4280
ECUADOR
Quito
Av. 12 de octubre y Colón
Torre Boreal Building, 8th, off. 803
Phone: (+593) 2 3945 331
Cuenca
Alfonso Cordero y Paucarbamba,
WorkCenter Palace, 4th, off. 410
Phone: (+593) 7 4103 792
US
EC
Contacts
26 Mobile + Cloud + IOT

User interaction plays a crucial role in every system. This is true for IoT too. Sensors, actuators and intelligent things connected together can cooperate and exchange information, but their ultimate goal is to provide value to people. Such value can be perceived only through appropriate user interfaces, which visualise information (through dashboard, reports, or infographics), let user navigate the information, and also interact with the devices, by setting properties or regulating their behavior. That's why it's important that in the IIoT development context we consider also user interaction. This presentation introduces IFML, the Interaction Flow Modeling Language, a new OMG standard that focuses on user interfaces and their integration with information systems, data sources, sensors and actuators. The presentation reports on some success stories from the industry, where IFML has been successfully applied. Large scale examples include consumer-oriented user interfaces, backend systems, data analysis dashboards, and interactions for command and control. Adopters include GE, Acer Computer, manufacturing, banks, utilities and military. Bio: Marco Brambilla is professor of software engineering at Polytechnic University of Milan, in Italy. He is also partner of WebRatio Inc. and founder of the startup Fluxedo. He has been visiting researcher at University of California, San Diego, CISCO, San José, and Dauphine University, Paris. He has extensive experience in model-driven methods and languages, and he published several books in the field of model-driven specifications of user interactions. He is the main promoter and designer of the language IFML (Interaction Flow Modeling Language), which has been adopted as a standard by the OMG in 2014. His research interest span model-driven development, web technologies, crowdsourcing, social network and big data analysis. WebRatio produces a model-driven tool for Web, Mobile and IoT development based on IFML, providing full code generation for enterprise-class applications.

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A large audience of users and typically a long time frame are needed to produce sensible and useful log data, making it an expensive task. To address this limit, we propose a method that focuses on the generation of REALISTIC NAVIGATIONAL PATHS, i.e., web logs . Our approach is extremely relevant because it can at the same time tackle the problem of lack of publicly available data about web navigation logs, and also be adopted in industry for AUTOMATIC GENERATION OF REALISTIC TEST SETTINGS of Web sites yet to be deployed. The generation has been implemented using deep learning methods for generating more realistic navigation activities, namely Recurrent Neural Network, which are very well suited to temporally evolving data Generative Adversarial Network: neural networks aimed at generating new data, such as images or text, very similar to the original ones and sometimes indistinguishable from them, that have become increasingly popular in recent years. We run experiments using open data sets of weblogs as training, and we run tests for assessing the performance of the methods. Results in generating new weblog data are quite good with respect to the two evaluation metrics adopted (BLEU and Human evaluation). Our study is described in detail in the paper published at ICWE 2020 – International Conference on Web Engineering with DOI: 10.1007/978-3-030-50578-3. It’s available online on the Springer Web site.

Analyzing rich club behavior in open source projects

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The network of collaborations in an open source project can reveal relevant emergent properties that influence its prospects of success. In this work, we analyze open source projects to determine whether they exhibit a rich-club behavior, i.e., a phenomenon where contributors with a high number of collaborations (i.e., strongly connected within the collaboration network) are likely to cooperate with other well-connected individuals. The presence or absence of a rich-club has an impact on the sustainability and robustness of the project. For this analysis, we build and study a dataset with the 100 most popular projects in GitHub, exploiting connectivity patterns in the graph structure of collaborations that arise from commits, issues and pull requests. Results show that rich-club behavior is present in all the projects, but only few of them have an evident club structure. We compute coefficients both for single source graphs and the overall interaction graph, showing that rich-club behavior varies across different layers of software development. We provide possible explanations of our results, as well as implications for further analysis.

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Community analysis using graph representation learning on social networks

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In a world more and more connected, new and complex interaction patterns can be extracted in the communication between people. This is extremely valuable for brands that can better understand the interests of users and the trends on social media to better target their products. In this paper, we aim to analyze the communities that arise around commercial brands on social networks to understand the meaning of similarity, collaboration, and interaction among users.We exploit the network that builds around the brands by encoding it into a graph model.We build a social network graph, considering user nodes and friendship relations; then we compare it with a heterogeneous graph model, where also posts and hashtags are considered as nodes and connected to the different node types; we finally build also a reduced network, generated by inducing direct user-to-user connections through the intermediate nodes (posts and hashtags). These different variants are encoded using graph representation learning, which generates a numerical vector for each node. Machine learning techniques are applied to these vectors to extract valuable insights for each user and for the communities they belong to. In the paper, we report on our experiments performed on an emerging fashion brand on Instagram, and we show that our approach is able to discriminate potential customers for the brand, and to highlight meaningful sub-communities composed by users that share the same kind of content on social networks.

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Data Cleaning for social media knowledge extraction

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Social media platforms let users share their opinions through textual or multimedia content. In many settings, this becomes a valuable source of knowledge that can be exploited for specific business objectives. Brands and companies often ask to monitor social media as sources for understanding the stance, opinion, and sentiment of their customers, audience and potential audience. This is crucial for them because it let them understand the trends and future commercial and marketing opportunities. However, all this relies on a solid and reliable data collection phase, that grants that all the analyses, extractions and predictions are applied on clean, solid and focused data. Indeed, the typical topic-based collection of social media content performed through keyword-based search typically entails very noisy results. We recently implemented a simple study aiming at cleaning the data collected from social content, within specific domains or related to given topics of interest. We propose a basic method for data cleaning and removal of off-topic content based on supervised machine learning techniques, i.e. classification, over data collected from social media platforms based on keywords regarding a specific topic. We define a general method for this and then we validate it through an experiment of data extraction from Twitter, with respect to a set of famous cultural institutions in Italy, including theaters, museums, and other venues. For this case, we collaborated with domain experts to label the dataset, and then we evaluated and compared the performance of classifiers that are trained with different feature extraction strategies.

Iterative knowledge extraction from social networks. The Web Conference 2018

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Knowledge in the world continuously evolves, and ontologies are largely incomplete, especially regarding data belonging to the so-called long tail. We propose a method for discovering emerging knowledge by extracting it from social content. Once initialized by domain experts, the method is capable of finding relevant entities by means of a mixed syntactic-semantic method. The method uses seeds, i.e. prototypes of emerging entities provided by experts, for generating candidates; then, it associates candidates to feature vectors built by using terms occurring in their social content and ranks the candidates by using their distance from the centroid of seeds, returning the top candidates. Our method can run iteratively, using the results as new seeds. In this paper we address the following research questions: (1) How does the reconstructed domain knowledge evolve if the candidates of one extraction are recursively used as seeds (2) How does the reconstructed domain knowledge spread geographically (3) Can the method be used to inspect the past, present, and future of knowledge (4) Can the method be used to find emerging knowledge?. This work was presented at The Web Conference 2018, MSM workshop.

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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. 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 groundtruth 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.

Myths and challenges in knowledge extraction and analysis from human-generate...

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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 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. The talk explores the mixed realistic-utopian domain of knowledge extraction and reports on some tools and cases where digital and physical world have brought together for better understanding our society.

Harvesting Knowledge from Social Networks: Extracting Typed Relationships amo...

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Knowledge bases like DBpedia, Yago or Google's Knowledge Graph contain huge amounts of ontological knowledge harvested from (semi-)structured, curated data sources, such as relational databases or XML and HTML documents. Yet, the Web is full of knowledge that is not curated and/or structured and, hence, not easily indexed, for example social data. Most work so far in this context has been dedicated to the extraction of entities, i.e., people, things or concepts. This poster describes our work toward the extraction of relationships among entities. The objective is reconstructing a typed graph of entities and relationships to represent the knowledge contained in social data, without the need for a-priori domain knowledge. The experiments with real datasets show promising performance across a variety of domains. The key distinguishing feature of the work is its focus on highly unstructured social data (tweets and Facebook posts) without reliable grammar structures. Traditional relation extraction approaches supervised , semi-supervised or unsupervised, commonly assume the availability of grammatically correct language corpora.

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Consumer-centered software applications nowadays are required to be available both as mobile and desktop versions. However, the app design is frequently made only for one of the two (i.e., mobile first or web first) while missing an appropriate design for the other (which, in turn, simply mimics the interaction of the first one). This results into poor quality of the interaction on one or the other platform. Current solutions would require different designs, to be realized through different design methods and tools, and that may require to double development and maintenance costs. In order to mitigate such an issue, this paper proposes a novel approach that supports the design of both web and mobile applications at once. Starting from a unique requirement and business specification, where web– and mobile–specific aspects are captured through tagging, we derive a platform independent design of the system specified in IFML. This model is subsequently refined and detailed for the two platforms, and used to automatically generate both the web and mobile versions. If more precise interactions are needed for the mobile part, a blending with MobML, a mobile-specific modeling language, is devised. Full traceability of the relations between artifacts is granted.

Web Science. An introduction

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The Web Science course focuses on the study of large-scale socio-technical systems associated with the World Wide Web. It considers the relationship between people and technology, the ways that society and technology complement one another and the way they impact on broader society. These analyses are inherently associated with Big Data management issues. The course is organised in four parts. 1. Syntax In the first part, the course introduces the basis of content analysis. If focuses on the syntactic aspects, covering the fundamentals of natural language processing and text mining. It describes the structure and typical characteristics of the different web sources, spanning search results, social media contents, social network structures, Web APIs, and so on. It also provides an overview of the basic Web analysis techniques applied in Web search and Web recommendation. 2. Semantics In the second part, the course presents semantic technologies. These technologies are very important nowadays because they allow to treat the "variety" dimension of Big Data, i.e., they enable integration of multiple and diverse sources of information, which is typical on the modern Web platform. Covered topics include: - RDF - a flexible data model to represent heterogeneous data - OWL - a flexible ontological language to model heterogeneous data sources - SPARQL - a query language for RDF. It shows how to put all the pieces together in order to achieve interoperability among heterogeneous information sources 3. Time The third part covers the realm of temporal-dependent data. The topics covered here allow to treat the "velocity" dimension of Big Data. It shows the importance for many Big Data analysis scenarios to process data stream, coming for instance from Internet of Things (IoT) and Social Media sources; and it describes how to apply semantic and syntactic techniques in the context of time-dependent information. For instance, it shows how to extend RDF to model RDF streams, how to extend SPARQL to continuously process RDF streams and how to reason on those RDF Streams 4. Applications In the fourth part, the course focuses on specific application scenarios and presents the typical settings and problems where the presented techniques can be applied. This part discusses settings such as: big data analysis for smart cities; data analytics for brand monitoring (marketing) and event monitoring; data analysis for trend detection and user engagement; and so on.

On the Quest for Changing Knowledge. Capturing emerging entities from social ...

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Massive data integration technologies have been recently used to produce very large ontologies. However, knowledge in the world continuously evolves, and ontologies are largely incomplete for what concerns low-frequency data, belonging to the so-called long tail. Socially produced content is an excellent source for discovering emerging knowledge: it is huge, and immediately reflects the relevant changes which hide emerging entities. Thus, we propose a method for discovering emerging entities by extracting them from social content. We start from a purely-syntactic method as a baseline, and we propose a semantics-based method based on entity recognition and DBpedia. The method associates candidate entities to feature vectors, built from social content by using co-occurrence, and then extracts the emerging entities by using feature similarity measures. Once instrumented by experts through very simple initialization, the method is capable of finding emerging entities and extracting their relevant relationships to given types; the method can be continuously or periodically iterated, using the already identified emerged knowledge as new starting point. We validate our method by applying it to a set of diverse domain-specific application scenarios, spanning fashion, literature, exhibitions and so on. We show the approach at work and we demonstrate its effectiveness on datasets with different characterization in terms of coverage, dynamics and size.

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Cities are growing as melting pots of people with different culture, religion, and language. In this paper, through multilingual analysis of Twitter contents shared within a city, we analyze the prevalent language in the different neighborhoods of the city and we compare the results with census data, in order to highlight any parallelisms or discrepancies between the two data sources. We show that the officially identified neighborhoods are actually representing significantly different communities and that the use of the social media as a data source helps to detect those weak signals that are not captured from traditional data.

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1. Mobile + Cloud + Internet of Things = New Business Opportunities Marco Brambilla Politecnico di Milano Matteo Sassi WebRatio Srl

2. Agenda Context: size of the market Case studies The global: Google Nest, Philips Hue The craftman: Arduino, IFTTT The engineer: Modeling, WebRatio Business scenarios Real world examples 2 Mobile + Cloud + IOT

3. Context – size of the market Gartner estimates that Internet of Things product and service suppliers will generate incremental revenue exceeding $300 billion in 2020. IDC forecasts that the worldwide market for Internet of Things solutions will grow from $1.9 trillion in 2013 to $7.1 trillion in 2020. Hardware, apps and services for healthb and fitness may generate up to $5 billion by 2016 (Gartner) 3 Mobile + Cloud + IOT

4. The global: Philips Hue Simple devices No installation burdens Control from web / mobile 4 Mobile + Cloud + IOT

5. The global: Google nest Intelligent thermostat? 5 Mobile + Cloud + IOT

6. Nest ecosystem Ecosystem of devices and cloud services Ecosystem of devices and cloud services data storage, analytics, machine intelligence 6 Mobile + Cloud + IOT

7. Nest: sensor or (valuable) data? 7 Mobile + Cloud + IOT

8. The craftman: Arduino Opensource hardware platform (from Italy!) Starts at less than 100$ 8 Mobile + Cloud + IOT

9. The craftman: If-This-Then-That 9 Mobile + Cloud + IOT

10. The engineer: modeling /abstracting Modeling languages (BPMN, IFML, UML) Modeling tools and automatic prototype builders Abstract from low-level details, everything is a resource Take a look at WebRatio www.webratio.com 10 Mobile + Cloud + IOT

11. Business scenarios: Payments Mobile payment: Checkout as you go: your bill is ready when done shopping NFC payments • Credit card business disruption • Fight between retail and virtual services (Apple Pay) Wallet Beacons e BLE (Bluetooth Low Energy) NFC-activated restaurant menu 11 Mobile + Cloud + IOT

12. Scenarios: Health Food and activity tracking Sensors in sport apparel 12 Mobile + Cloud + IOT

13. Health – intelligent cup 13 Mobile + Cloud + IOT

14. Scenarios: Advertising and awareness Presence detectors in stores: Beacon e iBeacon sensors in membership cards cameras and sensors in the stores Ad hoc special offers 14 Mobile + Cloud + IOT

15. Scenarioa: Sensors and actuators Consumer Domotics – home automation Mobile Enterprise Sensors and actuators Business processes 15 Mobile + Cloud + IOT

16. IoT + IoP (IoH) The people! Internet of humans – Internet of people Human as a sensor 16 Mobile + Cloud + IOT

17. IoT + IoP 17 Mobile + Cloud + IOT

18. Real-world examples

19. PROACTIVE project Infrastructure for public protection from natural disasters Heterogeneous sources Predictive models Monitoring tools Response planning Demonstrators PeopleOnTheMove: safety and prevention on crowds SnowWatch: water resources monitoring LeakWatch: utility improvements (water leaks in distribution network) 19 Mobile + Cloud + IOT

20. PROACTIVE architecture 20 Mobile + Cloud + IOT

21. SmartH2O ICT infrastructure for water consumption measurement Understand consumers’ behaviour Forecast consumption based on contingencies Increase engagement and responsibility of consumers 21 Mobile + Cloud + IOT

22. SmartH2O infrastructure 22 Mobile + Cloud + IOT

23. Typical IoT + Cloud + Mobile infrastructure 23 Mobile + Cloud + IOT

24. Simplified architectural view User Interface (HTML5, PhoneGap,…) Web/Mobile App Logic (Java, IFML,…) Web Services (soap, rest) Database (SQL) “Things” 24 Mobile + Cloud + IOT

25. Your ideas Come and share your ideas We will build a "Proof of Concept" (PoC) with WebRatio Platform and boost your business Get in touch! matteo.sassi@webratio.com marco.brambilla@polimi.it Twitter: @marcobrambi 25 Mobile + Cloud + IOT

26. Contacts Email address: Email address: contact@webratio.com Sales information: sales@webratio.com Support: helpdesk@webratio.com Customer support: support@webratio.com UNITED STATES IT Minneapolis 12100 Singletree Ln, Suite 109 Eden Prairie, MN - 55344 Phone: +1 (952) 746 1236 Santa Clara 2040 Martin Avenue Santa Clara, CA - 95050 Phone: +1 (408) 707 6565 ITALY Milan Piazza Cadorna, 10 20123 Milano Phone: (+39) 02 4548 5980 Como Via Cavour, 2 22074 Lomazzo c/o ComoNExT Phone: (+39) 02 3671 4280 ECUADOR Quito Av. 12 de octubre y Colón Torre Boreal Building, 8th, off. 803 Phone: (+593) 2 3945 331 Cuenca Alfonso Cordero y Paucarbamba, WorkCenter Palace, 4th, off. 410 Phone: (+593) 7 4103 792 US EC Contacts 26 Mobile + Cloud + IOT

Mobile + cloud + internet of things (iot) = nuove opportunità di business

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