Automatic Reuse of User Inputs to Services among End-Users in Service Composition

Wang, Shaohua; Zou, Ying; Keivanloo, Iman; Upadhyaya, Bipin; Ng, Joanna; Ng, Tinny

doi:10.1109/tsc.2014.2378278

Cited by 4 publications

(10 citation statements)

References 24 publications

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“…To help users fill in input parameters in different contexts, we need a mechanism to organize and store user inputs efficiently. We extend our prior meta-data model proposed in [21] to include the contextual information of interactions between user inputs and input parameters, in addition to the basic information defined in [21] (e.g., a textual humanreadable label in a web form). The description of our metadata model is illustrated in Figure 1.…”

Section: Collecting and Storing User Inputs With Contextual Informationmentioning

confidence: 99%

“…We formulate them into two sets of words. We adopt three algorithms: cosine similarity [25], Jaccard's coefficient [26], and WordNet-based approach (WNA) [21], to calculate the textual similarity between two sets of words.…”

Section: Building Ranking Featuresmentioning

confidence: 99%

“…Rationale and Explanation Features Textual similarity A UIN textually similar to an IP could be the right value and selected by a user. We adopt 3 approaches, cosine similarity [25], Jaccard's coefficient [26], and WordNet-based approach (WNA) [21] to calculate the textual similarity between the text of UIN and the text of IP.…”

Section: Categorymentioning

confidence: 99%

“…Filling the same values into multiple web forms can be tedious [4], especially when the number of web forms is high. Therefore, it is essential to help users fill out web forms which take user's inputs for underlying web services and share user's inputs among different web services requiring the same inputs in order to reuse inputs to prevent users from repetitive typing [21]. For example, users can be four times faster on a smart phone when just correcting pre-filled form entries compared to entering the information from scratch [5].…”

Section: Introductionmentioning

confidence: 99%

“…To address the aforementioned limitations, we propose a ranking-based framework that ranks a list of previous user inputs for an input parameter to save a user from unnecessary data entries. As a complement to our prior work in TSC [21] taking filled values in web forms and sharing them among web services to reduce the amount of user inputs, this work focuses on ranking values for input parameters. More specifically, our framework (1) collects and stores interactions between user inputs and input parameters along with the contextual information, then (2) learns and analyzes such interactions to rank user inputs for input parameters in different contexts.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Collecting and Storing User Inputs With Contextual Informationmentioning

confidence: 99%

Section: Building Ranking Featuresmentioning

confidence: 99%

Section: Categorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Context-aware Service Input Ranking by Learning from Historical Information

Wang

Zou

et al. 2017

IEEE Trans. Serv. Comput.

Self Cite

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Automatic Filling Values to Services: A Road Map

Wang

Zou

et al. 2015

2015 IEEE World Congress on Services

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

Belgacem

Bianculli

et al. 2023

ACM Trans. Softw. Eng. Methodol.

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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 377 ms per suggestion.

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Automatic Reuse of User Inputs to Services among End-Users in Service Composition

Cited by 4 publications

References 24 publications

Context-aware Service Input Ranking by Learning from Historical Information

Context-aware Service Input Ranking by Learning from Historical Information

Automatic Filling Values to Services: A Road Map

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

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