Uncertainty in big data analytics: survey, opportunities, and challenges
Abstract Big data analytics has gained wide attention from both academia and industry as the demand for understanding trends in massive datasets increases. Recent developments in sensor networks, cyber-physical systems, and the ubiquity of the Internet of Things (IoT) have increased the collection o...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2019-06-01
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| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s40537-019-0206-3 |
| _version_ | 1818286428110454784 |
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| author | Reihaneh H. Hariri Erik M. Fredericks Kate M. Bowers |
| author_facet | Reihaneh H. Hariri Erik M. Fredericks Kate M. Bowers |
| author_sort | Reihaneh H. Hariri |
| collection | DOAJ |
| description | Abstract Big data analytics has gained wide attention from both academia and industry as the demand for understanding trends in massive datasets increases. Recent developments in sensor networks, cyber-physical systems, and the ubiquity of the Internet of Things (IoT) have increased the collection of data (including health care, social media, smart cities, agriculture, finance, education, and more) to an enormous scale. However, the data collected from sensors, social media, financial records, etc. is inherently uncertain due to noise, incompleteness, and inconsistency. The analysis of such massive amounts of data requires advanced analytical techniques for efficiently reviewing and/or predicting future courses of action with high precision and advanced decision-making strategies. As the amount, variety, and speed of data increases, so too does the uncertainty inherent within, leading to a lack of confidence in the resulting analytics process and decisions made thereof. In comparison to traditional data techniques and platforms, artificial intelligence techniques (including machine learning, natural language processing, and computational intelligence) provide more accurate, faster, and scalable results in big data analytics. Previous research and surveys conducted on big data analytics tend to focus on one or two techniques or specific application domains. However, little work has been done in the field of uncertainty when applied to big data analytics as well as in the artificial intelligence techniques applied to the datasets. This article reviews previous work in big data analytics and presents a discussion of open challenges and future directions for recognizing and mitigating uncertainty in this domain. |
| first_indexed | 2024-12-13T01:24:26Z |
| format | Article |
| id | doaj.art-4788a7a9a3c24eb3b32299ab8d7732f5 |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-12-13T01:24:26Z |
| publishDate | 2019-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-4788a7a9a3c24eb3b32299ab8d7732f52022-12-22T00:04:09ZengSpringerOpenJournal of Big Data2196-11152019-06-016111610.1186/s40537-019-0206-3Uncertainty in big data analytics: survey, opportunities, and challengesReihaneh H. Hariri0Erik M. Fredericks1Kate M. Bowers2Oakland UniversityOakland UniversityOakland UniversityAbstract Big data analytics has gained wide attention from both academia and industry as the demand for understanding trends in massive datasets increases. Recent developments in sensor networks, cyber-physical systems, and the ubiquity of the Internet of Things (IoT) have increased the collection of data (including health care, social media, smart cities, agriculture, finance, education, and more) to an enormous scale. However, the data collected from sensors, social media, financial records, etc. is inherently uncertain due to noise, incompleteness, and inconsistency. The analysis of such massive amounts of data requires advanced analytical techniques for efficiently reviewing and/or predicting future courses of action with high precision and advanced decision-making strategies. As the amount, variety, and speed of data increases, so too does the uncertainty inherent within, leading to a lack of confidence in the resulting analytics process and decisions made thereof. In comparison to traditional data techniques and platforms, artificial intelligence techniques (including machine learning, natural language processing, and computational intelligence) provide more accurate, faster, and scalable results in big data analytics. Previous research and surveys conducted on big data analytics tend to focus on one or two techniques or specific application domains. However, little work has been done in the field of uncertainty when applied to big data analytics as well as in the artificial intelligence techniques applied to the datasets. This article reviews previous work in big data analytics and presents a discussion of open challenges and future directions for recognizing and mitigating uncertainty in this domain.http://link.springer.com/article/10.1186/s40537-019-0206-3Big dataUncertaintyBig data analyticsArtificial intelligence |
| spellingShingle | Reihaneh H. Hariri Erik M. Fredericks Kate M. Bowers Uncertainty in big data analytics: survey, opportunities, and challenges Journal of Big Data Big data Uncertainty Big data analytics Artificial intelligence |
| title | Uncertainty in big data analytics: survey, opportunities, and challenges |
| title_full | Uncertainty in big data analytics: survey, opportunities, and challenges |
| title_fullStr | Uncertainty in big data analytics: survey, opportunities, and challenges |
| title_full_unstemmed | Uncertainty in big data analytics: survey, opportunities, and challenges |
| title_short | Uncertainty in big data analytics: survey, opportunities, and challenges |
| title_sort | uncertainty in big data analytics survey opportunities and challenges |
| topic | Big data Uncertainty Big data analytics Artificial intelligence |
| url | http://link.springer.com/article/10.1186/s40537-019-0206-3 |
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