Content Analysis of Scientific Articles in Apache Hadoop Ecosystem

Dendek, Piotr Jan; Czeczko, Artur; Fedoryszak, Mateusz; Kawa, Adam; Wendykier, Piotr; Bolikowski, Łukasz

doi:10.1007/978-3-319-04714-0_10

Cited by 5 publications

(7 citation statements)

References 15 publications

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“…The automatic clustering approach of scientific text and newspaper articles was proposed by [15]. A content-level approach was proposed by [16] for the classification of scientific documents. They applied different algorithms like Naive Bayes, decision trees, k-Nearest neighbor (kNN), neural networks, and Support Vector Machines (SVM) for the classification of documents.…”

Section: Research Backgroundmentioning

confidence: 99%

A Novel Metadata Based Multi-Label Document Classification Technique

Sajid¹,

Ahmad²,

Rahman³

et al. 2023

Computer Systems Science and Engineering

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From the beginning, the process of research and its publication is an ever-growing phenomenon and with the emergence of web technologies, its growth rate is overwhelming. On a rough estimate, more than thirty thousand research journals have been issuing around four million papers annually on average. Search engines, indexing services, and digital libraries have been searching for such publications over the web. Nevertheless, getting the most relevant articles against the user requests is yet a fantasy. It is mainly because the articles are not appropriately indexed based on the hierarchies of granular subject classification. To overcome this issue, researchers are striving to investigate new techniques for the classification of the research articles especially, when the complete article text is not available (a case of nonopen access articles). The proposed study aims to investigate the multilabel classification over the available metadata in the best possible way and to assess, "to what extent metadata-based features can perform in contrast to content-based approaches." In this regard, novel techniques for investigating multilabel classification have been proposed, developed, and evaluated on metadata such as the Title and Keywords of the articles. The proposed technique has been assessed for two diverse datasets, namely, from the Journal of universal computer science (J.UCS) and the benchmark dataset comprises of the articles published by the Association for computing machinery (ACM). The proposed technique yields encouraging results in contrast to the state-ofthe-art techniques in the literature.

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Section: Research Backgroundmentioning

confidence: 99%

A Novel Metadata Based Multi-Label Document Classification Technique

Sajid¹,

Ahmad²,

Rahman³

et al. 2023

Computer Systems Science and Engineering

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“…The automatic clustering approach of scientific text and newspapers articles was proposed by Afonso and Duque [28]. A content level approach was proposed by Dendek et al [13] for the classification of scientific documents. They applied different algorithms such as: Naïve Bayes, decision tree, k-nearest neighbor (KNN), neural network, Support Vector Machine (SVM) for the classification of documents.…”

Section: Content-based Approachesmentioning

confidence: 99%

“…(1) single-label classification (i.e., classifying the items into a single class) and (2) multi-label classification (i.e., classifying the items into more than one class), since a research article can have an association with multiple categories. Therefore, multi-label classification has gained the attention of many researchers who have classified research articles into multiple categories [12,13]. Most of the multi-label classification schemes are of low accuracy and classify research articles into a limited number of categories [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Single vs. Multi-Label: The Issues, Challenges and Insights of Contemporary Classification Schemes

et al. 2023

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Over the decades, a tremendous increase has been witnessed in the production of documents available in digital form. The increased production of documents has gained so much momentum that their rate of production jumps two-fold every five years. These articles are searched over the internet via search engines, digital libraries, and citation indexes. However, the retrieval of relevant research papers for user queries is still a pipedream. This is because scientific documents are not indexed based on some subject classification hierarchies. Hence, the classification of these documents becomes a challenging task for the researchers. Classification of the documents can be two-fold: one way is to assign a single label to each document and the other is to assign multi-labels to each document based on its belonging domains. Classification of the documents can be performed by using either the available metadata or the whole content of the documents. While performing classification, there are many challenges which may belong to the dataset, feature selection technique, preprocessing methodology, and which classification model is suitable for the classification of the documents. This paper highlights the issues for single-label and multi-label classification by using either metadata or content of the documents and why metadata-based approaches are better than content-based approaches in terms of feasibility.

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“…This tool is a mature solution for defining and executing workflows (that can be triggered by a user, time event or data arrival) [12]. It supports useful functionalities such as the persistence of an execution history.…”

Section: Bug-source Software Selectionmentioning

confidence: 99%

Concurrency bugs in open source software: a case study

Asadollah

Sundmark

Eldh

et al. 2017

J Internet Serv Appl

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Concurrent programming puts demands on software debugging and testing, as concurrent software may exhibit problems not present in sequential software, e.g., deadlocks and race conditions. In aiming to increase efficiency and effectiveness of debugging and bug-fixing for concurrent software, a deep understanding of concurrency bugs, their frequency and fixing-times would be helpful. Similarly, to design effective tools and techniques for testing and debugging concurrent software, understanding the differences between non-concurrency and concurrency bugs in real-word software would be useful. This paper presents an empirical study focusing on understanding the differences and similarities between concurrency bugs and other bugs, as well as the differences among various concurrency bug types in terms of their severity and their fixing time, and reproducibility. Our basis is a comprehensive analysis of bug reports covering several generations of five open source software projects. The analysis involves a total of 11860 bug reports from the last decade, including 351 reports related to concurrency bugs. We found that concurrency bugs are different from other bugs in terms of their fixing time and severity while they are similar in terms of reproducibility. Our findings shed light on concurrency bugs and could thereby influence future design and development of concurrent software, their debugging and testing, as well as related tools.

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Content Analysis of Scientific Articles in Apache Hadoop Ecosystem

Cited by 5 publications

References 15 publications

A Novel Metadata Based Multi-Label Document Classification Technique

A Novel Metadata Based Multi-Label Document Classification Technique

Single vs. Multi-Label: The Issues, Challenges and Insights of Contemporary Classification Schemes

Concurrency bugs in open source software: a case study

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