Modeling of control flow and data flow in MLS workflows

被引:0
|
作者
Hua, ZG [1 ]
Ding, LZ [1 ]
机构
[1] Huazhong Univ Sci & Technol, Dept Comp Sci & Applicat, Wuhan 430074, Peoples R China
关键词
MLS workflow; colored Petri net; control flow subnet; data flow subnet;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A multilevel secure workflow may consist of tasks of different security levels. The needed data items also are assigned different security levels. In a complex business process it can be difficult to detect if a process specification complies with a given multi-level security (MLS) policy. The policy may be too strict and means to relax the policy have to be found. It's necessary to find a way to model the control flow and the needed data items simultaneously for the purpose of analysis. This paper shows how to systematically model the information flow in workflows under a MLS policy. The information flow in a workflow under a MLS policy will be modeled with Colored Petri nets (CP-nets), and the information flow CP-net will be divided into two parts: control flow subnet and data flow subnet. The control flow subnet specifies which tasks need to be executed and in what order. The data flow subnet states how the data items move between the different tasks.
引用
收藏
页码:322 / 326
页数:5
相关论文
共 50 条
  • [31] Seamless integration of control flow and data flow in a visual language
    Randriamparany, H
    Ibrahim, B
    ACS/IEEE INTERNATIONAL CONFERENCE ON COMPUTER SYSTEMS AND APPLICATIONS, PROCEEDINGS, 2001, : 428 - 434
  • [32] MOTIVATION FOR A COMBINED DATA FLOW-CONTROL FLOW PROCESSOR
    OXLEY, DW
    PROCEEDINGS OF THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS, 1981, 298 : 305 - 311
  • [33] An execution model for the seamless integration of control flow and data flow
    Ibrahim, B
    Randriamparany, F
    ACS/IEEE INTERNATIONAL CONFERENCE ON COMPUTER SYSTEMS AND APPLICATIONS, PROCEEDINGS, 2001, : 402 - 404
  • [34] Reducing data transfer in big-data workflows: the computation-flow delegated approach
    Rickey T. P. Nunes
    Santosh L. Deshpande
    Journal of Data, Information and Management, 2019, 1 (3-4): : 129 - 145
  • [35] Distributed Workflows for Modeling Experimental Data
    Lynch, Vickie E.
    Calvo, Jose Borreguero
    Deelman, Ewa
    da Silva, Rafael Ferreira
    Goswami, Monojoy
    Hui, Yawei
    Lingerfelt, Eric
    Vetter, Jeffrey S.
    2017 IEEE HIGH PERFORMANCE EXTREME COMPUTING CONFERENCE (HPEC), 2017,
  • [36] Recent progress of machine learning in flow modeling and active flow control
    Li, Yunfei
    Chang, Juntao
    Kong, Chen
    Bao, Wen
    CHINESE JOURNAL OF AERONAUTICS, 2022, 35 (04) : 14 - 44
  • [37] Flow and flow control modeling for a drilling riser system with auxiliary lines
    Wu, Wenbo
    Wang, Jiasong
    Jiang, Shiquan
    Xu, Liangbin
    Sheng, Leixiang
    OCEAN ENGINEERING, 2016, 123 : 204 - 222
  • [38] Recent progress of machine learning in flow modeling and active flow control
    Yunfei Li
    Juntao Chang
    Chen Kong
    Wen Bao
    Chinese Journal of Aeronautics, 2022, 35 (04) : 14 - 44
  • [39] EnzymeML: seamless data flow and modeling of enzymatic data
    Lauterbach, Simone
    Dienhart, Hannah
    Range, Jan
    Malzacher, Stephan
    Spoering, Jan-Dirk
    Rother, Doerte
    Pinto, Maria Filipa
    Martins, Pedro
    Lagerman, Colton E.
    Bommarius, Andreas S.
    Host, Amalie Vang
    Woodley, John M.
    Ngubane, Sandile
    Kudanga, Tukayi
    Bergmann, Frank T.
    Rohwer, Johann M.
    Iglezakis, Dorothea
    Weidemann, Andreas
    Wittig, Ulrike
    Kettner, Carsten
    Swainston, Neil
    Schnell, Santiago
    Pleiss, Juergen
    NATURE METHODS, 2023, 20 (03) : 400 - +
  • [40] EnzymeML: seamless data flow and modeling of enzymatic data
    Simone Lauterbach
    Hannah Dienhart
    Jan Range
    Stephan Malzacher
    Jan-Dirk Spöring
    Dörte Rother
    Maria Filipa Pinto
    Pedro Martins
    Colton E. Lagerman
    Andreas S. Bommarius
    Amalie Vang Høst
    John M. Woodley
    Sandile Ngubane
    Tukayi Kudanga
    Frank T. Bergmann
    Johann M. Rohwer
    Dorothea Iglezakis
    Andreas Weidemann
    Ulrike Wittig
    Carsten Kettner
    Neil Swainston
    Santiago Schnell
    Jürgen Pleiss
    Nature Methods, 2023, 20 : 400 - 402