Débora Pina, L. Kunstmann, Felipe Bevilaqua, Isabela Siqueira, Alan Lyra, Daniel de Oliveira, M. Mattoso
Due to the exploratory nature of DNNs, DL specialists often need to modify the input dataset, change a filter when preprocessing input data, or fine-tune the models’ hyperparameters, while analyzing the evolution of the training. However, the specialist may lose track of what hyperparameter configurations have been used and tuned if these data are not properly registered. Thus, these configurations must be tracked and made available for the user’s analysis. One way of doing this is to use provenance data derivation traces to help the hyperparameter’s fine-tuning by providing a global data picture with clear dependencies. Current provenance solutions present provenance data disconnected from W3C PROV recommendation, which is difficult to reproduce and compare to other provenance data. To help with these challenges, we present Keras-Prov, an extension to the Keras deep learning library to collect provenance data compliant with PROV. To show the flexibility of Keras-Prov, we extend a previous Keras-Prov demonstration paper with larger experiments using GPUs with the help of Google Colab. Despite the challenges of running a DBMS with virtual environments, DL analysis with provenance has added trust and persistence in databases and PROV serializations. Experiments show Keras-Prov data analysis, during training execution, to support hyperparameter fine-tuning decisions, favoring the comparison, and reproducibility of such DL experiments. Keras-Prov is open source and can be downloaded from https://github.com/dbpina/keras-prov.
{"title":"Capturing Provenance from Deep Learning Applications Using Keras-Prov and Colab: a Practical Approach","authors":"Débora Pina, L. Kunstmann, Felipe Bevilaqua, Isabela Siqueira, Alan Lyra, Daniel de Oliveira, M. Mattoso","doi":"10.5753/jidm.2022.2544","DOIUrl":"https://doi.org/10.5753/jidm.2022.2544","url":null,"abstract":"Due to the exploratory nature of DNNs, DL specialists often need to modify the input dataset, change a filter when preprocessing input data, or fine-tune the models’ hyperparameters, while analyzing the evolution of the training. However, the specialist may lose track of what hyperparameter configurations have been used and tuned if these data are not properly registered. Thus, these configurations must be tracked and made available for the user’s analysis. One way of doing this is to use provenance data derivation traces to help the hyperparameter’s fine-tuning by providing a global data picture with clear dependencies. Current provenance solutions present provenance data disconnected from W3C PROV recommendation, which is difficult to reproduce and compare to other provenance data. To help with these challenges, we present Keras-Prov, an extension to the Keras deep learning library to collect provenance data compliant with PROV. To show the flexibility of Keras-Prov, we extend a previous Keras-Prov demonstration paper with larger experiments using GPUs with the help of Google Colab. Despite the challenges of running a DBMS with virtual environments, DL analysis with provenance has added trust and persistence in databases and PROV serializations. Experiments show Keras-Prov data analysis, during training execution, to support hyperparameter fine-tuning decisions, favoring the comparison, and reproducibility of such DL experiments. Keras-Prov is open source and can be downloaded from https://github.com/dbpina/keras-prov.","PeriodicalId":293511,"journal":{"name":"Journal of Information and Data Management","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114233017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article introduces EERCASE, a Computer Aided Software Engineering tool that is based on the best practices of the Model Driven Development paradigm to provide a consistent environment for relational database design. EERCASE follows the graphical notation of the Enhanced Entity–Relationship model according to Elmasri and Navathe, implements the EERMM metamodel to avoid syntactically invalid constructs, shows and describes static semantic errors, and generates data definition code that takes into account advanced structural validations. The theoretical and technical framework used for the implementation of EERCASE is discussed, with emphasis on the restrictive and informative validations performed by it. In addition, considering feedbacks on modeling errors and code generation, EERCASE is also presented as a computational environment that favors active learning.
{"title":"Consistent Design of Relational Databases using EERCASE","authors":"Robson N. Fidalgo, Edson A. Silva","doi":"10.5753/jidm.2022.2537","DOIUrl":"https://doi.org/10.5753/jidm.2022.2537","url":null,"abstract":"This article introduces EERCASE, a Computer Aided Software Engineering tool that is based on the best practices of the Model Driven Development paradigm to provide a consistent environment for relational database design. EERCASE follows the graphical notation of the Enhanced Entity–Relationship model according to Elmasri and Navathe, implements the EERMM metamodel to avoid syntactically invalid constructs, shows and describes static semantic errors, and generates data definition code that takes into account advanced structural validations. The theoretical and technical framework used for the implementation of EERCASE is discussed, with emphasis on the restrictive and informative validations performed by it. In addition, considering feedbacks on modeling errors and code generation, EERCASE is also presented as a computational environment that favors active learning.","PeriodicalId":293511,"journal":{"name":"Journal of Information and Data Management","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132131908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mariana D. A. Salgueiro, Verônica dos Santos, André L. C. Rêgo, Daniel S. Guimarães, Jefferson B. Santos, Edward H. Haeusler, Marcos V. Villas, Sérgio Lifschitz
This work presents the design and implementation of two web-based search systems, Busc@NIMA and Quem@PUC. Both systems allow the identification of research and development projects, besides existing competencies in laboratories and departments involving professors and researchers at PUC-Rio University. Our applications are based on a list of search-related terms that are matched to the dataset composed of PUC-Rio’s Lattes CVs offered courses, information from administrative systems, and specific keywords that are input by the professors/researchers themselves. To integrate all the needed data, we consider multiple database and search technologies, such as XML, RDF, TripleStores, and Relational Databases. Search results include professor’s name, academic papers, teaching activities, contact links, keywords, and laboratories of those involved with the subject represented by the set of keywords input. We describe the main features that show how our systems work.
{"title":"Searching for Researchers: an Ontology-based NoSQL Database System Approach and Practical Implementation","authors":"Mariana D. A. Salgueiro, Verônica dos Santos, André L. C. Rêgo, Daniel S. Guimarães, Jefferson B. Santos, Edward H. Haeusler, Marcos V. Villas, Sérgio Lifschitz","doi":"10.5753/jidm.2022.2601","DOIUrl":"https://doi.org/10.5753/jidm.2022.2601","url":null,"abstract":"This work presents the design and implementation of two web-based search systems, Busc@NIMA and Quem@PUC. Both systems allow the identification of research and development projects, besides existing competencies in laboratories and departments involving professors and researchers at PUC-Rio University. Our applications are based on a list of search-related terms that are matched to the dataset composed of PUC-Rio’s Lattes CVs offered courses, information from administrative systems, and specific keywords that are input by the professors/researchers themselves. To integrate all the needed data, we consider multiple database and search technologies, such as XML, RDF, TripleStores, and Relational Databases. Search results include professor’s name, academic papers, teaching activities, contact links, keywords, and laboratories of those involved with the subject represented by the set of keywords input. We describe the main features that show how our systems work.","PeriodicalId":293511,"journal":{"name":"Journal of Information and Data Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128852363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Scientific collaboration networks can present different views of researchers’ interactions. This work presents SCI-synergy, an online navigable artifact aiming to promote mechanisms and views of scientific collaboration networks. The artifact focuses on the researchers’ interaction in the co-authorship of publications considering intra- and interprogram relationships. SCI-synergy is developed upon the design science research paradigm using scientific publication data available on the large Digital Bibliography & Library Project (DBLP) repository. Official data from the Sucupira repository of six Brazilian graduate program members including Federal University of Minas Gerais (UFMG), State University of São Paulo (USP), Federal University of Rio Grande do Norte (UFRN), Federal University of Amazonas (UFAM), University of Brasília (UnB), and University of Vale do Rio dos Sinos (UNISINOS) is used. Data from these graduate programs illustrate the artifact usage regarding the scientific collaboration network of each program, how each researcher cooperates, and what relationship patterns exist in intra- and inter-programs views. We advocate that, even though it is necessary to consider data from each program’s history and current contextualization regarding politics, economics, and administration, the collaboration network views provided by SCI-synergy might help to understand collaboration network patterns.
{"title":"Scientific Collaboration Network Views: A Brazilian Computer Science Graduate Programs Case","authors":"Aurelio Ribeiro Costa, Vanessa Tavares Nunes, Célia Ghedini Ralha","doi":"10.5753/jidm.2022.2695","DOIUrl":"https://doi.org/10.5753/jidm.2022.2695","url":null,"abstract":"Scientific collaboration networks can present different views of researchers’ interactions. This work presents SCI-synergy, an online navigable artifact aiming to promote mechanisms and views of scientific collaboration networks. The artifact focuses on the researchers’ interaction in the co-authorship of publications considering intra- and interprogram relationships. SCI-synergy is developed upon the design science research paradigm using scientific publication data available on the large Digital Bibliography & Library Project (DBLP) repository. Official data from the Sucupira repository of six Brazilian graduate program members including Federal University of Minas Gerais (UFMG), State University of São Paulo (USP), Federal University of Rio Grande do Norte (UFRN), Federal University of Amazonas (UFAM), University of Brasília (UnB), and University of Vale do Rio dos Sinos (UNISINOS) is used. Data from these graduate programs illustrate the artifact usage regarding the scientific collaboration network of each program, how each researcher cooperates, and what relationship patterns exist in intra- and inter-programs views. We advocate that, even though it is necessary to consider data from each program’s history and current contextualization regarding politics, economics, and administration, the collaboration network views provided by SCI-synergy might help to understand collaboration network patterns.","PeriodicalId":293511,"journal":{"name":"Journal of Information and Data Management","volume":"58 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123304080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
User reviews are readily available on the Web and widely used for sentiment analysis tasks. Sentiment lexicons plays an important role in sentiment analysis, where each sentiment word is given a sentiment label (positive or negative) or score (1 or -1). However, a sentiment lexicon may express different sentiment polarity according different domain. In addition, only a few studies on Portuguese sentiment analysis are reported due to the lack of resources including domain-specific sentiment lexical corpora. In this paper, we present an effective methodology, called SentiLexBR, using probabilities of the Bayes’ Theorem for building a set of sentiment lexicons. An unsupervised algorithm is proposed to automatically identify sentiment lexicons with their polarities for the Portuguese language. Experimental results on user reviews datasets in 12 different domains indicate the effectiveness of our methodology in domain-specific sentiment lexicon generation for Portuguese. In addition, the sentiment lexicon produced by SentiLexBR also significantly outperforms several alternative approaches of building domain-specific sentiment lexicons.
{"title":"SentiLexBR: An Automatic Methodology of Building Sentiment Lexicons for the Portuguese Language","authors":"Tiago de Melo","doi":"10.5753/jidm.2022.2504","DOIUrl":"https://doi.org/10.5753/jidm.2022.2504","url":null,"abstract":"User reviews are readily available on the Web and widely used for sentiment analysis tasks. Sentiment lexicons plays an important role in sentiment analysis, where each sentiment word is given a sentiment label (positive or negative) or score (1 or -1). However, a sentiment lexicon may express different sentiment polarity according different domain. In addition, only a few studies on Portuguese sentiment analysis are reported due to the lack of resources including domain-specific sentiment lexical corpora. In this paper, we present an effective methodology, called SentiLexBR, using probabilities of the Bayes’ Theorem for building a set of sentiment lexicons. An unsupervised algorithm is proposed to automatically identify sentiment lexicons with their polarities for the Portuguese language. Experimental results on user reviews datasets in 12 different domains indicate the effectiveness of our methodology in domain-specific sentiment lexicon generation for Portuguese. In addition, the sentiment lexicon produced by SentiLexBR also significantly outperforms several alternative approaches of building domain-specific sentiment lexicons.","PeriodicalId":293511,"journal":{"name":"Journal of Information and Data Management","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116037388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Regis Pires Magalhães, Daniel Jean Rodrigues Vasconcelos, Guilherme Sales Fernandes, Lívia Almada Cruz, Matheus Xavier Sampaio, José Antônio Fernandes de Macêdo, Ticiana Linhares Coelho da Silva
Automatic Speech Recognition (ASR) is essential for many applications like automatic caption generation for videos, voice search, voice commands for smart homes, and chatbots. Due to the increasing popularity of these applications and the advances in deep learning models for transcribing speech into text, this work aims to evaluate the performance of commercial solutions for ASR that use deep learning models, such as Facebook Wit.ai, Microsoft Azure Speech, Google Cloud Speech-to-Text, Wav2Vec, and AWS Transcribe. We performed the experiments with two real and public datasets, the Mozilla Common Voice and the Voxforge. The results demonstrate that the evaluated solutions slightly differ. However, Facebook Wit.ai outperforms the other analyzed approaches for the quality metrics collected like WER, BLEU, and METEOR. We also experiment to fine-tune Jasper Neural Network for ASR with four datasets different with no intersection to the ones we collect the quality metrics. We study the performance of the Jasper model for the two public datasets, comparing its results with the other pre-trained models.
自动语音识别(ASR)对于视频的自动字幕生成、语音搜索、智能家居的语音命令和聊天机器人等许多应用都是必不可少的。由于这些应用程序的日益普及以及用于将语音转录为文本的深度学习模型的进步，本工作旨在评估使用深度学习模型(如Facebook Wit)的ASR商业解决方案的性能。ai、微软Azure语音、谷歌云语音转文本、Wav2Vec和AWS转录。我们用两个真实的公共数据集，Mozilla Common Voice和Voxforge进行了实验。结果表明，评估的解略有不同。然而，Facebook机智。ai在收集质量指标方面优于其他分析方法，如WER、BLEU和METEOR。我们还用四个不同的数据集对Jasper神经网络进行了微调，这些数据集与我们收集的质量指标没有交集。我们研究了Jasper模型在两个公共数据集上的性能，并将其结果与其他预训练模型进行了比较。
{"title":"Evaluation of Automatic Speech Recognition Approaches","authors":"Regis Pires Magalhães, Daniel Jean Rodrigues Vasconcelos, Guilherme Sales Fernandes, Lívia Almada Cruz, Matheus Xavier Sampaio, José Antônio Fernandes de Macêdo, Ticiana Linhares Coelho da Silva","doi":"10.5753/jidm.2022.2514","DOIUrl":"https://doi.org/10.5753/jidm.2022.2514","url":null,"abstract":"Automatic Speech Recognition (ASR) is essential for many applications like automatic caption generation for videos, voice search, voice commands for smart homes, and chatbots. Due to the increasing popularity of these applications and the advances in deep learning models for transcribing speech into text, this work aims to evaluate the performance of commercial solutions for ASR that use deep learning models, such as Facebook Wit.ai, Microsoft Azure Speech, Google Cloud Speech-to-Text, Wav2Vec, and AWS Transcribe. We performed the experiments with two real and public datasets, the Mozilla Common Voice and the Voxforge. The results demonstrate that the evaluated solutions slightly differ. However, Facebook Wit.ai outperforms the other analyzed approaches for the quality metrics collected like WER, BLEU, and METEOR. We also experiment to fine-tune Jasper Neural Network for ASR with four datasets different with no intersection to the ones we collect the quality metrics. We study the performance of the Jasper model for the two public datasets, comparing its results with the other pre-trained models.","PeriodicalId":293511,"journal":{"name":"Journal of Information and Data Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115998589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Glória de Fátima B. Lima, Marcelo Iury S. Oliveira, Bernadette Farias Lóscio
The growing availability of data in digital media has contributed to the creation of a large number of data ecosystems. However, having successful Data Ecosystem is still a challenge. In order to prevent the failure of a Data Ecosystem and ensure its survival, evaluating its health becomes fundamental. In a general way, the health of a Data Ecosystem can be defined as its ability to grow and survive over time. Indicators such as productivity, robustness, niche creation and sustainability can be employed to evaluate the health of a Data Ecosystem. In this paper, we propose a framework for data Ecosystem health evaluation composed of a set of indicators and metrics, which assess the Data Ecosystem’s current state and its ability to stay healthy over time. The results obtained when using the proposed framework offers evidence to assist in decision making on how data has being published and consumed in a Data Ecosystem, as well as to evaluate which ecosystems are more prosperous or need more investments.
{"title":"FASED: A Framework for Data Ecosystems Health Evaluation","authors":"Glória de Fátima B. Lima, Marcelo Iury S. Oliveira, Bernadette Farias Lóscio","doi":"10.5753/jidm.2022.2511","DOIUrl":"https://doi.org/10.5753/jidm.2022.2511","url":null,"abstract":"The growing availability of data in digital media has contributed to the creation of a large number of data ecosystems. However, having successful Data Ecosystem is still a challenge. In order to prevent the failure of a Data Ecosystem and ensure its survival, evaluating its health becomes fundamental. In a general way, the health of a Data Ecosystem can be defined as its ability to grow and survive over time. Indicators such as productivity, robustness, niche creation and sustainability can be employed to evaluate the health of a Data Ecosystem. In this paper, we propose a framework for data Ecosystem health evaluation composed of a set of indicators and metrics, which assess the Data Ecosystem’s current state and its ability to stay healthy over time. The results obtained when using the proposed framework offers evidence to assist in decision making on how data has being published and consumed in a Data Ecosystem, as well as to evaluate which ecosystems are more prosperous or need more investments.","PeriodicalId":293511,"journal":{"name":"Journal of Information and Data Management","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125675480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mirela T. Cazzolato, Lucas C. Scabora, Guilherme F. Zabot, Marco A. Gutierrez, Caetano Traina Jr., Agma J. M. Traina
Real-world applications generate large amounts of images every day. With the generalized use of social media, users frequently share images acquired by smartphones. Also, hospitals, clinics, exhibits, factories, and other facilities generate images with potential use for many applications. Processing the generated images usually requires feature extraction, which can be time-consuming and laborious. In this paper, we present FeatSet+, a compilation of color, texture and shape visual features extracted from 17 open image datasets reported in the literature. FeatSet+ provides a collection of 11 distinct visual features, extracted by well-known Feature Extraction Methods (FEMs) such as LBP, Haralick, and Color Layout. We organized the available features in a standard collection, including the metadata and labels, when available. Eleven of the datasets also contain classes, which aid the evaluation of supervised methods such as classifiers and clustering tasks. FeatSet+ is available for download in a public repository as sql scripts and csv files. Additionally, FeatSet+ provides a description of the domain of each dataset, including the reference to the original work and link. We show the potential applicability of FeatSet+ in four computational tasks: multi-attribute analysis and retrieval, visual analysis using Multidimensional Scaling (MDS) and Principal Components Analysis (PCA), global feature classification, and dimensionality reduction. FeatSet+ can be employed to evaluate supervised and non-supervised learning tasks, also widely supporting Content-Based Image Retrieval (CBIR) applications and complex data indexing using Metric Access Methods (MAMs).
{"title":"FeatSet+: Visual Features Extracted from Public Image Datasets","authors":"Mirela T. Cazzolato, Lucas C. Scabora, Guilherme F. Zabot, Marco A. Gutierrez, Caetano Traina Jr., Agma J. M. Traina","doi":"10.5753/jidm.2022.2328","DOIUrl":"https://doi.org/10.5753/jidm.2022.2328","url":null,"abstract":"Real-world applications generate large amounts of images every day. With the generalized use of social media, users frequently share images acquired by smartphones. Also, hospitals, clinics, exhibits, factories, and other facilities generate images with potential use for many applications. Processing the generated images usually requires feature extraction, which can be time-consuming and laborious. In this paper, we present FeatSet+, a compilation of color, texture and shape visual features extracted from 17 open image datasets reported in the literature. FeatSet+ provides a collection of 11 distinct visual features, extracted by well-known Feature Extraction Methods (FEMs) such as LBP, Haralick, and Color Layout. We organized the available features in a standard collection, including the metadata and labels, when available. Eleven of the datasets also contain classes, which aid the evaluation of supervised methods such as classifiers and clustering tasks. FeatSet+ is available for download in a public repository as sql scripts and csv files. Additionally, FeatSet+ provides a description of the domain of each dataset, including the reference to the original work and link. We show the potential applicability of FeatSet+ in four computational tasks: multi-attribute analysis and retrieval, visual analysis using Multidimensional Scaling (MDS) and Principal Components Analysis (PCA), global feature classification, and dimensionality reduction. FeatSet+ can be employed to evaluate supervised and non-supervised learning tasks, also widely supporting Content-Based Image Retrieval (CBIR) applications and complex data indexing using Metric Access Methods (MAMs).","PeriodicalId":293511,"journal":{"name":"Journal of Information and Data Management","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127017013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Companies or institutions can use survey questionnaires to evaluate items or products, analyze their employees/customers’ satisfaction or collect any data they consider helpful. Furthermore, questionnaires can be used to collect data that can be used in research studies. Some problems in creating such questionnaires involve: deciding which questions to ask, how to ask them, and how to organize them. Many research communities, especially in the healthcare field, maintain repositories that are publicly accessible and include different questionnaires that help professionals and researchers analyze the results of questions, add new questions, or even point out nonsense questions. In this paper, we describe: (i) web crawler, which scans the Web searching for sites that possibly contain questionnaires; (ii) an extractor, which extracts the questionnaires from the list of pages collected by the crawler and saves them into a relational database; and (iii) the public dataset we have created to persist the questionnaires. The database created can then serve to analyze these data and/or as a centralized base of examples to prepare new questionnaires or reuse existing questions. The experiments we have conducted demonstrate that our crawler has achieved 94,47%, and the extractor has achieved a precision between 90% and 92%.
{"title":"Collecting, extracting and storing web research survey questionnaires data","authors":"Carina F. Dorneles, Gilney N. Mathias","doi":"10.5753/jidm.2022.2318","DOIUrl":"https://doi.org/10.5753/jidm.2022.2318","url":null,"abstract":"Companies or institutions can use survey questionnaires to evaluate items or products, analyze their employees/customers’ satisfaction or collect any data they consider helpful. Furthermore, questionnaires can be used to collect data that can be used in research studies. Some problems in creating such questionnaires involve: deciding which questions to ask, how to ask them, and how to organize them. Many research communities, especially in the healthcare field, maintain repositories that are publicly accessible and include different questionnaires that help professionals and researchers analyze the results of questions, add new questions, or even point out nonsense questions. In this paper, we describe: (i) web crawler, which scans the Web searching for sites that possibly contain questionnaires; (ii) an extractor, which extracts the questionnaires from the list of pages collected by the crawler and saves them into a relational database; and (iii) the public dataset we have created to persist the questionnaires. The database created can then serve to analyze these data and/or as a centralized base of examples to prepare new questionnaires or reuse existing questions. The experiments we have conducted demonstrate that our crawler has achieved 94,47%, and the extractor has achieved a precision between 90% and 92%.","PeriodicalId":293511,"journal":{"name":"Journal of Information and Data Management","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127426504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gabriel P. Oliveira, Gabriel R. G. Barbosa, Bruna C. Melo, Juliana E. Botelho, Mariana O. Silva, Danilo B. Seufitelli, Mirella M. Moro
Music is not only a worldwide essential cultural industry but also one of the most dynamic. The increasing volume of complex music-related data defines new challenges and opportunities for extracting knowledge, benefiting not only different music segments but also the Music Information Retrieval research field. In this article, we assess musical success in the United States and Brazil, two of the biggest music markets in the world. We first introduce MUHSIC and MUHSIC-BR, two novel datasets with enhanced success information that combine chart-related data with acoustic metadata to describe the temporal evolution of musical careers. Then, we use such enriched and curated data to cluster artists according to their success level by considering their high-impact periods (hot streaks). Our results reveal three groups with distinct success behavior over time. Furthermore, Brazil and the US present specific music success patterns regarding artists and genres, reflecting the importance of analyzing regional markets individually.
{"title":"Musical Success in the United States and Brazil: Novel Datasets and Temporal Analyses","authors":"Gabriel P. Oliveira, Gabriel R. G. Barbosa, Bruna C. Melo, Juliana E. Botelho, Mariana O. Silva, Danilo B. Seufitelli, Mirella M. Moro","doi":"10.5753/jidm.2022.2350","DOIUrl":"https://doi.org/10.5753/jidm.2022.2350","url":null,"abstract":"Music is not only a worldwide essential cultural industry but also one of the most dynamic. The increasing volume of complex music-related data defines new challenges and opportunities for extracting knowledge, benefiting not only different music segments but also the Music Information Retrieval research field. In this article, we assess musical success in the United States and Brazil, two of the biggest music markets in the world. We first introduce MUHSIC and MUHSIC-BR, two novel datasets with enhanced success information that combine chart-related data with acoustic metadata to describe the temporal evolution of musical careers. Then, we use such enriched and curated data to cluster artists according to their success level by considering their high-impact periods (hot streaks). Our results reveal three groups with distinct success behavior over time. Furthermore, Brazil and the US present specific music success patterns regarding artists and genres, reflecting the importance of analyzing regional markets individually.","PeriodicalId":293511,"journal":{"name":"Journal of Information and Data Management","volume":"1770 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127139647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：