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IEEE/ACM international symposium on Big Data computing (BDC)

作者： Joo-Eun Bae Jin-Hyong Lee Young-In Park So-Hyang Park Kyoung-Sook Kim Kwanghoon Pio Kim Data & Process Engineering Research Laboratory Divison of AI Computer Science and Engineering College of Software & Management Kyonggi University Suwon-si Republic of Korea

In this paper, we carry out an experimental analysis to show how much perfectly the conceptual mining framework is working on mining process patterns and their enacted proportions from the business process enactment event histories logged with the IEEE-XES standardized format. In principle, the framework must be able to properly handle all the process patterns based upon the four types of control-flow primitives, such as linear (sequential), disjunctive (selective), conjunctive (parallel), and iterative (loop) patterns, together with the execution proportions. The paper focuses on not only verifying the conceptual feasibility of the procedural process mining framework through a series of experimental activities by using the implemented algorithmic mining framework and system, but also proving the functional correctness of the implemented process mining framework by applying to the real process instance enactment event histories of 10,000 workcases instantiated from the Large Bank Transaction Process Model and visualizing the details of the analytical artifacts and results, as well.

关键词： Analytical models Visualization Computational modeling Process control Big Data Knowledge discovery Data models

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Blind Spectrum Sensing Scheme Based on Harmonic Mean of Eigenvalues of Sample Covariance Matrix

Blind Spectrum Sensing Scheme Based on Harmonic Mean of Eige...

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IEEE international symposium on Parallel and distributed Processing with applications and IEEE international Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Tingting Liu Kejun Lei Zhewen Tan Xinxin Tian Yuhao Tan Xi Yang School of Communication and Electronic Engineering Jishou University Jishou P. R. China

Maximum eigenvalue-harmonic mean (MEHM) detection is a very promising algorithm for spectrum sensing in cognitive radio. However, the asymptotic analysis method currently used greatly limits the improvement of the detection performance of MEHM. For this, a novel maximum eigenvalue-harmonic mean (NMEHM) based spectrum sensing algorithm is proposed, using the results of the distribution of the limiting eigenvalues of the sample covariance matrix in random matrix theory. The proposed algorithm significantly improves the detection performance of the traditional MEHM algorithm and achieves better detection results than the classical eigenvalues based algorithms; meanwhile, the proposed algorithm does not require a priori knowledge of the primary user signal and wireless channel, which can effectively overcome the impact of noise uncertainty. Simulation results demonstrate the validity of the proposed algorithm.

关键词： Wireless sensor networks Uncertainty Limiting Simulation Reliability theory Ubiquitous computing Eigenvalues and eigenfunctions

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Towards Fast Edge Detection Approach for Industrial Products

Towards Fast Edge Detection Approach for Industrial Products

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IEEE international symposium on Parallel and distributed Processing with applications and IEEE international Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Dhafer G. Honi Ali Hasan Ali Zaid Ameen Abduljabbar Junchao Ma Vincent Omollo Nyangaresi Keyan Abdul-Aziz Mutlaq Samir M. Umran College of Education for Pure Sciences University of Basrah Basrah Iraq College of Big Data and Internet Shenzhen Technology University Shenzhen China Faculty of Biological and Physical Tom Mboya University Homabay Kenya It and Communication center University of Basrah Basrah Iraq School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China

Edge detection is a critical component of many elements of image processing applications and computer visualization, such as image segmentation, detection of imperfections in industrial products, medical image processing, and object identification. The edge detection technique seeks to accelerate image analysis by limiting the quantity of information processed. The object’s dimensions are a major component in manufacturing, engineering and other crucial fields, therefore the less time this takes, the more efficient the whole process becomes. This study details the use of unique image processing technology to advance the automated quantifying of an industrial object’s dimensions. Firstly, an image improvement approach founded on fuzzy entropy was devised to clarify the original deteriorated image. Secondly, the edges of the object were detected via the method of quick fuzzy edge detection. Finally, using Freeman Chain Codes (FCC), the essential corner locations were determined. Thus, the edges and corners of any given object image are accurately detected. This strategy is uncomplicated to implement, can efficiently identify size, shape, and any defects, and helps to determine acceptance or rejection of the final product. The results of the corresponding experiment indicate that the proposed method has a much lower computational cost in general compared to the related works.

关键词： Visualization Image analysis Shape Image edge detection FCC Ubiquitous computing Entropy

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Data centers with quantum random access memory and quantum networks

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Physical Review A 2023年第3期108卷 032610-032610页

作者： Junyu Liu Connor T. Hann Liang Jiang Pritzker School of Molecular Engineering University of Chicago Chicago Illinois 60637 USA Kadanoff Center for Theoretical Physics University of Chicago Chicago Illinois 60637 USA Chicago Quantum Exchange University of Chicago Chicago Illinois 60637 USA Institute for Quantum Information and Matter California Institute of Technology Pasadena California 91125 USA Walter Burke Institute for Theoretical Physics California Institute of Technology Pasadena California 91125 USA AWS Center for Quantum Computing Pasadena California 91125 USA

In this paper we propose the Quantum Data Center (QDC), an architecture combining Quantum Random Access Memory (QRAM) and quantum networks. We give a precise definition of QDC and discuss its possible realizations and extensions. We discuss applications of QDC in quantum computation, quantum communication, and quantum sensing, with a primary focus on QDC for T-gate resources, QDC for multiparty private quantum communication, and QDC for distributed sensing through data compression. We show that QDC will provide efficient, private, and fast services as a future version of data centers.

关键词： Quantum algorithms Quantum error correction Quantum memories Quantum networks

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A distributed business-Aware Storage Execution Environment Towards Large-Scale applications 20th

A Distributed Business-Aware Storage Execution Environment T...

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20th international Conference on Algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Jiang, Feng Cheng, Yongyang Dong, Changkun Hui, Zhao Yan, Ruibo China Telecom Cloud Comp Corp Beijing Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

ISBN: (纸本)9783030602390;9783030602383

The rapid development of distributed storage technology has attracted growing attention from both industry and academia. Although distributed system has been studied for many years, however, it is widely applied to engineering practice since the rise of cloud computing recently. Cloud storage constructs a storage resource pool with massive common storage devices through networks, which could be allocated to authorized users on demand. In this paper, we present a distributed business-aware storage execution environment towards large-scale applications. Initially, we present the overall of our proposed storage execution environment and different data delivery models according to corresponding business needs. Furthermore, we design a globally shared and cross-regional deployed metadata service to minimize the access delay of metadata. Finally, the approach presented in this paper has been validated to be effective through a series of evaluation experiments and actual use cases.

关键词： Cloud computing Object-oriented storage distributed system Large scale applications High-performance architecture

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Pervasive Container computing–––a Fine-Grained Loosely Coupled Cloud Cluster

Pervasive Container Computing–––a Fine-Grained Loosely Co...

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IEEE international symposium on Parallel and distributed Processing with applications and IEEE international Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Xinhong Hei Dan Wu YiFan Liu Wenjiang Ji Yichuan Wang Lei Zhu School of computer science and engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

With the emergence of CaaS, a cloud service model that can quickly build a production environment, this model has been widely used in the industry. However, the degree of loose coupling of various containers in the cluster and the fine-grained level of function division are often difficult to achieve people’s expectations. In addition, it is difficult to achieve the desired effect in terms of cloud cluster security. Therefore, this paper takes container-based cloud computing clusters as the main research object, and proposes a fine-grained, loosely coupled cloud cluster model based on containers, is called Pervasive-container computing. The experimental results show that Pervasive-container computing not only meets the fine-grained loose coupling characteristics required by microservices, but also adopts the strategy of “created with time” and “destroy after use”, which provides guarantees in terms of endogenous security. In addition, the proposed scheduling algorithm based on Pervasive-container calculation reduces the time consumption of file transfer by 20% compared to the time consumption without the algorithm.

关键词： Couplings Industries Cloud computing Scheduling algorithms Computational modeling Microservice architectures Production

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An RNN Based Mechanism for File Prefetching 18

An RNN Based Mechanism for File Prefetching

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18th international symposium on distributed computing and applications for business engineering and science (DCABES)

作者： Chen, Hui Zhou, Enqiang Liu, Jie Zhang, Zhicheng Natl Univ Def Technol Sch Comp Sci Changsha Hunan Peoples R China

ISBN: (纸本)9781728128658

Many applications spend a large proportion of the execution time to access files. To narrow the increasing gap between computing and I/O performance, several optimization techniques were adopted, such as data prefetching and data layout optimization. However, the effectiveness of these optimization processes heavily depends on the understanding of the I/O behavior. Traditionally, spatial locality and temporal locality are mainly considered for data prefetching and scheduling policy. Whereas for most real-world workloads, the file access pattern is hard to capture. For the goal of deeply and intelligently understanding the I/O access pattern of modern applications, and efficiently optimizing the performance of current file systems, we propose a new mechanism to embed file names to vectors and train a gated recurrent neural network to provide policies for file prefetching and cache replacing.

关键词： distributed File System File Prefetching Word Embedding RNN

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A Security Enhancement Scheme For Raft Consensus Algorithm Against Term Forgery Attacks

A Security Enhancement Scheme For Raft Consensus Algorithm A...

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IEEE international symposium on Parallel and distributed Processing with applications and IEEE international Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Yichuan Wang Mengjie Tian Yaling Zhang Xiaoxue Liu Yeqiu Xiao Xinhong Hei School of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

The consensus algorithm is the core component of blockchain technology and a key link to maintaining the security of the blockchain network. In the Raft consensus algorithm, since the term growth range is not specified in the algorithm, malicious nodes can obtain the leader status by increasing their term value, and the fairness of the Raft cluster leader election is damaged. To solve the above problems, in this paper, we restore the attack experiment, which is a malicious node gaining leader status by increasing its term value. Identify malicious nodes by dynamically adjusting the growth range of the term, thus achieving a defensive effect. The optimized algorithm resists malicious node attacks with a 79.4% probability and reduces the election time by 11.4% compared to the Raft consensus algorithm in the presence of malicious nodes. On the premise of ensuring high efficiency, the fault tolerance and security of the algorithm have also been significantly improved.

关键词： Fault tolerance Voting Fault tolerant systems Clustering algorithms Consensus algorithm Resists Ubiquitous computing

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Parallel Approximations of the Tukey g-and-h Likelihoods and Predictions for Non-Gaussian Geostatistics

Parallel Approximations of the Tukey g-and-h Likelihoods and...

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international symposium on Parallel and distributed Processing (IPDPS)

作者： Sagnik Mondal Sameh Abdulah Hatem Ltaief Ying Sun Marc G. Genton David E. Keyes Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Kingdom of Saudi Arabia Statistics Program King Abdullah University of Science and Technology Extreme Computing Research Center King Abdullah University of Science and Technology

Maximum likelihood estimation is an essential tool in the procedure to impute missing data in climate/weather applications. By defining a particular statistical model, the maximum likelihood estimation can be used to understand the underlying structure of given geospatial data. The Gaussian random field has been widely used to describe geospatial data, as one of the most popular models under the hood of maximum likelihood estimation. Computation of Gaussian log-likelihood demands operations on a dense symmetric positive definite matrix, often parameterized by the Matérn correlation function. This computation of the log-likelihood requires $\mathcal{O}(n^{2})$ storage and $\mathcal{O}(n^{3})$ operations, which can be a huge task considering that the number of geographical locations, $n$ , now commonly reaches into the millions. However, despite its appealing theoretical properties, the assumptions of Gaussianity may be unrealistic since real data often show signs of skewness or have some extreme values. Herein, we consider the Tukey ${g-}$ and $-h$ (TGH) random field as an example of a non-Gaussian random field that shows more robustness in modeling geospatial data by including two more parameters to incorporate skewness and heavy tail features in the model. This work provides the first HPC implementation of the TGH random field's inference on parallel hardware architectures. Using task-based programming models associated with dynamic runtime systems, our implementation leverages the high concurrency of current parallel systems. This permits to run the exact log-likelihood evaluation of the Tukey g-and-h (TGH) random fields for a decent number of geospatial locations. To tackle large-scale problems, we provide additionally an implementation of the given model using two different low-rank approximations. We compress the aforementioned positive-definite symmetric matrix for computing the log-likelihood and rely on the Tile Low-Rank (TLR) and the Hierarchical Off-Diagon

关键词： Maximum likelihood estimation Symmetric matrices Runtime Computational modeling Linear algebra Tail Predictive models

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Design and Implementation of engineering Standard Database System Based on Data Mining 18

Design and Implementation of Engineering Standard Database S...

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18th international symposium on distributed computing and applications for business engineering and science (DCABES)

作者： Shen, Yi Wang, Mengdi Zhou, Hong Zhu, Quanyin Ma, Siwei Cao, Meng Li, Jiadong Huaiyin Inst Technol Fac Comp & Software Engn Huaian Peoples R China

ISBN: (纸本)9781728128658

In order to establish a public service platform for construction drawing intelligent review, an engineering standard database system based on Web crawler was proposed by building an engineering standard database system to improve the efficiency of querying engineering standards. The system mainly obtains the unstructured engineering standard information on the Web site through the crawler module, and extracts the information in the image on the Web site through the image recognition module, and then removes the non-critical information into the structured information through the data cleaning module, and finally the data update module ensures real-time data updates. When the user uses the system, the information in the database can be queried through the web page. Experimental research shows that the engineering standard database system can meet the daily needs of users.

关键词： Data Mining Image Identification engineering Standard Database System

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