Big data and quality: A literature review

Lakshen, Guma Abdulkhader; Vraneš, Sanja; Janev, Valentina

doi:10.1109/telfor.2016.7818902

Cited by 19 publications

(11 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They suggested that a more systematic analysis for both BDT and DQD is essential for reducing the number of missing quality requirements while accounting for BDT. Likewise, Lakshen et al [50] argued on the various technical challenges that must be addressed before the potential of BDT and DQD can be fully realised. Haryadi et al [14] confirmed that BDT and DQD are the central issues for implementing BDA.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring big data traits and data quality dimensions for big data analytics application using partial least squares structural equation modelling

et al. 2021

View full text Add to dashboard Cite

The popularity of big data analytics (BDA) has boosted the interest of organisations into exploiting their large scale data. This technology can become a strategic stimulation for organisations to achieve competitive advantage and sustainable growth. Previous BDA research, however, has focused more on introducing more traits, known as Vs for big data traits, while ignoring the quality of data when examining the application of BDA. Therefore, this study aims to explore the effect of big data traits and data quality dimensions on BDA application. This study has formulated 10 hypotheses that comprised of the relationships of big data traits, accuracy, believability, completeness, timeliness, ease of operation, and BDA application constructs. This study conducted a survey using a questionnaire as a data collection instrument. Then, the partial least squares structural equation modelling technique was used to analyse the hypothesised relationships between the constructs. The findings revealed that big data traits can significantly affect all constructs for data quality dimensions and that the ease of operation construct has a significant effect on BDA application. This study contributes to the literature by bringing new insights to the field of BDA and may serve as a guideline for future researchers and practitioners when studying BDA application.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Exploring big data traits and data quality dimensions for big data analytics application using partial least squares structural equation modelling

et al. 2021

View full text Add to dashboard Cite

show abstract

“…It provides valuable insights that may be accustomed leverage massive information science activities. [13] Big data and its analysis are at the middle of contemporary science and business. These information are generated from on-line transactions, emails, videos, audios, images, click streams, logs, posts, search queries, health records, social networking interactions, science information, sensors and mobile phones and their applications [4] .…”

Section: Literature Surveymentioning

confidence: 99%

Voice Recognition System Through Machine Learning

Sindhu.¹,

Sujatha²

2019

IJITEE

View full text Add to dashboard Cite

Human voice recognition by computers has been ever developing area since 1952. It is challenging task for a computer to understand and act according to human voice rather than to commands or programs. The reason is that no two human’s voice or style or pitch will be similar and every word is not pronounced by everyone in a similar fashion. Background noises and disturbances may confuse the system. The voice or accent of the same person may change according to the user’s mood, situation, time etc. despite of all these challenges, voice recognition and speech to text conversion has reached a successful stage. Voice processing technology deserves still more research. As a tip of iceberg of this research we contribute our work on this are and we propose a new method i.e., VRSML (Voice Recognition System through Machine Learning) mainly focuses on Speech to text conversion, then analyzing the text extracted from speech in the form of tokens through Machine Learning. After analyzing the derived text, reports are created in textual as well graphical format to represent the vocabulary levels used in that speech. As Supervised learning algorithm from Machine Learning is employed to classify the tokens derived from text, the reports will be more accurate and will be generated faster.

show abstract

“…Veracity Veracity refers to the overall quality of the data available. The quality of data can be impacted by noise or abnormalities in the original data gathering process, or through improper processing (Lakshen, Vraneš, & Janev, 2016). Big Data presents some unique issues due to the methods of data collection-slight variations in both the state of the recording equipment and the environmental conditions of the subject can dramatically alter the quality of incoming data.…”

Section: Terminologymentioning

confidence: 99%

Big Earth Data: a comprehensive analysis of visualization analytics issues

Merritt

Davis

et al. 2018

Big Earth Data

View full text Add to dashboard Cite

Big Earth Data analysis is a complex task requiring the integration of many skills and technologies. This paper provides a comprehensive review of the technology and terminology within the Big Earth Data problem space and presents examples of state-of-the-art projects in each major branch of Big Earth Data research. Current issues within Big Earth Data research are highlighted and potential future solutions identified.

show abstract

Big data and quality: A literature review

Cited by 19 publications

References 9 publications

Exploring big data traits and data quality dimensions for big data analytics application using partial least squares structural equation modelling

Exploring big data traits and data quality dimensions for big data analytics application using partial least squares structural equation modelling

Voice Recognition System Through Machine Learning

Big Earth Data: a comprehensive analysis of visualization analytics issues

Contact Info

Product

Resources

About