The amount of data handled by real-time and embedded applications is increasing. This calls for data-centric approaches when designing embedded systems, where data and its metainformation (e.g., temporal correctness requirements) are stored centrally. The focus of this thesis is on efficient data management, especially maintaining data freshness and guaranteeing correct age on data. The contributions of our research are updating algorithms and concurrency control algorithms using data similarity. The updating algorithms keep data items up-to-date and can adapt the number of updates of data items to state changes in the external environment. Further, the updating algorithms can be extended with a relevance check allowing for skipping of unnecessary calculations. The adaptability and skipping of updates have positive effects on the CPU utilization, and freed CPU resources can be reallocated to, e.g., more extensive diagnosis of the system. The proposed multiversion concurrency control algorithms guarantee calculations reading data that is correlated in time. Performance evaluations show that updating algorithms with a relevance check give significantly better performance compared to well-established updating approaches, i.e., the applications use more fresh data and are able to complete more tasks in time. The proposed multiversion concurrency control algorithms perform better than HP2PL and OCC and can at the same time guarantee correct age on data items, which HP2PL and OCC cannot guarantee. Thus, from the perspective of the application, more precise data is used to achieve a higher data quality overall, while the number of updates is reduced. / <p>Report code: LiU-Tek-Lic-2004:67.</p>

The design and construction of high-performance buildings have emerged as a preferred solution for reducing energy consumption and greenhouse gas emissions. However, sometimes there is a considerable gap between the design performance and the actual performance of the buildings. Post Occupancy Evaluations (POE) provide tools to quantify the performance relative to the occupant's health, well-being, and comfort. POE is getting widely accepted to obtain feedback for various parameters such as water, energy, indoor environmental quality, and occupant comfort. Key Performance Indicators (KPIs) can be derived based on the obtained feedback to determine the performance gaps. POE has evolved to be a robust scientific methodology; however, traditional methods of conducting POE have been proven time-consuming, inconsistent, and inefficient. This research aims to conceptualize the next generation of post occupancy evaluations that leverages cutting-edge technologies such as Building Information Modeling (BIM), Internet of Things based sensors (IoT), Geographic Information Systems (GIS), and digital twins. The key contributions of this research are presented in a series of manuscripts. In the first paper, the gaps in the existing POE were determined by conducting a thorough literature review. The observed gaps were classified in data collection, analysis, and visualization categories. Broader POE definition, spot measurements of parameters, and 2D plans and charts for visualization made the existing POE procedure time-consuming. Using digital twins that combine the geometric and parametric data from BIM models and built-environment data from GIS and sensor measurements were recommended as potential solutions to address the observed gaps. The second paper explored the application of BIM-IoT-GIS integration to conduct POE. Use case scenarios were developed to derive system requirements to host the BIM-IoT-GIS-integrated POE. Four sequential tests were conducted to integrate a BIM model from Revit and sensors' data from Excel with ArcGIS pro that contained the surrounding environment data. Based on lessons learned from the tests, an optimized workflow was recommended that can be used across a variety of projects. The third paper used the BIM-IoT-GIS-integration concept to create a holistic proof of concept for digital-twin-enabled POE. The proof of concept was validated by conducting a digital-twin-based POE on the STTC building on the Red River College campus in Winnipeg. The indoor thermal comfort was visualized within the STTC digital twin developed in ArcGIS Pro. The preliminary energy consumption analysis concluded that the STTC buildings' average energy savings were approximately 70,000 KWH/year. The potential users for digital-twin-enabled POE were presented with a comparison of iv existing POE and digital-twin-based POE over a survey and a focus group discussion. Based on opinion-based feedback, the conclusion can be made that digital twins improve the overall efficiency of POE. The fourth paper recommended the digital-twin-enabled POE procedure for UVic's engineering expansion project. It established the semantics for POE, followed by a digital twin execution plan that can be used for developing a digital twin during each phase (from planning to operations) of the project. Furthermore, the benefits of the digital-twin-enabled POE procedure were demonstrated by comparison with the existing POE procedure relative to the project phases. This study concluded that conducting the POE on the UVic ECS expansion project will enable the researchers to determine the effectiveness of sustainable features by comparing the performance of existing and proposed facilities. In conclusion, BIM-IoT-GIS-integrated digital twins address the limitations of data collection, analysis, and visualization. These digital twins will enable multi-objective analysis and spatial-temporal visualization and provide deeper insights into the way these high-performance buildings function. / Graduate / 2023-05-24

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Cloud computing is a computing paradigm where large numbers of devices are connected through networks that provide a dynamically scalable infrastructure for applications, data and storage. Currently, many businesses, from small scale to big companies and industries, are changing their operations to utilize cloud services because cloud platforms could increase company’s growth through process efficiency and reduction in information technology spending [Coles16]. Companies are relying on cloud platforms like Amazon Web Services, Google Compute Engine, and Microsoft Azure, etc., for their business development. Due to the emergence of new technologies, devices, and communications, the amount of data produced is growing rapidly every day. Big data is a collection of large dataset, typically hundreds of gigabytes, terabytes or petabytes. Big data storage and the analytics of this huge volume of data are a great challenge for companies and new businesses to handle, which is a primary focus of this paper. This research was conducted on Amazon’s Elastic Compute Cloud (EC2) and Microsoft Azure platforms using the HiBench Hadoop Big Data Benchmark suite [HiBench16]. Processing huge volumes of data is a tedious task that is normally handled through traditional database servers. In contrast, Hadoop is a powerful framework is used to handle applications with big data requirements efficiently by using the MapReduce algorithm to run them on systems with many commodity hardware nodes. Hadoop’s distributed file system facilitates rapid storage and data transfer rates of big data among the nodes and remains operational even when a node failure has occurred in a cluster. HiBench is a big data benchmarking tool that is used for evaluating the performance of big data applications whose data are handled and controlled by the Hadoop framework cluster. Hadoop cluster environment was enabled and evaluated on two cloud platforms. A quantitative comparison was performed on Amazon EC2 and Microsoft Azure along with a study of their pricing models. Measures are suggested for future studies and research.

Indiana University-Purdue University Indianapolis (IUPUI) / Previous research has found that the clarity of information provided to raters about women managers’ performance affects ratings of their competence, likeability, and overall evaluation. The current study sought to contribute to this literature by examining whether individual differences of raters can explain the reason for differential performance evaluations of male and female managers, despite them both performing equally. For this purpose, the current research extended the findings of Heilman and colleagues by replicating their methodology while introducing a moderator variable, the rater’s Implicit Person Theory (IPT). The IPT differentiates people into either entity theorists (that is, those who believe that behavior is trait-based and therefore fixed and stable) and incremental theorists (those who believe that behavior is situationally mediated and hence, changeable). Specifically, it was proposed that the effects found in the previous study would be stronger when the rater possessed an entity theory as opposed to an incremental theory. In doing so, this research attempted to provide an understanding of why male and female managers might be given different ratings, all other things being equal. Analyses revealed results that were consistent with, as well as some that were quite inconsistent with, previous findings. Rater IPT was found to have a significant effect on ratings provided by male participants but not those of female participants. Other findings and implications are discussed and limitations and future research directions are stated.