Title: An intelligent framework for auto-filling web forms from different web applications

Authors: Shaohua Wang; Ying Zou; Iman Keivanloo; Bipin Upahyaya; Joanna Ng

Addresses: School of Computing, Queen's University, Kingston, Ontario, Canada ' Department of Electrical and Computer Engineering, Queen's University, Kingston, Ontario, Canada ' Department of Electrical and Computer Engineering, Queen's University, Kingston, Ontario, Canada ' Department of Electrical and Computer Engineering, Queen's University, Kingston, Ontario, Canada ' CAS Research, IBM Canada Software Laboratory, Markham, Canada

Abstract: End-users compose ad-hoc business processes by integrating web applications to conduct online tasks. Generally, end-users have to enter information into web forms of web applications, and often repetitively type the same information into applications. It could be a tedious job for end-users to fill in web forms with identical information. To save end-users from repetitive typing and increase composition productivity, it is critical to propagate and pre-fill user inputs to web applications. In this paper, we propose an intelligent auto-filling framework collecting and propagating user inputs across web applications, identifying user usage patterns and contexts. The empirical results show that our framework, on average, achieves a precision of 74.5% and a recall of 58% on pre-filling web forms, and a precision of 82.25% and a recall of 68.4% on suggesting values to end-users if the end-users edit the initial pre-filled values.

Keywords: web form auto-filling; clustering; usage patterns; context-aware; web form filling; web forms; web applications; intelligent auto-filling; user inputs; form pre-filling.

DOI: 10.1504/IJBPIM.2017.082747

International Journal of Business Process Integration and Management, 2017 Vol.8 No.1, pp.16 - 30

Received: 15 Apr 2014
Accepted: 17 Nov 2014
Published online: 10 Mar 2017 *

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