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Activity Graph

The Experts below are selected from a list of 291 Experts worldwide ranked by ideXlab platform

Zhi-li Zhang – 1st expert on this subject based on the ideXlab platform

  • application traffic Activity Graph analysis
    Springer Optimization and Its Applications, 2012
    Co-Authors: Esam Sharafuddin, Zhi-li Zhang

    Abstract:

    Understanding and analyzing traffic characteristics are fundamental to the design, development and implementation of networks. The traditional emphasis of network traffic analysis has been on the statistical properties of traffic, leading to the important discoveries such as heavy-tails and long range dependence. As networking technologies continue to mature and evolve, and more sophisticated network applications are invented and deployed, operating, managing and securing networks have become increasingly challenging tasks, and require us to understand, analyze and model the behavioral characteristics of network traffic, such as communication patterns, interaction structures and trends of applications, users and other entities in the networks.

  • Inferring applications at the network layer using collective traffic statistics
    2010 22nd International Teletraffic Congress – Proceedings ITC 22, 2010
    Co-Authors: Yu Jin, Nick Duffield, Patrick Haffner, Subhabrata Sen, Zhi-li Zhang

    Abstract:

    Traffic Analysis at Network Layer using Traffic Activity Graph

  • unveiling core network wide communication patterns through application traffic Activity Graph decomposition
    Measurement and Modeling of Computer Systems, 2009
    Co-Authors: Esam Sharafuddin, Zhi-li Zhang

    Abstract:

    As Internet communications and applications become more complex,operating, managing and securing networks have become increasingly challenging tasks. There are urgent demands for more sophisticated techniques for understanding and analyzing the behavioral characteristics of network traffic. In this paper, we study the network traffic behaviors using traffic Activity Graphs (TAGs), which capture the interactions among hosts engaging in certain types of communications and their collective behavior. TAGs derived from real network traffic are large, sparse, yet seemingly complex and richly connected, therefore difficult to visualize and comprehend. In order to analyze and characterize these TAGs, we propose a novel statistical traffic Graph decomposition technique based on orthogonal nonnegative matrix tri-factorization (tNMF) to decompose and extract the core host interaction patterns and other structural properties. Using the real network traffic traces, we demonstrate that our tNMF-based Graph decomposition technique produces meaningful and interpretable results. It enables us to characterize and quantify the key structural properties of large and sparse TAGs associated with various applications, and study their formation and evolution.

Esam Sharafuddin – 2nd expert on this subject based on the ideXlab platform

  • application traffic Activity Graph analysis
    Springer Optimization and Its Applications, 2012
    Co-Authors: Esam Sharafuddin, Zhi-li Zhang

    Abstract:

    Understanding and analyzing traffic characteristics are fundamental to the design, development and implementation of networks. The traditional emphasis of network traffic analysis has been on the statistical properties of traffic, leading to the important discoveries such as heavy-tails and long range dependence. As networking technologies continue to mature and evolve, and more sophisticated network applications are invented and deployed, operating, managing and securing networks have become increasingly challenging tasks, and require us to understand, analyze and model the behavioral characteristics of network traffic, such as communication patterns, interaction structures and trends of applications, users and other entities in the networks.

  • unveiling core network wide communication patterns through application traffic Activity Graph decomposition
    Measurement and Modeling of Computer Systems, 2009
    Co-Authors: Esam Sharafuddin, Zhi-li Zhang

    Abstract:

    As Internet communications and applications become more complex,operating, managing and securing networks have become increasingly challenging tasks. There are urgent demands for more sophisticated techniques for understanding and analyzing the behavioral characteristics of network traffic. In this paper, we study the network traffic behaviors using traffic Activity Graphs (TAGs), which capture the interactions among hosts engaging in certain types of communications and their collective behavior. TAGs derived from real network traffic are large, sparse, yet seemingly complex and richly connected, therefore difficult to visualize and comprehend. In order to analyze and characterize these TAGs, we propose a novel statistical traffic Graph decomposition technique based on orthogonal nonnegative matrix tri-factorization (tNMF) to decompose and extract the core host interaction patterns and other structural properties. Using the real network traffic traces, we demonstrate that our tNMF-based Graph decomposition technique produces meaningful and interpretable results. It enables us to characterize and quantify the key structural properties of large and sparse TAGs associated with various applications, and study their formation and evolution.

  • SIGMETRICS/Performance – Unveiling core network-wide communication patterns through application traffic Activity Graph decomposition
    Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems – SIGMETRICS '09, 2009
    Co-Authors: Esam Sharafuddin, Zhi-li Zhang

    Abstract:

    As Internet communications and applications become more complex,operating, managing and securing networks have become increasingly challenging tasks. There are urgent demands for more sophisticated techniques for understanding and analyzing the behavioral characteristics of network traffic. In this paper, we study the network traffic behaviors using traffic Activity Graphs (TAGs), which capture the interactions among hosts engaging in certain types of communications and their collective behavior. TAGs derived from real network traffic are large, sparse, yet seemingly complex and richly connected, therefore difficult to visualize and comprehend. In order to analyze and characterize these TAGs, we propose a novel statistical traffic Graph decomposition technique based on orthogonal nonnegative matrix tri-factorization (tNMF) to decompose and extract the core host interaction patterns and other structural properties. Using the real network traffic traces, we demonstrate that our tNMF-based Graph decomposition technique produces meaningful and interpretable results. It enables us to characterize and quantify the key structural properties of large and sparse TAGs associated with various applications, and study their formation and evolution.

S Zanlongo – 3rd expert on this subject based on the ideXlab platform

  • An Automated Methodology for Worker Path Generation and Safety Assessment in Construction Projects
    IEEE Transactions on Automation Science and Engineering, 2018
    Co-Authors: Mahbubur Rahman, Ali Mostafavi, Triana Carmenate, Luis Bobadilla, S Zanlongo

    Abstract:

    Collisions between automated moving equipment and human workers in job sites are one of the main sources of fatalities and accidents during the execution of construction projects. In this paper, we present a methodology to identify and assess project plans in terms of hazards before their execution. Our methodology has the following steps: 1) several potential plans are extracted from an initial Activity Graph; 2) plans are translated from a high-level Activity Graph to a discrete-event simulation model; 3) trajectories and safety policies are generated that avoid static and moving obstacles using existing motion planning algorithms; 4) safety scores and risk-based heatmaps are calculated based on the trajectories of moving equipment; and 5) managerial implications are provided to select an acceptable plan with the aid of a sensitivity analysis of different factors (cost, resources, and deadlines) that affect the safety of a plan. Finally, we present illustrative case study examples to demonstrate the usefulness of our model. Note to Practitioners —Currently, construction project planning does not explicitly consider safety due to a lack of automated tools that can identify a plan’s safety level before its execution. This paper proposes an automated construction safety assessment tool which is able to evaluate the alternate construction plans and help to choose considering safety, cost, and deadlines. Our methodology uses discrete-event modeling along with motion planning to simulate the motions of workers and equipment, which account for most of the hazards in construction sites. Our method is capable of generating safe motion trajectories and coordination policies for both humans and machines to minimize the number of collisions. We also provide safety heatmaps as a spatiotemporal visual display of construction site to identify risky zones inside the environment throughout the entire timeline of the project. Additionally, a detailed sensitivity analysis helps to choose among plans in terms of safety, cost, and deadlines.

  • An Automated Methodology for Worker Path Generation and Safety Assessment in Construction Projects
    IEEE Transactions on Automation Science and Engineering, 2018
    Co-Authors: Md Mahbubur Rahman, Ali Mostafavi, Triana Carmenate, Luis Bobadilla, S Zanlongo

    Abstract:

    Collisions between automated moving equipment and human workers in job sites are one of the main sources of fatalities and accidents during the execution of construction projects. In this paper, we present a methodology to identify and assess project plans in terms of hazards before their execution. Our methodology has the following steps: 1) several potential plans are extracted from an initial Activity Graph; 2) plans are translated from a high-level Activity Graph to a discrete-event simulation model; 3) trajectories and safety policies are generated that avoid static and moving obstacles using existing motion planning algorithms; 4) safety scores and risk-based heatmaps are calculated based on the trajectories of moving equipment; and 5) managerial implications are provided to select an acceptable plan with the aid of a sensitivity analysis of different factors (cost, resources, and deadlines) that affect the safety of a plan. Finally, we present illustrative case study examples to demonstrate the usefulness of our model.

  • ICRA – A coupled discrete-event and motion planning methodology for automated safety assessment in construction projects
    2015 IEEE International Conference on Robotics and Automation (ICRA), 2015
    Co-Authors: Mahbubur Rahman, Luis Bobadilla, Triana Carmenate, S Zanlongo, Ali Mostafavi

    Abstract:

    Collisions between moving machinery and human workers in construction job sites are one of the main sources of fatalities and accidents during the execution of construction projects. In this paper, we present a methodology to identify and assess construction project plan dangers before their execution. Our methodology has the following steps: 1) Plans are translated from a high-level Activity Graph to a discrete event simulation model; 2) Trajectories are simulated using sampling based and combinatorial motion planning algorithms; and 3) Safety scores and risk-based heatmaps are calculated based on the trajectories of moving equipment. Finally, we present an illustrative case study to demonstrate the usability of our model.