Big Data – What is it and why it matters

Tattersall, Andy; Grant, Maria J.

doi:10.1111/hir.12147

Cited by 4 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Big data analytics is the concept of examining massive data sets containing heterogeneous data types to uncover hidden patterns, unknown correlations, and other actionable information using advanced analytic methods [25]. To perform the computational requirements of large data analysis, a powerful framework is fundamental to design, implement, and manage the required algorithms and techniques such as machine learning, deep learning [26].…”

Section: Apache Sparkmentioning

confidence: 99%

Deep Transfer Learning Pipelines with Apache Spark and Keras TensorFlow combined with Logistic Regression to Detect COVID-19 in Chest CT Images

Benbrahim

Hachimi

Amine

2021

Walailak J Sci & Tech

View full text Add to dashboard Cite

The SARS-CoV-2 (COVID-19) has propagated rapidly around the world, and it became a global pandemic. It has generated a catastrophic effect on public health. Thus, it is crucial to discover positive cases as early as possible to treat touched patients fastly. Chest CT is one of the methods that play a significant role in diagnosing 2019-nCoV acute respiratory disease. The implementation of advanced deep learning techniques combined with radiological imaging can be helpful for the precise detection of the novel coronavirus. It can also be assistive to surmount the difficult situation of the lack of medical skills and specialized doctors in remote regions. This paper presented Deep Transfer Learning Pipelines with Apache Spark and KerasTensorFlow combined with the Logistic Regression algorithm for automatic COVID-19 detection in chest CT images, using Convolutional Neural Network (CNN) based models VGG16, VGG19, and Xception. Our model produced a classification accuracy of 85.64, 84.25, and 82.87 %, respectively, for VGG16, VGG19, and Xception. HIGHLIGHTS Deep Transfer Learning Pipelines with Apache Spark and Keras TensorFlow combined with Logistic Regression using CT images to screen for Corona Virus Disease (COVID-19) Automatic detection of COVID-19 in chest CT images Convolutional Neural Network (CNN) based models VGG16, VGG19, and Xception to predict COVID-19 in Computed Tomography image GRAPHICAL ABSTRACT

show abstract

Section: Apache Sparkmentioning

confidence: 99%

Deep Transfer Learning Pipelines with Apache Spark and Keras TensorFlow combined with Logistic Regression to Detect COVID-19 in Chest CT Images

Benbrahim

Hachimi

Amine

2021

Walailak J Sci & Tech

View full text Add to dashboard Cite

show abstract

“…When examining big data at the macro level in library and information settings, big data can examine patterns of use for materials, such as how often materials are checked out and which materials are the most popular or underutilized. This data can assist library and information professionals to know which materials can be weeded and which materials should stay in circulation . When using big data to interact with a system, rather than individuals, privacy issues can be mitigated.…”

Section: Big Data As It Relates To Librariesmentioning

confidence: 99%

“…Although such data is generally in the public domain, it is often used without explicit consent. Even though individuals on social media or search engine platforms are aware data could be used, they often view the information or inquiries as private . For example, researchers have previously used networked data on Facebook to predict sexual orientation of users.…”

Section: Big Data As It Relates To Librariesmentioning

confidence: 99%

Big Data's Impact on Privacy for Librarians and Information Professionals

Harper

Oltmann

2017

Bull of the Asso Info Sci

View full text Add to dashboard Cite

EDITOR'S SUMMARY In a digital age, it is very difficult to maintain complete privacy when posting on social media or making purchases. Individual activity on the internet is increasingly collected by corporations, even with the user's knowledge, and can be used to predict future behavior, purchasing choices or other sensitive subjects. This data analysis is often done without a user's consent and in many cases presents unethical behavior and breaches of privacy. In the world of libraries, the privacy of patrons has been tantamount for decades, but trying to keep up with privacy codes and still make use of this big data can be challenging for librarians. Big data can be beneficial to libraries in many ways, and if pointed at library systems, rather than the habits of patrons, can also keep privacy intact.

show abstract

“…The big data era promoted by next-generation sequencing (NGS) has irreversibly changed genetics and systems biology. The amount of biological data available to scientists has been rapidly increasing during the last 15 years, 1 which makes the intersection between experimental sciences and computational biology even more imperative to overcome new scientific barriers.…”

Section: Introductionmentioning

confidence: 99%

Understanding small ORF diversity through a comprehensive transcription feature classification

2021

View full text Add to dashboard Cite

Small open reading frames (small ORFs/sORFs/smORFs) are potentially coding sequences smaller than 100 codons that have historically been considered junk DNA by gene prediction software and in annotation screening; however, the advent of next-generation sequencing has contributed to the deeper investigation of junk DNA regions and their transcription products, resulting in the emergence of smORFs as a new focus of interest in systems biology. Several smORF peptides were recently reported in noncanonical mRNAs as new players in numerous biological contexts; however, their relevance is still overlooked in coding potential analysis. Hence, this review proposes a smORF classification based on transcriptional features, discussing the most promising approaches to investigate smORFs based on their different characteristics. First, smORFs were divided into nonexpressed (intergenic) and expressed (genic) smORFs. Second, genic smORFs were classified as smORFs located in noncoding RNAs (ncRNAs) or canonical mRNAs. Finally, smORFs in ncRNAs were further subdivided into sequences located in small or long RNAs, whereas smORFs located in canonical mRNAs were subdivided into several specific classes depending on their localization along the gene. We hope that this review provides new insights into large-scale annotations and reinforces the role of smORFs as essential components of a hidden coding DNA world.

show abstract

Big Data – What is it and why it matters

Cited by 4 publications

References 11 publications

Deep Transfer Learning Pipelines with Apache Spark and Keras TensorFlow combined with Logistic Regression to Detect COVID-19 in Chest CT Images

Deep Transfer Learning Pipelines with Apache Spark and Keras TensorFlow combined with Logistic Regression to Detect COVID-19 in Chest CT Images

Big Data's Impact on Privacy for Librarians and Information Professionals

Understanding small ORF diversity through a comprehensive transcription feature classification

Contact Info

Product

Resources

About