Edge Computing Approach for Vessel Monitoring System

A vessel monitoring system (VMS) is responsible for real-time vessel movement tracking. At sea, most of the tracking systems use satellite communications, which have high associated costs. This leads to a less frequent transmission of data, which reduces the reliability of the vessel location. Our research work involves the creation of an edge computing approach on a local VMS, creating an intelligent process that decides whether the collected data needs to be transmitted or not. Only relevant data that can indicate abnormal behavior is transmitted. The remaining data is stored and transmitted only at ports when communication systems are available at lower prices. In this research, we apply this approach to a fishing control process increasing the data collection process from once every 10 min to once every 30 s, simultaneously decreasing the satellite communication costs, as only relevant data is transmitted in real-time to the competent central authorities. Findings show substantial communication savings from 70% to 90% as only abnormal vessel behavior is transmitted. Even with a data collection process of once every 30 s, findings also show that the use of more stable fishing techniques and fishing areas result in higher savings. The proposed approach is assessed as well in terms of the environmental impact of fishing and potential fraud detection and reduction. View Full-Text

Master in Integrated Business Intelligence Systems (MIBIS) - ISCTE-IUL

Master in Integrated Business Intelligence Systems (MIBIS) - ISCTE-IUL

Master in Integrated Business Intelligence Systems (MIBIS) - ISCTE-IUL

Higher Education, ISCTE-IUL, Lisbon, Portugal - Information on the functioning of the school year 2020/2021

Because I am receiving questions, in social media, from both Portuguese and foreign students, I share here the email I received from the rectory on 31-07-2020.

Citation:

Preparation of the 2020/2021 school year 

The Iscte is also awaiting guidance from the Ministry of Science, Technology and Higher Education (MSTHE) and the Directorate General of Health (DGH) regarding the operating conditions for the next school year.

However, for the start of the 2020/2021 school year, the Iscte has already defined the following principles:

1. in line with the practice of previous years, it is planned to start classes, for most courses, in September 2020.

2. In the case of the 1st year of undergraduate and integrated master's degrees, given the schedule of the National Access Competition, classes will begin only on October 6 and end in December, according to the school calendar.

3. It is anticipated that classes will be taught in face-to-face from the Iscte rooms.

4. Classes are expected to be held in a mixed regime (face-to-face and online simultaneously).

5. For foreign or international students, who are unable to travel to Lisbon due to the pandemic, the classes can be transmitted online, in English. 

The ways of implementing these principles, as well as their exceptions, will be defined after the approval and dissemination of the MSTHE and DGH guidelines.

Academic Greetings,

Maria de Lurdes Rodrigues

Dean

Avenida das Forças Armadas, Edifício Sedas Nunes, Reitoria

1649-026 LISBON Portugal

www.iscte-iul.pt | Facebook | Twitter | IULtv | Flickr

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For any additional information about the functioning of the Master in Integrated Business Intelligence Systems (MIBIS), you may contact me by email:

Master in Integrated Business Intelligence Systems (MIBIS) - ISCTE-IUL

Management

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IoT Power Monitoring System for Smart Environments

In this research work, we describe the development and subsequent validation of EnerMon a flexible, efficient, edge-computing based Internet of Things (IoT) LoRa (LongRange) System to monitor power consumption. This system provides real-time information and a descriptive analytics process to provide a ‘big picture’ about energy consumption over time and identify energetic waste. The solution is based on Arduinos, current transformer sensors, Raspberry Pi as an application server and LoRa communication alongside a description and information on what is to be expected of it, describing the development process from the design phase to the validation phase with all steps in between. Due to LoRa low debit communication, an edge computing approach was implemented to create a real-time monitoring process based on this technology. This solution, with the help of descriptive analysis, allows the creation of an energetic local footprint, using a low-cost developed solution for less than 80€ per three-phases monitoring device. This solution also allows for easy installation without communication range and obstacles limitations making it easy use in different situations from big complex building to smaller consumers, such as electric boilers, or simply to measure the energetic footprint of tourists in a small local tourist apartment. View Full-Text

Master in Integrated Business Intelligence Systems (MIBIS) - ISCTE-IUL

Master in Integrated Business Intelligence Systems (MIBIS) - ISCTE-IUL

Master in Integrated Business Intelligence Systems (MIBIS) - ISCTE-IUL

Vehicle Electrification: New Challenges and Opportunities for Smart Grids

Nowadays, concerns about climate change have contributed significantly to changing the paradigm in the urban transportation sector towards vehicle electrification, where purely electric or hybrid vehicles are increasingly a new reality, supported by all major automotive brands. Nevertheless, new challenges are imposed on the current electrical power grids in terms of a synergistic, progressive, dynamic and stable integration of electric mobility. Besides the traditional unidirectional charging, more and more, the adoption of a bidirectional interconnection is expected to be a reality. In addition, whenever the vehicle is plugged-in, the on-board power electronics can also be used for other purposes, such as in the event of a power failure, regardless if the vehicle is in charging mode or not. Other new opportunities, from the electrical grid point of view, are even more relevant in the context of off-board power electronics systems, which can be enhanced with new features as, for example, compensation of power quality problems or interface with renewable energy sources. In this sense, this paper aims to present, in a comprehensive way, the new challenges and opportunities that smart grids are facing, including the new technologies in the vehicle electrification, towards a sustainable future. A theoretical analysis is also presented and supported by experimental validation based on developed laboratory prototypes. View Full-Text

Master in Integrated Business Intelligence Systems (MIBIS) - ISCTE-IUL

Child’s Target Height Prediction Evolution

This study is a contribution for the improvement of healthcare in children and in society generally. This study aims to predict children’s height when they become adults, also known as “target height”, to allow for a better growth assessment and more personalized healthcare. The existing literature describes some existing prediction methods, based on longitudinal population studies and statistical techniques, which with few information resources, are able to produce acceptable results. The challenge of this study is in using a new approach based on machine learning to forecast the target height for children and (eventually) improve the existing height prediction accuracy. The goals of the study were achieved. The extreme gradient boosting regression (XGB) and light gradient boosting machine regression (LightGBM) algorithms achieved considerably better results on the height prediction. The developed model can be usefully applied by pediatricians and other clinical professionals in growth assessment. View Full-Text

Master in Integrated Business Intelligence Systems (MIBIS) - ISCTE-IUL

Attend a Portuguese university?

Hello, everyone!

This page is an individual initiative as part of my doctoral research, at #ISTAR, #ISCTE. The theme is the internationalization of higher education. I intend to promote the debate of desires and opinions between those who seek to study abroad and those who have already had experience of internationalization.

Would you please answer the 2 questions below?

Thank you in advance!

Salespeople Performance Evaluation with Predictive Analytics in B2B

Master in Integrated Business Intelligence Systems (MIBIS) - ISCTE-IUL

"Performance Evaluation is a process that occurs multiple times per year on a company. During this process, the manager and the salesperson evaluate how the salesperson performed on numerous Key Performance Indicators (KPIs). To prepare the evaluation meeting, managers have to gather data from Customer Relationship Management System, Financial Systems, Excel files, among others, leading to a very time-consuming process. The result of the Performance Evaluation is a classification followed by actions to improve the performance where it is needed. Nowadays, through predictive analytics technologies, it is possible to make classifications based on data. In this work, the authors applied a Naive Bayes model over a dataset that is composed by sales from 594 salespeople along 3 years from a global freight forwarding company, to classify salespeople into pre-defined categories provided by the business. The classification is done in 3 classes, being: Not Performing, Good, and Outstanding. The classification was achieved based on KPI’s like growth volume and percentage, sales variability along the year, opportunities created, customer base line, target achievement among others. The authors assessed the performance of the model with a confusion matrix and other techniques like True Positives, True Negatives, and F1 score. The results showed an accuracy of 92.50% for the whole model." View Full-Text

BIM in People2People and Things2People Interactive Process

In this research work, we present an IoT solution to environment variables using a LoRa transmission technology to give real-time information to users in a Things2People process and achieve savings by promoting behavior changes in a People2People process. These data are stored and later processed to identify patterns and integrate with visualization tools, which allow us to develop an environmental perception while using the system. In this project, we implemented a different approach based on the development of a 3D visualization tool that presents the system collected data, warnings, and other users’ perception in an interactive 3D model of the building. This data representation introduces a new People2People interaction approach to achieve savings in shared spaces like public buildings by combining sensor data with the users’ individual and collective perception. This approach was validated at the ISCTE-IUL University Campus, where this 3D IoT data representation was presented in mobile devices, and from this, influenced user behavior toward meeting campus sustainability goals. View Full-Text

Master in Integrated Business Intelligence Systems (MIBIS) - ISCTE-IUL

Machine learning for quality control system

Attending the Master in Integrated Business Intelligence Systems you can learn to do this, for example:

"In this work, we propose and develop a classification model to be used in a quality control system for clothing manufacturing using machine learning algorithms. The system consists of using pictures taken through mobile devices to detect defects on production objects. In this work, a defect can be a missing component or a wrong component in a production object. Therefore, the function of the system is to classify the components that compose a production object through the use of a classification model. As a manufacturing business progresses, new objects are created, thus, the classification model must be able to learn the new classes without losing previous knowledge. However, most classification algorithms do not support an increase of classes, these need to be trained from scratch with all . Thus. In this work, we make use of an incremental learning algorithm to tackle this problem. This algorithm classifies features extracted from pictures of the production objects using a convolutional neural network (CNN), which have proven to be very successful in image classification problems. We apply the current developed approach to a process in clothing manufacturing. Therefore, the production objects correspond to clothing items." Read full article

Named Entity Recognition for Sensitive Data Discovery in Portuguese

Here is an example of work you can learn to do by attending the Master in Integrated Business Intelligence Systems

The process of protecting sensitive data is continually growing and becoming increasingly important, especially as a result of the directives and laws imposed by the European Union. The effort to create automatic systems is continuous, but, in most cases, the processes behind them are still manual or semi-automatic. In this work, we have developed a component that can extract and classify sensitive data, from unstructured text information in European Portuguese. The objective was to create a system that allows organizations to understand their data and comply with legal and security purposes. We studied a hybrid approach to the problem of Named Entity Recognition for the Portuguese language. This approach combines several techniques such as rule-based/lexical-based models, machine learning algorithms, and neural networks. The rule-based and lexical-based approaches were used only for a set of specific classes. For the remaining classes of entities, two statistical models were tested—Conditional Random Fields and Random Forest and, finally, a Bidirectional-LSTM approach as experimented. Regarding the statistical models, we realized that Conditional Random Fields is the one that can obtain the best results, with a f1-score of 65.50%. With the Bi-LSTM approach, we have achieved a result of 83.01%. The corpora used for training and testing were HAREM Golden Collection, SIGARRA News Corpus, and DataSense NER Corpus. View Full-Text

LoBEMS—IoT for Building and Energy Management Systems

Here is an example of work you can learn to do by attending the Master in Integrated Business Intelligence Systems

This work presents the efforts on optimizing energy consumption by deploying an energy management system using the current IoT component/system/platform integration trends through a layered architecture. LoBEMS (LoRa Building and Energy Management System), the proposed platform, was built with the mindset of proving a common platform that would integrate multiple vendor locked-in systems together with custom sensor devices, providing critical data in order to improve overall building efficiency. The actions that led to the energy savings were implemented with a ruleset that would control the already installed air conditioning and lighting control systems. This approach was validated in a kindergarten school during a three-year period, resulting in a publicly available dataset that is useful for future and related research. The sensors that feed environmental data to the custom energy management system are composed by a set of battery operated sensors tied to a System on Chip with a LoRa communication interface. These sensors acquire environmental data such as temperature, humidity, luminosity, air quality but also motion. An already existing energy monitoring solution was also integrated. This flexible approach can easily be deployed to any building facility, including buildings with existing solutions, without requiring any remote automation facilities. The platform includes data visualization templates that create an overall dashboard, allowing management to identify actions that lead to savings using a set of pre-defined actions or even a manual mode if desired. The integration of the multiple systems (air-conditioning, lighting and energy monitoring) is a key differentiator of the proposed solution, especially when the top energy consumers for modern buildings are cooling and heating systems. As an outcome, the evaluation of the proposed platform resulted in a 20% energy saving based on these combined energy saving actions. View Full-Text

Smart Health

Remote monitoring of patients becomes increasingly necessary, especially for patients with chronic diseases, the elderly, and others who need special support. Technology and data science are already sufficiently developed to not only enable remote monitoring but to make real-time decisions based on the extraction of knowledge from captured data. There is a great demand for people specialised in IoT, remote sensing, data mining.

Here is a master's course where you can learn and practice on these subjects.

Pictures from IoT for Smart Cities, Summer School 2020, ISCTE, ISTAR-IUL

João Ferreira, Assistant Professor and Master's Director

Luís Rosário, Cardiologist at Santa Maria Hospital

Studying abroad

Hello, everyone!

This page is an individual initiative as part of my doctoral research, at #ISTAR, #ISCTE. The theme is the internationalization of higher education. I intend to promote the debate of desires and opinions between those who seek to study abroad and those who have already had experience of internationalization.

Would you please answer the 2 questions below?

Thank you in advance!

Why study at ISCTE?

My interest in ISCTE-IUL began when I met the first students of ISCTE-IUL. I noticed that the materials created by the teachers were, in general, very good and that a lot of care was taken in accompanying the students. 

For having such a good idea of ISCTE-IUL, I influenced my daughter to do her higher studies at ISCTE-IUL. She did her bachelor's and master's degrees and finished in 2018/2019, with an average of 16 out of 20, without ever leaving subjects behind and never having needed a tutor. That's why, when I thought about going back to school, it was natural that I went to search ISCTE-IUL's website for the formative offer, which could interest me.

I was interested in the Master in Integrated Business Intelligence Systems (MIBIS) and in the beginning of January 2020 I sent an email to the master's director, professor João Carlos Ferreira, explaining my interest and asking if I would be able to be accepted in that master's course. 

His answer was unexpectedly quick and very encouraging. He advised me to prepare my professional CV as complete as possible, because at ISCTE-IUL they also value the professional path of candidates.  He suggested that I attend the Internet Things Specialization Seminar for Smart Cities, which would take place in early February. I did so, I followed all his advice and it was proved that he guided me properly, because I was accepted in the master's course, getting second position in the list of places in 1st phase, for the school year 2020/2021.

Since it was the beginning of the semester, even though it was already the 2nd of 2019/2020, once again I sent an email to professor João Carlos Ferreira, telling him that I would like to start attending some classes, if I could, to move forward. The answer came again quickly and with a suggestion of which subjects I could attend without missing the subjects taught in the 1st semester. He also mentioned that I would have to address requests to the respective professors and they would have to give me the requested permission.

It was for that reason that I went to talk with professor Sérgio Moro and, once again, I was pleasantly surprised by the work of ISCTE-IUL teachers. He was the one who told me that I was eligible to apply for a PhD. That same day he was tireless in accompanying me to the ISTA secretariat to ask for information, asking questions to the doctoral director, encouraging me to believe that I would be able to fulfill my dreams of achieve a PhD, be a researcher and teach in higher education.

From that date until April 26, when I submitted my doctoral application, I could always count on the support and quick response to all my doubts, from professors João Carlos Ferreira and Sérgio Moro, who naturally became my PhD advisors.

I am very grateful to ISCTE-IUL and to ISTAR, making this gratitude come true in the professors I have already met and who have supported me, cherishing my dream. I will do everything in my power to repay the trust placed in me, dedicating all my commitment to contribute to the success of ISTAR and ISCTE-IUL. As far as I am concerned, you can count on me.

Maria da Conceição Pereira

PhD student at ISCTE-IUL

Master in Integrated Business Intelligence Systems (MIBIS)

The Master in Integrated Business Intelligence Systems was created in 2005 with the goal of training specialized professionals in Business Intelligence capable of managing, explaining, implementing and successfully using systems that support decision-making procedures integrated into the management of organizational information.

It is a differentiated course focused on an integrated view of Business Intelligence (BI) systems.

Since its creation, the program has continued to be the only option at the national level that integrates the various facets of BI from support to decision-making.

It aims to train professionals with skills in three key areas:

• Data Warehouse systems or analytical information systems;
• Data Mining or knowledge extraction in the areas of data science;
• Strategic BI (with application of balanced scorecard approaches and ABC/ABM-Activity-based costing/Activity-based management).

Alongside the academic component, the program connects its students to actual businesses through the use of real cases in teaching. Students also have the opportunity to obtain Master's scholarships for the realization of applied projects associated with real-world cases in international (H2020) and national research, as well as company sponsorships for the resolution of real-world problems and connection to the Laboratory of Open Data at CMLisboa and the Data of the IoT Laboratory of Iscte.  

There is great demand on the part of companies for professionals in BI, and a very limited supply of such professionals. Graduates in BI therefore have a high employability, as demonstrated in their higher-than-average salaries. These trained specialists will play a role of increasing intervention and value-creation in management and innovation processes in the fields of industry, service, and society in general in the coming years.

The Master in Integrated Business Intelligence Systems has also created opportunities in partnership with various companies, including a set of themed workshops. Examples of such workshops: pizza lunches sponsored by Inov on Saturdays under the theme "Tertúlia tec&ciência," with eight sections held in the year 2019:

• 1) Multicriteria Decision Analysis 4 Cybersecurity;
• 2) Dimensional Modeling: In a Business Intelligence Environment;
• 3) ETL and Data Integration: Improving Data Quality and Analytics;
• 4) Big Data Web Applications: Pentaho and UX to the Rescue;
• 5) High Performance Trading;
• 6) Machine Learning 4 Credit Risk;
• 7) Decision Analysis 4 Data Science;
• 8) Multicriteria Methods 4 Data Science

 

And two more workshops:

• 1) Health Care Workshop held in the JJLaginha auditorium on 30-10-2019;
• 2) Workshop on Intelligent Systems - Auditorium 2 on 18-10-2019.

Management

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The Internet of Things' lab (IoT), ISTAR, ISCTE-IUL

See the laboratory where you can learn and practice using sensors to build intelligent systems.

We've all seen or heard of fridges that communicate with the Internet and warn us that they're malfunctioning, even when we're away from home.

We are all familiar with clothes dryers that automatically regulate the drying time according to the humidity they detect in the clothes.

I could go on and on about the intelligent systems (Smart Systems) that exist today, but better than hearing about them is knowing how to design them.

Come and learn with a motivated team, competent teachers and sympathetic to the interests of their students, in one of the best Portuguese universities, ISCTE-IUL.

Come complete your training with the Master in Integrated Decision Support Systems (MSIAD).

Open applications, here!

Don't let the deadline pass or the vacancies finish!

Internet of Things Laboratory (IoT), ISTAR, ISCTE-IUL

MIBIS - Open applications

Want to know more about:

- Internet of Things (IoT)

- Use of sensors of all types

- Capturing information from sensors and social media

- Store captured data in the cloud in real-time

- Clean and structure the data (Data Wharehouse)

- Extracting knowledge from the data

- Design decision support systems (DSS) based on knowledge extraction

and much more on current topics in the field of Artificial Intelligence (AI), Smart Cities, Smart Transportation, Smart Health,..., Smart Anything.

This is the right master's degree to you, grow in knowledge and prepare a future full of good opportunities in a propitious learning environment, with all the support of teachers and colleagues.

Don't miss the deadline!

Master in Integrated Business Intelligence Systems (MIBIS) - ISCTE-IUL

IoT Laboratory