8,452 research outputs found
ELICA: An Automated Tool for Dynamic Extraction of Requirements Relevant Information
Requirements elicitation requires extensive knowledge and deep understanding
of the problem domain where the final system will be situated. However, in many
software development projects, analysts are required to elicit the requirements
from an unfamiliar domain, which often causes communication barriers between
analysts and stakeholders. In this paper, we propose a requirements ELICitation
Aid tool (ELICA) to help analysts better understand the target application
domain by dynamic extraction and labeling of requirements-relevant knowledge.
To extract the relevant terms, we leverage the flexibility and power of
Weighted Finite State Transducers (WFSTs) in dynamic modeling of natural
language processing tasks. In addition to the information conveyed through
text, ELICA captures and processes non-linguistic information about the
intention of speakers such as their confidence level, analytical tone, and
emotions. The extracted information is made available to the analysts as a set
of labeled snippets with highlighted relevant terms which can also be exported
as an artifact of the Requirements Engineering (RE) process. The application
and usefulness of ELICA are demonstrated through a case study. This study shows
how pre-existing relevant information about the application domain and the
information captured during an elicitation meeting, such as the conversation
and stakeholders' intentions, can be captured and used to support analysts
achieving their tasks.Comment: 2018 IEEE 26th International Requirements Engineering Conference
Workshop
Adaptive development and maintenance of user-centric software systems
A software system cannot be developed without considering the various facets of its environment. Stakeholders – including the users that play a central role – have their needs, expectations, and perceptions of a system. Organisational and technical aspects of the environment are constantly changing. The ability to adapt a software system and its requirements to its environment throughout its
full lifecycle is of paramount importance in a constantly changing environment. The continuous involvement of users is as important as the constant evaluation of the system and the observation of evolving environments. We present a methodology for adaptive software systems development and
maintenance. We draw upon a diverse range of accepted methods including participatory design, software architecture, and evolutionary design. Our focus is on user-centred software systems
Eliciting Expertise
Since the last edition of this book there have been rapid developments in the use and exploitation of formally elicited knowledge. Previously, (Shadbolt and Burton, 1995) the emphasis was on eliciting knowledge for the purpose of building expert or knowledge-based systems. These systems are computer programs intended to solve real-world problems, achieving the same level of accuracy as human experts. Knowledge engineering is the discipline that has evolved to support the whole process of specifying, developing and deploying knowledge-based systems (Schreiber et al., 2000) This chapter will discuss the problem of knowledge elicitation for knowledge intensive systems in general
Collective awareness platforms and digital social innovation mediating consensus seeking in problem situations
In this paper we show the results of our studies carried out in the framework of the European Project SciCafe2.0 in the area of Participatory Engagement models. We present a methodological approach built on participative engagements models and holistic framework for problem situation clarification and solution impacts assessment. Several online platforms for social engagement have been analysed to extract the main patterns of participative engagement. We present our own experiments through the SciCafe2.0 Platform and our insights from requirements elicitation
The Requirements for Ontologies in Medical Data Integration: A Case Study
Evidence-based medicine is critically dependent on three sources of
information: a medical knowledge base, the patients medical record and
knowledge of available resources, including where appropriate, clinical
protocols. Patient data is often scattered in a variety of databases and may,
in a distributed model, be held across several disparate repositories.
Consequently addressing the needs of an evidence-based medicine community
presents issues of biomedical data integration, clinical interpretation and
knowledge management. This paper outlines how the Health-e-Child project has
approached the challenge of requirements specification for (bio-) medical data
integration, from the level of cellular data, through disease to that of
patient and population. The approach is illuminated through the requirements
elicitation and analysis of Juvenile Idiopathic Arthritis (JIA), one of three
diseases being studied in the EC-funded Health-e-Child project.Comment: 6 pages, 1 figure. Presented at the 11th International Database
Engineering & Applications Symposium (Ideas2007). Banff, Canada September
200
Towards Question-based Recommender Systems
Conversational and question-based recommender systems have gained increasing
attention in recent years, with users enabled to converse with the system and
better control recommendations. Nevertheless, research in the field is still
limited, compared to traditional recommender systems. In this work, we propose
a novel Question-based recommendation method, Qrec, to assist users to find
items interactively, by answering automatically constructed and algorithmically
chosen questions. Previous conversational recommender systems ask users to
express their preferences over items or item facets. Our model, instead, asks
users to express their preferences over descriptive item features. The model is
first trained offline by a novel matrix factorization algorithm, and then
iteratively updates the user and item latent factors online by a closed-form
solution based on the user answers. Meanwhile, our model infers the underlying
user belief and preferences over items to learn an optimal question-asking
strategy by using Generalized Binary Search, so as to ask a sequence of
questions to the user. Our experimental results demonstrate that our proposed
matrix factorization model outperforms the traditional Probabilistic Matrix
Factorization model. Further, our proposed Qrec model can greatly improve the
performance of state-of-the-art baselines, and it is also effective in the case
of cold-start user and item recommendations.Comment: accepted by SIGIR 202
B2C Mass Customization in the Classroom
The purpose of this article is to describe an internet-based mass customization assignment in Operations Management/Supply Chain Management classes where students utilize the Web site of a company that offers a customized product. Students evaluate the user interface, judge the value proposition of the product they demonstrate, and discuss issues of product design, process design and scheduling, inventory management, Supply Chain Management, marketing, and competitors. The students learn about mass customization from both the producer\u27s perspective and the consumer\u27s perspective. Through their own research and the class presentations students are able to develop a better understanding of the implementation requirements and challenges of mass customization. The assignment is highly interactive and has been successfully used in Operations Management and Supply Chain Management courses at under-graduate and graduate levels and at multiple universities. In addition, practitioners interested in implementing a mass customization process can use the assignment as a brainstorming or benchmarking exercise
Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study
Recommender systems engage user profiles and appropriate filtering techniques
to assist users in finding more relevant information over the large volume of
information. User profiles play an important role in the success of
recommendation process since they model and represent the actual user needs.
However, a comprehensive literature review of recommender systems has
demonstrated no concrete study on the role and impact of knowledge in user
profiling and filtering approache. In this paper, we review the most prominent
recommender systems in the literature and examine the impression of knowledge
extracted from different sources. We then come up with this finding that
semantic information from the user context has substantial impact on the
performance of knowledge based recommender systems. Finally, some new clues for
improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science &
Engineering Survey (IJCSES) Vol.2, No.3, August 201
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