Jenny A. Harding

Bio: Jenny A. Harding is an academic researcher from Loughborough University. The author has contributed to research in topics: Ontology (information science) & Knowledge extraction. The author has an hindex of 12, co-authored 26 publications receiving 1308 citations.

Data mining in manufacturing: a review based on the kind of knowledge

Abstract: In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, including product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection etc. Data mining has emerged as an important tool for knowledge acquisition from the manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with a special emphasis on the type of functions to be performed on the data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been used on the abstracts and keywords of 150 papers to identify the research gaps and find the linkages between knowledge area, knowledge type and the applied data mining tools and techniques.

An engineering design knowledge reuse methodology using process modelling

Abstract: This paper describes an approach for reusing engineering design knowledge. Many previous design knowledge reuse systems focus exclusively on geometrical data, which is often not applicable in early design stages. The proposed methodology provides an integrated design knowledge reuse framework, bringing together elements of best practice reuse, design rationale capture and knowledge-based support in a single coherent framework. Best practices are reused through the process model. Rationale is supported by product information, which is retrieved through links to design process tasks. Knowledge-based methods are supported by a common design data model, which serves as a single source of design data to support the design process. By using the design process as the basis for knowledge structuring and retrieval, it serves the dual purpose of design process capture and knowledge reuse: capturing and formalising the rationale that underpins the design process, and providing a framework through which design knowledge can be stored, retrieved and applied. The methodology has been tested with an industrial sponsor producing high vacuum pumps for the semiconductor industry.

Identifying helpful online reviews: A product designer's perspective

Abstract: Large amounts of online reviews, the valuable voice of the customer, benefit consumers and product designers. Identifying and analyzing helpful reviews efficiently and accurately to satisfy both current and potential customers' needs have become a critical challenge for market-driven product design. Existing evaluation methods only use the review voting ratios given by customers to measure helpfulness. Due to the issues such as viewpoints of interest, technical proficiency and domain knowledge involved, it may mislead designers in identifying those truly valuable and insightful opinions from designers' perspective. Thus, in this study, we initiate our work to explore a possible approach that bridges the opinions expressed by consumers and the understanding gathered by designers in terms of how helpful these opinions are. Our ultimate research focus is on how to automatically evaluate the helpfulness of an online review from a designer's viewpoint entirely based on the review content itself. We start our work by first conducting an exploratory study to understand the fundamental question of what makes an online customer review helpful from product designers' viewpoint. Through our study, we propose four categories of features that reflect designers' concerns in judging review helpfulness. Based on our experiments, it reveals that discrepancy does exist between both online customer voting and designers' rating. Furthermore, for the cases where review ratings are not steadily available, we have proposed to use regression to predict and interpret review helpfulness with the help of the aforementioned four categories of features that are entirely extracted from review content. Finally, using review data crawled from Amazon.com and real ratings given by design personnel, it demonstrates the effectiveness of our proposal and it also suggests that helpful product reviews can be identified from a designer's angle by automatically analyzing the review content. We argue that the study reported is able to improve designer's ability in business intelligence processing by offering more targeted customer opinions. It highlights the urgency to uncover sensible user requirements from such quality opinions in our future research.

A framework to integrate design knowledge reuse and requirements management in engineering design

Abstract: This paper presents a framework to integrate requirements management and design knowledge reuse The research approach begins with a literature review in design reuse and requirements management to identify appropriate methods within each domain A framework is proposed based on the identified requirements The framework is then demonstrated using a case study example: vacuum pump design Requirements are presented as a component of the integrated design knowledge framework The proposed framework enables the application of requirements management as a dynamic process, including capture, analysis and recording of requirements It takes account of the evolving requirements and the dynamic nature of the interaction between requirements and product structure through the various stages of product development

A survey on opinion mining and sentiment analysis

Abstract: With the advent of Web 2.0, people became more eager to express and share their opinions on web regarding day-to-day activities and global issues as well. Evolution of social media has also contributed immensely to these activities, thereby providing us a transparent platform to share views across the world. These electronic Word of Mouth (eWOM) statements expressed on the web are much prevalent in business and service industry to enable customer to share his/her point of view. In the last one and half decades, research communities, academia, public and service industries are working rigorously on sentiment analysis, also known as, opinion mining, to extract and analyze public mood and views. In this regard, this paper presents a rigorous survey on sentiment analysis, which portrays views presented by over one hundred articles published in the last decade regarding necessary tasks, approaches, and applications of sentiment analysis. Several sub-tasks need to be performed for sentiment analysis which in turn can be accomplished using various approaches and techniques. This survey covering published literature during 2002-2015, is organized on the basis of sub-tasks to be performed, machine learning and natural language processing techniques used and applications of sentiment analysis. The paper also presents open issues and along with a summary table of a hundred and sixty-one articles.