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SOFTWARE-PRACTICE & EXPERIENCE 2016年第1期46卷 79-105页

作者： Kune, Raghavendra Konugurthi, Pramod Kumar Agarwal, Arun Chillarige, Raghavendra Rao Buyya, Rajkumar Adv Data Proc Res Inst Dept Space Hyderabad Andhra Pradesh India Univ Hyderabad Sch Comp & Informat Sci Hyderabad 500134 Andhra Pradesh India Univ Melbourne Sch Engn Dept Comp & Informat Syst CLOUDS Lab Melbourne Vic Australia

Advances in information technology and its widespread growth in several areas of business, engineering, medical, and scientific studies are resulting in information/data explosion. Knowledge discovery and decision-making from such rapidly growing voluminous data are a challenging task in terms of data organization and processing, which is an emerging trend known as big data computing, a new paradigm that combines large-scale compute, new data-intensive techniques, and mathematical models to build data analytics. big data computing demands a huge storage and computing for data curation and processing that could be delivered from on-premise or clouds infrastructures. This paper discusses the evolution of big data computing, differences between traditional data warehousing and big data, taxonomy of big data computing and underpinning technologies, integrated platform of big data and clouds known as big data clouds, layered architecture and components of big data cloud, and finally open-technical challenges and future directions. Copyright (c) 2015 John Wiley & Sons, Ltd.

关键词： big data big data computing big data analytics as a service big data cloud architecture

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Fast-FFA: a fast online scheduling approach for big data stream computing with future features-aware

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2017年第3期10卷 205-217页

作者： Sun, Dawei Tang, Hao China Univ Geosci Sch Informat Engn Beijing 100083 Peoples R China

Awareness of future features is more important than that of historical features for online scheduling in a big data stream computing environment. In this paper, a fast future feature-aware online scheduling approach fast-FFA is put forward, exhibiting the following contributions;1) Modelling the online resource scheduling from viewpoints of user and data centre, considering multi-dimensional features of online data stream and quantitating preferences and utilities of each dimension. 2) Obtaining future features from historical features of multidimensional data stream with a hybrid particle swarm optimisation, back propagation (PSO-BP) algorithm and optimising online scheduling with an immune clonal algorithm. 3) Evaluating fast-FFA and balancing both fast future feature awareness and acceptable accuracy objectives. Experimental results demonstrate that the proposed fast-FFA approach has high potential as the approach provides significant system efficiency enhancements in online big data environments.

关键词： big data computing data stream online scheduling feature awareness intelligent optimisation

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A multi-factor prediction algorithm in big data computing environments

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INTERNATIONAL JOURNAL OF computing SCIENCE AND MATHEMATICS 2016年第4期7卷 312-322页

作者： Tang, Hao Sun, Dawei China Univ Geosci Sch Informat Engn Beijing 100083 Peoples R China

In big data environments, many problems become more huge and complex. This makes it harder for us to deal with the data and forecast the result. Traditional prediction algorithm is more suitable for less factor problem, and how to solve multi-factor problem is one of the major challenges. In this paper, a more accurate and effective algorithm was proposed based on the good performance of weight optimisation of PSO and generalisation ability of BPNN. Further, the convergence and feasibility of the combinational algorithm were analysed with actual data. The simulation revealed that with the increase of influencing factors, the BP neutral network optimised by PSO possessed a more rapid convergence rate of MSE and higher accuracy of network output values.

关键词： multi-factor data prediction PSO-BP big data computing

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The Direction of Lightweight Ciphers in Mobile big data computing 3rd

The Direction of Lightweight Ciphers in Mobile Big Data Comp...

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3rd Information Systems International Conference

作者： Buja, Alya Geogiana Latip, Shekh Faisal Abdul Univ Teknikal Malaysia INSFORNET Crypto Res Grp CRYPTREG C ACT Durian Tunggal 76100 Melaka Malaysia Univ Teknol MARA Melaka Jasin Fac Comp & Math Sci Merlimau 77300 Melaka Malaysia

It is too fast. The advances of the computing technology are moving very fast and far from the era of gigantic machine. This advanced technology offers easy, fast and wide range of computing activities particularly users who want to use the Internet, regardless of time and place. In addition, this advanced technology can also connect more communication tool. At the same time, greater storage platform is also available as mobile computing cloud computing architecture adopted to carry out computer activities. However, the larger the network which is connected to a computer, the more susceptible the computer to the outside threats. Indirectly, the communication system and the information stored in the computer are also exposed. Therefore, in this paper, we has discussed on the evolution of the computing which begin with the distributed system until recent computing technology which we called Mobile big data computing. Besides, in this paper, we define the term Mobile big data computing. Our discussion focuses on the information security aspects for the security of storage and transmitted data. Ultimately, this paper discusses the direction of the lightweight cipher design consideration towards Mobile big data computing. (C) 2015 The Authors. Published by Elsevier B.V.

关键词： big data computing Cipher Cloud computing Lightweight Mobile computing

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The Direction of Lightweight Ciphers in Mobile big data computing

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Procedia Computer Science 2015年 72卷 469-476页

作者： Alya Geogiana Buja Shekh Faisal Abdul Latip INSFORNET – Crypto Research Group (CRYPTREG) Centre of Advanced Computing Technology (C-ACT) Universiti Teknikal Malaysia Melaka 76100 Durian Tunggal Melaka Malaysia

关键词： big data computing Cipher Cloud computing Lightweight Mobile computing

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data Driven Neural Speech Enhancement for Smart Healthcare in Consumer Electronics Applications

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第2期70卷 4828-4838页

作者： Paikrao, Pavan D. Mukherjee, Amrit Ghosh, Uttam Goswami, Pratik Novak, Milan Jain, Deepak Kumar Al-Numay, Mohammed S. Narwade, Pradeep Dr DY Patil Inst Technol Dept Elect & Telecommun Engn Pune 411018 India South Bohemia Univ Ceske Budejovice Fac Sci Comp Sci Dept Ceske Budejovice 37005 Czech Republic Meharry Med Coll CS & DS Dept Nashville TN 37208 USA Yeungnam Univ Sch Comp Sci & Engn Gyongsan 38541 South Korea Dalian Univ Technol Key Lab Intelligent Control & Optimizat Ind Equipm Minist Educ Dalian 116024 Peoples R China Symbiosis Int Univ Symbiosis Inst Technol Pune 412115 India King Saud Univ Coll Engn Elect Engn Dept Riyadh 11421 Saudi Arabia Shri Guru Gobind Singhji Inst Engn & Technol Dept Elect & Telecommun Engn Nanded 431606 India

This paper presents the practical response and performance-aware development of online speech enhancement from a consumer electronic perspective. To improve the efficiency of human-machine interaction, speech can play a vital role as a transmission medium on the Internet of Medical Things (IoM). However, some intelligent speech recognition systems cannot preserve the confidentiality of speech data. Additionally, the preservation of privacy is onerous, especially for model training and speech recognition in real-time. The recent development of big data-oriented wireless technologies associated with edge computing, interconnected devices of the Internet of Medical Things (IoMT), and big data analytics has great demand for connected human-machine interaction for many applications like automated cars, health monitoring, and consumer personal health care monitoring systems. Although big data-oriented wireless technologies serve these applications, the challenge remains of ignoring emotional care. This paper starts by explaining how to make a neural network-based architecture that can improve the speech of multichannel first-order Ambisonics mixtures and lower the need for human intervention through ambient intelligence (AmI). This will make the system work better overall in medical situations. Second, we demonstrate the effectiveness of different noise estimation techniques on proposed modulation domain processing (MDP) applications in smart hospitals, including electronic medical documentation, disease diagnosis, and evaluation. The proposed approach outperforms the enhancement of the conventional modulation domain in the cortex with several objective evaluation parameters such as Log Likelihood Ratio (LLR), Weighted Spectral Slope (WSS), Perceptual Evaluation of Speech Quality (PESQ), Csig and segmental (SNR seg.) Different noise estimators are used to figure out what effect the system has on different spectral modification parameters, like the over-subtraction factor

关键词： Medical services Speech enhancement Speech recognition Consumer electronics Monitoring Noise measurement Real-time systems Consumer healthcare system speech recognition ambient intelligence big data computing

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big data technology for teaching quality monitoring and improvement in higher education - joint K-means clustering algorithm and Apriori algorithm

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SYSTEMS AND SOFT computing 2024年 6卷

作者： Li, Yang Zhang, Haiyu Minzu Normal Univ Xingyi Sch Educ Sci Xingyi 562400 Peoples R China

With the development of big data technology, the field of monitoring and improving teaching quality in universities has ushered in new opportunities and challenges. big data technology enables the capture and analysis of massive amounts of data generated during the teaching process, providing the possibility for a deeper understanding of teaching activities. However, how to extract useful information from these vast amounts of data and transform it into strategies for teaching improvement is a challenge. The research aims to propose a teaching quality monitoring and improvement method based on big data technology, which combines K-means clustering algorithm and association rule mining algorithm to improve the accuracy of teaching monitoring and the effectiveness of teaching improvement. In order to cope with these challenges, the study proposes a research method of big data technology based on joint K-mean clustering algorithm and association rule mining algorithm. The study first analyzes the teaching quality monitoring and evaluation indexes using the K-mean algorithm. Then the association rule mining algorithm is utilized to mine the data in the teaching quality monitoring indicators with association rules on the basis of the obtained cluster analysis. Finally, on the basis of association rule mining, the study constructs the assessment model of teaching quality monitoring indicators by utilizing the fused method. The outcomes revealed that the average of data analysis accuracy and the average of recall rate of the modeling method were 93.79 % and 91.95 %, respectively. Meanwhile, the evaluation time of the modeling method in the process of teaching quality monitoring data processing was 17.3 s, and the evaluation precision was 93.15 % respectively. Additionally, the process's overall confidence and enhancement are 95.01 % and 86.73 %, respectively, and the modeling method's performance is compared to other approaches. This shown that the approach may significant

关键词： Teaching quality big data computing K -means clustering algorithm Apriori algorithm Test scores

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Hierarchical risk parity: Efficient implementation and real world analysis

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2025年 167卷

作者： Dekovic, Dario Simovic, Petra Posedel Fac Elect Engn & Comp Unska Ul 3 Zagreb 10000 Croatia Dept Informat Sci & Math Fac Agr Svetosimunska 25 Zagreb 10000 Croatia

In this paper, we present an efficient implementation of the Hierarchical Risk Parity (HRP) portfolio optimization algorithm. HRP was designed to allocate portfolio weights by building a hierarchical tree of asset clusters and reducing risk through inverse variance allocation across the clusters. Our implementation improves the performance of the original algorithm by reducing its time complexity and making it more suitable for real-time systems. We evaluate the performance of our implementation on various constituents of the S&P 500 index, a market-capitalization-weighted index of 500 leading publicly traded companies in the U.S., using historical price data from 2005 to 2023. We compare the out-of-sample risk-adjusted returns of the HRP algorithm to those of a simple 1/N allocation method and find that the 1/N method outperforms HRP across all experimental setups. However, the HRP generated portfolios had a lower standard deviation by approximately 1% across all experimental setups. These results show that HRP can be of great use in generating portfolios when risk is the primary concern.

关键词： Hierarchical risk parity Portfolio optimization Risk management Efficient computing big data computing

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Scalable big data computing for the Personalization of Machine Learned Models and its Application to Automatic Speech Recognition Service 2

Scalable Big Data Computing for the Personalization of Machi...

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IEEE International Conference on big data

作者： Ahnn, Jong Hoon Samsung Res Amer Silicon Valley Cloud Res Lab Mountain View CA 94043 USA

ISBN: (纸本)9781479956661

We observe that the recent advances in big data computing have empowered model-based services such as speech recognition, face recognition, context-aware service, and many other services. Various sources of user's logs can be utilized in remodeling or adapting existing models to improve the quality of service. We propose a system that can support store/retrieve data and process them in a scalable manner. Recently advances in ASR and big data technologies drive more personalized services in many areas of services. A speaker adaptation is one good example which requires huge computation cost in creating a personalized acoustic model and corresponding language model over 100s millions of Samsung product users. We propose a personalized and scalable ASR system powered by the big data infrastructure which brings data-driven personalized opportunities to voice-enabled services such as voice-to-text transcriber, voice-enabled web search in a peta bytes scale. We verify the feasibility of speaker adaptation based on 107 testers' recordings and obtain about 10% of recognition accuracy. We study an optimal set of execution environments by executing jobs running either on Hadoop 1 or Hadoop 2 cluster, and move forward performance optimization strategies: workflow compaction, file compression, best file system selection among several distributed file systems. We devise a metric for the cost of personalized model creation to compare the efficiency of one cluster with the other cluster, and it provides the estimated total execution time for the given number of machines. We finally introduce our in-house object storage and data storage design, and their high performance compared to state-of-the art systems, optimized for voice-enabled services to effectively support small and large files.

关键词： big data learning (artificial intelligence) parallel processing speech recognition big data computing big data infrastructure Hadoop 1 cluster Hadoop 2 cluster Samsung product users automatic speech recognition service best file system selection computation cost context-aware service data storage design distributed file systems face recognition file compression in-house object storage language model machine learned models model-based services performance optimization strategies personalized ASR system personalized acoustic model personalized model creation quality of service scalable ASR system speaker adaptation voice-enabled Web search voice-enabled services voice-to-text transcriber workflow compaction Acoustics Adaptation models big data Computational modeling Memory Speech Speech recognition

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big data Oriented Graph Division and Storage 8

Big Data Oriented Graph Division and Storage

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IEEE 8th International Conference on big data Analytics (ICBDA)

作者： Zhang, Zheng Zhao, Yanbin Fan, Xiaoxi Zhang, Bo Nankai Univ Coll Software Tianjin Peoples R China

ISBN: (纸本)9798350310764

Parameters of machinery parts have dependency relationships between each other. Given a part, our task is to compute all the combinations of all its parameters constrained by the dependency relationships between them. A directed graph can be used to represent the dependency relationships between those parameters. However, simply using the width-first extension to solve the combinations would lead to the problem of combinatorial explosion, even with the constraint imposed by the dependency relationships. This problem causes consequences of too long the computing time, too large the required computer memory, and too slow the subsequent data querying. To solve the problem, we propose a method of graph division to reduce the computation scale. Based on specific characteristics of our task, the method divides the directed graph into various regions, and further divides complex ones of the regions into various sub-graphs. By the strategy of divide-and-conquer, processing of the entire graph is decomposed into processing of simple sub-graphs. Even with the application of above graph division, rows number of tables used to store the result of the extension can still be as large as one billion. Conventional DBMSs would perform poorly at such large a data scale. To combat this problem, we use a tool of persistent memory programming, Metall, to process the big data. Utilizing the tool gains a high I/O throughout. Our experiments show that, among 3,581 machinery parts being processed, for 97% of them, each part can be processed within 6 hours. Moreover, the technique of graph division improves the efficiency of subsequent data querying by 77%.

关键词： graph division big data computing big data storage

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