Abstract
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
Abstract
This work addresses the planning process of a public passenger transport operator, including the generation of schedules and services for vehicles and drivers, in the framework of a previously agreed service. This problem will be studied in the context of all stages of the planning process: parameterization, preparation, production of performance indicators and the generation of results for different operational scenarios. View Full-Text
Master in Integrated Business Intelligence Systems (MIBIS) - ISCTE-IUL
Abstract
This paper presents an Android-based mobile app designed to provide real time context aware public transportation information and advice to its users through the combination of the user’s preferences and geographic context with data retrieved from a public transportation information system called XTraN Passenger. Thus, this mobile app contributes to fulfill the necessities of the passengers, and also provides an incentive for people to use the public transportation infrastructure more frequently. The proposed mobile app allows the users to benefit from the access to real time public transportation data in a simple and intuitive way. The validation of the features and operation of the developed app was assessed with results from use cases and real-world experimental tests using public transportation data from a Brazilian bus fleet operator. View Full-Text
Index Terms—Geographic Information System, Global Positioning System, Intelligent Transportation System, Mobile App, Personalization,Real Time Information.
Master in Integrated Business Intelligence Systems (MIBIS) - ISCTE-IUL
Abstract
As the adoption of Electronic Medical Records (EMRs) rises in the healthcare institutions, these resources’ importance increases because of the clinical information they contain about patients. However, the unstructured information in the form of clinical narratives present in those records, makes it hard to extract and structure useful clinical knowledge. This unstructured information limits the potential of the EMRs, because the clinical information these records contain can be used to perform important tasks inside healthcare institutions such as searching, summarization, decision support and statistical analysis, as well as be used to support management decisions or serve for research. These tasks can only be done if the unstructured clinical information from the narratives is properly extracted, structured and transformed in clinical knowledge. Usually, this extraction is made manually by healthcare practitioners, which is not efficient and is error-prone. This research uses Natural Language Processing (NLP) and Information Extraction (IE) techniques, in order to develop a pipeline system that can extract clinical knowledge from unstructured clinical information present in Portuguese EMRs, in an automated way, in order to help EMRs to fulfil their potential. View Full-Text
Keywords— Information Extraction, Knowledge Extraction, Natural Language Processing, Text Mining
Master in Integrated Business Intelligence Systems (MIBIS) - ISCTE-IUL
ISTAR, an R&D Unit of ISCTE along with its Internet of Everything Lab, will provide a 2-week intensive Summer School on
IoT technologies and applications with focus on Health applications for Smart Cities.
See: https://istar.iscte-iul.pt/summerschool2021/
Participantes will be acquainted with emergent computing paradigms, technologies,
services and business models associated with Internet of Things (IoT).
The objective of this school is provide interested participants with theoretical-practical and
experimental knowledge about key IoT technologies and user-centered co-creation design methodologies, for development of intelligent systems towards healthy smart cities.
The school provides an overview of future trends and ongoing research in this fast-growing area:
· Study the principles, research problems and applications of device, software and IoT
· Acquire experience with IoT technologies and operating systems
· Understand, in practical experience, how to run user-centered projects based in Design Thinking
· Help students to develop self-study skills to keep up with the rapidly changing technologies, tools and techniques in this knowledge area.
The school runs from September 6th to 17th 2021, and consists of 10 hours of expert lectures, anchored by 19 1/2 hours of hands-on training on key topics that include:
· Introduction to IoT
· Design Thinking
· Security
· Tiny Machine Learning and Applications
· Applications and challenges on Health and Crowdsourcing
· There is an industrial session with presentations by three companies in Portugal that develop and apply IoT techniques.
Participants will work on supervised, hands-on projects, developed at the ISTAR Internet of Everything Lab,
as well as provided basic training on:
· IoT frameworks (e.g. Arduino, LORA communications, and sensors)
· Data science tools (Python, Data Analytics introduction)
· Design Thinking methodologies.
Duration: 10 days | 6 ECTS
More information: https://istar.iscte-iul.pt/summerschool2021/
Pre-Registration is already open! Express your interest at
Questions? pre-award.istar@iscte-iul.pt
Master in Integrated Business Intelligence Systems (MIBIS) - ISCTE-IUL
Abstract
This paper presents DisBot, the first Portuguese speaking chatbot that uses social media retrieved knowledge to support citizens and first-responders in disaster scenarios, in order to improve community resilience and decision-making. It was developed and tested using Design Science Research Methodology (DSRM), being progressively matured with field specialists through several design and development iterations. DisBot uses a state-of-the-art Dual Intent Entity Transformer (DIET) architecture to classify user intents, and makes use of several dialogue policies for managing user conversations, as well as storing relevant information to be used in further dialogue turns. To generate responses, it uses real-world safety knowledge, and infers a dynamic knowledge graph that is dynamically updated in real-time by a disaster-related knowledge extraction tool, presented in previous works. Through its development iterations, DisBot has been validated by field specialists, who have considered it to be a valuable asset in disaster management. View Full-Text
Publication regarding the second artifact (work held in Chapter 4 and the artifact’s validation of Chapter 5) of the MSc thesis in MIBIS, from Boné J.
Master in Integrated Business Intelligence Systems (MIBIS) - ISCTE-IUL
Abstract
This research is aimed at creating and presenting DisKnow, a data extraction system with the capability of filtering and abstracting tweets, to improve community resilience and decision-making in disaster scenarios. Nowadays most people act as human sensors, exposing detailed information regarding occurring disasters, in social media. Through a pipeline of natural language processing (NLP) tools for text processing, convolutional neural networks (CNNs) for classifying and extracting disasters, and knowledge graphs (KG) for presenting connected insights, it is possible to generate real-time visual information about such disasters and affected stakeholders, to better the crisis management process, by disseminating such information to both relevant authorities and population alike. DisKnow has proved to be on par with the state-of-the-art Disaster Extraction systems, and it contributes with a way to easily manage and present such happenings. View Full-Text
Publication regarding the first artifact (work held in Chapter 3) of the MSc thesis in MIBIS, from Boné J.
Master in Integrated Business Intelligence Systems (MIBIS) - ISCTE-IUL
Abstract
Buildings in Lisbon are often the victim of several types of events (such as accidents, fires, collapses, etc.). This study aims to apply a data-driven approach towards knowledge extraction from past incident data, nowadays available in the context of a Smart City. We apply a Cross Industry Standard Process for Data Mining (CRISP-DM) approach to perform incident management of the city of Lisbon. From this data-driven process, a descriptive and predictive analysis of an events dataset provided by the Lisbon Municipality was possible, together with other data obtained from the public domain, such as the temperature and humidity on the day of the events. The dataset provided contains events from 2011 to 2018 for the municipality of Lisbon. This data mining approach over past data identified patterns that provide useful knowledge for city incident managers. Additionally, the forecasts can be used for better city planning, and data correlations of variables can provide information about the most important variables towards those incidents. This approach is fundamental in the context of smart cities, where sensors and data can be used to improve citizens’ quality of life. Smart Cities allow the collecting of data from different systems, and for the case of disruptive events, these data allow us to understand them and their cascading effects better. View Full-Text
Master in Integrated Business Intelligence Systems (MIBIS) - ISCTE-IUL
Master in Integrated Business Intelligence Systems (MIBIS) - ISCTE-IUL