Integration and formal representation in civil engineering supervision based on data-driven

被引:0
|
作者
Wu, Shifeng [1 ]
Song, Huazhu [1 ,2 ]
Li, Ting [1 ]
Zhong, Xian [1 ,2 ]
机构
[1] Wuhan Univ Technol, Sch Comp Sci & Technol, Wuhan 430070, Hubei, Peoples R China
[2] Hubei Key Lab Transportat Internet Things, Wuhan 430070, Hubei, Peoples R China
关键词
Civil engineering supervision; Data-driven; Data integration; Formal representation; BUILDING INFORMATION; CONSTRUCTION; MANAGEMENT; MODEL; SCHEDULE; SAFETY;
D O I
10.1007/s10586-018-2277-x
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In the civil engineering supervision, the management processes about personnel, materials, quality, safety, schedule and etc. are complex, which include large amount of data, involve more participants, and timely coordination and feedback is difficult. In this paper, we propose the data integration idea based on data-driven, which could guide the whole life cycle of civil engineering supervision from the top-level data organization, supervision of business processes, the interactive units and the users, the four-elements method TDTM and so on. Then, the data integration algorithms of civil engineering supervision are put forward to integrate the civil engineering supervision data from outside to inside and from coarse-grained to fine-grained, eliminate contradictions and redundancy and ensure data consistency. Finally, the civil engineering supervision data entities are determined, and we further analyze and discuss its query and report, data maintenance and conversion, and compare the functions in the different supervision platform. The civil engineering supervision unified data platform proposed could maintain the independence of the data, have the good scalability and support the more functions.
引用
收藏
页码:S6499 / S6516
页数:18
相关论文
共 50 条
  • [41] Towards integration of data-driven agronomic experiments with data provenance
    Serra da Cruz, Sergio Manuel
    Pires do Nascimento, Jose Antonio
    COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2019, 161 : 14 - 28
  • [42] DATA-DRIVEN HARMONIC FILTERS FOR AUDIO REPRESENTATION LEARNING
    Won, Minz
    Chun, Sanghyuk
    Nieto, Oriol
    Serra, Xavier
    2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, 2020, : 536 - 540
  • [43] Ontological representation, classification and data-driven computing of phenotypes
    Alexandr Uciteli
    Christoph Beger
    Toralf Kirsten
    Frank A. Meineke
    Heinrich Herre
    Journal of Biomedical Semantics, 11
  • [44] Data-Driven Sketch Beautification With Neural Feature Representation
    Shen, I-Chao
    IEEE COMPUTER GRAPHICS AND APPLICATIONS, 2021, 42 (04) : 72 - 79
  • [45] Symbolization of dynamic data-driven systems for signal representation
    Sarkar, Soumalya
    Chattopdhyay, Pritthi
    Ray, Asok
    SIGNAL IMAGE AND VIDEO PROCESSING, 2016, 10 (08) : 1535 - 1542
  • [46] Symbolization of dynamic data-driven systems for signal representation
    Soumalya Sarkar
    Pritthi Chattopdhyay
    Asok Ray
    Signal, Image and Video Processing, 2016, 10 : 1535 - 1542
  • [47] Data-driven background representation method to video surveillance
    Li, Zhihui
    Xia, Yingji
    Qu, Zhaowei
    JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION, 2017, 34 (02) : 193 - 202
  • [48] Ontological representation, classification and data-driven computing of phenotypes
    Uciteli, Alexandr
    Beger, Christoph
    Kirsten, Toralf
    Meineke, Frank A.
    Herre, Heinrich
    JOURNAL OF BIOMEDICAL SEMANTICS, 2020, 11 (01)
  • [49] A Scalable Framework for Data-Driven Subspace Representation and Clustering
    Kim, Eunwoo
    Lee, Minsik
    Oh, Songhwai
    PATTERN RECOGNITION LETTERS, 2019, 125 : 742 - 749
  • [50] Data Integration: Data-driven Discovery from Diverse Data Sources
    Allen, Genevera
    GENETIC EPIDEMIOLOGY, 2019, 43 (07) : 864 - 864