Big Data Analytics

By Arsalan Zahid Piprani, Amjad Ali, Adeel Shah. Lecture Notes in Computer Science, 2014

1,807 citations recorded by the source corpus.

Subfields: Health Information Management

View Paper: https://www.semanticscholar.org/paper/cc017a62c605a0749e35a1264a46d62e78fb68b7

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In Health Information Management

Citations in the corpus

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda (2022, background): “The challenges in the operational dynamism of AI technologies in healthcare systems are immeasurable despite the information that this is one of the most vital expansion areas in biomedical research (Kumar et al. 2020).”
A review of clustering techniques and developments (2017, background): “Big data [161-168] is also an emerging issue.”
A Survey on Big Data Market: Pricing, Trading and Protection (2018, background): “In addition, approximately 74% of information technology organizations use at least one of the advanced analytics methods mentioned [47].”
Big Data computing and clouds: Trends and future directions (2013, background): “Furthermore, market research shows that inadequate staffing and skills, lack of business support, and problems with analytics software are some of the barriers faced by corporations when performing analytics [27].”
Data Mining for Internet of Things: A Survey (2014, background): “2The volume of data, the variety of data types, and the velocity of data generated are the major characteristics of big data [19].”
A critical investigation of Industry 4.0 in manufacturing: theoretical operationalisation framework (2018, background): “This can, consequently, provide novel opportunities in terms of efficiency improvements, cost savings and revenue growth (Bechtold et al. 2014; Zhong et al. 2015).”
Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations (2018, methodology): “According to a 2011 investigation by the TDWI research (Russom, 2011), the benefits of analyzing unstructured data capability are illustrated by the successful implementation of targeted marketing, providing revenue-generating insights and building customer segmentation.”
Understandable Big Data: A survey (2015, methodology): “Due to the characteristics of big data, mainly variety, there are many techniques used for analytics on big data [32].”
Big Data technologies: A survey (2017, positive): “By 2015, digital data grew to 8 ZB (Rajaraman, 2016).”
Changes in air passenger demand as a result of the COVID-19 crisis: using Big Data to inform tourism policy (2020, background): “Vs currently identified as the characteristics that define Big Data: volume, velocity, variety, variability, veracity, visualisation and value (El Alaoui et al., 2017; Gandomi & Haider, 2015).”
Achieving superior organizational performance via big data predictive analytics: A dynamic capability view (2019, background): “Some of these skills include mastery of machine learning, artificial intelligence, statistical analytics, data extraction and cleaning, and last but not the least the understanding of programming languages (Davenport, 2014; Russom, 2011).”
Big IoT Data Analytics: Architecture, Opportunities, and Open Research Challenges (2017, background): “The capability to analyze large amounts of data can help an organization deal with considerable information that can affect the business [6].”
Data science, predictive analytics, and big data in supply chain management: Current state and future potential (2015, positive): “It was also surprising that about one-third of the respondents were not familiar with analytics (28.4%), especially in light of a survey conducted two years prior, which reported only 7% not being familiar with data analytics (Russom 2011).”