Computer Science ›› 2023, Vol. 50 ›› Issue (2): 237-243.doi: 10.11896/jsjkx.220600203

• Artificial Intelligence • Previous Articles     Next Articles

Cross-domain DOM Pickup and Automation Scheme of RPA System Based on Browser Extension

YI Renke, CAI Yuhui, YANG Shenghong, WU Fan, LI Kenli   

  1. School of Computer Science and Engineering,Hunan University,Changsha 410082,China
  • Received:2022-06-22 Revised:2022-11-14 Online:2023-02-15 Published:2023-02-22
  • Supported by:
    High-performance Computing Application Software Co-develop Tools and Environmental Research(2017YFB0202201)

Abstract: Robotic process automation(RPA) is one of today's research hotspots.The pickup and automation of web page elements is one of the important functions of RPA.RPA injects scripts into the web page to process web pages by using browser extensions,using web page element positioning path to locate to the target node for automated operations.When there is a cross-domain frame in the source web page,due to the limitations of the same-origin strategy,the script injected into the source web page can not obtain the DOM object of the target node,resulting in the inability to generate a web page element positioning path,so that it can not be automated.When processing a web page containing a third-party cross-domain frame,the scheme treats it as a frame process equal to the status of the source web page frame,and the web page element positioning path is designed to contain the url of the frame and the form of the web page element Xpath to achieve cross-domain web page element pickup and automation.Experiments show that the scheme can effectively pick up and automate the elements of cross-domain web pages,and support chrome,firefox,and other browsers that support browser extensions.

Key words: Browser extensions, Cross-domain, RPA

CLC Number: 

  • TP312
[1]VAJGEL B,CORRÊA P L P,DE SOUSA T T,et al.Development of intelligent robotic process automation:A utility case study in Brazil[J].IEEE Access,2021,9:71222-71235.
[2]HUANG S Q,LIU Y B,HUANG X S.Research on Automatic Testing Technology of Model Driven Development Tools[J].Computer Science,2021,48(6A):568-571.
[3]KEDZIORA D,SMOLANDER K.Responding to HealthcareEmergency Outbreak of COVID-19 Pandemic with Robotic Process Automation(RPA)[C]//HICSS.2022:1-10.
[4]TØMMERVÅG A S,BACH T,JÆGER B.Leveraging the competition:Robotic Process Automation(RPA) enabling competitive Small and Medium sized Auditing Firms[C]//2022 IEEE/SICE International Symposium on System Integration(SII).IEEE,2022:833-837.
[5]VILLAR A S,KHAN N.Robotic process automation in banking industry:a case study on Deutsche Bank[J].Journal of Banking and Financial Technology,2021,5(1):71-86.
[6]KOCH O,BUCHKREMER R,KNEISEL E.Graph Databasesand Robotic Process Automation:Achieving Improvement in Project Knowledge Management[C]//33rd Blede Conference Enabling Technology for a Sustainable Society.2020:171-184.
[7]BAIDYA A.Document Analysis and Classification:A Robotic Process Automation(RPA) and Machine Learning Approach[C]//2021 4th International Conference on Information and Computer Technologies(ICICT).IEEE,2021:33-37.
[8]LI X D,GU Y Q.DOM-based Information Extraction for theWeb Sources[J].Chinese Journal of Computers,2002(5):526-533.
[9]ZHANG J,LIU X F.Intelligent Discernment and AutomaticManipulation of Web-Page Controls[J].Computer Systems & Applications,2009,18(4):163-166.
[10]WANG H.Design and implementation of a breadth-first themed crawler[D].Shanghai:Fudan University,2011.
[11]SHARMA A,GULERIA K.A Framework for Hotel InventoryControl System for Online Travel Agency using Robotic Process Automation[C]//2021 International Conference on Advance Computing and Innovative Technologies in Engineering(ICACITE).IEEE,2021:764-768.
[12]YATSKIV S,VOYTYUK I,YATSKIV N,et al.Improvedmethod of software automation testing based on the robotic process automation technology[C]//2019 9th International Conference on Advanced Computer Information Technologies(ACIT).IEEE,2019:293-296.
[13]SHIDAGANTI G,SALIL S,ANAND P,et al.Robotic ProcessAutomation with AI and OCR to Improve Business Process[C]//2021 Second International Conference on Electronics and Sustainable Communication Systems(ICESC).IEEE,2021:1612-1618.
[14]MA Y W,LIN D P,CHEN S J,et al.System design and deve-lopment for robotic process automation[C]//2019 IEEE International Conference on Smart Cloud(SmartCloud).IEEE,2019:187-189.
[15]DO ROSÁRIO CABRITA M,PARGANA F,COSTA J.Robotic Process Automation implementation framework in a financial institution[C]//2021 16th Iberian Conference on Information Systems and Technologies(CISTI).IEEE,2021:1-9.
[16]DONG R,HUANG Z,LAM I I,et al.WebRobot:web robotic process automation using interactive programming-by-demonstration[C]//Proceedings of the 43rd ACM SIGPLAN International Conference on Programming Language Design and Implementation.2022:152-167.
[17]WEWERKA J,MICUS C,REICHERT M.Seven Guidelines for Designing the User Interface in Robotic Process Automation[C]//2021 IEEE 25th International Enterprise Distributed Object Computing Workshop(EDOCW).Gold Coast,Australia,2021:157-165.
[18]MARAGATHASUNDARI S,MUTHANANTHAM M,VANALAKSHMI R,et al.Queuing Analysis in Robotic Process Automation[C]//2022 8th International Conference on Advanced Computing and Communication Systems(ICACCS).IEEE,2022:174-181.
[19]MARTINS P,SÁF,MORGADO F,et al.Using machine lear-ning for cognitive Robotic Process Automation(RPA) [C]//2020 15th Iberian Conference on Information Systems and Technologies (CISTI).2020:1-6.
[20]MARTINS P,SÁ F,MORGADO F,et al.Using machine learning for cognitive Robotic Process Automation(RPA) [C]//2020 15th Iberian Conference on Information Systems and Technologies(CISTI).2020:1-6.
[21]WANG Y S,QIN Y C,CAI Y H,et al.Design and Implementation of RPA System Based on UIA Interface [J].Computer Science,2022,49(8):225-229.
[22]LUO W,SHEN Q N,WU Z H,et al.State-of-the-art survey of research on browser's same origin policy security[J].Ruan Jian Xue Bao/Journal of Software,2021,32(8):2469-2504.
[1] HU Zhongyuan, XUE Yu, ZHA Jiajie. Survey on Evolutionary Recurrent Neural Networks [J]. Computer Science, 2023, 50(3): 254-265.
[2] ZHANG Jia, DONG Shou-bin. Cross-domain Recommendation Based on Review Aspect-level User Preference Transfer [J]. Computer Science, 2022, 49(9): 41-47.
[3] FANG Yi-qiu, ZHANG Zhen-kun, GE Jun-wei. Cross-domain Recommendation Algorithm Based on Self-attention Mechanism and Transfer Learning [J]. Computer Science, 2022, 49(8): 70-77.
[4] WANG Yan-song, QIN Yun-chuan, CAI Yu-hui, LI Ken-li. Design and Implementation of RPA System Based on UIA Interface [J]. Computer Science, 2022, 49(8): 225-229.
[5] CHEN Yan-bing, ZHONG Chao-ran, ZHOU Chao-ran, XUE Ling-yan, HUANG Hai-ping. Design of Cross-domain Authentication Scheme Based on Medical Consortium Chain [J]. Computer Science, 2022, 49(6A): 537-543.
[6] LIU Lin-yun, CHEN Kai-yan, LI Xiong-wei, ZHANG Yang, XIE Fang-fang. Overview of Side Channel Analysis Based on Convolutional Neural Network [J]. Computer Science, 2022, 49(5): 296-302.
[7] MA Ji, LIN Shang-jing, LI Yue-ying, ZHUANG Bei, JIA Rui, TIAN Jin. Traffic Prediction for Wireless Communication Networks with Multi-source and Cross-domain Data Fusion [J]. Computer Science, 2022, 49(11A): 210800165-7.
[8] WANG Chi, CHANG Jun. CSI Cross-domain Gesture Recognition Method Based on 3D Convolutional Neural Network [J]. Computer Science, 2021, 48(8): 322-327.
[9] DENG Li, WU Jin-da, LI Ke-xue, LU Ya-kang. SpaRC Algorithm Hyperparameter Optimization Methodology Based on TPE [J]. Computer Science, 2021, 48(2): 70-75.
[10] JIAN Song-lei, LU Kai. Survey on Representation Learning of Complex Heterogeneous Data [J]. Computer Science, 2020, 47(2): 1-9.
[11] JIANG Ze-tao, XU Juan-juan. Efficient Heterogeneous Cross-domain Authentication Scheme Based on Proxy Blind Signature in Cloud Environment [J]. Computer Science, 2020, 47(11): 60-67.
[12] GE Meng-fan, LIU Zhen, WANG Na-na, TIAN Jing-yu. Cross-domaing Item Recommendation Algorithm Including Tag Transfer [J]. Computer Science, 2019, 46(10): 1-6.
[13] TONG Wei-guo, LI Min-xia, ZHANG Yi-ke. Research on Optimization Algorithm of Deep Learning [J]. Computer Science, 2018, 45(11A): 155-159.
[14] TANG Cheng-hua, ZHANG Xin, WANG Lu, WANG Yu and QIANG Bao-hua. Security Interoperation Model of Cross-domain Network Resources [J]. Computer Science, 2016, 43(6): 141-145.
[15] WU Fei, ZHANG Yu-hong and HU Xue-gang. Approach of Cross-domain Word Sentiment Orientation Identification on Reviews [J]. Computer Science, 2015, 42(6): 220-222.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!