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The Rise of Big Data Science: A Survey of Techniques, Methods and Approaches in the Field of Natural Language Processing and Network Theory

Jeffrey Ray, Olayinka Johnny, Marcello Trovati, Stelios Sotiriadis, Nik Bessis
2018 Big Data and Cognitive Computing  
The continuous creation of data has posed new research challenges due to its complexity, diversity and volume. Consequently, Big Data has increasingly become a fully recognised scientific field.  ...  This article provides an overview of the current research efforts in Big Data science, with particular emphasis on its applications, as well as theoretical foundation.  ...  real-world entity [48] .  ... 
doi:10.3390/bdcc2030022 fatcat:fdo7e5asvjbwlg2wibdc47mzzm

A Big Data Approach for Traffic Classification using Data Mining

Srashti Chouhan
2018 International Journal for Research in Applied Science and Engineering Technology  
The collected data samples are the real world data sample which evaluates the performance of the work. For distributed and parallel processing and fetching results MapReduce component is used.  ...  for carefully which leads to generate large network traffic data.  ...  This increasing data which is of large amount is called as Big Data. Big data requires some specialized technique for the efficient classification. A.  ... 
doi:10.22214/ijraset.2018.1457 fatcat:db2356szhnc37h2qwlgyiqmoc4

A Structure Fidelity Approach for Big Data Collection in Wireless Sensor Networks

Mou Wu, Liansheng Tan, Naixue Xiong
2014 Sensors  
The simulation results based on synthetic and real world datasets verify the effectiveness of SFDC framework both on energy saving and data fidelity.  ...  A structural distortion based on the image quality assessment approach is used to perform the nodes work/sleep scheduling, such that the number of the working nodes is reduced while the remainder of nodes  ...  WSNs have great potential that are exploited for many applications in real world scenarios.  ... 
doi:10.3390/s150100248 pmid:25609045 pmcid:PMC4327017 fatcat:2drgceby5bgkdflnwrfdwrz6he

A Big Data Approach for Traffic Classification using Data Mining

Srashti Chouhan
2018 International Journal for Research in Applied Science and Engineering Technology  
The collected data samples are the real world data sample which evaluates the performance of the work. For distributed and parallel processing and fetching results MapReduce component is used.  ...  for carefully which leads to generate large network traffic data.  ...  This increasing data which is of large amount is called as Big Data. Big data requires some specialized technique for the efficient classification. A.  ... 
doi:10.22214/ijraset.2018.6149 fatcat:i6fhjlwxw5aulor6qcrd6taelm

Developing an Intrusion Detection Framework for High-Speed Big Data Networks: A Comprehensive Approach

2018 KSII Transactions on Internet and Information Systems  
As such, we present a comprehensive approach to building an efficient IDS with the aim of strengthening academic anomaly detection research in real-world operational environments.  ...  big data computing framework and utilizing a contemporary dataset to deal with ongoing advancements.  ...  Conclusion In this paper, we followed a comprehensive approach to develop an efficient IDS with emphasis on tackling big data problems in large-scale networks.  ... 
doi:10.3837/tiis.2018.08.026 fatcat:cfh544m3qreipl5qglpbla6sh4

A spatiotemporal compression based approach for efficient big data processing on Cloud

Chi Yang, Xuyun Zhang, Changmin Zhong, Chang Liu, Jian Pei, Kotagiri Ramamohanarao, Jinjun Chen
2014 Journal of computer and system sciences (Print)  
To tackle the challenges, we propose a novel technique for effectively processing big graph data on Cloud. Specifically, the big data will be compressed with its spatiotemporal features on Cloud.  ...  It is well known that processing big graph data can be costly on Cloud.  ...  They are original scheduling based on the physical topology of the real world network.  ... 
doi:10.1016/j.jcss.2014.04.022 fatcat:yksb3x2gerbchewchyh722nhka

A graph-based big data optimization approach using hidden Markov model and constraint satisfaction problem

Imad Sassi, Samir Anter, Abdelkrim Bekkhoucha
2021 Journal of Big Data  
To address this issue, we propose a graph-based big data optimization approach using a CSP to enhance the results of learning and prediction tasks of HMMs.  ...  To verify the validity of the model, the proposed approach is evaluated on real-world data using the mean absolute percentage error (MAPE) and other metrics as measures of the prediction accuracy.  ...  The objective of this big data optimization approach is to reduce the state and/or the observation space so that this approach can be used in a big data context.  ... 
doi:10.1186/s40537-021-00485-z fatcat:h3x3aas4mzeilowfoyc4flztj4

Towards Near Real-Time BGP Deep Analysis: A Big-Data Approach [article]

Joel Obstfeld, Xiaoyu Chen, Olivier Frebourg, Pavan Sudheendra
2017 arXiv   pre-print
In this paper, we present a BGP Deep-analysis application developed using the PNDA (Platform for Network Data Analytics) 'Big-Data' platform.  ...  In addition, such techniques are challenged by a lack of sufficient resources to store and process data feeds in real-time from multiple BGP Vantage Points (VPs).  ...  PNDA provides a Hadoop Towards Near Real-Time BGP Deep Analysis: A Big-Data Approach IMC 2017 Figure 5 : 5 Figure 5: PNDA block diagram Figure 6 : 6 Figure 6: Top N analysisUsing further mapping techniques  ... 
arXiv:1705.08666v1 fatcat:xlienzux3nds7c6lc4auofnec4

A Novel Pipeline Approach for Efficient Big Data Broadcasting

Chi-Jen Wu, Chin-Fu Ku, Jan-Ming Ho, Ming-Syan Chen
2016 IEEE Transactions on Knowledge and Data Engineering  
In this paper, we investigate the Big Data Broadcasting problem for a single source node to broadcast a big chunk of data to a set of nodes with the objective of minimizing the maximum completion time.  ...  We model the Big-data broadcasting problem into a LockStep Broadcast Tree (LSBT) problem.  ...  In science, the Large Hadron Collider (LHC) can generate about fifteen petabytes of data annually, and thousands of scientists around the world need to access and analyze those big data sets [3] .  ... 
doi:10.1109/tkde.2015.2468714 fatcat:t4tst547ezh6bbr66jnh27wohq

GFP-X: A parallel approach to massive graph comparison using spark

Stephen Bonner, John Brennan, Georgios Theodoropoulos, Ibad Kureshi, Andrew Stephen McGough
2016 2016 IEEE International Conference on Big Data (Big Data)  
The approach acts by producing a 'Graph Fingerprint' which represents both vertex level and global level topological features from a graph.  ...  Importantly, the approach is shown to not only be comparable to existing approaches, but on when comparing topology and size, more sensitive at detecting variation between graphs.  ...  INTRODUCTION Network science is an interdisciplinary field for the study of detailed real-world phenomena by viewing them as a graph.  ... 
doi:10.1109/bigdata.2016.7840989 dblp:conf/bigdataconf/BonnerBTKM16a fatcat:3nsam4qrvbco5n636p3vgs6uhm

Dependable Content Distribution in D2D-Based Cooperative Vehicular Networks: A Big Data-Integrated Coalition Game Approach

Zhenyu Zhou, Houjian Yu, Chen Xu, Yan Zhang, Shahid Mumtaz, Jonathan Rodriguez
2018 IEEE transactions on intelligent transportation systems (Print)  
Finally, we evaluate the proposed algorithm based on real-world map and realistic vehicular traffic.  ...  In this paper, we investigate how to achieve dependable content distribution in device-todevice (D2D)-based cooperative vehicular networks by combining big data-based vehicle trajectory prediction with  ...  developed based on ideal theoretical models without considering real-world street topologies and big data based vehicle trajectory prediction.  ... 
doi:10.1109/tits.2017.2771519 fatcat:lb4223qyana6tiu6hnpp4pvizu

A Time Efficient Approach for Detecting Errors in Big Sensor Data on Cloud

Chi Yang, Chang Liu, Xuyun Zhang, Surya Nepal, Jinjun Chen
2015 IEEE Transactions on Parallel and Distributed Systems  
For fast data error detection in big sensor data sets, in this paper, we develop a novel data error detection approach which exploits the full computation potential of cloud platform and the network feature  ...  of a whole big data set.  ...  Secondly, due to the scale-free network systems being a special topology, the partition has to form the data clusters according to the real world situation of scale-free network or Cluster-head based WSN  ... 
doi:10.1109/tpds.2013.2295810 fatcat:7lrkukyer5bxpmokswsmobzzki

A meta-graph approach to analyze subgraph-centric distributed programming models

Ravikant Dindokar, Neel Choudhury, Yogesh Simmhan
2016 2016 IEEE International Conference on Big Data (Big Data)  
These address the short-comings of Big Data abstractions/platforms like MapReduce/Hadoop for large-scale graph processing.  ...  Here, we propose a analytical approach based on a meta-graph sketch to examine the characteristics of component-centric graph programming models at a coarse granularity.  ...  We consider two classes of large-scale real-world graphs, namely graphs with powerlaw distribution and spatial networks.  ... 
doi:10.1109/bigdata.2016.7840587 dblp:conf/bigdataconf/DindokarCS16 fatcat:ala4ecbsxjcfjhf3mnycw3osea

Routing of Electric Vehicles With Intermediary Charging Stations: A Reinforcement Learning Approach

Marina Dorokhova, Christophe Ballif, Nicolas Wyrsch
2021 Frontiers in Big Data  
The algorithm was implemented and tested on a case study of a road network in Switzerland. The training procedure requires low computing and memory demands and is suitable for online applications.  ...  As a possible solution method, we present an off-policy model-free reinforcement learning approach that aims to generate energy feasible paths for EV from source to target.  ...  FUNDING Frontiers in Big Data | www.frontiersin.org May 2021 | Volume 4 | Article 586481  ... 
doi:10.3389/fdata.2021.586481 pmid:34124649 pmcid:PMC8187862 fatcat:fzrj6w62j5errde6ijgv6nvyle

A Novel Data-Driven Situation Awareness Approach for Future Grids--Using Large Random Matrices for Big Data Modeling [article]

Xing He, Lei Chu, Robert C. Qiu, Qian Ai, Zenan Ling
2018 arXiv   pre-print
It is a challenge, however, to efficiently turn these massive datasets into useful big data analytics.  ...  To address such a challenge, this paper, based on random matrix theory (RMT), proposes a datadriven approach.  ...  As a result, (5) is turned into ∆w =f x (x 0 ,y 0 ) ∆x+f y (x 0 ,y 0 ) ∆y +f xy (x 0 ,y 0 ) ∆x∆y. Suppose the network topology is unchanged, i.e., ∆y = 0.  ... 
arXiv:1610.05076v2 fatcat:zsqdlmvz7rghbfulvrwsew5ksi
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