Data-intensive applications, challenges, techniques and technologies: A survey on Big Data
By Philip Chen, Chunming Zhang. Information Sciences, 2014
3,010 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Information Systems, Information Systems and Management
View Paper: https://www.semanticscholar.org/paper/3b217403302f9cb9d9685404c7646de7bc0db428
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- George K. Karagiannidis
- Sergio Ramírez-Gallego
- Alexandra L’Heureux
- O. Elijah
- Angappa Gunasekaran
- Abdullah Bin Gani
- Yong Yuan
- Wei Yu
In Information Systems
- Sérgio Moro
- Paulo Rita
- George K. Karagiannidis
- Yong Yuan
- Feiyue Wang
- Angappa Gunasekaran
- Stephen J. Childe
- Rameshwar Dubey
In Information Systems and Management
- Tsan-Ming (Jason) Choi
- Abdul Nasser El-Kassar
- Sanjay Kumar Singh
- Alexandra L’Heureux
- Angappa Gunasekaran
- R. Ranjan
- Paulo Cortez
- Vishanth Weerakkody
Citations in the corpus
- kNN-IS: An Iterative Spark-based design of the k-Nearest Neighbors classifier for big data (2017, background): “All rights reserved. tackle data-intensive applications [10] based on the principle of data locality [11] , which is implemented through its distributed file system.”
- Digital Twin: Values, Challenges and Enablers From a Modeling Perspective (2019, neutral): “[395] C.”
- Local Receptive Fields Based Extreme Learning Machine (2015, background): “Introduction machine learning and big data analysis have recently drawn great attentions from researchers in different disciplinaries [1]–[5].”
- Machine Learning With Big Data: Challenges and Approaches (2017, background): “Table of Acronyms ACRONYM DEFINITION FPGA Field-Programmable Gate Array GPU Graphic Processing Units IoT Internet of Things i.i.d Independent and Identically Distributed LLE Locally Linear Embedding ML Machine Learning MLlib Machine Learning Library MLP Multi-Layer Perceptron MOA Massive Online Anal”
- Big IoT Data Analytics: Architecture, Opportunities, and Open Research Challenges (2017, methodology): “Different analytic types are used according to the requirements of IoT applications [32].”
- Big Data Analytics in Operations Management (2018, background): “Chen and Zhang (2014) review the literature and present the big data-related techniques and meth-ods from an information science technical perspective.”
- Remote sensing big data computing: Challenges and opportunities (2015, background): “For big data, the data availability is on top priority of the data processing and knowledge discovering [19].”