University of Alberta
This project will explore enhancements in artificial intelligence.
Team:
- Dr James Miller, Professor, Faculty of Engineering – Electrical & Computer Engineering Dept
** Title: Usability Research
Description: Software development strategies is an ever-evolving topic, gone are the days of waterfall development which have been increasingly replaced by continuous development, integration and deployment. Product cycle time has been slashed from years (development of the entire product as a single monolithic process) to days (develop a small incremental addition or fix to the current, often incomplete, product). Within these upheavals, ticketing systems have become a central and essential mechanism for managing the workflow. These are accompanied with a switch in ideology from “build the product” to “service the customer”. Customers with issues produce tickets and the organizational push is now to service (solve) these tickets quickly and accurately. This collaboration will focus on improving this process in a wide variety of scenarios.
PUBLISHED PAPERS:
Title: The Current State of Software License Renewals in the IT Industry
Authors: Aindrila Ghosh, Mona Nashaat, and James Miller
Published in: Information and Software Technology Journal (Elsevier)
Abstract:
Context: The software industry has changed significantly in the 21st century; no longer is it dominated by organizations seeking to sell products directly to customers, instead most multinational organizations nowadays provide services via licensing agreements. These licenses are for a fixed-duration; and hence, the question of their renewal becomes of paramount importance for the selling organization’s revenue.
Objective: Despite its financial impact, the topic of license renewal strategies, processes, tools, and support receives very limited attention in the research literature. Hence, it is believed that an interesting research question is: What is the state of current industrial practice in this essential field?
Method: To initially explore the topic of license renewals, this paper implements the Grounded theory method. To implement the method, semi-structured, cross-sectional, anonymous, self-reported interviews are carried out with 20 professionals from multiple organizations, later the Constant Comparative Method is used to analyze the collected data.
Results: This paper presents a synthesized picture of the current industrial practice of the end-to-end soft- ware license renewal process. Alongside, it also identifies a set of challenges and risk factors that impact on renewal decisions of customers, hence on the overall revenue of seller organizations. Finally, using structured brainstorming techniques, this paper identifies 11 future research directions, that can help organizations with the mitigation of the risks in the license renewal process.
Conclusion: It is concluded that lack of effective communication among stakeholders, the absence of customer trust, and scarcity of value generated from purchased licenses are among the primary drivers that influence renewal decisions. Also, there is a need to invest in intelligent automation along with artificial intelligence enabled analytics in order to enhance customer satisfaction.
Title: A comprehensive review of tools for exploratory analysis of tabular industrial datasets
Authors: Aindrila Ghosh, Mona Nashaat, James Miller, Shaikh Quader, and Chad Marston
Published in: Visual Informatics Journal (Elsevier)
Abstract:
Exploratory data analysis plays a major role in obtaining insights from data. Over the last two decades, researchers have proposed several visual data exploration tools that can assist with each step of the analysis process. Nevertheless, in recent years, data analysis requirements have changed significantly. With constantly increasing size and types of data to be analyzed, scalability and analysis duration are now among the primary concerns of researchers. Moreover, in order to minimize the analysis cost, businesses are in need of data analysis tools that can be used with limited analytical knowledge. To address these challenges, traditional data exploration tools have evolved within the last few years. In this paper, with an in-depth analysis of an industrial tabular dataset, we identify a set of additional exploratory requirements for large datasets. Later, we present a comprehensive survey of the recent advancements in the emerging field of exploratory data analysis. We investigate 50 academic and non-academic visual data exploration tools with respect to their utility in the six fundamental steps of the exploratory data analysis process. We also examine the extent to which these modern data exploration tools fulfill the additional requirements for analyzing large datasets. Finally, we identify and present a set of research opportunities in the field of visual exploratory data analysis.
Title: M-Lean: An End-to-end Development Framework for Predictive Models in B2B Scenarios
Authors: Mona Nashaat, Aindrila Ghosh, James Miller, Shaikh Quader, Chad Marston
Published in: Information and Software Technology Journal
Abstract:
Context: The need for business intelligence has led to advances in machine learning in the business domain, especially with the rise of big data analytics. However, the resulting predictive systems often fail to maintain a satisfactory level of performance in production. Besides, for predictive systems used in business-to-business scenarios, user trust is subject to the model performance. Therefore, the processes of creating, evaluating, and deploying machine learning systems in the business domain need innovative solutions to solve the critical challenges of assuring the quality of the resulting systems.
Objective: Applying machine learning in business-to-business situations imposes specific requirements. This paper aims at providing an integrated solution to businesses to help them transform their data into actions.
Method: The paper presents MLean, an end-to-end framework, that aims at guiding businesses in designing, developing, evaluating, and deploying business-to-business predictive systems. The framework employs the Lean Startup methodology and aims at maximizing the business value while eliminating wasteful development practices.
Results: To evaluate the proposed framework, with the help of our industrial partner, we applied the framework to a case study to build a predictive product. The case study resulted in a predictive system to predict the risks of software license cancellations. The system was iteratively developed and evaluated while adopting management and end-user perspectives.
Conclusion: It is concluded that, in industry, it is important to be aware of the businesses requirements before considering the application of machine learning. The framework accommodates business perspective from the beginning to produce a holistic product. From the results of the case study, we think that this framework can help businesses define the right opportunities for applying machine learning, developing solutions, evaluating the effectiveness of these solutions, and maintaining their performance in production.
Title: LaBiD: Automating Weak Supervision to Find Missing Labels for Big Data
IBM CASTLE PRESENTATION: MAY 2019 (click to view PDF)
IBM CASTLE 2019 – Presentation 1
IBM CASTLE 2019 – Presentation 2
IBM CASTLE 2019 – Presentation 3
** Note: The “Usability Research” project is funded entirely by IBM Canada.