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CISUC – Centre of Informatics and Systems of the University of Coimbra - University of Coimbra; ISEC – Superior Engineering Institute of Coimbra – Polytechnic Institute of Coimbra
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50 Papers

NoSQL Document Databases Assessment: Couchbase, CouchDB, and MongoDB

Topics: Nosql Databases; Database Architecture and Performance

Authors: Inês Carvalho 1 , Filipe Sá 2 ; 1 and Jorge Bernardino 3 ; 1

Affiliations: 1 Polytechnic of Coimbra – ISEC, Rua Pedro Nunes, Quinta da Nora, 3030-199 Coimbra, Portugal , 2 CISeD – Research Centre in Digital Services of Polytechnic of Viseu, Campus Politécnico, 3504-510 Viseu, Portugal , 3 CISUC – Centre of Informatics and Systems of University of Coimbra, Pinhal de Marrocos, 3030-290 Coimbra, Portugal

Digital Object Identifier: 10.5220/0011352700003269

Proceedings of DATA 2022, ISBN: 978-989-758-583-8

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NoSQL document databases emerged as an alternative to relational databases to deal with large volumes of data. In this paper, we assess the top three free and open-source NoSQL document databases: Couchbase, CouchDB, and MongoDB. Through this analysis, we identify the main characteristics of each database. The OSSpal methodology, which combines quantitative and qualitative measures to assess open-source software was used. This methodology defines seven categories: functionality, operational software characteristics, software technology attributes, documentation, support and service, community and adoption, and development process. At the end, it is obtained a score that identify which is the best NoSQL document database.

Analysis of Data Anonymization Techniques

Topics: Applications and Case-Studies; Education and Training

Authors: Joana Ferreira Marques 1 and Jorge Bernardino 2 ; 1

Affiliations: 1 Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Rua Pedro Nunes, 3030-199 Coimbra, Portugal , 2 CISUC - Centre of Informatics and Systems of University of Coimbra, Pinhal de Marrocos, 3030-290 Coimbra, Portugal

Digital Object Identifier: 10.5220/0010142302350241

Proceedings of KEOD 2020/IC3K, ISBN: 978-989-758-474-9

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The privacy of personal data is a very important issue these days. How to process the data and use it for analysis without compromising the individual’s identity is a critical task and must be done in order to ensure the anonymity of this data. To try to unanimously unify this anonymity, laws and regulations such as GDPR were created. In this paper, GDPR will be described and the concepts of anonymization and pseudonymization will be explained. We present some of the main anonymization techniques and efficient software to support the application of these techniques. The main objective is to understand which techniques offer a higher level of anonymization, the strengths and weakness of each one and the advantages in its use.

Big Data Streaming Platforms to Support Real-time Analytics

Topics: Decision Support Systems; Real-Time Systems; Big Data and Data Science

Authors: Eliana Fernandes 1 , Ana Carolina Salgado 2 and Jorge Bernardino 3

Affiliations: 1 Polytechnic of Coimbra – ISEC, Rua Pedro Nunes, Quinta da Nora, 3030-199 Coimbra, Portugal , 2 Centre for Informatics, Universidade Federal de Pernambuco, Recife, Brazil , 3 Polytechnic of Coimbra – ISEC, Rua Pedro Nunes, Quinta da Nora, 3030-199 Coimbra, Portugal, Centre for Informatics and Systems of the University of Coimbra (CISUC), Portugal

Digital Object Identifier: 10.5220/0009817304260433

Proceedings of ICSOFT 2020, ISBN: 978-989-758-443-5

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In recent years data has grown exponentially due to the evolution of technology. The data flow circulates in a very fast and continuous way, so it must be processed in real time. Therefore, several big data streaming platforms have emerged for processing large amounts of data. Nowadays, companies have difficulties in choosing the platform that best suits their needs. In addition, the information about the platforms is scattered and sometimes omitted, making it difficult for the company to choose the right platform. This work focuses on helping companies or organizations to choose a big data streaming platform to analyze and process their data flow. We provide a description of the most popular platforms, such as: Apache Flink, Apache Kafka, Apache Samza, Apache Spark and Apache Storm. To strengthen the knowledge about these platforms, we also approached their architectures, advantages and limitations. Finally, a comparison among big data streaming platforms will be provided, using as attributes the characteristics that companies usually most need.

Evaluating Self-Service BI and Analytics Tools for SMEs

Topics: Data Visualization; Business Analytics

Authors: Antonio Oliveira 1 and Jorge Bernardino 2

Affiliations: 1 Polytechnic of Coimbra – ISEC, Rua Pedro Nunes, Quinta da Nora, 3030-199 Coimbra, Portugal , 2 Polytechnic of Coimbra – ISEC, Rua Pedro Nunes, Quinta da Nora, 3030-199 Coimbra, Portugal, CISUC - Centre of Informatics and Systems of University of Coimbra, Pinhal de Marrocos, 3030-290 Coimbra, Portugal

Digital Object Identifier: 10.5220/0009820400890097

Proceedings of ICE-B 2020, ISBN: 978-989-758-447-3

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In the vast Data Science domain, Business Intelligence (BI) and Data Analytics are two of the most relevant topics nowadays. Regardless of the data type an end-user needs to work on, data visualization and/or analytics can be a valuable support for a successful decision-making process in management. For that, free and open-source business intelligence and analytics solutions are on the market as an indispensable opportunity for companies to start benefiting from data analytics at no cost. In addition, that task has been currently eased by a group of BI and Analytics tools named as “Self-service”, which is an Advanced Analytics topic and designed to enable users with no IT background to perform analyses of data and find business opportunities themselves with minimal or no assistance from IT technicians. Considering that, to help Small and Medium-sized Enterprises (SMEs) decide on a free self-service data tool according to their needs, we compare in this paper, on a functionality basis, 5 popular Self-Service BI and Analytics tools: Metabase, Pentaho Community, Power BI Free, QlikView, and Tableau Public.

An Application of OSSpal for the Assessment of Open Source Project Management Tools

Topics: Communities of Interest and Practice; Information Systems and SaaS, IaaS, PaaS, Cloud Computing; Application, Research project and Service Based IS; Methodologies for Services and Architecture

Authors: Hugo Carvalho de Paula 1 and Jorge Bernardino 2

Affiliations: 1 Polytechnic of Coimbra, ISEC, Rua Pedro Nunes, Quinta da Nora, 3030-199 Coimbra and Portugal , 2 Polytechnic of Coimbra, ISEC, Rua Pedro Nunes, Quinta da Nora, 3030-199 Coimbra, Portugal, CISUC - Centre of Informatics and Systems of University of Coimbra, Pinhal de Marrocos, 3030-290 Coimbra and Portugal

Digital Object Identifier: 10.5220/0008412404110417

Proceedings of WEBIST 2019, ISBN: 978-989-758-386-5

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Projects are a necessity within any competitive business, and as the execution of complex projects becomes the norm, so grows the need for advances in project management. The use of project management tools is key towards taming said complexity. There are many such tools available; the current challenge resides in picking the right one. In this paper, we evaluate three different tools - OpenProject, dotProject, and Odoo - using the OSSpal methodology.

Comparison of Naïve Bayes, Support Vector Machine, Decision Trees and Random Forest on Sentiment Analysis

Topics: Machine Learning; Data Mining in Electronic Commerce; Process Mining; Information Extraction

Authors: Márcio Guia 1 , Rodrigo Rocha Silva 2 and Jorge Bernardino 3

Affiliations: 1 Polytechnic of Coimbra – ISEC, Rua Pedro Nunes, Quinta da Nora, 3030-199 Coimbra and Portugal , 2 CISUC – Centre of Informatics and Systems of University of Coimbra, Pinhal de Marrocos, 3030-290 Coimbra, Portugal, FATEC Mogi das Cruzes, São Paulo Technological College, 08773-600 Mogi das Cruzes and Brazil , 3 Polytechnic of Coimbra – ISEC, Rua Pedro Nunes, Quinta da Nora, 3030-199 Coimbra, Portugal, CISUC – Centre of Informatics and Systems of University of Coimbra, Pinhal de Marrocos, 3030-290 Coimbra and Portugal

Digital Object Identifier: 10.5220/0008364105250531

Proceedings of KDIR 2019/IC3K, ISBN: 978-989-758-382-7

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Every day, we deal with a lot of information on the Internet. This information can have origin from many different places such as online review sites and social networks. In the midst of this messy data, arises the opportunity to understand the subjective opinion about a text, in particular, the polarity. Sentiment Analysis and Text Classification helps to extract precious information about data and assigning a text into one or more target categories according to its content. This paper proposes a comparison between four of the most popular Text Classification Algorithms - Naive Bayes, Support Vector Machine, Decision Trees and Random Forest - based on the Amazon Unlocked mobile phone reviews dataset. Moreover, we also study the impact of some attributes (Brand and Price) on the polarity of the review. Our results demonstrate that the Support Vector Machine is the most complete algorithm of this study and achieve the highest values in all the metrics such as accuracy, precision, recall, and F1 score.
Collapse Button Related Ontology Subjects/Areas/Topics (11)
Artificial Intelligence

Information Extraction

Process Mining

Computational Intelligence

Knowledge Discovery and Information Retrieval

Soft Computing

Data Mining in Electronic Commerce

Knowledge-Based Systems

Symbolic Systems
Evolutionary Computing

Machine Learning

Data Mining Techniques for Early Detection of Breast Cancer

Topics: Machine Learning; Data Analytics; Information Extraction

Authors: Maria Inês Cruz 1 and Jorge Bernardino 2

Affiliations: 1 Polytechnic of Coimbra – ISEC, Rua Pedro Nunes, Quinta da Nora, 3030-199 Coimbra and Portugal , 2 Polytechnic of Coimbra – ISEC, Rua Pedro Nunes, Quinta da Nora, 3030-199 Coimbra, Portugal, CISUC – Centre of Informatics and Systems of University of Coimbra, Pinhal de Marrocos, 3030-290 Coimbra and Portugal

Digital Object Identifier: 10.5220/0008346504340441

Proceedings of KDIR 2019/IC3K, ISBN: 978-989-758-382-7

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Nowadays, millions of people around the world are living with the diagnosis of cancer, so it is very important to investigate some forms of detection and prevention of this disease. In this paper, we will use an ensemble technique with some data mining algorithms applied to a dataset related to the diagnosis of breast cancer using biological markers found in routine blood tests, in order to diagnose this disease. From the results obtained, it can be verified that the model got an AUC of 95% and a precision of 87%. Thus, through this model it is possible to create new screening tools to assist doctors and prevent healthy patients from having to undergo invasive examinations.
Collapse Button Related Ontology Subjects/Areas/Topics (12)
Artificial Intelligence

Data Engineering

Knowledge-Based Systems

Business Analytics

Evolutionary Computing

Machine Learning

Computational Intelligence

Information Extraction

Soft Computing

Data Analytics

Knowledge Discovery and Information Retrieval

Symbolic Systems

Evaluating GitLab, OpenProject, and Redmine using QSOS Methodology

Topics: Web Tools and Languages; Communities of Interest and Practice; Services with Methodological Approaches, Quality, Metrics, BAM; Methodologies for Services and Architecture

Authors: André Vicente 1 and Jorge Bernardino 2

Affiliations: 1 Polytechnic of Coimbra, ISEC, Rua Pedro Nunes, Quinta da Nora, 3030-199 Coimbra and Portugal , 2 Polytechnic of Coimbra, ISEC, Rua Pedro Nunes, Quinta da Nora, 3030-199 Coimbra, Portugal, CISUC - Centre of Informatics and Systems of University of Coimbra, Pinhal de Marrocos, 3030-290 Coimbra and Portugal

Digital Object Identifier: 10.5220/0008364603800387

Proceedings of WEBIST 2019, ISBN: 978-989-758-386-5

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Measure and planning all aspects and variables of a project is extremely important to have success. Therefore, having the right tool is extremely important. To evaluate project management tools, we can use several methodologies, that allow to choose the best tools according to our criteria. QSOS is one of the methodologies that allows to make a more weighted choose. In this paper, we evaluate popular open source project management tools GitLab, OpenProject, and Redmine, using QSOS methodology.

Evaluating Open Source Project Management Tools using OSSPal Methodology

Topic: Information Systems and SaaS, IaaS, PaaS, Cloud Computing

Authors: Antonio Oliveira 1 and Jorge Bernardino 2

Affiliations: 1 Polytechnic of Coimbra – ISEC, Rua Pedro Nunes, Quinta da Nora, 3030-199 Coimbra and Portugal , 2 Polytechnic of Coimbra – ISEC, Rua Pedro Nunes, Quinta da Nora, 3030-199 Coimbra, Portugal, CISUC - Centre of Informatics and Systems of University of Coimbra, Pinhal de Marrocos, 3030-290 Coimbra and Portugal

Digital Object Identifier: 10.5220/0008351003430350

Proceedings of WEBIST 2019, ISBN: 978-989-758-386-5

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One of the major differences between a successful project and a failed one is the project management abilities. Project management leads to better alignment of projects within the business’ strategy, so that companies can reduce their costs, accelerate product development, and focus on meeting their customers’ needs. To help with that, project management tools are highly recommended, once they ease planning, scheduling, resource allocation, communication and documentation tasks. In this paper, we assess three popular open source project management tools: OpenProject, Orangescrum and ProjectLibre with the help of the OSSPal methodology. This study can help project managers and programmers on choosing an adequate, current, high quality and affordable tool to perform their projects.

Evaluation of Asana, Odoo, and ProjectLibre Project Management Tools using the OSSpal Methodology

Topic: Business Process Management

Authors: Joana Ferreira Marques 1 and Jorge Bernardino 2

Affiliations: 1 Polytechnic of Coimbra – ISEC, Rua Pedro Nunes, Quinta da Nora, 3030-199 Coimbra and Portugal , 2 Polytechnic of Coimbra – ISEC, Rua Pedro Nunes, Quinta da Nora, 3030-199 Coimbra, Portugal, CISUC - Centre of Informatics and Systems of University of Coimbra, Pinhal de Marrocos, 3030-290 Coimbra and Portugal

Digital Object Identifier: 10.5220/0008351903970403

Proceedings of KEOD 2019/IC3K, ISBN: 978-989-758-382-7

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In order to successfully complete projects it is essential for companies to acquire a project management tool that assists in their planning, cost and resource management. Currently, there are several open source project management tools on the market that have much of the functionality required. Thus, it is important to choose the most appropriate one according to the needs of the user. To help with this choice, open source software evaluation methodologies can be used. In this paper, we use the OSSpal methodology to evaluate three popular open source project management tools: Asana, Odoo, and ProjectLibre.
Collapse Button Related Ontology Subjects/Areas/Topics (8)
Artificial Intelligence

Enterprise Engineering

Knowledge-Based Systems

Business Process Management

Enterprise Information Systems

Symbolic Systems
e-Business

Knowledge Management and Information Sharing

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