In short, three different fellowships are available:

1. Research Fellowship: For individuals without a PhD who are considering pursuing research studies.
2. PhD Scholarship: For those who wish to pursue a PhD in Switzerland for a period of 3+ years.
3. Post-Doc Scholarship: For individuals with a PhD who are looking to spend one year in Switzerland conducting postdoctoral research.

Please note that all fellowships are intended for individuals who have not previously spent time in Switzerland. Consequently, if you are awarded one of these fellowships, you will be ineligible to apply for the others in the future as you technicaly already have a Phd or Postdoc in Switzerland.

Let’s see each one of them in dettail:

Research Fellowship

Research Fellowships are 12-month programs aimed at highly qualified postgraduate researchers from all academic disciplines. Applicants must have obtained a master’s degree or equivalent by July 31, 2024, except for ETH Zurich applicants, who must meet this requirement by June 30, 2024. Candidates should be born after December 31, 1988. The application requires a support letter from an academic host professor at a Swiss university, including a brief CV (maximum 2 pages), explaining their willingness to supervise and support the research. This letter differs from the confidential recommendation letter also required. Additionally, applicants must submit a research proposal with a defined time frame. Notably, this fellowship is not available to those who already hold a PhD degree

PhD Scholarship

PhD Scholarships are designed for highly qualified PhD students from all academic disciplines, offering funding for 12 to 36 months, extendable in three 12-month increments based on academic progress. Applicants must have obtained a master’s degree or equivalent by July 31, 2024, except for ETH Zurich applicants, who must meet this requirement by June 30, 2024. Eligible candidates should be born after December 31, 1988. The application requires a support letter from an academic host professor at a Swiss university, including a brief CV (maximum 2 pages), detailing their commitment to supervise and support the PhD throughout its duration. If an assistant professor supports the application, a full professor must co-sign the letter. This support letter is distinct from the confidential recommendation letter also required. For PhD studies at a University of Applied Sciences, an additional support letter from a faculty member of the host research university is mandatory. The doctoral degree must be awarded by a research university. A detailed research proposal with a defined time frame is the centerpiece of the application and requires thorough attention.

Post-Doc Scholarship

Postdoctoral Scholarships are 12-month programs aimed at highly qualified early career scientists from all scientific fields. Eligible candidates must have completed their PhD between December 31, 2020, and July 31, 2024 (June 30, 2024, for ETH Zurich), and primarily for those who have not previously studied in Switzerland. The program does not fund sabbaticals. Applicants need a support letter from an academic host professor at a Swiss university, including a brief CV (maximum 2 pages), explaining their willingness to supervise the research. Former PhD mentors and co-directors for co-tutelle PhDs cannot be supervisors. A detailed research proposal with a timeframe is the centerpiece of the application. This scholarship is exclusively for full-time postdoctoral research in Switzerland, prohibiting fieldwork outside the country, and recipients are expected to reside in Switzerland during the grant period. For double doctorates, the date of the first degree is considered. The scholarship is not valid for mixed research conducted partly in Switzerland and partly in another country.

Where to find more Information

The official link of the scholarship contains all the relevant information and is the “GO TO” resources:

• https://www.sbfi.admin.ch/sbfi/en/home/education/scholarships-and-grants/swiss-government-excellence-scholarships.html

Additionally, each country has specific information and details regarding the application procedure, which can be found at the following link.

• https://www.sbfi.admin.ch/dam/sbfi/en/dokumente/2022/07/all-countries.pdf.download.pdf/2023_ESKAS_all_country_contacts_A_to_Z_for_application_documents.pdf

I recommend relying on the official information provided at the link above. The program itself warns about potential scams, and while there are numerous YouTube videos on the topic, some are generated by ChatGPT and others are, for lack of a better word, “dubious.” Here, I share videos from two YouTubers who, in my opinion, offer a reliable overview of the program. From Jama, part 1:

And part 2:

As well as from Aatiya:

My personal suggestions:

If you are interested in applying for a Phd or Postdoc in Switzerland, here are my personal suggestions:

1. Start Early (Paperwork): The application process is deliberately complex to filter out unmotivated candidates. The responsibility for managing the paperwork falls entirely on you, so begin early to ensure you have ample time to complete everything accurately.
2. Start Early (Connections in Switzerland): A critical aspect of the application is obtaining a support letter from a professor in Switzerland who is willing to endorse your candidacy and confirm that your proposal aligns with their and their institution’s research. Building these connections early is essential.
3. Start Early (Your Research Proposal): Develop a meaningful and well-written research proposal as soon as possible. Without a solid proposal, it will be challenging to secure support from a professor. You are solely responsible for writing this proposal. Once you have a polished version formatted according to the required templates, use it as your business card when reaching out to potential professors in Switzerland. Without this document, your communication is likely to be ignored or flagged as spam.

In other words, your commitment and the importance of contacting your potential supervisor as early as possible, armed with all the relevant information, cannot be overstated. This official video from the program provides additional valuable suggestions:

In addition, please note that each professor will not/cam not endorse more then one (or two) candidates per year. Therefore many of the request may simply be not answered.

Can I help you further for your Phd or Postdoc in Switzerland?

It really depends. For this specific grant you (the applicant) are really responsible for doing the heavy lifting as well as for dealing with the dettails. As for the rest, (i) if you believe that you have what it takes for this fellowship, (ii) you fulfill all the requirements, (iii) you have a nice idea to pursue and (iv) it is related to one of the following areas:

1. Internet of Things (IoT): Refers to the network of interconnected devices and objects that can communicate and share data. IoT enables smart homes, industrial automation, and more.
2. Extended Reality (XR): Encompasses virtual reality (VR), augmented reality (AR), and mixed reality (MR). XR technologies blend digital content with the real world, enhancing user experiences.
3. Human Behavioral Informatics: The study of human behavior using data analytics and computational methods. It involves understanding patterns, preferences, and decision-making based on digital interactions.
4. Computer Vision: A field of artificial intelligence that focuses on enabling machines to interpret visual information from images or videos. Applications include facial recognition, object detection, and medical imaging.
5. Language Processing: Also known as natural language processing (NLP), it involves teaching computers to understand and generate human language. NLP powers chatbots, language translation, and sentiment analysis.
6. Security and Privacy: Addresses safeguarding data, systems, and networks from unauthorized access or breaches. It includes encryption, authentication, and privacy-preserving techniques.
7. Big Data Processing: Deals with managing and analyzing large volumes of data. Technologies like Hadoop and Spark enable efficient processing of massive datasets.
8. Distributed Systems: Involves designing and managing systems that run on multiple interconnected computers. Examples include cloud computing, peer-to-peer networks, and distributed databases.
9. Serverless Computing: A cloud computing model where developers focus on writing code without managing servers. Serverless platforms automatically scale resources based on demand.
10. Digital Decision Making: The process of using data-driven insights and algorithms to make informed choices in various domains, such as business, healthcare, and finance.

You can consider dropping me a line! Are you still finishing your thesis instead? These guides may help!

Good Luck!

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It was a really fun and formative experience where we had the possibility to interact with many colleagues across Europe. We can clearly declare the workshop successful as we had to cap the contribution to only 11 despite the very large contribution that we received (total 22, acceptance rage 50%). If that is not enough for declaring it a success, it also was the largest (most participated) event of the conference.

A big thanks also goes to our sponsors and keynote speaker that stimulated the conversation as well as provided a valuable industrial experience.

Here you can find a set of useful links in case you would like to profit of the outcomes:

• Call for paper for the first workshop on AI Beyond Efficiency
• Modafferi, S., Nuñez, M. J., Lelli, F., & Dalle Carbonare, D. (2022). Artificial Intelligence beyond Efficiency. CEUR Workshop Proceedings, 3214

Full report can be downloaded here: PDF Version of the report on the workshop

The paper that where discussed are available at the following links:

• Introducing Building Blocks for Industry 4.0, an Analytics Application for the Federated EFPF Platform
  Jose Maria Gonzalez Castro Robert Sanfeliu Prat, Francesc Bonada, Fernando Gigante Valencia
• AI Ethics for Industry 5.0 – From Principles to Practice
  Alexandru Constantin Ciobanu, Gabriela Meșniță
• A Practical Experience of AI Solution Used to Improve Varnishing Process Efficiency in Furniture Manufacturing
  Juan Del Agua, Gabriel Modia
• Industry5.0 and Sociotechnical Theory: Theoretical Underpinnings
  Nicholas Fair
• Relevance of Visualization and Interaction Technologies for Industry 5.0
  Ander Garcia, Marco Quartulli, Igor G. Olaizola, Iñigo Barandiaran
• Teaming.AI: Enabling Human-AI Teaming Intelligence in Manufacturing
  Thomas Hoch, Bernhard Heinzl, Gerald Czech, Maqbool Khan, Philipp Waibel, Stefan Bachhofner, Elmar Kiesling, Bernhard Moser
• On Exploring the Possibilities and the Limits of AI for an Interoperable and Empowering Industry 4.0
  Francesco Lelli
• Artificial Intelligence from Industry 5.0 perspective: Is the Technology Ready to Meet the Challenge?
  Igor García Olaizola Marco Quartulli, Ander Garcia, Iñigo Barandiaran
• Towards Zero-Defect Manufacturing: Machine Selection through Unsupervised Learning in the Printing Industry
  Sotirios T. Spantideas, Anastasios E. Giannopoulos, Nikolaos C. Kapsalis, Angelos Angelopoulos, Stamatis Voliotis, Panagiotis Trakadas
• Towards Industry 5.0 – A Trustworthy AI Framework for Digital Manufacturing with Humans in Control
  Usman Wajid, Alexandros Nizamis, Victor Anaya
• On Developing Human Centric Digital Twins in Industry 4.0 and Beyond
  Akpofure A. Enughwure, Francesco Lelli

As organizer I hope that the connections that the participants build will remain strong, will grow, and will materialize in a tangible advance of the field.

First Workshop on AI Beyond Efficiency: proceedings of the event. #ArtificialIntelligence #workshops #interoperability #Industry40 #BigData #MachineLearning
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In both cases you have my respect and support for trying to understand how HPC, AI, Big Data and Industry 4.0 can be used in your business.

I am honored and humbled that you selected me as part of your portfolio of experts for understanding how to leverage AI based technologies in your company. I am also expressing my gratitude to the StarwAI for supporting candidature as expert.

Please consider the following, prior contacting me:

• StairwAI is using a matchmaking tool to propose to each SME the list of AI Experts that fit the pilot’s scope and needs. Obviously you make the final decision on the Expert or Experts that would like to cooperate with. In addition, you free to select any Experts from the list of validated Experts, not proposed by the matchmaking tool. However, you may want to give a shot to the matchmaking tool first.
• You may want to drop me a line informally before moving forward, I will be happy to answer to your questions and take a virtual (or physical) coffee and discuss your ideas. In this way I may be able to understand if we are a good fit for each other and if I can really provide value to you.
• StairwAI is planning a second support program in 2023. Therefore if we will not have the possibility to work together in this round, do not despair; we will be able to do it at the next iteration.

Where to know more:

• AI4Europe
• StarwAI Project

Finally, let me thanks AI4EU for bring together AI stakeholders and resources, overcoming fragmentation issues and encouraging the acceleration of AI-based innovations. In particular for supporting SMEs that may be left behind due to their limited capability to acquire competences in this current market.

Happy to support the StarwAI Project and AI4EU in their mission of supporting SMEs in their strategies for #DataScience #AI #industry40 and #hpc .
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A knowledge graph is also known as a semantic network and represents a network of real-world entities. In other words represent “reality” as a combination of objects, events, situations, or concepts taking into account the relationship between them. This information is usually stored in a graph database and visualized as a graph structure, prompting the term knowledge “graph.”. The figure below can give you a visual idea of how these entities interlink among each other.

Intuitively, we can note that knowledge graphs take advantages of triples: subject-verb-qualifier in order to infer relationships among the data.

The knowledge graphs is at the core of the semantic web vision and standards like RDF and query languages like SPARQL are maintained by the w3c. A set of pointers for more information is available in this paper that I wrote specifically for interoperability in industry 4.0 and internet of things. However sections 2.3. (of the paper) give an overview of the semantic stack.

Existing tools and technologies that use knowledge graph

A core set of technologies that are currently in productions and used companies like Google, Facebook, Apple includes the following:

• Wikidata: Wikidata is a collaboratively edited multilingual knowledge graph hosted by the Wikimedia Foundation. It is a free and open knowledge base that can be read and edited by both humans and machines.
• schema.org is an organization that publishes documentations and guidelines to using structured data mark-up on web-pages. Its main objective is to standardize HTML tags to be used by webmasters for creating rich results about a certain topic of interest.
• Google Knowledge Graph is a knowledge base used by Google and its services to enhance its search engine’s results with information gathered from a variety of sources.

These technologies are strictly focusing on the data instead and the video below share a practical approach that focus on the data and how to combine them. Note that it does not consider any AI approach but focus on how to combine existing available data and their relationships.

However, it contains very nice examples and give an intuitive idea on how Wikidata, Schema.org and Knowledge graph works.

Research on Knowledge Graph and Artificial Intelligence

Now that we have an idea of what are the basic techniques for representing information we need to understand how we can use machine learning techniques for enriching these information and the expressiveness of the models. What follows is a set of seminars that discuss the knowledge graph from a more theoretical point of view and consider open research questions. This is the first part:

Below you can find the second part. It also contains very nice real life case studies. Including how Siri works!

If you are curious and would like to know more about the topic I encourage you in browsing the YouTube channel for more videos.

On #Knowledge Graph and #ArtificialIntelligence : how to combine data and models for better information. #BigData #DataScience
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Adopting healthy scientific practices is essential for conducting good quality research. This may be intuitive but it is hard to achieve and does not happen naturally. This video from the Royal Society introduces the problem:

The term “replication crisis” emphasizes that replication, a corner stone in science, is often considered of secondary importance. If we want to fight this phenomena it is important to conduct research using practices that promote the possibility to do that.

The open science movement not only offers you a way to get credited for your work (see this post on open access for more), but also tries to solve the problem of replicability. It takes into account several aspects at the level of researchers, scientific venues, funding organizations, etc.

In this article, we will focus on aspects related to the way research is conducted and the direct implications (and advantages) for your thesis.

I am assuming that you are not interested in falsifying your data or following any other intentional malpractices. In case you wonder, if you get caught, you could face serious consequences that range from immediate nullification of your thesis to expulsion and/or immediate termination of your internship.

As a single researcher (or small group of researchers) your attention should go towards offering to other scientists the possibility to access the data that you used for the analysis as well as your procedures. This video explain in a funny way why:

In other words, these are a set of good research practices that you should take into account:

• Carefully describe the procedures that you followed for obtaining the data.
• Make your data publicly available so that other scientists can check what you are doing. This includes your mentor and your second reader.
• Document each step of the process in order to be able to share this information with fellow researchers.
• Disclose the meaning of each field in the data.
• Disclose and share the programming code that you used for developing the data as well as the analysis.

Adopting these practices has several direct benefits:

• Your supervisors and/or reviewers will be positively impressed and predispose towards a higher grade.
• You will obtain a (very) good grade independently from your findings. In fact, your thesis is not about your findings: Showing you capability to follow the scientific method is way more important.
• Your work could, starting from your data, be published in a relatively easy manner as a tangible follow up of your thesis. Thus, differentiating your CV from other graduated students.
• A fellow student could decide to continue your work. Thus, you will be able to leave something tangible after you, instead of having your thesis completely forgotten.

In a nutshell, the bottom line is that good research practices will give you a better grade.

In this article, I limited myself to describing the scientific practices that a single individual should follow in pursuing his/her thesis. Adopting a more transparent approach clearly has several benefits and a direct implication on your grade. However, the problem of replicability is bigger than it may look like from this post. It is taken into serious consideration by the entire scientific community. In case you are interested in knowing more, this video gives you a general (and insightful) view on the problem:

#good #research practices like the one promoted by #OpenScience and #OpenData lead to better grades for your #Thesis as well as to a better #Science and more visibility of your work via #openaccess.
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This article (Adopting good scientific practices increase your visibility and the grade of your thesis) is part of the miniseries on how to do a good thesis, you can see the full list of posts at the following link:

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Data come in all shapes and forms. If you are doing your thesis, you are in search of a proof of concept In other words, you are attempting to prove the validity of an idea or concept, not to produce an industry/ready solution . Therefore, most of the time, you do not need particularly large datasets. However, you want to be sure that they are of a sufficient quality. After all, you need to elaborate them without introducing too much noise.

What follows is a selected set of resources. It is not exhaustive and I am pretty sure that you will be able to find more by searching the Internet and asking your supervisor. However, this list is a good starting point in case you are trying to build a research/thesis proposal or you are stuck and in search of an idea.

Google datasets search:

• Link: https://datasetsearch.research.google.com/

You guessed right. Google has a dedicated search engine for datasets. It is freely available and index data that implement a particular schema.org format. Some of the data may be behind paywalls. However, academics and students usually can contact the data provider in order to get access to them or to a selected portion that is sufficient for their study.

EU Open Data Portal:

• Link: https://data.europa.eu/euodp/en/data/

Once again, you guessed right. The European Union is committed to foster a strong data transparency policy. At the time of writing this article, I was able to find 15399 different datasets that are freely available. They cover a large variety of topics and are related to all the various arguments under the jurisdiction of the EU.

Yahoo Finance:

• Link: https://finance.yahoo.com/
• Link to the help center: https://help.yahoo.com/kb/download-historical-data-yahoo-finance-sln2311.html

There are plenty of financial DB in the web. Yahoo finance is the most known and straightforward. In case you are looking for daily quotations of various financial assets, this is the place for you. The second link will explain how to download historical data.

More on financial data:

• Link Thomson Reuters API: https://customers.reuters.com/developer/apis_tech.aspx

Some programming knowledge is required. An Application Programming Interface (API) gives you the possibility to access to the data of a website in a programmatic way. Reuters has a dataset that includes financial news and press releases. All you have to do is to write a few lines of code for accessing this information.

Kaggle.com, a community approach:

• Link: https://www.kaggle.com/datasets

Kaggle offers for its members the possibility to access and share data. In addition, it offers also a set of demo code for accessing and manipulating the data. The community is data science driven. However, some of the datasets do not require particular programming skills.

Get your data by yourself:

• link: https://www.programmableweb.com/

There is an ever growing amount of websites that offer the possibility to access them programmatically. Programmableweb is a directory that tries to list them. In addition, it lists links to the proper resources for using them. Note that use the APIs will require some programming. However, you may end up creating original datasets and this is already a valuable outcome of your thesis.

Where to get data for your thesis? In summary:

I hope that by now you realized that datasets are far from been a sparse resource. This collection or resources can serve you as the starting point for finding what you are looking for or for refining/developing your research idea.

Do you need #data? Here you can find a collection of resources for finding your dream datasets. #DataScience #BigData #OpenData
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This article (Where to get Data: a collection of resources for your thesis) is part of the miniseries on how to do a good thesis, you can see the full list of posts at the following link:

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The contemporary media landscape demands that media content producers approach continuously changing consumer preferences in an agile manner, adapting content to audiences. Insights gleaned from large data streams can support creative staff in adapting to trends and preferences of this moving target. This project aims to identify how and when (big) data can inspire Talpa’s cross-media creative innovation processes. The objective of this research is twofold. First, it aims to build new scientific insights about the relationship between (big) data and creative innovation processes (CIPs) in general, and those of the media industry in specific. Second, these scientific insights will be fed back into CIPs of the private partner of this research project (Talpa), who will provide the data as well as a testing ground. Changes in the global media landscape provide audiences with a wider range of content and media platform choices. As audiences cope with abundant choices in different ways, it is crucial for media companies to understand how audiences discern content between media platforms. Yet little is known about how audiences actively engage with content across media platforms. In this project, we study how “big data” in the fast-changing media industry can support the development of new media formats in the innovation process. We particularly focus on studying challenges and opportunities for cross-media companies that could generate valuable insights from combining data from each of the different platforms they own.

Curious to know more? Check the following links

• Check possible internships and master thesis at Talpa
• Official Website of the research project
• Data Inspired Creativity at Tilburg University
• Talpa Network

Would you like to be involved? Drop me a line, research collaborations as well as master thesis may be available.

The project is supported by NWO

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Mentor: Francesco Lelli

External Supervisor: Assigned by ESA case by case

The European Space Agency (ESA) offer a set of internship for students that want to do a thesis and contribute to the success of the European space program.  Some require you to be (just) graduated and some can be done in conjunction of your master thesis. They span in all the areas of Engineering, Computer Science and Information Management.  The department of Management has a collaboration in the areas of Software Engineering and Information Management with particular interest in AI and Big Data. In this respect, you are welcome to be an intern at ESA while pursuing your thesis for graduating in Information Management.   

4 different types of internship are available:

Student Internship

This internship cover a period of 3-6 months and is specifically focused on building a thesis at ESA. There are windows of time when they can apply and they are renewed every 6 months.

Link for more information:

• https://www.esa.int/About_Us/Careers_at_ESA/Student_Internships2

YGT: Young graduate trainee

This program is for students that are willing to consider a longer staying at ESA, the duration is 1+1 year and require you to be a graduate students or that the thesis is the only thing that you are missing. You are going to receive a proper salary and you will be employed in one of the 4 centers (Frankfurt, Leiden, Madrid or Rome) of the agency for the duration of your full time contract.

Link for more information:

• https://www.esa.int/About_Us/Careers_at_ESA/Apply_now_for_new_Young_Graduate_Trainee_opportunities

National trainee

These internships follow a similar trajectory of the YGT program, however a specific for a particular country and will require you to have a specific nationality.

Link for more information:

• https://www.esa.int/About_Us/Careers_at_ESA/Graduates_National_Trainees

These positions also require an interaction with you and the national initiatives.

Specific Trainee positions:

Link for more information (look for “trainee”) : 

• https://career2.successfactors.eu/career?company=esa

In this DB you can find all the positions that are currently open at ESA, if a particular research group requires a specific intern is advertise it here. You should look using the keyword “trainee”.

A Final Note on Internships and Fellowships at the European Space Agency

Our specific collaboration focus on the ESOC and ESTEC programs nevertheless the internships program is a single program for all the initiatives that are currently pursued at ESA.

Within this respect, the first selection process at ESA is centralized and equal for all the candidates. Consequently, if you are interested in pursuing a “thesis plus internship” you need to pass the first stage of the selection before been able to propose/define a topic of investigation.  The selection process and definition of the topic will follow this trajectory:

Phase 1: Interested candidates should apply for the proper internship program.

Phase 2: the candidate will be assessed by the ESA-HR

Phase 3: If successful, the candidate will be part of the trainee and inserted in an internal DB at ESA where scientists will look at your profile in search for a match with their interest. 

Phase 4: you will be contacted by ESA with a few possibilities for pursuing the internship

Phase 5: You will get in touch with me and we will decide how to finalize your thesis proposal in order to be able to do your thesis in Information Management as part of the ESA program.

#thesis plus #internships or Fellowships at the European Space Agency ( #ESA ) in the areas of #BigData and #AI
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For a complete list of available thesis please visit the following link: 
https://francescolelli.info/thesis/

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At a first look these questions look really obvious, to the point that they do not really need to be answered. However, “the obvious” is what most often shapes our behavior without us being aware of it. In particular, our relation with the objects around us, from an anthropological perspective, is what literally allowed us to become humans and helped us in interacting and modifying the surrounding environment.

This study intends to provide an introspective analysis of the way we approach the relationship we have with “things”. As a result, this study may foster a deeper understanding of ourselves and help us in prioritizing what we need, what we want and what we desire in life.

Dr. Heidi Toivonen and me decided to develop a multidisciplinary research on human-smart devices interaction that will try to answer some of these questions in a scientifically sound manner.  

In this respect we need your help! In particular, we are inviting you to take part in a little survey regarding your relationship with smart devices. In taking this survey, you will be able to reflect on how you relate to technology. At the same time you will be contributing to a study that aims to foster a deeper understanding of human interaction with personal smart devices that are becoming a more and more pervasive element of our everyday life. 

We sincerely appreciate your support and collaboration. 

Completing the survey will take you no more than 5 minutes.

Follow this link to the Survey:
Human-Smart Device Interaction

You are also welcome to extend this invitation to anybody you think may be interested in participating in this research. Please share this link and help us spread the voice!

Participate to a scientific #research that will #help #understanding the #interactive #relationships between #humans and smart devices. It takes only 5 minutes of your time! #IoT #HCI #psychology #AI #BigData
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You can contact us if you have any questions or feedback to share. In addition, if you have done work that you believe is relevant for this research, we look forward to learning from you! 

Thanks for your contribution to science!

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In order to set up the mood for this exercise I recommend you to watch the following video:

The rest of this article share with you the current situation of your mission (should you chose to accept it) and is structured as follows: First (section 1), the background is introduced. Then, the case study (Section 2) is briefly described and the interview that you had with various stakeholders (section 2.1 and 2.2) are articulated. Finally (Section 3) presents the details of your tasks.

1 – What happen before: the background story

The University of Nowhere made an agreement with a popular web based social network[1], named ACME Social, in order to share its information about its students and its lecturers.

The business agreement is on the line of what follows: “we will provide you the information that you require and, if one of your customers, would like to use these information you will pay us 10% of your sale”.

The legal department of the University of Nowhere validated this agreement. They decided to amend the subscription of each students with offering the possibility to opt out for preserving their privacy. However, if the student do that he/she will not be able to subscribe to the exams (i.e. he will never graduate!).

Student associations have been consulted and they do not see any difference from how this information are currently handled by popular eCommerce websites[2]. Consequently, they decide to support this initiative. Possible reasons for giving their support includes: (i) a potential (but not certain) reduction in the tuition costs of all the students and (ii) a share of the 1% (this is certain) of the revenue that the University of Nowhere will make. It will be transferred to the bank accounts of the associations that will support the initiative (and only to them).

Now that the various business agreements are in place, the rector magnificus has decided to investigate on the real possibility to implement what has been promised to ACME Social (really!?!? Not before!?!?). In his infinite wisdom, he decided to appoint you as his adviser and he is expecting concrete and actionable insights in order to move forward this initiative.

2 – BPI: The Case Study

You did not waste your time and you immediately started working on it. After all this is real life, not just an example of Business Process Integration. You see this as a fantastic opportunity for advancing your studies in the right way. Clearly a lot of professors of the Department of Impossible Computing are looking at what you are doing and, if successful, you will probably have the luxury of choosing your mentor for the master thesis freely. In addition, he/she will help you in securing a state of the art internship that will boost your future career.

You identified the IT department and ACME Social as your core stakeholders. After all, the various divisions of the university agreed to support this initiative and they all use the IT department for storing their information. Therefore, in principle, the IT is the gatekeeper of all the information of the University of Nowhere.

You did a few meetings with:

• ACME Social: in order to understand what kind of information they want and how and when they would like these data. In other words what kind of format they would like to process and how they envision the transferring of /accessing to these data.
• The IT department: in order to understand what information are available and what systems are involved.

The following subsections describe the outcome of these meetings.

2.1 Minute of the Meeting with ACME Social

ACME Social would like to enrich its data with a precise location of the students. After all, the university is a “very compact environment” where different students coexists. They initially thought about using GPS coordinates from the mobile phones of the students. Unfortunately, the precision, in particular if inside a building, is not accurate enough. Consequently, they would like to combine the GPS information with additional information. In particular they would like to leverage the fact that students follow classes. By combining them they would be able to achieve a more precise geo-location insight the campus.

Some of the functionalities that they would like to implement are:

• Retrieve a list of unique ID of the students
• Retrieve the email(s) of each student
• Get the class that the student is supposed to follow
• Retrieving the exact coordinates of the location of a given student according to the University of Nowhere.

Note that in a real case the information that ACME Social would like to acquire will be much more. However, for the purpose of this exercise we will limit to these four.

2.2 Minute of the Meeting with the IT Department

You had a very, very, very long discussion with the IT department and you analyzed 15-een possible systems that may provide the information that you are seeking.  You noticed that most of the systems collect redundant information and one is actually from 1980. Nobody actually knows how it works and what exactly is doing. What is clear is that if they try to turn it off, 50% of their systems stop working.

You manage to restrict the list to two systems that may provide the relevant information to you. Proper access to the data need to be programmed as there is not an existing module that is capable to do what you are envisioning. By the way, the IT Department thinks that it is not a particularly difficult task (really?!? Mmmm sound suspicious).

The systems are, University of Nowhere Student CRM that offer the following information:

• Retrieve the list of Unique ID (ANR) of all the students
• Retrieve the email associated to a particular Unique ID (ANR). All the information related to a student’s profile (address, phone number etc etc) are there as well.

And the system, University of Nowhere ACME digital learning with the following information:

• Retrieve the list of classes that are currently offered by the university
• Retrieve the list of students (with their unique ID or ANR) that are following a particular class

3 – The Mission of the Business Process Integrator

Your mission (should you choose to accept it) is to understand and model the needs of University of Nowhere. You will give to all the stakeholders (students, professors, ACME Social) what they need for implementing the foreseen business agreement. In addition, the rector magnificus need a clear road map to present the board for securing the budget for the project. Due to your extensive experience in Business Process Integration you know what the stakeholders need. You intend to produce is a document that summarizes the state of the art the the planned actions. In particular the document should contains :

• One or more Use Case diagrams that describe the functionalities of the BPI solution that the University of Nowhere should support. You also count of the fact that you can explain each Use Case using natural language (English) to make them simple and more readable. They key challenge in this task is to try to identify all the actors involved.
• One or more Activity Diagrams for illustrating the dynamic of the Use Cases and model the overall end-to-end process. This Diagram will give you the possibility to specify the internal and external systems involved as well as their interactions. 
• An UML class diagram describing all the concepts used among all the selected processes and systems. Once you will be able to specify all the attributes and the methods of the classes they IT Department will know exactly what to do.
• A set of Sequence Diagrams that leverage the Use Cases and the Class Diagram in order to clearly specify: (i) how the system interacts and (ii) how your solution will implement the process as defined by the business requirements.

Take home message for this example of business process integration

This is just an example of business process integration with a “big data flavor”. It is fictional and represent a simplified use case. Nevertheless is not trivial and still represents relatively well a real cases. You can have a look at the syllabus of BPI for more information.

More information regarding the diagrams that we envision is available at this link.

[1] Possible (but this is fictional) real life examples include: Facebook, Twitter, LinkedIn, Google, Academia, Researcher

[2] Possible (but this is fictional) real life examples include: Bol or Amazon or eBay

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