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Substructure Discovery in Heterogeneous Multilayer networks

Substructure Discovery in Heterogeneous Multilayer Networks

引用

IEEE International conference on Big Knowledge (ICBK)

作者： Kiran Bolaj Abhishek Santra Sharma Chakravarthy IT Lab and Department of Computer Science & Engineering The University of Texas at Arlington

ISBN: (数字)9798331508821

ISBN: (纸本)9798331508838

Graph mining analyzes graphs to find core substructures (connected subgraphs) in applications that are modeled using graphs. These identified substructures, based on frequency or some other metric, are important as they reveal an inherent feature or property in the given graph/forest. Interesting substructures that are frequent or compress the graph well offer insights into hidden regularities. The process of finding these interesting patterns using unsupervised learning is termed substructure discovery. SUBDUE is an early main-memory algorithm developed for substructure discovery. Since then, this algorithm has been extended using disk-based, database-oriented approaches, and more recently using the Map/Reduce paradigm to exploit distributed and parallel processing. Graph sizes have increased steadily, thanks to the advent of the Internet and social network applications. To model complex datasets – with multiple types of entities and relationships - multilayer networks (or MLNs) have been shown to be effective. MLNs have also been shown to be superior as compared to simple and attributed graphs for modeling complex data. MLNs are also useful for modeling different data types using distinct layers. This paper focuses on substructure discovery in heterogeneous multilayer networks (one type of MLN) using the novel decoupling-based approach. In this approach, each layer is processed independently and then the results from two or more layers are composed to identify substructures in the entire MLN. The algorithm is designed and implemented, including the composition part, using the Map/Reduce paradigm for understanding speedup. After validating accuracy of results with ground truth, we analyze speedup and response time of the proposed algorithm and approach through extensive experimental analysis on large synthetic datasets with diverse graph characteristics.

关键词： Measurement Social networking (online) Knowledge graphs parallel processing Nonhomogeneous media Data models Internet Time factors Unsupervised learning Synthetic data

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Research on Design of High-Performance Graph Computing System based on CPU Heterogeneous Architecture 1

Research on Design of High-Performance Graph Computing Syste...

引用

1st International conference on Electronic Information Technology and Smart Agriculture, ICEITSA 2021

作者： Li, Yanjiang King's College London London United Kingdom

ISBN: (纸本)9781665413008

High performance computing is also known as supercomputing, mainly refers to more data in a short time to help the computer system to complete more computing tasks. Cloud computing, as a parallel distributed computing application concept proposed by the current technological innovation, will use the network to connect a large number of computing resources in practical application, thus forming a computing resource pool with stronger computing capacity and parallel processing capacity, so as to bring more superior computing services to users. From the perspective of overall development, cloud computing, as a new processing mode proposed to achieve high performance computing, is very novel in both application scope and promotion form, which plays a positive role in the future research of computer technology. Therefore, based on the understanding of high performance computing research significance and the central processing unit (CPU) implementation on the basis of the advantages of high performance computing, start from a computer unified device architecture (CUDA) for fast image processing system, in-depth studies are proving CPU heterogeneous architecture as the core of high performance computing system has a certain application advantages. © 2021 IEEE.

关键词： Program processors

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Simulation of Big Data Order-Preserving Matching and Retrieval Model based on Deep Learning

Simulation of Big Data Order-Preserving Matching and Retriev...

引用

Power, Electrical Engineering, Electronics and Control (PEEEC), International conference on

作者： Ziqi Zhu Yingying Fang Zhejiang University School of Management Hangzhou Zhejiang China Zhejiang University College of Computer Science and Technology Hangzhou Zhejiang China

Although the speed of big data processing can be effectively improved through advanced infrastructure such as cloud computing and supercomputing and parallel distributed processing means, it is extremely important to seek an accurate and fast matching and retrieval algorithm for improving the order-preserving matching and retrieval accuracy of big data applications. This article is based on the DL (Deep Learning) big data order preserving matching and retrieval model. By implementing a layer by layer initialization strategy for encoding, the stack style self coding neural network is initialized, and the activation state of the hidden layer neural unit is used as input for the next level. The DBN (deep belief network) with RBM (Restricted Boltzmann Machine) as the basic unit is trained. based on the distribution of text themes and image feature vectors, big data ordering matching and retrieval are achieved. The simulation results show that for the same random data series, with the increase of pattern length, the number of matching results will gradually decrease, and the proportion of similarity calculation time to the overall time of the algorithm will gradually decrease. In multi-data retrieval, the proposed method shows the trend of more data and higher accuracy, which has obvious advantages.

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SPEAR: Dynamic Spatio-Temporal Query processing over High Velocity Data Streams 37

SPEAR: Dynamic Spatio-Temporal Query Processing over High Ve...

引用

37th IEEE International conference on Data Engineering (IEEE ICDE)

作者： Baig, Furqan Teng, Dejun Kong, Jun Wang, Fusheng SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA SUNY Stony Brook Dept Biomed Informat Stony Brook NY 11794 USA Georgia State Univ Dept Comp Sci Dept Math & Stat Atlanta GA 30303 USA

ISBN: (纸本)9781728191843

With the advent of IoT and emerging 5G technology, real-time streaming data are being generated at unprecedented speed and volume having both temporal and spatial dimensions. Effective analysis at such scale and speed require support for dynamically adjusting querying capabilities in real-time. In the spatio-temporal domain, this warrants data as well as query optimization strategies especially for objects with changing motion states. Contemporary spatio-temporal data stream management systems in the distributed domain are mostly dominated by specify-once-apply-continuously query model. Any modification in query state requires query restart limiting system responsiveness and producing outdated or in worst case erroneous results. In this paper, we propose adaptations of principles from streaming databases, spatial data management, and distributed computing to support dynamic spatio-temporal query processing over high-velocity big data streams. We first formulate a set of spatio-temporal data types and functions to seamlessly handle changes in distributed query states. We develop a comprehensive set of streaming spatio-temporal querying methods and propose geohash based dynamic spatial partitioning for effective parallel processing. We implement a prototype on top of Apache Flink, where the in-memory stream processing fits nicely with our spatio-temporal models. Comparative evaluation of our prototype demonstrates the effectiveness of our strategy by maintaining high consistent processing rates for both stationary as well as moving queries over high velocity spatio-temporal big data streams.

关键词： spatio-temporal stream-processing spatiotemporal-stream real-time-spatio-temporal spatial-stream distributed-stream

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MPI Scaling Up for Powerlist based parallel Programs 27

MPI Scaling Up for Powerlist Based Parallel Programs

引用

27th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Niculescu, Virginia Bufnea, Darius Sterca, Adrian Babes Bolyai Univ Fac Math & Comp Sci Cluj Napoca Romania

ISBN: (纸本)9781728116440

Powerlists are recursive data structures that together with their associated algebraic theories could offer both a methodology to design parallel algorithms and parallel programming abstractions to ease the development of parallel applications This has been also proved by a concrete development of such a framework that allows easy, efficient, and reliable implementation of Java parallel programs on shared memory systems. The paper presents a highly scalable version of this framework by extending it to distributed memory systems based on an MPI implementation. Through this extension we may use the framework to develop Java parallel programs also on distributed memory systems such as clusters. The design of the framework enables flexibility in defining the appropriate execution type depending on the execution system and its characteristics. Therefore, it is possible to choose MPI execution (ithat also could be combined with multithreading) if the available system includes an MPI platform, or simple multithreading execution. Examples are given and performance experiments are conducted. The performance analysis of these applications emphasises the utility and the efficiency of this framework extension.

关键词： parallel programming scaling recursive structures Java MPI performance models

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Accelerating Mega-Scale Satellite network Simulation in NS-3 via MPI-based parallelization

Accelerating Mega-Scale Satellite Network Simulation in NS-3...

引用

IEEE International conference on Communications (ICC)

作者： Haibin Song Tian Pan Guohao Ruan Yan Zheng Ying Wan Jiao Zhang Tao Huang Yunjie Liu Beijing University of Posts and Telecommunications Beijing China Purple Mountain Laboratories Nanjing China Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China China Mobile (Suzhou) Software Technology Co. Ltd Suzhou China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Due to the high costs of low Earth orbit (LEO) satellite manufacturing and launch, as well as the complexity of in-orbit network protocol debugging, simulating and verifying satellite network protocols on the ground before satellite launch holds significant importance. Compared to the expensive emulation with one-to-one replication, simulation (e.g., using ns-3) can achieve discrete event processing at a relatively lower cost by extending the wall clock time. However, very few studies have used ns-3 for LEO satellite network simulation, facing challenges such as faithfully simulating the on/off state switching of inter-satellite links (ISLs) and achieving simulation performance scalability for high-density satellite constellations. In this work, we propose a system to accelerate mega-scale LEO satellite network simulation in ns-3 via MPI-based parallelization. Specifically, we simulate ISLs based on ns-3's P2P channels/P2P remote channels and achieve runtime link connection/disconnection by implementing stateful traffic dropping inside the network interface. Then, we conduct concurrent simulation with ns-3's parallel and distributed simulation capability and partition the satellite constellation into multiple simulation processes through a hierarchical clustering algorithm and automated scripts, considering satellite locality and inter-process workload balance. Our evaluation shows significant speed improvements via parallelization, e.g., a 373% speedup with 12 processes for LEO-192, and a 156% speedup with 3 processes for LEO-3072.

关键词： Satellite constellations Satellites Runtime Protocols Costs Low earth orbit satellites Clustering algorithms

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Dynamic Source Localization via Finite-Element Underwater Acoustic Field Estimation 29

Dynamic Source Localization via Finite-Element Underwater Ac...

引用

29th European Signal processing conference (EUSIPCO)

作者： Manduzio, Graziano A. Forti, Nicola Braca, Paolo Battistelli, Giorgio Chisci, Luigi Sabatinit, Roberto Univ Firenze Florence Italy NATO STO Ctr Maritime Res & Expt La Spezia Italy

ISBN: (纸本)9789082797060

In this paper we address the problem of dynamic source localization in spatially distributed systems governed by Partial Differential Equations (PDEs) which aims to detect and localize a mobile source from a passive array of acoustic sensors. We consider an underwater environment where the space-time dynamics of the source-induced field is modeled by a finite-element (FE) approximation of the full acoustic wave PDE. based on recent advancements in large-scale state estimation of PDE systems, we present a novel Multiple Model (MM) filtering approach to underwater dynamic source localization. The proposed framework sequentially estimates the acoustic field and the source location by running in parallel a bank of FE-based field estimators, each conditioned to the source being placed in a given element of the FE mesh. We adopt the Ensemble Kalman Filter (EnKF) implementation for computationally efficient estimation of the large-scale acoustic field. The effectiveness of the proposed Finite-Element Multiple Model Ensemble Kalman Filter (FE-MM-EnKF) is demonstrated via simulation experiments in the underwater acoustic environment.

关键词： Location awareness Computational modeling Acoustic sensors Signal processing Iron Finite element analysis Acoustic field

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High-performance network Traffic Classification based on Graph Neural network

High-performance Network Traffic Classification Based on Gra...

引用

IEEE Information Technology, networking, Electronic and Automation Control conference

作者： Bo Pang Yongquan Fu Siyuan Ren Yan Jia College of Computer Science and Technology Harbin Institute of Technology Shenzhen China National Key Laboratory for Parallel and Distributed Processing College of Computer National University of Defense Technology Changesha China Peng Cheng Laboratory Shen Zhen China

network traffic classification is crucial for network security and network management and is one of the most important network tasks. Current state-of-the-art traffic classifiers are based on deep learning models to automatically extract features from packet streams. Unfortunately, current approaches fail to effectively combine the structural information of traffic packets with the content features of the packets, resulting in limited classification accuracy. In this paper, we propose a graph neural network model for network traffic classification, which can well perceive the interaction feature of packets in traffic. Firstly, we design a graph structure for packets’ flows to hold the interaction information between packets, which embeds both packet contents and sequence relationships into a unified graph. Secondly, we propose a graph neural network framework for graph classification to automatically learn the structural features of the packets’ flows together with the packets’ features. Extensive evaluation results on real-world traffic data show that the proposed model improves the prediction accuracy of improves the prediction accuracy by 2% to 37% for malicious traffic classification.

关键词： Deep learning Automation Telecommunication traffic Predictive models network security Feature extraction Graph neural networks

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OneGenE: Regulatory Gene network Expansion via distributed Volunteer Computing on BOINC 27

OneGenE: Regulatory Gene Network Expansion via Distributed V...

引用

27th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Asnicar, Francesco Masera, Luca Pistore, Davide Valentini, Samuel Cavecchia, Valter Blanzieri, Enrico Univ Trento Dept Informat Engn & Comp Sci Trento Italy CNR IMEM Inst Mat Elect & Magnetism Trento Unit Trento Italy Univ Trento CIBIO Trento Italy

ISBN: (纸本)9781728116440

Gene regulatory network expansion is a task of the foremost importance in computational biology that aims at finding new genes to expand a given known gene regulatory network. To this end we present OneGenE, a novel framework for gene regulatory network expansion that relies on the BOINC platform. OneGenE is an evolution of the NES2RA algorithm, with the aim to overcome its main criticality, i.e. long response time for the final user. To achieve this goal, candidate expansion lists are pre-computed for each gene in the organism and then aggregated at runtime to produce the the final expansion list for a given known gene regulatory network. We validated OneGenE on the expression data of Pseudomonas aeruginosa, comparing its results with the one obtained by NES2RA and through a biological literature review.

关键词： distributed volunteer computing gene regulatory network expansion BOINC

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Challenges of distributed Computing for Pandemic Spread Prediction based on Large-Scale Human Interaction Data 14

Challenges of Distributed Computing for Pandemic Spread Pred...

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IEEE 14th International conference on Cloud Computing (CLOUD)

作者： Shubha, Sudipta Saha Mahmud, Shohaib Shen, Haiying Univ Virginia Dept Comp Sci Charlottesville VA 22903 USA

ISBN: (纸本)9781665400602

Pandemic like COVID-19 poses severe challenges to public health and causes great damages to human lives and economy. Computational epidemiology allows the authorities (e.g., federal, state, city governments) to predict the future states of pandemic and take preventive measures based on that. However, due to widespread human mobility, epidemic prediction on a small-scale (e.g., city) may not produce effective results. Therefore, to obtain a clearer picture of pandemics, authorities often rely on large-scale human mobility data. processing such a large graph dataset is highly computation intensive, and thus generates long latency. distributed computing, in which the graph is partitioned and processed by multiple servers in parallel, is a solution to this problem. However, as human mobility changes over time, the graph varies over time, which may make the previous graph partition not effective anymore in limiting the communication overhead. In this paper, we study the existing works in the literature for processing large-scale human mobility graph data to predict pandemic spread. We conducted comprehensive experiments based on real-world large-scale human mobility data that covers the entire state of Virginia, USA. based on the experimental evaluations, we present our findings and discuss the possible future research directions for the aforementioned challenge.

关键词： Cloud computing Limiting Pandemics Design methodology conferences Urban areas Government

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