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Faculty & Research

Our school brings together scholars and practitioners from a range of disciplines—engineering and science, business, information systems, health, social sciences and the humanities—to study the issues critical to success in the new digital economy.

Their past and current involvement in the industry means that they bring fresh, relevant ideas to the classroom. While some are involved in groundbreaking research, others are frequently asked to speak at professional and academic conferences. It's more than just their accomplishments and research that make them valuable to the program - they're also experienced instructors and expert communicators.

Faculty Research Spotlight

September 7, 2018

TRSITM Faculty Members, Dr. Margaret Plaza & Dr. Farid Shirazi along with MBA graduate, Iulian David's manuscript entitled "Management of inventory under market fluctuations: The case of a Canadian high tech company" has been accepted for publication in International Journal of Production Economics, 2018, 205C, pages215-227, external link.

Abstract

The argument presented in the paper revolves around the Canadian firm from a computer industry, which struggled to find a suitable inventory management strategy. In an effort to help the company, we developed a hybrid inventory management model that combines the demand driven delivery of a “pull” system with forecasting techniques typical for a “push” system. The model was successfully implemented and generated a 27% reduction in a total value of inventory within just 11 months. Additionally, the customer service, cycle times, and even warehouse handling were all significantly improved.

The behavior of inventory is analyzed based on a real case study (practical contribution), which is supported by a decision model (theoretical contribution). The case study was developed by our MBA student who worked under my supervision on his MRP project. It demonstrated that a lean practice can be used to cost effectively manage the inventory within a non-repetitive environment even if demand fluctuates and purchasing lead time is much longer than production lead time. 

September 12, 2018

Dr. Ana-Maria Herman's research explores decision-making processes in the case of a 'digital' museum exhibit 

A recent article published by Dr. Ana-Maria Herman in Museum & Society explores both human and nonhuman involvement in decision-making processes related to a 'digital' exhibit. 

Through a study conducted at the McCord Museum, Dr. Herman examines the case of the MTL Urban Museum App (or MUM App), an augmented reality application used to display a collection of historical photographs across the city centre of Montreal. In the study, Dr. Herman employs a 'sociotechnical' approach (drawn from science and technology studies) to reconstruct how the MUM App was re-made. Therefore, the study takes into account both the social and the technical (and considers the human and the nonhuman), and thereby charts the roles of heterogeneous actors in re-making the App and, relatedly, in re-negotiating the Museum’s exhibition-making practices. By doing so, Dr. Herman takes a closer look at often taken-for-granted practices and processes, and looks to answer three questions related to the re-making of this 'digital' display: What actors were involved in its re-making? How did they participate in decision-making processes? And, what are the implications of the negotiations made? 

The research reveals: 1) how the re-making of the App redistributed tasks associated with exhibition-making practices by displacing them across unexpected actors both inside and outside the Museum, 2) how some aspects of design became non-negotiable or irreversible, and 3) how the re-negotiation of display practices established unanticipated gatekeepers in the Museum’s exhibition-making practices. The study also reveals that the 'digital', while embedded in sociotechnical practices and processes, is still couched in complex ‘sociomaterial’ arrangements. Therefore, while such apps may appear to exist in an entirely 'digital' (and thereby dematerialized) form, they actually continue to be well embedded in 'material' arrangements – they emerge and stabilize in networks that are as much related to standardized code, frameworks and operating systems as they are to city spaces, buildings, mobile devices, maps and bodies. 

The findings described in this article are part of a larger study that will be published in a book by Dr. Herman.

Read the article online

July 18, 2018

New TRSITM Faculty Member, Dr. M. Kargar publishes research results in top-tier journal.

Congratulations to Dr. Kargar on becoming the first Ryerson Faculty Member to have a paper published in the the  prestigious VLDBJ (The International Journal on Very Large Data Bases). His paper entitled "Effective and Complete Discovery of Bidirectional Order Dependencies via Set-based Axioms" was published in June 2018 and is the first submission from Canada for the year.

Abstract

In business intelligence and analytics, as well as in data management, integrity constraints (ICs) ensure accuracy and consistency of data in a business database. Formulating ICs manually, however, requires domain expertise, is prone to human error, and can be exceedingly time-consuming in the big data era. Thus, methods for automatic discovery have been developed for some classes of ICs, such as functional dependencies (FDs), and recently, order dependencies (ODs). An FD is a constraint between two sets of attributes in a database. For example, for a given postal code, city could be determined. Thus, postal code determines city. However, if two entities with the same postal code have different cities, the input data is erroneous and must be cleaned before using in any business analytics process. ODs properly subsume FDs and can express business rules involving order; e.g., an employee who pays higher taxes has a higher salary than another employee. If for two given employees, one has higher salary and pays less tax, the input data contains error. We address the limitations of prior work on automatic OD discovery, which has factorial complexity, is incomplete, and is not concise. We present an efficient bidirectional OD discovery algorithm enabled by a novel polynomial mapping to a canonical form, and a sound and complete set of axioms for canonical bidirectional ODs to prune the search space. Our algorithm has exponential worst-case time complexity in the number of attributes and linear complexity in the number of tuples. We prove that it produces a complete and minimal set of bidirectional ODs, and we experimentally show orders of magnitude performance improvements over the prior state-of-the-art methodologies. For example, in one of the experiments, and over a real dataset, our proposed algorithm found a set of ODs in about 1 second while previous work did not terminate after 5 hours.

The results of this research have been published in the prestigious VLDBJ (The International Journal on Very Large Data Bases) which is a top-tier journal in the field of data management and information systems. This is a collaborative research that involves Jaroslaw Szlichta (University of Ontario), Parke Godfrey (York University), Lukasz Golab (University of Waterloo), Mehdi Kargar (Ted Rogers School of Management at Ryerson University), and Divesh Srivastava (AT&T Labs-Research).

Read the paper online, external link

Continuing Education Instructors

Instructor Name Email Address
Ana Barcus abarcus@ryerson.ca
Khalil Abuosba abuosba@ryerson.ca
Claude Sam-Foh csamfoh@ryerson.ca
David Chan d22chan@ryerson.ca
Djordje Jankovic d2jankov@ryerson.ca
Emad Samwel esamwel@ryerson.ca
Hamid Faridani faridani@ryerson.ca
George Foltak gfoltak@ryerson.ca
Helen Chen helend.chen@ryerson.ca
Ilia Nika inika@ryerson.ca
Inka Bari inka.bari@ryerson.ca
Irene Lee irene.lee@ryerson.ca
Luminata Stubbs lstubbs@ryerson.ca
Mourad Michael michael@ryerson.ca
Mahmoud Jahani mjahani@ryerson.ca
Nawar Hakeem nawar.hakeem@ryerson.ca
Roger DePeiza r2depeiz@ryerson.ca
Roy Ng royng@ryerson.ca
Soheila Bashardoust-Tajali sbtajali@ryerson.ca
Selcuk Savas selcuk.savas@ryerson.ca