Uncertainty in Big Data Analytics: Survey, Opportunities, and Challenges
Open accessAbstract
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.
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Many sources of data remain siloed, unstructured, and/or unavailable for complementary and secondary research.
“However, many sources of data remain siloed, unstructured, and/or unavailable for complementary and secondary research.”
From Sysrev: A FAIR Platform for Data Curation and Systematic Evidence Review (2021) · cites this paper as Hariri et al., 2019
The Big data section describes unstructured data and the difficulty of analysing it alongside data from heterogeneous sources. It does not say that many data sources are siloed or unavailable for complementary or secondary research; searches for data silos, access, and secondary use found no supporting passage. Our copy provides no page numbers for these passages. (LLM, conf=0.82)
Experimental — accuracy not yet measuredRe-use of the information can be limited due to controls on database access, limited labeling of semi-structured or unstructured data, and the structures of databases that prevent inclusion of some experimental data, among others.
“However, re-use of the information can be limited due to controls on database access, limited labeling of semi-structured or unstructured data (Hariri et al., 2019), and the structures of databases that prevent inclusion of some experimental data (Wilkinson et al., 2016), among others.”
From Sysrev: A FAIR Platform for Data Curation and Systematic Evidence Review (2021) · cites this paper as Hariri et al., 2019
The 'Machine learning and big data' section says that data can lack labels and that learning from unlabeled data is difficult. The 'Big data' section separately describes challenges in analyzing semi-structured and unstructured data, but does not specifically link those challenges to limited labeling. Our copy also does not establish controls on database access as a limit on re-use; the database-structure point is cited to Wilkinson, not Hariri. Page numbers are unavailable in our copy. (LLM, conf=0.84)
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- Authors
- Hariri, R. H., Fredericks, E. M., & Bowers, K. M.
- Affiliations
- Oakland University (US)
- Corresponding author
- Hariri, Reihaneh H.
- Venue
- Journal Of Big Data
- Published
- 2019-06-04
- Volume
- 6
- Issue
- 1
- ISSN
- 2196-1115 (ISSN-L)
- OpenAlex
- W2948646149
- Cited by
- 620
- References
- 74
- Citations by year
- 2026: 29 · 2025: 100 · 2024: 140 · 2023: 131 · 2022: 107 · 2021: 70 · 2020: 37 · 2019: 6
- FWCI
- 64.94
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Journal Of Big Data (publishedVersion, cc-by)
- ELARTU (Ternopil National Technical University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation (CNS-1657061)
- Arizona Space Grant Consortium
- Comcast Innovation
- Oakland University
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Keywords
- big data analytics, uncertainty, noisy data, missing data, data inconsistency, artificial intelligence, machine learning, natural language processing, computational intelligence, decision-making, Internet of Things, sensor networks, cyber-physical systems, open problems
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