Enhancing Domain Knowledge for Requirements Elicitation with Web Mining

2010 ◽  
Author(s):  
Haruhiko Kaiya ◽  
Yuutarou Shimizu ◽  
Hirotaka Yasui ◽  
Kenji Kaijiri ◽  
Motoshi Saeki
Keyword(s):  
Domain Knowledge ◽  
Web Mining ◽  
Requirements Elicitation

scholarly journals Data-Driven Requirements Elicitation: A Systematic Literature Review

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Sachiko Lim ◽  
Aron Henriksson ◽  
Jelena Zdravkovic
Keyword(s):  
Big Data ◽  
Requirements Engineering ◽  
Domain Knowledge ◽  
Final Analysis ◽  
Online Reviews ◽  
Requirements Elicitation ◽  
Supervised Machine Learning ◽  
Data Driven ◽  
Related Information ◽  
Current State

AbstractRequirements engineering has traditionally been stakeholder-driven. In addition to domain knowledge, widespread digitalization has led to the generation of vast amounts of data (Big Data) from heterogeneous digital sources such as the Internet of Things (IoT), mobile devices, and social networks. The digital transformation has spawned new opportunities to consider such data as potentially valuable sources of requirements, although they are not intentionally created for requirements elicitation. A challenge to data-driven requirements engineering concerns the lack of methods to facilitate seamless and autonomous requirements elicitation from such dynamic and unintended digital sources. There are numerous challenges in processing the data effectively to be fully exploited in organizations. This article, thus, reviews the current state-of-the-art approaches to data-driven requirements elicitation from dynamic data sources and identifies research gaps. We obtained 1848 hits when searching six electronic databases. Through a two-level screening and a complementary forward and backward reference search, 68 papers were selected for final analysis. The results reveal that the existing automated requirements elicitation primarily focuses on utilizing human-sourced data, especially online reviews, as requirements sources, and supervised machine learning for data processing. The outcomes of automated requirements elicitation often result in mere identification and classification of requirements-related information or identification of features, without eliciting requirements in a ready-to-use form. This article highlights the need for developing methods to leverage process-mediated and machine-generated data for requirements elicitation and addressing the issues related to variety, velocity, and volume of Big Data for the efficient and effective software development and evolution.

Automated Assessment of Domain Knowledge With Online Knowledge Mapping

2006 ◽  
Author(s):  
Gregory K. W. K. Chung ◽  
Eva L. Baker ◽  
David G. Brill ◽  
Ravi Sinha ◽  
Farzad Saadat ◽  
...  
Keyword(s):  
Domain Knowledge ◽  
Knowledge Mapping ◽  
Automated Assessment

A Model for Structured Data Entry Based on Explicit Descriptional Knowledge

1994 ◽  
Vol 33 (05) ◽  
pp. 454-463 ◽  
Author(s):  
A. M. van Ginneken ◽  
J. van der Lei ◽  
J. H. van Bemmel ◽  
P. W. Moorman
Keyword(s):  
Domain Knowledge ◽  
Data Entry ◽  
Knowledge Bases ◽  
Structured Data ◽  
Research Quality ◽  
Free Text ◽  
Specific Knowledge ◽  
User Input ◽  
The One ◽  
Research Quality Assessment

Abstract:Clinical narratives in patient records are usually recorded in free text, limiting the use of this information for research, quality assessment, and decision support. This study focuses on the capture of clinical narratives in a structured format by supporting physicians with structured data entry (SDE). We analyzed and made explicit which requirements SDE should meet to be acceptable for the physician on the one hand, and generate unambiguous patient data on the other. Starting from these requirements, we found that in order to support SDE, the knowledge on which it is based needs to be made explicit: we refer to this knowledge as descriptional knowledge. We articulate the nature of this knowledge, and propose a model in which it can be formally represented. The model allows the construction of specific knowledge bases, each representing the knowledge needed to support SDE within a circumscribed domain. Data entry is made possible through a general entry program, of which the behavior is determined by a combination of user input and the content of the applicable domain knowledge base. We clarify how descriptional knowledge is represented, modeled, and used for data entry to achieve SDE, which meets the proposed requirements.

Sign in / Sign up

Export Citation Format

Share Document