User-Driven Automation of Web Form Filling

Díaz, Óscar; Otaduy, Itziar; Puente, Gorka

doi:10.1007/978-3-642-39200-9_16

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…In the field of information retrieval, crawlers automatically fill and submit web forms to crawl the data returned from databases [38,46]; in the context of data crawling, automated form filling aims to automatically generate input values that can pass the field validation and retrieve more data, instead of helping users find the correct value they intend to fill. In the field of data mining, several approaches [68,49,21] fill data entry forms with the metadata extracted from data-rich text files. For example, these approaches can automatically use the metadata taken from a resume text file to fill several fields of forms in different job search sites [68].…”

Section: Related Workmentioning

confidence: 99%

A Machine Learning Approach for Automated Filling of Categorical Fields in Data Entry Forms

Belgacem,

Li,

Bianculli

et al. 2022

Preprint

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Users frequently interact with software systems through data entry forms. However, form filling is time-consuming and error-prone. Although several techniques have been proposed to auto-complete or pre-fill fields in the forms, they provide limited support to help users fill categorical fields, i.e., fields that require users to choose the right value among a large set of options.In this paper, we propose LAFF, a learning-based automated approach for filling categorical fields in data entry forms. LAFF first builds Bayesian Network models by learning field dependencies from a set of historical input instances, representing the values of the fields that have been filled in the past. To improve its learning ability, LAFF uses local modeling to effectively mine the local dependencies of fields in a cluster of input instances. During the form filling phase, LAFF uses such models to predict possible values of a target field, based on the values in the already-filled fields of the form and their dependencies; the predicted values (endorsed based on field dependencies and prediction confidence) are then provided to the end-user as a list of suggestions.We evaluated LAFF by assessing its effectiveness and efficiency in form filling on two datasets, one of them proprietary from the banking domain. Experimental results show that LAFF is able to provide accurate suggestions with a Mean Reciprocal Rank value above 0.73. Furthermore, LAFF is efficient, requiring at most 317 ms per suggestion.

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Section: Related Workmentioning

confidence: 99%

A Machine Learning Approach for Automated Filling of Categorical Fields in Data Entry Forms

Belgacem,

Li,

Bianculli

et al. 2022

Preprint

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show abstract

“…Toda et al [47] propose a probabilistic approach using the information extracted from data-rich text as the input source to fill values into web forms. WebFeeder [45] leverages iMarcos [48] from managing script code to script models to automatically fill the form-intensive websites requiring a large quantify of data with external data sources already digitalized in terms of documents, spread-sheets or databases. Kristjansson et al [46] propose an interactive form filling system to help data entry workers with the tedious and error-prone database form filling.…”

Section: Related Workmentioning

confidence: 99%

Context-aware Service Input Ranking by Learning from Historical Information

Wang

Zou

et al. 2017

IEEE Trans. Serv. Comput.

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Users visit on-line services and compose them to accomplish on-line tasks, such as shopping on-line. Quite often, users enter the same information into various on-line services to finish on-line tasks. However, repetitively typing the same information into web forms is a tedious job for users. In this paper, we propose a context-aware ranking framework to rank values for input parameters. We propose 6 categories of ranking features constructed from various types of information, such as user contexts and patterns of user inputs. Our framework adopts learning-to-rank (LtR) algorithms that consist of a set of machine learned models to automatically construct ranking models by integrating the ranking features. When a user enters a value to an input parameter, an interaction between the user input and the input parameter is established. Our framework learns information relevant to such interactions and ranks user inputs in different contexts. Through empirical studies on the real-world on-line services, we obtain the following main results: (1) Among the 8 state-of-the-art learning-to-rank models, RankBoost can outperform other LtR models on ranking user inputs for input parameters; (2) Our framework using IRSVM that performs the worst among the LtR models outperforms the two baseline conventional ranking models and Google Chrome Autofilling, an industrial tool, on ranking user inputs to input parameters; and (3) We observe that the textual information of user inputs and input parameters is the most influential factor on ranking user inputs. Among the various types of contextual data, user locations and time matter the most to the ranking of user inputs.

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“…There are also various ways to fill in data when collecting corresponding data in the control of the form. The most basic filling method is for users to manually fill in each field in the form sequentially, which is relatively inefficient and accurate [4]. In addition, another common filling method is to complete data filling through data communication between different systems.…”

Section: Introductionmentioning

confidence: 99%

Research on a Web System Data-Filling Method Based on Optical Character Recognition and Multi-Text Similarity

Su,

Kang,

Fan

2024

Applied Sciences

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In the development of web systems, data uploading is a relatively important function. The traditional method of uploading data is to manually fill out forms, but when the data to be uploaded mostly exist in the form of form images, and the form content contains a lot of similar field information and irrelevant edge information, using traditional methods is not only time-consuming and labor-intensive, but also prone to errors. This requires a technology that can automatically fill in complex form images. OCR is an optical character recognition technology that can convert images into digitized text data using computer vision methods. However, using this technology alone cannot complete the tasks of extracting relevant data and filling corresponding fields. To address this issue, this article proposes a method that combines OCR technology and Levenshtein multi-text similarity. This method can effectively solve the problem of data filling after parsing complex form images, and the application results of this method in web systems show that the filling accuracy for complex form images can reach over 90%.

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User-Driven Automation of Web Form Filling

Cited by 6 publications

References 7 publications

A Machine Learning Approach for Automated Filling of Categorical Fields in Data Entry Forms

A Machine Learning Approach for Automated Filling of Categorical Fields in Data Entry Forms

Context-aware Service Input Ranking by Learning from Historical Information

Research on a Web System Data-Filling Method Based on Optical Character Recognition and Multi-Text Similarity

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