Due to rapid development of technology, such as the Internet of Things, collecting data is easier and cheaper than ever before. As a result, municipal governments and urban centres across Canada are being inundated with dataâdata that have potential to improve public service. Despite this, local governments do not have enough data expertise to extract insight from these overwhelming datasets, which are often unstructured and âdirtyâ (i.e., incomplete, inaccurate, and/or erroneous).
Business Technology Management (BTM) is a rapidly emerging trans-disciplinary research area and professional discipline in Business Administration. It seeks to provide an integrated framework for the strategic use of Information and Communication Technology (ICT) and the digital transformation of organizations. This research project will develop the first BTM Body of Knowledge (BOK) and provide a systematic, exhaustive, and evolving framework for professional practice standards.
Our proposed research investigates how K-12 teachers learn and customize digital classroom tools and learning management systems and how they share this information with each other. In particular, we will be working with our partner Microsoft to investigate the use and customization of the recently developed OneNote Class Notebooks software that is increasingly being used by teachers for various content delivery and content management tasks.
The output of this project which is a recommender system can be used by the partner organization in order to improve their automated taxi dispatching system. So, the process of dispatching a taxi request to drivers can be done faster with more customersâ satisfaction rate, because the driver which is assigned to drive a customer would have common interests with the customer in language, accent, pet-freeness of his/her vehicle, smoke-freeness of his/her vehicle, â¦.
Also, the taxi companies can defeat other competitor companies by using this new feature in their automated dispatching system.
Upon completion of the project the interns work will allow the partner organization to better understand
how inventory management is handled in small businesses. The project will also help understand what
inventory levels should be for small businesses based on predictive analytics.
This project is dedicated to the development of a new business corpus as a novel data for the company’s business intelligence. It focuses on linguistic pre-processing for the business domain using two types of collected corpora: text and speech. An automatic annotation of the pre-processed business corpus will be completed using labels related to sentiment analysis and emotion mining technologies. Specific rules will be used to strengthen these labels. Last, a cognitive social analysis on human behaviors and team dynamics will be completed within a business meeting.
Workflow management frameworks support the creation of task dependencies and make efficient use of resources while running those workloads. Typically, these tasks can be long running processes like machine learning algorithms or access data from databases. Workflow management consists of mapping tasks to suitable resources and the management of workflow execution in a cloud environment. The goal of this project is to optimize the job scheduling algorithm using machine learning techniques in a workflow orchestration framework that manage workloads across a heterogeneous system.
Search is an important way people get the information they want. Whether we want to find more content about a specific topic, or get general information on a subject, search engines lie at the core of this process. At Flipp, search plays a crucial role in the overall user experience and drives relevant content to consumers. Consequently, improving search by assisting consumers in finding a larger volume of relevant products will be of growing importance to Flipp. The proposed project aims to improve Flippâs search experience by achieving greater relevancy, volume and ease of use.
Machine learning is a discipline of teaching computers repeatable tasks that humans do well but slowly. At Interdata we are on a mission to use Artificial intelligence to understand the data being stored by organizations and the relationships between those data assets. As such Darrell will be working on methodologies and tools to expand our understanding of the algorithms we develop in order to improve them. He will then use those methodologies and tools to engineer new algorithms to be used by the organization to categorize and tranform data.
As a result of recent advances in high-throughput technologies, rapidly increasing amounts of mass spectrometry (MS) data pose new opportunities as well as challenges to existing analysis methods. Novel computational approaches are needed to take advantage of latest breakthroughs in high-performance computing for the large-scale analysis of big data from MS-based proteomics. In this project, we aim to develop new applications of deep learning and neural networks for the analysis of MS data.