Results 1 to 10 of about 589,342 (25)
Several Typical Paradigms of Industrial Big Data Application [PDF]
David C.Wyld et al.(Eds):
ITCSE,ICDIPV,NC,CBIoT,CAIML,CRYPIS,ICAIT,NLCA-2021,pp.61-68,2021.CS&IT-CSCP
2021, 2021 Industrial big data is an important part of big data family, which has important application value for industrial production scheduling, risk perception, state identification, safety monitoring and quality control, etc. Due to the particularity of the industrial field, some concepts in the existing big data research field are unable to reflect ...
arxiv +1 more source
NNSC-Cobordism of Bartnik Data in High Dimensions [PDF]
SIGMA 16 (2020), 030, 5 pages, 2020 In this short note, we formulate three problems relating to nonnegative scalar curvature (NNSC) fill-ins. Loosely speaking, the first two problems focus on: When are $(n-1)$-dimensional Bartnik data $\big(\Sigma_i ^{n-1}, \gamma_i, H_i\big)$, $i=1,2$, NNSC-cobordant? (i.e., there is an $n$-dimensional compact Riemannian manifold $\big(\Omega^n, g\big)$
arxiv +1 more source
, 2020 The Internet of Things, crowdsourcing, social media, public authorities, and other sources generate bigger and bigger data sets. Big and open data offers many benefits for emergency management, but also pose new challenges. This chapter will review the sources of big data and their characteristics.
arxiv +1 more source
Journey from Data Mining to Web Mining to Big Data [PDF]
International Journal of Computer Trends and Technology (IJCTT)
10(1): 1-3, April 2014. Published by Seventh Sense Research Group, 2014 This paper describes the journey of big data starting from data mining to web mining to big data. It discusses each of this method in brief and also provides their applications. It states the importance of mining big data today using fast and novel approaches.
arxiv +1 more source
Characterizing and Subsetting Big Data Workloads [PDF]
, 2014 Big data benchmark suites must include a diversity of data and workloads to be useful in fairly evaluating big data systems and architectures. However, using truly comprehensive benchmarks poses great challenges for the architecture community. First, we need to thoroughly understand the behaviors of a variety of workloads.
arxiv +1 more source
A Survey on Blockchain for Big Data: Approaches, Opportunities, and Future Directions [PDF]
arXiv, 2020 Big data has generated strong interest in various scientific and engineering domains over the last few years. Despite many advantages and applications, there are many challenges in big data to be tackled for better quality of service, e.g., big data analytics, big data management, and big data privacy and security.
arxiv
Big Data: Opportunities and Privacy Challenges [PDF]
arXiv, 2015 Recent advances in data collection and computational statistics coupled with increases in computer processing power, along with the plunging costs of storage are making technologies to effectively analyze large sets of heterogeneous data ubiquitous. Applying such technologies (often referred to as big data technologies) to an ever growing number and ...
arxiv
Video Big Data Analytics in the Cloud: Research Issues and Challenges [PDF]
arXiv, 2020 On the rise of distributed computing technologies, video big data analytics in the cloud have attracted researchers and practitioners' attention. The current technology and market trends demand an efficient framework for video big data analytics. However, the current work is too limited to provide an architecture on video big data analytics in the ...
arxiv
Identifying Dwarfs Workloads in Big Data Analytics [PDF]
arXiv, 2015 Big data benchmarking is particularly important and provides applicable yardsticks for evaluating booming big data systems. However, wide coverage and great complexity of big data computing impose big challenges on big data benchmarking. How can we construct a benchmark suite using a minimum set of units of computation to represent diversity of big ...
arxiv
A Taxonomy on Big Data: Survey [PDF]
arXiv, 2018 The Big Data is the most popular paradigm nowadays and it has almost no untouched area. For instance, science, engineering, economics, business, social science, and government. The Big Data are used to boost up the organization performance using massive amount of dataset.
arxiv