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big data intelligence and computing (datacom), international conference on

作者： Brian Setz Subrahmanya VRK Rao Alexander Lazovik Marco Aiello Department of Service Computing University of Stuttgart Stuttgart Germany Cognizant Technology Solutions Chennai India Distributed Systems Group University of Groningen Groningen The Netherlands

ISBN: (数字)9781728141176

ISBN: (纸本)9781728141183

A colocation data center rents out rack space and equipment to third parties, while providing core services such as power, cooling, and bandwidth. Energy consumption, power distribution, and cooling account for the majority of the operational costs. Reducing these operational costs is important in order to increase the profit margins of the data center. When implementing cost-saving measures, the Service Level Agreements and the high Quality of Service have to be maintained. Therefore, monitoring the effect of these measures is key. In a colocation data center however, monitoring the effect on the IT equipment owned by third parties is challenging, due to limited access to the equipment. This work addresses the question: can we monitor third party IT equipment in a colocation data center, without requiring access to the operating system? To answer this, we collect 2.5 billion data points from over 160 servers in a data center, monitoring the server state and the environmental parameters. We utilize this dataset to discover and train multiple models that allow colocation data centers to monitor third party servers without requiring access to the server’s operating system. These models enable data Center Operators to monitor the effect of cost-saving measures on the thermal state of the servers. As well as to monitor the computational load, in order to assist in the expansion planning process.

关键词： Temperature sensors Temperature measurement data centers Atmospheric modeling Computational modeling data models Servers

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big data intelligence and computing (datacom), international conference on

作者： Tingting Sun Chunyang Mo Liang Zhao Zhikui Chen Dalian Natural Resources Affairs Service Center Dalian China School of Software Technology Dalian University of Technology Dalian China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian University of Technology Dalian China

ISBN: (数字)9781728141176

ISBN: (纸本)9781728141183

In the real world, data instances are usually represented from various perspectives, thus multi-view feature learning and clustering have attracted more and more attention. However, most existing works assume that the datasets are pure to learn the common features in the subspace, which ignore the noises and uncorrelated information. Therefore, the performance of them may be seriously degraded. To tackle it, in this paper, we develop a new subspace fusion model for multi-view data clustering. Specifically, we learn the correlated and uncorrelated features simultaneously for multi-view data in the subspace. Thus, the uncorrelated items can be separated from the correlated features and promising latent common features can be obtained. Moreover, the geometric structures of both data and feature manifold are constructed to preserve the distribution of data points in the common subspace. A new objective function is designed to solve the model and the optimization processes are presented. Experiments on real-world datasets show that the proposed method outperforms the compared state-of-the-art methods.

关键词： Representation learning Manifolds Noise big data Linear programming data models Optimization

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Combining Multiple Ranking Systems on the Generalized Permutation Rank Space

Combining Multiple Ranking Systems on the Generalized Permut...

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big data intelligence and computing (datacom), international conference on

作者： X. Zhong L. Hurley S. Sirimulla C. Schweikert D. F. Hsu Department of Computer and Information Sciences Laboratory of Informatics and Data Mining Fordham University New York NY Department of Pharmaceutical Sciences School of Pharmacy University of Texas at El Paso El Paso TX Division of Computer Science Mathematics and Science St John’s University Queens NY

ISBN: (数字)9781728141176

ISBN: (纸本)9781728141183

Given t ranking systems of n objects, consensus ranking (CR) aims to derive a ranking which best represents the consensus goal of these ranking systems. Since a ranking system of n objects is equivalent to a permutation of the n natural numbers [1, n] from 1 to n, the CR problem has been studied on the bubble-sort graph rank space B n which consists of the set of all permutations of order n. However, it remains a challenging issue when a combination of ranking systems (consensus ranking) has ties. But B n does not include permutation (or rank systems) with ties. In this paper, we propose a multi-layer combinatorial fusion algorithm for combining multiple ranking systems on the generalized permutation rank space where ties are allowed. Using two simulated data sets and an empirical data set in the molecular docking domain, we demonstrate the robustness of our approach. This study also provides a viable approach to data analytics, machine learning, and combinatorial fusion in the non-parametric rank space.

关键词： Machine learning algorithms data analysis Machine learning big data Robustness

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Tiered Financial Fraud Detection Utilizing Precision Stratified Random Forest Assembly

Tiered Financial Fraud Detection Utilizing Precision Stratif...

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big data intelligence and computing (datacom), international conference on

作者： Charles Gardner Dan Chia-Tien Lo Johng-Chern Chern Pascal Paschos Chung Ng Department of Computer Science Kennesaw State University Marietta Georgia U.S.A Department of Computer Science Chicago State University Chicago Illinois U.S.A Department of Computer Science Morehouse College Atlanta Georgia U.S.A

ISBN: (数字)9781728141176

ISBN: (纸本)9781728141183

This study focuses on financial fraud detection via the creation of a three-tiered anomaly detection system. The system is constructed by tuning multiple random forest classifiers, each with different fitness functions. The process is done using a randomized grid search that optimizes the random forest parameters to match the fitness function. Once complete, the models are compared to form three-tiers of detected frauds with each tier containing a different level of precision. Separating detected frauds into different tiers allows for both high precision and high recall values. With this strategy, 96% of frauds are classified correctly while still maintaining a high precision of over 90% for 85% of detected frauds detected. Our studies show that the tiered random forest outperforms other algorithms such as SVM and logistic regression with a precision at 85% and a recall of 72%.

关键词： Support vector machines Logistic regression Computational modeling Finance Forestry Predictive models data models

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Multiple Kernel K-means Clustering : A Self-weighted Learning Approach

Multiple Kernel K-means Clustering : A Self-weighted Learnin...

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big data intelligence and computing (datacom), international conference on

作者： Hong Yu Tiantian Zhang Beilei Cui Linlin Zong School of Software Dalian University of Technology Dalian China

ISBN: (数字)9781728141176

ISBN: (纸本)9781728141183

The k-means algorithm is one of the most widely used methods for data clustering. However, traditional k-means can only be applied in the original feature space, limited to handle nonlinear separable clusters. To address this issue, kernel k-means is proposed to map low-dimensional linearly inseparable data to a high-dimensional space, and the mapped data are expected to be linearly separable. However, same as other kernel methods, the success of kernel k-means is highly depended on the choice of the kernel function. Unfortunately, it is difficult to determine which kernel is the most suitable for a specific task in practical applications. Naturally, it is thought that multiple kernel learning can perform better than a single kernel, but it is inevitable to introduce multiple weight parameters. In this paper, we propose a novel multiple kernel k-means method, which adopts a self-weighted learning approach. Unlike other multiple kernel k-means methods in the literature, our method can learn a consensus kernel and automatically assign an optimal weight to each kernel without introducing additional parameters. Extensive experiments on eight benchmark datasets compared with several state-of-the-art multiple kernel clustering methods demonstrate our new multiple kernel k-means method can achieve comparable performance and can be used more practically.

关键词： Clustering methods Clustering algorithms Benchmark testing big data Classification algorithms Kernel Optimization

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SCNNSC: Spelling Check based Neural Network for Sentiment Classification

SCNNSC: Spelling Check based Neural Network for Sentiment Cl...

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big data intelligence and computing (datacom), international conference on

作者： Yiwen Li Yi Song Kai Shuang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China China Telecom Shanghai Besttone Information Service Branch China Telecom Shanghai Branch Shanghai China

ISBN: (数字)9781728141176

ISBN: (纸本)9781728141183

Sentiment classification, as a core part in Natural Language Processing, has achieved great improvements in recent years. With the development of the Internet, online social platforms have become increasingly more popular, attracting people to publish numerous user-generated contents that contain important voice and sentiment information. Existing sentiment classification works are mostly based on the original text, while these texts always suffer a lot from typos, some of which even change the original meaning of sentence. In this paper, a Spelling Check based Neural Network for Sentiment Classification (SCNNSC) is purposed. First, a spelling check method is used to conduct text correction in advance, in order to avoid being influenced by common spelling or typing error. Then, we introduce CNN-GRU-Cross (CGC) model for sentiment classification. By utilizing multiple word embedding learning algorithms, and the cross structure of CNN and bigRU, our model can extract both local semantic features as well as long term dependency feature, thus capturing more comprehensive sentence representation. Finally, by integrating spelling check method and CGC model, SCNNSC can be obtained. Experiments are conducted on Chinese Microblog dataset and Taobao dataset. Experimental results indicate that our SCNNSC can achieve satisfying performance on sentiment classification.

关键词： Sentiment analysis Neural networks User-generated content Semantics Blogs Interference big data

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Rethinking Software Defined Cloud Storage for Disaggregation

Rethinking Software Defined Cloud Storage for Disaggregation

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big data intelligence and computing (datacom), international conference on

作者： Yi Zou Arun Raghunath Anjaneya Chagam Sujoy Sen Tushar Gohad Research Labs Intel Corp. Hillsboro OR USA Data Center Group Intel Corp. Chandler AZ USA Data Center Group Intel Corp. Hillsboro OR USA

ISBN: (数字)9781728141176

ISBN: (纸本)9781728141183

Software Defined Storage (SDS) architectures abstract away underlying physical hardware and provide durability, availability and reliability guarantees by transparently creating replicas, erasure coding, and performing periodic background data checks. However, current SDS architectures are at odds with the evolving trend of data Centers towards physically separating server resources like storage, into distinct sleds connected via high speed interconnects such as NVMe over Fabric (NVMe-oF), for finer grained control and flexibility. The assumption of locally attached storage and a monolithic server design exacerbate the overheads of the internal operations performed within the SDS framework in a disaggregated data Center. We propose redefining the boundaries of separation within SDS architectures to address disaggregation overheads. Specifically, we decouple control and data plane operations and transfer block ownership to execute on remote storage targets. This redistribution of responsibilities provides a 40% reduction in bandwidth overheads and a 2X end-to-end latency reduction associated with internal SDS data replication traffic. Using Ceph as an example, we provide detailed analysis on the potential reduction in bandwidth and latency on the proposed architecture. We demonstrate decoupling the Ceph BlueStore functionality and integrating with NVMe-oF target layer. We further present preliminary results collected on a testing PoC Ceph cluster currently being developed.

关键词： data centers Architecture Computer architecture Bandwidth Streaming media Software Fabrics

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Dysphasia’s Voice Extraction and Period Selection in Deep Learning Framework

Dysphasia’s Voice Extraction and Period Selection in Deep L...

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big data intelligence and computing (datacom), international conference on

作者： Hung-Shing Huang Yue-Shan Chang Bo-Yu Lin Dept. of Computer Science and Information Engineering National Taipei University New Taipei Taiwan R.O.C

ISBN: (数字)9781728141176

ISBN: (纸本)9781728141183

As the advance of technology, it is increasingly speech recognition tools on mobile devices, such as Google Voice and Apple Siri, those have been widely used and have high recognition rate of people’s speech. However, those tools can hardly work with the people who have the disease called "Dysphasia". And, we’d like to use deep learning technique to train their own acoustic model. Before training, there is an issue about how to prepare training data since the data generated by them via recording is in different duration. Due to their disability, they can only speak a sentence using the method which is word-by-word. Through observation of waveform of the voice spoken by them, we find the waveform of each word is like pulse signal. We propose a method that is based on simple pulse detection to detect how many words in a Chinese sentence and extract each voice of word. We also propose a method to detect the length of the voice contained one Chinese word. Then, we can know the proper time duration of one-word voice, which is an important parameter in training acoustic model. The evaluation reveals that the proposed model with length of 0.7, 0.8 or 0.9 second has better accuracy than others, and the percentage of extraction of correctness is about 90%.

关键词： Training Deep learning Virtual assistants Training data Speech recognition Acoustics Mobile handsets

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Predicting Performance of Social Media Postings using data Mining Methods

Predicting Performance of Social Media Postings using Data M...

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big data intelligence and computing (datacom), international conference on

作者： Li Szu-Chuang Chen Yi-wen Huang Yennun CITI Academia Sinica Taipei Taiwan Dept. of Information and Communication Tamkang University Taipei Taiwan

ISBN: (数字)9781728141176

ISBN: (纸本)9781728141183

Social media service such as Facebook has been enhancing its capability to provide more varied contents to its users. For content publishers, posting article links to Facebook is still a popular way to direct traffic to own websites. However, past studies have shown that only a small number of contents could become popular among Internet users. Being able to predict the performance of a particular piece of content before posting it would give the content publisher a significant competitive *** researchers attempted to build such a predictive model in this study. Features of the social media posts posted by our research partner were collected, including temporal, textual, and monetary ones. The features were used for further statistical and data mining analysis, and the resultant predictive model, albeit with mediocre accuracy, can effectively reduce the number of social media posts needed to achieve the same monetary return. Using the model enables the publisher to post less and earn the *** contribution of this study is as follows. First, in this study, we were able to build a model to predict advertisement income, which is unique in content popularity and performance studies. Second, this study focuses on the influence of social media posting on web contents performance, which has yet to be looked into in previous studies. Finally, the use of data-mining methods enables us not only to look at the research questions from an academic perspective but also the opportunity to build a predictive model to use in the real-world scenario.

关键词： Support vector machines Measurement Social networking (online) Soft sensors Linear regression Predictive models data models

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Preface

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CEUR Workshop Proceedings 2022年 3304卷

作者： Jia, Weijia Yin, Jian Hu, Shaolin Peng, Shaoliang Niu, Shaozhang Srinivasa Rao, P.C. Lu, Yao Mirsaeidi, Sohrab Cao, Bin Wu, Yilin Wang, Xingbo Subramaniyam, Kannimuthu Boddu, Raja Sarath Kumar Li, Zhonghua Tao, Qian Zhang, Lei Li, Yi Ding, Deqing Wang, Dahan Zhang, Ziyan AlShawabkeh, Mahmoud Aslam, Muhammad Dib, Omar Sahibuddin, Shamsul Bin Wagh, Sanjeev J. Pardalos, Panos M. Al-Sammarraie, Najeeb Abbas Liu, Linfeng Kong, Jin-Heung Borrego, Carlos Tahir, Sabeen Wu, Zhiheng Li, Jiongcheng Xu, Yong Xie, Zhi Wang, Jiahai Cai, Hongmin Yang, Yuedong Qin, Xiaolin Beijing Normal University Zhuhai China Sun Yat-sen University China Guangdong University of Petrochemical Technology China Hunan University China Beijing University of Posts and Telecommunications China India Beijing Jiaotong University China Hebei University of Technology China Guangdong University of Education China Foshan University China Karpagam College of Engineering India Lenora College of Engineerin India Yat-sen University Guangdong High Performance Computing Society China South China University of Technology China Henan University China Wenzhou University China National University of Defense Technology China Xiamen University of Technology China Hainan Tropical Ocean University China Guangxi Normal University for Nationalities China Wuhan University China Wenzhou-Kean University China School of Software Engineering Universiti Teknologi Malaysia Information Technology Government College of Engineering Karad India Department of Industrial & Systems Engineering University of Florida United States Al-Madinah International University Faculty of Computer and Information Technology Malaysia School of Computer Science and Technology Nanjing University of Posts and Telecommunications China School of Computer and Information Engineering Kwangwoon University Korea Republic of Departament Enginyeria of Information Comunicacions Autonomous University of Barcelona Spain Information Technology Department King AbdulAziz University Jeddah Saudi Arabia Institute of Manufacturing GDAS China Guangdong Planning and Designing Institute of Telecommunications Co. Ltd China Guangzhou Institutes of Biomedicine and Health Chinese Academy of Sciences China Cheng Du Information Technology of Chinese Academy of Sciences Co. Ltd China

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