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A Big Data Lake for Multilevel Streaming Analytics [article]

Ruoran Liu, Haruna Isah, Farhana Zulkernine
2020 arXiv   pre-print
Finally, we present a real-world data lake development use case for data stream ingestion, staging, and multilevel streaming analytics which combines structured and unstructured data.  ...  This study can serve as a guide for individuals or organizations planning to implement a data lake solution for their use cases.  ...  ACKNOWLEDGMENT We would like to express a special thanks to the Southern Ontario Smart Computing for Innovation Platform (SOSCIP), Ontario Centres of Excellence (OCE) and IBM Canada for supporting this  ... 
arXiv:2009.12415v1 fatcat:riscmxr55vaolj2or7izhgcrkm

The Top 10 Challenges in Extreme-Scale Visual Analytics

Pak Chung Wong, Han-Wei Shen, Christopher R. Johnson, Chaomei Chen, Robert B. Ross
2012 IEEE Computer Graphics and Applications  
Acknowledgments The article benefited from a discussion with Pat Hanrahan. We  ...  Navigating an exceedingly deep multilevel hierarchy and searching for optimal resolution are major challenges for scalable analysis.  ...  Scalability and Multilevel Hierarchy Multilevel hierarchy is a prevailing approach to many VA scalability issues. But as data size grows, so do the hierarchy's depth and complexity.  ... 
doi:10.1109/mcg.2012.87 pmid:24489426 pmcid:PMC3907777 fatcat:styns5pstbb7jffqygbp75pwwi

2020 Index IEEE Transactions on Parallel and Distributed Systems Vol. 31

2021 IEEE Transactions on Parallel and Distributed Systems  
Pei, J., +, HSDC: A Highly Scalable Data Center Network Architecture for Greater Incremental Scalability.  ...  ., +, TPDS Feb. 2019 361-374 HSDC: A Highly Scalable Data Center Network Architecture for Greater Incremental Scalability.  ... 
doi:10.1109/tpds.2020.3033655 fatcat:cpeatdjlpzhqdersvsk5nmzjkm

An understanding of machine learning techniques in big data analytics: a survey

S Josephine Isabella, Sujatha Srinivasan
2018 International Journal of Engineering & Technology  
This study reviews the various challenges and innovative ideas for big data analytics with machine learning in different fields over the past ten years.  ...  This paper mainly organized to identify the research projects based on the discussions over machine learning techniques for big data analytics and provide suggestions to develop the new projects.  ...  techniques. [15] Designed a framework for a real-time network traffic anomaly detection system using Machine Learning algorithms like Naive Bayesian, SVM, and Decision Tree.  ... 
doi:10.14419/ijet.v7i2.33.15471 fatcat:lshqfqsv2zh3val6ykfhzhlo5y

Table of contents

2019 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC)  
LSTM 183 Neda Tavakoli (Georgia Institute of Technology) A Scalable Framework for Multilevel Streaming Data Analytics using Deep Learning 189 Shihao Ge (Queen's University, Canada), Haruna Isah  ...  Modeling of Big Clinical Trials Data for Adverse Outcome Prediction: A Case Prototyping for Internet of Things with Web Technologies: A Case on Project-Based Learning using An Approach for Developing  ...  SAPSE: Security Aspects for Process & Services  ... 
doi:10.1109/compsac.2019.10167 fatcat:gkeovgrv2zejpd3bkuk2xxu7u4

Social Media Big Data Analysis: Towards Enhancing Competitiveness of Firms in a Post-Pandemic World

Abdelaziz Darwiesh, Mohammed. I Alghamdi, A. H. El-Baz, Mohamed Elhoseny, M. Praveen Kumar Reddy
2022 Journal of Healthcare Engineering  
The proposed framework utilizes some of the most significant tools in this era, such as social media and big data analysis for business intelligence systems.  ...  In this paper, we proposed an advanced business intelligence framework for firms in a post-pandemic phase to increase their performance and productivity.  ...  Scroll around deep multilevel hierarchy and finding an optimal resolution are real challenges for scalability analysis [64] .  ... 
doi:10.1155/2022/6967158 pmid:35281539 pmcid:PMC8913073 fatcat:gfjf2zrbefe6bhaarqw5l243ta

Twitter Sentiment Analysis Approaches: A Survey

Omar Yousef Adwan, Marwan Al-Tawil, Ammar Huneiti, Rawan Shahin, Abeer Abu Zayed, Razan Al-Dibsi
2020 International Journal of Emerging Technologies in Learning (iJET)  
Such analysis can be valuable for several researchers and applications that require understanding people views about a particular topic or event.  ...  The study carried out in this paper provides an overview of the algorithms and approaches that have been used for sentiment analysis in twitter.  ...  The work in [4] proposed a scalable framework for multilevel streaming analytics of social media data by leveraging distributed open-source tools and deep learning architectures.  ... 
doi:10.3991/ijet.v15i15.14467 fatcat:uapdaucwevchrgvrrw5mrfilvi

Guest Editors Introduction: Special Issue on Advanced Management of Softwarized Networks

Wolfgang Kellerer, Giovanni Schembra, Jinho Hwang, Noriaki Kamiyama, Joon-Myung Kang, Barbara Martini, Rafael Pasquini, Dimitrios Pezaros, Hongke Zhang, Mohamed Faten Zhani, Thomas Zinner
2021 IEEE Transactions on Network and Service Management  
Various multilevel security policies are constructed using the well adopted notions and frameworks using multilevel security lattices and then translated to OpenFlow rules.  ...  In "Optimized IoT Services Chain Implementation in Edge Cloud Platform: A Deep Learning Framework," Pham et al.  ...  He is a Chartered Engineer, a Fellow of BCS and IET, and a Senior Member of ACM.  ... 
doi:10.1109/tnsm.2021.3058871 fatcat:yg3kanbewvd7lp3nz6b4rzh5vm

2021 Index IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems Vol. 40

2021 IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  Hoseinghorban, A., +, TCAD March 2021 419-429 DLUX: A LUT-Based Near-Bank Accelerator for Data Center Deep Learn-ing Training Workloads.  ...  ., +, TCAD Sept. 2021 1850-1863 DLUX: A LUT-Based Near-Bank Accelerator for Data Center Deep Learn-ing Training Workloads.  ... 
doi:10.1109/tcad.2021.3136047 fatcat:ppooj4g65nc2zonj7szclerc2y

Using Semantic Modelling to Improve the Processing Efficiency of Big Data in the Internet of Things Domain

A. Gladun, Y. Rogushina, A. Andrushevich
2019 Kibernetika i vyčislitelʹnaâ tehnika  
The purpose of the article is to use deep machine learning, based on convolutional neural networks because this model of machine learning corresponds to processing of unstructured and complex nature of  ...  Machine learning is used as an instrument for Big Data of analyzes: it provides logical inference of the rules that can be applied to processing of information generated by Smart Home system.  ...  MACHINE LEARNING FOR BIG DATA ANALYTICS Deep learning is a particular learning model that combines well with unstructured and complex nature of the IoT domain.  ... 
doi:10.15407/kvt196.02.027 fatcat:b3yzwnp2prbjlnqm4ll33kuxxq

A Novel Real-Time Edge-Cloud Big Data Management and Analytics Framework for Smart Cities

Roberto Cavicchioli, Riccardo Martoglia, Micaela Verucchi
2022 Journal of universal computer science (Online)  
In the context of the H2020 CLASS project, we describe an innovative framework aiming to facilitate the design of advanced big-data analytics workflows.  ...  We specifically focus on obstacle detection and tracking for edge processing, and consider a traffic density monitoring application, with hierarchical data aggregation features for cloud processing; the  ...  Acknowledgements The authors would like to thank Francesco Barbanti, who contributed to the development of a preliminary version of the cloud data management techniques during his bachelor internship.  ... 
doi:10.3897/jucs.71645 fatcat:gte5a4f44naaxafig2ncndz2fa

A Tale of Two Data-Intensive Paradigms: Applications, Abstractions, and Architectures [article]

Shantenu Jha, Judy Qiu, Andre Luckow, Pradeep Mantha, Geoffrey C.Fox
2014 arXiv   pre-print
We use a simple and broadly used Ogre (K-means clustering), characterize its performance on a range of representative platforms, covering several implementations from both paradigms.  ...  We analyze the ecosystems of the two prominent paradigms for data-intensive applications, hereafter referred to as the high-performance computing and the Apache-Hadoop paradigm.  ...  A vibrant, manifold open-source ecosystem consisting of higher-level data stores, data processing/analytics and machine learning frameworks has evolved around a stable, non-monolithic kernel: the Hadoop  ... 
arXiv:1403.1528v2 fatcat:dnyrpncqfneofaxyuvq3tzffz4

Scalable Security Analytics Framework Using NoSQL Database

Rizwan ur Rahman, Deepak Singh Tomar
2017 International Journal of Database Theory and Application  
In this paper the scalable framework for security analytics is proposed using MongoDB NoSQL database. An attack scenario is created to simulate the zero-day malware.  ...  Supervised and unsupervised learning techniques are applied for analytics on data collected from live application and experimental set-up.  ...  Clustering is an example of unsupervised learning. In the proposed framework, clustering the unsupervised learning is used for anomaly detection.  ... 
doi:10.14257/ijdta.2017.10.11.03 fatcat:kqyb77wei5cmljwy5fac3b74l4

Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning [article]

Julius Berner, Markus Dablander, Philipp Grohs
2020 arXiv   pre-print
We show that a single deep neural network trained on simulated data is capable of learning the solution functions of an entire family of PDEs on a full space-time region.  ...  We present a deep learning algorithm for the numerical solution of parametric families of high-dimensional linear Kolmogorov partial differential equations (PDEs).  ...  One can then use simulated training data in order to learnū by means of deep learning.  ... 
arXiv:2011.04602v1 fatcat:qjohbcbkvvc3din4mchcvue3vy

Table of contents

2019 2019 IEEE 35th International Conference on Data Engineering (ICDE)  
a Visual Analytics Database 1466 Michael R.  ...  ), and Reynold Cheng (The University of Hong Kong) Research (23) -Learning, Temporal and Spatial Data Computing Trajectory Similarity in Linear Time: A Generic Seed-Guided Neural Metric Learning Approach  ... 
doi:10.1109/icde.2019.00004 fatcat:i2a2ozgicngw5gvaktjgncde5y
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