"Big data" is an emerging topic and has attracted the attention of many researchers and practitioners in industrial systems engineering and cybernetics. Big data analytics would definitely lead to valuable knowledge for many organizations. Business operations and risk management can be a beneficiary as there are many data collection channels in the related industrial systems (e.g., wireless sensor networks, Internet-based systems, etc.). Big data research, however, is still in its infancy. Its focus is rather unclear and related studies are not well amalgamated. This paper aims to present the challenges and opportunities of big data analytics in this unique application domain. Technological development and advances for industrial-based business systems, reliability and security of industrial systems, and their operational risk management are examined. Important areas for future research are also discussed and revealed.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1109/TCYB.2015.2507599 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
This study intends to optimize the carbon footprint management model of power enterprises through artificial intelligence (AI) technology to help the scientific formulation of carbon emission reduction strategies. Firstly, a carbon footprint calculation model based on big data and AI is established, and then machine learning algorithm is used to deeply mine the carbon emission data of power enterprises to identify the main influencing factors and emission reduction opportunities. Finally, the driver-state-response (DSR) model is used to evaluate the carbon audit of the power industry and comprehensively analyze the effect of carbon emission reduction.
View Article and Find Full Text PDFMulti-feature fusion-based consumer perceived risk prediction and its interpretability study.
PLoS One
January 2025
School of Economics & Management, Beijing Information Science & Technology University, Beijing, China.
E-commerce faces challenges such as content homogenization and high perceived risk among users. This paper aims to predict perceived risk in different contexts by analyzing review content and website information. Based on a dataset containing 262,752 online reviews, we employ the KeyBERT-TextCNN model to extract thematic features from the review content.
View Article and Find Full Text PDFBACT: nonparametric Bayesian cell typing for single-cell spatial transcriptomics data.
Brief Bioinform
November 2024
Institute of Statistics and Big Data, Renmin University of China, No. 59 Zhongguancun Street, 100872 Beijing, China.
The spatial transcriptomics is a rapidly evolving biological technology that simultaneously measures the gene expression profiles and the spatial locations of spots. With progressive advances, current spatial transcriptomic techniques can achieve the cellular or even the subcellular resolution, making it possible to explore the fine-grained spatial pattern of cell types within one tissue section. However, most existing cell spatial clustering methods require a correct specification of the cell type number, which is hard to determine in the practical exploratory data analysis.
View Article and Find Full Text PDFBasic Science and Pathogenesis.
Alzheimers Dement
December 2024
Picower Institute, MIT, Cambridge, MA, USA.
Background: The ability to profile gene expression at the single-cell resolution offers the unprecedent opportunity to define the complex cellular heterogeneity of the brain in response to pathology. However, single-cell transcriptomics, particularly within the context of postmortem human brain samples, only provide a static snapshot of the underlying transcriptional mechanisms driving the initiation and progression of diseases.
Method: To gain a more comprehensive picture of disease-associated transcriptional programs, our research integrates single-cell genomics with cellular reprogramming techniques for data-driven mechanistic studies with human-based cellular models of the brain.
kMetaShot: a fast and reliable taxonomy classifier for metagenome-assembled genomes.
Brief Bioinform
November 2024
Department of Biosciences, Biotechnology and Environment, University of Bari Aldo Moro, Via E. Orabona 4, 70126, Bari, Italy.
The advent of high-throughput sequencing (HTS) technologies unlocked the complexity of the microbial world through the development of metagenomics, which now provides an unprecedented and comprehensive overview of its taxonomic and functional contribution in a huge variety of macro- and micro-ecosystems. In particular, shotgun metagenomics allows the reconstruction of microbial genomes, through the assembly of reads into MAGs (metagenome-assembled genomes). In fact, MAGs represent an information-rich proxy for inferring the taxonomic composition and the functional contribution of microbiomes, even if the relevant analytical approaches are not trivial and still improvable.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!