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检索条件"任意字段=5th IEEE International Conference on Big Data Intelligence and Computing, DataCom 2019"

共 603 条记录，以下是21-30 订阅

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Fuzzy-based approach for identifying epistasis in large-scale data 5

Fuzzy-based approach for identifying epistasis in large-scal...

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5th ieee international conference on big data intelligence and computing, datacom 2019

作者： Yang, Cheng-Hong Moi, Sin-Hua Chuang, Li-Yeh Lin, Yu-Da National Kaohsiung University of Science and Technology Department of Electronic Engineering Kaohsiung Taiwan I-Shou University Department of Chemical Engineering Institute of Biotechnology and Chemical Engineering Kaohsiung Taiwan

ISBN: (纸本)9781728141176

Identifying epistasis is critical for determining susceptibility to human genetic diseases. Multifactor dimensionality reduction (MDR) have enabled efficient calculation for obtaining superior detection. However, a vital issue that has not been studied extensively is the binary classification of multifactor genotypes into high-risk (H) and low-risk (L) teams, especially for large-scale data. In this study, the membership degree of a fuzzy logic system was introduced to overcome the limitations of binary classification. A fuzzy logic system combined with multiobjective MDR (FMOMDR) can enhance flexibility in multifactor genotypes at similar frequencies. Based on multiobjective MDR, FMOMDR can obtain higher detection rates than alternative MDR-based measurements when applied to large-scale simulated data. the results demonstrated that fuzzy classification of multifactor genotypes can provide insight into the uncertainty of H/L classification in large-scale datasets. © 2019 ieee.

关键词： Dimensionality reduction

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A big data based intelligent decision support system for sustainable regional development

A big data based intelligent decision support system for sus...

引用

ieee international conference on Smart City, SmartCity 2015

作者： Zhou, Hong Noble, Christopher Cotter, Julie School of Mechanical and Electrical Engineering Faculty of Health Engineering and Science University of Southern Queensland Toowoomba Australia University of Southern Queensland Australia

ISBN: (纸本)9781509018932

Timely intelligent decision making is increasingly important for modern society. With the availability of big data and advanced artificial intelligence in decision making, more objective and evidence-based quantitative smart decisions can be made in a timely manner. this research proposed a big data based intelligent decision support system (B-IDSS) for sustainable business development. the system can be used by both the government agencies and corporate business (e.g. farms. mining) in advanced planning, collaboration and management. this paper also addresses the performance optimization as bilevel decision-making problem with one leader and multiple followers. An extended Kuhn-Tucker approach is introduced as one of the algorithms that can be adapted in the system. © 2015 ieee.

关键词： Sustainable development

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Architecture to Speed up Redundant data Processing in a MapReduce Cloud 5

Architecture to Speed up Redundant Data Processing in a MapR...

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5th ieee international conference on big data intelligence and computing, datacom 2019

作者： Huang, Tzu-Chi Chu, Kuo-Chih Zeng, Xue-Yan Jou, Fu-Lin Tsai, Ming-Fong Shieh, Ce-Kuen Lunghwa University of Science and Technology Department of Electronic Engineering Taoyuan Taiwan National United University Department of Electronic Engineering Miaoli Taiwan National Cheng Kung University Department of Electrical Engineering Tainan Taiwan

ISBN: (纸本)9781728141176

A cloud owns many resources usable to applications, but economically utilizing or saving the resources always is an important issue on cloud computing nowadays. A cloud probably wastes resources to do extra unnecessary computations due to processing redundant data such as MapReduce applications calculating the page ranking of hot news or rendering map zooms of a geographic location. Now, a cloud can utilize the Architecture to Speed up Redundant data Processing (ASRDP) proposed in this paper in order to speed up the processing of redundant data in MapReduce. With the help of ASRDP, A cloud can greatly save the execution time of an application that processes redundant input data, especially when the application has high complexity in its Map function. According to the prototype implementation and related experiments with several popular applications in this paper, a cloud utilizes ASRDP to not only greatly improve performance when processing redundant input data but also intelligently avoid overheads of ASRDP when non-redundant input data. © 2019 ieee.

关键词： MapReduce

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引用

5th ieee international conference on big data intelligence and computing, datacom 2019

作者： Sun, Tingting Mo, Chunyang Zhao, Liang Chen, Zhikui Dalian Natural Resources Affairs Service Center Dalian116000 China Dalian University of Technology School of Software Technology Dalian116600 China Dalian University of Technology Key Laboratory for Ubiquitous Network Service Software of Liaoning Province Dalian116600 China

ISBN: (纸本)9781728141176

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. © 2019 ieee.

关键词： Contrastive Learning

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Robust Classification of Step data of Exercise 5

Robust Classification of Step Data of Exercise

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5th ieee international conference on big data intelligence and computing, datacom 2019

作者： Fu, Haoqi Shi, Keqin Sun, Weiqiang Shanghai Jiao Tong University Department of Electronic Engineering Shanghai China

ISBN: (纸本)9781728141176

the step data of exercise(SDE) is a type of daily life data that is generated by humans and recorded by mobile devices. Collection of such data has become more and more popular, as it provides users with information regarding his/her level of activity, and a stimulus for persisting in an active lifestyle. When SDE becomes more widely available, it also provides health professionals with a new tool to interact with their clients. Most existing methods for classifying exercise modes rely on training models that are manually labeled, or micro-pattern analysis using manually selected lengths and micro-pattern quantities. However, SDE datasets are highly personalized, inaccurate and the cost of these methods is high. So we propose a new method to classify the SDE by utilizing its inherent characteristics. With the understanding that SDE reflects daily lifestyle, is personalized and stable over at least a period of time, this paper takes the understanding of users' exercise preference, obtained from dataset of respective users, as prior knowledge. then, instead of using raw step data, we quantize the raw data into different levels of exercise and takes into account the moment of exercise occurrence. Results show that our method provides an easy to interpret and robust characterization of user's exercise habit. © 2019 ieee.

关键词： classification exercise habit prior knowledge SDE smart device

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Computational modelling of speech data integration to assess interactions in B2B sales calls 5

Computational modelling of speech data integration to assess...

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5th ieee international conference on big data intelligence and computing, datacom 2019

作者： Silber-Varod, Vered Lerner, Anat Carmi, Nehoray Amit, Daphna Guttel, Yonathan Orlob, Chris Allouche, Omri The Open University of Israel Open Media and Information Lab Ra'anana Israel The Open University of Israel Mathematics and Computer Science Department Ra'anana Israel Gong.io Herzliya Israel Gong.io San Fransico United States

ISBN: (纸本)9781728141176

the business sector now recognizes the value of Conversation intelligence in understanding patterns, structures and insights of authentic conversation. Using machine learning methods, companies process massive amount of data about conversation content, vocal features and even speaker body gestures of spoken conversations. this study is a Work-in-Progress (WIP), aimed to modeling the dynamics between sales representatives and customers in business-to-business (B2B) sales calls, by relying solely on the acoustic signal. To this end, we analyze 358 sales calls at the Discovery phase. To model the conversations, we compute a basic set of acoustic features: Talk proportions, F0, intensity, harmonics-to-noise ratio (HNR), jitter, and shimmer. the plots of each acoustic feature reveal the interactions and common behavior across calls, on one hand, and within calls, on the other. the study demonstrates that using delta metrics to assess the interactions leads to new insights. © 2019 ieee.

关键词： acoustic features Conversation intelligence conversation modelling sales calls speech data

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Stream Processing of Software-Defined Video Analytics on a Smart Campus 5

Stream Processing of Software-Defined Video Analytics on a S...

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5th ieee international conference on big data intelligence and computing, datacom 2019

作者： Hsieh, Chia-Ying Li, Yen-Chun Hsu, Chia-Hsin Kuo, Yung-Ching Chen, Chung-Chiu Hsu, Cheng-Hsin Sheu, Jang-Ping National Tsing Hua University Department of Computer Science Hsin-Chu Taiwan

ISBN: (纸本)9781728141176

the demand for real-time video analytics is increasing due to the prosperity of surveillance cameras in smart spaces. In this extended abstract, we present our stream processing platform to dynamically deploy, manage, and upgrade software-defined video analytics. Our key innovation is to divide a smart space into multiple Internet-of-things (IoT) zones. Each zone consists of a set of geo-collocated IoT devices, heterogeneous networks, and edge servers. By creating a hierarchical architecture of IoT zones, our distributed platform achieves better scalability and flexibility. We implement our stream processing platform using a set of opensource projects. We deploy it on a real testbed of eight smart street lamps on our campus, in which four surveillance cameras stream live videos to an edge server. the edge server runs multiple video analytics following the user demands, and the analytics results are stored in a time-series database for future usage. In this demo, we will demonstrate two sample video analytics: illegal parking detection and people counting. the conference attendees will get to navigate through our Web user interface for various operations, e.g., selecting surveillance cameras, rewinding to earlier date/time, and visualizing analytics results in plots. We believe our demo will stimulate constructive discussion at the conference venue in this emerging research area. © 2019 ieee.

关键词： Fog computing

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Combining Multiple Ranking Systems on the Generalized Permutation Rank Space 5

Combining Multiple Ranking Systems on the Generalized Permut...

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5th ieee international conference on big data intelligence and computing, datacom 2019

作者： Zhong, X. Hurley, L. Sirimulla, S. Schweikert, C. Hsu, D.F. Fordham University Laboratory of Informatics and Data Mining Department of Computer and Information Sciences New YorkNY United States University of Texas at El Paso School of Pharmacy Department of Pharmaceutical Sciences El PasoTX United States St John's University Division of Computer Science Mathematics and Science QueensNY United States

ISBN: (纸本)9781728141176

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 Bn 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 Bn 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. © 2019 ieee.

关键词： data Analytics

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A data-Driven Approach to Monitoring Colocation data Centers 5

A Data-Driven Approach to Monitoring Colocation Data Centers

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5th ieee international conference on big data intelligence and computing, datacom 2019

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

ISBN: (纸本)9781728141176

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. © 2019 ieee.

关键词： Quality of service

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Rethinking Software Defined Cloud Storage for Disaggregation 5

Rethinking Software Defined Cloud Storage for Disaggregation

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5th ieee international conference on big data intelligence and computing, datacom 2019

作者： Zou, Yi Raghunath, Arun Chagam, Anjaneya Sen, Sujoy Gohad, Tushar Intel Corp. Research Labs HillsboroOR United States Intel Corp. Data Center Group ChandlerAZ United States Intel Corp. Data Center Group HillsboroOR United States

ISBN: (纸本)9781728141176

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. © 2019 ieee.

关键词： Cloud storage

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