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<p>Vibrating screens are mechanical machines used to sort granulated materials based on their particle size. Utilized in the mining industry, these machines can sort tonnes of materials per hour. In the past, McMaster University developed sensor devices that measure and transmit vibration data of these machines to a central data acquisition unit for analysis, tuning, and maintenance purposes. In this thesis, I present the development of two new software systems that are used to process, manage, and present the information gained from these measurements. The first system, the offline vibration analysis software, is used to analyze the vibration data in both time and frequency domain, and presents the measured and calculated data in textual and graphical forms. The second system, the online vibration analysis software, is used by vibrating screens manufacturers and their customers to gather and manage vibration data collected from their vibrating screens by utilizing a central storage. The development process of these systems followed an iterative and incremental approach with continuous feedback from stakeholders. It included extensive requirements gathering to define a model, in terms of data representation, that captures the business logic and practices of the industry. Furthermore, it used standard architectures such as Model View Controller (MVC) and advanced technologies such as Object Relationship Mapping (ORM) for data access to increase flexibility and maintainability. Finally, comprehensive unit testing and thorough security risks evaluation were conducted in order to ensure that these systems are secure and bug free.</p> / Master of Applied Science (MASc)

<p>Modern advances in biometeorological monitoring technology have improved the capacity for measuring ecosystem exchanges of mass, energy and scalars (such as CO₂). Translating these measurements into robust and accurate scientific information (and ultimately, understanding) requires careful assessment of operations throughout the biometeorological data life cycle. In response, this research analyzed and optimized aspects of data collection, management and filtering for an ecosystem exchange measurement program over an age-sequence of temperate white pine forests.</p> <p>A comprehensive data workflow and management system (DWMS) was developed and implemented to support the entire data life cycle for all past, present and future measurement operations in our research group, and meet the needs of a collaborative, student-led data management environment. Best practices for biometeorological data management were introduced and used as standards to assess system performance.</p> <p>Roving eddy covariance (rEC) systems were examined as a means of producing reliable time-integrated carbon exchange estimates at multiple sites, by rotating an EC system in a resource-mindful approach. When used with an optimal gap-filling model and rEC rotation schedule (2 sites with 15-day rotations), the results suggested its viability, as annual NEE estimate uncertainties ranged between 35 and 63% of the annual NEE flux magnitude at our study sites – even though approximately 70% of half-hours were filled.</p> <p>Lastly, a data-driven approach was used to investigate the effects of different friction velocity and footprint filtering methods on time-integrated carbon exchange estimates at our fetch-limited forests. Though predicted flux source areas varied considerably between footprint models, our objective analyses identified the model (Kljun et al., 2004) and within-fetch requirement (80%) that optimized reliability and representativeness of carbon exchange estimates. Applying this footprint model decreased annual NEE by 31 to 129% (59 to 207 g C m^-2 y^-1) relative to no footprint application, and highlighted the importance of objective analyses of EC flux filtering methods.</p> / Doctor of Philosophy (PhD)

By 2004, microprocessor design focused on multicore scaling"increasing the number of cores per die in each generation "as the primary strategy for improving performance. These multicore processors typically equip multiple memory subsystems to improve data throughput. In addition, these systems employ heterogeneous processors such as GPUs and heterogeneous memories like non-volatile memory to improve performance, capacity, and energy efficiency. With the increasing volume of hardware resources and system complexity caused by heterogeneity, future systems will require intelligent ways to manage hardware resources. Early research to improve performance and energy efficiency on heterogeneous, multi-core, multi-memory systems focused on tuning a single primitive or at best a few primitives in the systems. The key limitation of past efforts is their lack of a holistic approach to resource management that balances the tradeoff between performance and energy consumption. In addition, the shift from simple, homogeneous systems to these heterogeneous, multicore, multi-memory systems requires in-depth understanding of efficient resource management for scalable execution, including new models that capture the interchange between performance and energy, smarter resource management strategies, and novel low-level performance/energy tuning primitives and runtime systems. Tuning an application to control available resources efficiently has become a daunting challenge; managing resources in automation is still a dark art since the tradeoffs among programming, energy, and performance remain insufficiently understood. In this dissertation, I have developed theories, models, and resource management techniques to enable energy-efficient execution of parallel applications through thread and data management in these heterogeneous multi-core, multi-memory systems. I study the effect of dynamic concurrent throttling on the performance and energy of multi-core, non-uniform memory access (NUMA) systems. I use critical path analysis to quantify memory contention in the NUMA memory system and determine thread mappings. In addition, I implement a runtime system that combines concurrent throttling and a novel thread mapping algorithm to manage thread resources and improve energy efficient execution in multi-core, NUMA systems. In addition, I propose an analytical model based on the queuing method that captures important factors in multi-core, multi-memory systems to quantify the tradeoff between performance and energy. The model considers the effect of these factors in a holistic fashion that provides a general view of performance and energy consumption in contemporary systems. Finally, I focus on resource management of future heterogeneous memory systems, which may combine two heterogeneous memories to scale out memory capacity while maintaining reasonable power use. I present a new memory controller design that combines the best aspects of two baseline heterogeneous page management policies to migrate data between two heterogeneous memories so as to optimize performance and energy. / Ph. D.

Smart infrastructure has become increasingly prevalent in recent decades due to the emergence of sophisticated and affordable sensing technologies. As sensors are deployed more widely and higher sampling rates are feasible, managing the massive scale of real-time data collected by these systems has become fundamental to providing relevant and timely information to decision-makers. To address this task, a novel open-source framework has been developed to manage and intuitively present high-volume data in near real-time. This design is centered around the goals of making data accessible, supporting decision-making, and providing flexibility to modify and reuse this framework in the future. In this work, the framework is tailored to vibration-based structural health monitoring, which can be used in near real-time to screen building condition. To promote timely intervention, distributed computing technologies are employed to accelerate the processing, storage, and visualization of data. Vibration data is processed in parallel using a publish-subscribe messaging queue and then inserted into a NoSQL database that stores heterogeneous data across several nodes. A REST-based web application allows interaction with this stored data via customizable visualization interfaces. To illustrate the utility of this framework design, it has been implemented to support a frequency domain monitoring dashboard for a 5-story classroom building instrumented with 224 accelerometers. A simulated scenario is presented to capture how the dashboard can aid decisions about occupant safety and structural maintenance. / Master of Science / Advances in technology have made it affordable and accessible to collect information about the world around us using sensors. When sensors are used to aid decision-making about structures, it is frequently referred to as Structural Health Monitoring (SHM). SHM can be used to monitor long-term structural health, inform maintenance decisions, and rapidly screen structural conditions following extreme events. Accelerometers can be used in SHM to capture vibration data that give insight into deflection patterns and natural frequencies in a structure. The challenge with vibration-based SHM and many other applications that leverage sensors is that the amount of data collected has the potential to grow to massive scales. To communicate relevant information to decision-makers, data must be processed quickly and presented intuitively. To facilitate this process, a novel open-source framework was developed for processing, storing, and visualizing high-volume data in near real-time. This framework combines multiple computers to extend the processing and storage capacity of our system. Data is processed in parallel and stored in a database that supports efficient data retrieval. A web application enables interaction with stored data via customizable visualization interfaces. To demonstrate the framework functionality, it was implemented in a 5-story classroom building instrumented with 224 accelerometers. A frequency-domain dashboard was developed for the building, and a simulated scenario was conducted to capture how the dashboard can aid decisions about occupant safety and structural maintenance.

The growing disparity in data storage and retrieval needs of modern applications is driving the proliferation of a wide variety of storage systems (e.g., key-value stores, cloud storage services, distributed filesystems, and flash cache, etc.). While extant storage systems are designed and tuned for a specific set of applications targeting a range of workload characteristics, they lack the flexibility in adapting to the ever-changing workload behaviors. Moreover, the complexities in implementing modern storage systems and adapting ever-changing storage requirements present unique opportunities and engineering challenges. In this dissertation, we design and develop a series of novel data management and storage systems solutions by applying a simple yet effective rule---workload awareness. We find that simple workload-aware data management strategies are effective in improving the efficiency of modern storage systems, sometimes by an order of magnitude. The first two works tackle the data management and storage space allocation issues at distributed and cloud storage level, while the third work focuses on low-level data management problems in the local storage system, which many high-level storage/data-intensive applications rely on. In the first part of this dissertation (Chapter 3), we propose and develop MBal, a high-performance in-memory object caching framework with adaptive multi-phase load balancing, which supports not only horizontal (scale-out) but vertical (scale-up) scalability as well. MBal is able to make efficient use of available resources in the cloud through its fine-grained, partitioned, lockless design. In the second part of this dissertation (Chapter 4 and Chapter5), we design and build CAST (Chapter 4), a Cloud Analytics Storage Tiering solution that cloud tenants can use to reduce monetary cost and improve performance of analytics workloads. The approach takes the first step towards providing storage tiering support for data analytics in the cloud. Furthermore, we propose a hybrid cloud object storage system (Chapter 5) that could effectively engage both the cloud service providers and cloud tenants via a novel dynamic pricing mechanism. In the third part of this dissertation (Chapter 6), targeting local storage, we explore offline algorithms for flash caching in terms of both hit ratio and flash lifespan. We design and implement a multi-stage heuristic by synthesizing several techniques that manage data at the granularity of a flash erasure unit (which we call a container) to approximate the offline optimal algorithm. In the fourth part of this dissertation (Chapter 7), we are focused on how to enable fast prototyping of efficient distributed key-value stores targeting a proxy-based layered architecture. In this work, we design and build {con}, a framework that significantly reduce the engineering effort required to build a full-fledged distributed key-value store. Our dissertation shows that simple workload-aware data management strategies can bring huge benefit in terms of both efficiency (i.e., performance, monetary cost, etc.) and flexibility (i.e., ease-of-use, ease-of-deployment, programmability, etc.). The principles of leveraging workload dynamicity and storage heterogeneity can be used to guide next-generation storage system software design, especially when being faced with new storage hardware technologies. / Ph. D. / Modern storage systems often manage data without considering the dynamicity of user behaviors. This design approach does not consider the unique features of underlying storage medium either. To this end, this dissertation first studies how the combinational factors of random user workload dynamicity and inherent storage hardware heterogeneity impact the data management efficiency. This dissertation then presents a series of practical and efficient techniques, algorithms, and optimizations to make the storage systems workload-aware. The experimental evaluation demonstrates the effectiveness of our workload-aware design choices and strategies.

In today's digital landscape, data is akin to oil, pivotal for decision-making and innovation, especially in retail with its vast customer data. However, accumulating data presents challenges, notably in costeffective management. This thesis explores strategies for retail firms to optimize data management without sacrificing the data’s potential benefits. Drawing insights from interviews with five retail companies and implementing a product recommendation model. The study reveals that while storage costs are perceived as low, the prevalent "store it all" approach results in storing vast amounts of unused data, incurring unnecessary expenses. Furthermore, compliance with GDPR primarily shapes companies’ data retention policies, with some companies opting for automated deletion or anonymization to align with regulations. However, inconsistencies exist in practice regarding data storage intentions. The thesis culminates in a strategic framework to enhance data management. A four-step framework is proposed: assessing data lifespan, implementing archiving routines, anonymizing and aggregating data, and evaluating cost versus utility. The research underscores the need for deletion strategies to prevent data overload and maintain cost-effectiveness. This thesis contributes to understanding data value and offers practical guidance for retail firms to navigate data management efficiently while complying with regulations like GDPR. Future research could delve into the long-term impacts of retention policies on business operations, assessing data deletion or archiving over extended periods. Longitudinal studies with company data access would enrich this exploration.

No / Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. There is growing emphasis on sharing and reusing dementia care-related datasets to improve the quality of dementia care. Consequently, there is a need to develop data management solutions for collecting, integrating and storing these data in formats that enhance opportunities for reuse. Dementia Care Mapping (DCM) is an observational tool that is in widespread use internationally. It produces rich, evidence-based data on dementia care quality. Currently, that data is primarily used locally, within dementia care services, to assess and improve quality of care. Information-rich DCM data provides opportunities for secondary use including research into improving the quality of dementia care. But an effective data management solution is required to facilitate this. A rationale for the warehousing of DCM data as a technical data management solution is suggested. The authors also propose a data model for a DCM data warehouse and present user-identified challenges for reusing DCM data within a warehouse.