Enabling autoscaling for in-memory storage in cluster computing framework

2019 Spring.Includes bibliographical references.IoT enabled devices and observational instruments continuously generate voluminous data. A large portion of these datasets are delivered with the associated geospatial locations. The increased volumes of geospatial data, alongside the emerging geospatial services, pose computational challenges for large-scale geospatial analytics. We have designed and implemented STRETCH , an in-memory distributed geospatial storage that preserves spatial proximity and enables proactive autoscaling for frequently accessed data. STRETCH stores data with a delayed data dispersion scheme that incrementally adds data nodes to the storage system. We have devised an autoscaling feature that proactively repartitions data to alleviate computational hotspots before they occur. We compared the performance of S TRETCH with Apache Ignite and the results show that STRETCH provides up to 3 times the throughput when the system encounters hotspots. STRETCH is built on Apache Spark and Ignite and interacts with them at runtime

Towards federated learning over large-scale streaming data

2020 Spring.Includes bibliographical references.Distributed Stream Processing Engines (DSPEs) have seen significant deployment growth along with an increase in streaming data sources such as sensor networks. These DSPEs enable processing large amounts of streaming data in a cluster of commodity machines to extract knowledge and insights in real-time. Due to fluctuating data arrival rates in real-world applications, modern DSPEs often provide auto-scaling. However, the existing designs of advanced analytical frameworks are not effectively aligned with scalable streaming computing environments. We have designed and developed ORCA, a federated learning architecture that supports the training of traditional Artificial Neural Networks as well as Convolutional Neural Networks and Long Short-term Memory Network based models while ensuring resiliency during scaling. ORCA also introduces dynamic adjustment of the 'elasticity' hyper-parameter for rescaled computing environments. We estimate this elasticity hyper-parameter using reinforcement learning. Our empirical benchmarks show that ORCA is capable of achieving an MSE of 0.038 over real-world streaming datasets

Prediction based scaling in a distributed stream processing cluster

2020 Spring.Includes bibliographical references.Proliferation of IoT sensors and applications have enabled us to monitor and analyze scientific and social phenomena with continuously arriving voluminous data. To provide real-time processing capabilities over streaming data, distributed stream processing engines (DSPEs) such as Apache STORM and Apache FLINK have been widely deployed. These frameworks support computations over large-scale, high frequency streaming data. However, current on-demand auto-scaling features in these systems may result in an inefficient resource utilization which is closely related to cost effectiveness in popular cloud-based computing environments. We propose ARSTREAM, an auto-scaling computing environment that manages fluctuating throughputs for data from sensor networks, while ensuring efficient resource utilization. We have built an Artificial Neural Network model for predicting data processing queues and this model captures non-linear relationships between data arrival rates, resource utilization, and the size of data processing queue. If a bottleneck is predicted, ARSTREAM scales-out the current cluster automatically for current jobs without halting them at the user level. In addition, ARSTREAM incorporates threshold-based re-balancing to minimize data loss during extreme peak traffic that could not be predicted by our model. Our empirical benchmarks show that ARSTREAM forecasts data processing queue sizes with RMSE of 0.0429 when tested on real-time data

Sensor web geoprocessing on the grid

Recent standardisation initiatives in the fields of grid computing and geospatial sensor middleware provide an exciting opportunity for the composition of large scale geospatial monitoring and prediction systems from existing components. Sensor middleware standards are paving the way for the emerging sensor web which is envisioned to make millions of geospatial sensors and their data publicly accessible by providing discovery, task and query functionality over the internet. In a similar fashion, concurrent development is taking place in the field of grid computing whereby the virtualisation of computational and data storage resources using middleware abstraction provides a framework to share computing resources. Sensor web and grid computing share a common vision of world-wide connectivity and in their current form they are both realised using web services as the underlying technological framework. The integration of sensor web and grid computing middleware using open standards is expected to facilitate interoperability and scalability in near real-time geoprocessing systems. The aim of this thesis is to develop an appropriate conceptual and practical framework in which open standards in grid computing, sensor web and geospatial web services can be combined as a technological basis for the monitoring and prediction of geospatial phenomena in the earth systems domain, to facilitate real-time decision support. The primary topic of interest is how real-time sensor data can be processed on a grid computing architecture. This is addressed by creating a simple typology of real-time geoprocessing operations with respect to grid computing architectures. A geoprocessing system exemplar of each geoprocessing operation in the typology is implemented using contemporary tools and techniques which provides a basis from which to validate the standards frameworks and highlight issues of scalability and interoperability. It was found that it is possible to combine standardised web services from each of these aforementioned domains despite issues of interoperability resulting from differences in web service style and security between specifications. A novel integration method for the continuous processing of a sensor observation stream is suggested in which a perpetual processing job is submitted as a single continuous compute job. Although this method was found to be successful two key challenges remain; a mechanism for consistently scheduling real-time jobs within an acceptable time-frame must be devised and the tradeoff between efficient grid resource utilisation and processing latency must be balanced. The lack of actual implementations of distributed geoprocessing systems built using sensor web and grid computing has hindered the development of standards, tools and frameworks in this area. This work provides a contribution to the small number of existing implementations in this field by identifying potential workflow bottlenecks in such systems and gaps in the existing specifications. Furthermore it sets out a typology of real-time geoprocessing operations that are anticipated to facilitate the development of real-time geoprocessing software.EThOS - Electronic Theses Online ServiceEngineering and Physical Sciences Research Council (EPSRC) : School of Civil Engineering & Geosciences, Newcastle UniversityGBUnited Kingdo

Essays in financial technology: banking efficiency and application of machine learning models in Supply Chain Finance and credit risk assessment

The financial landscape is undergoing a significant transformation, driven by technological innovations that are reshaping traditional banking practices. This thesis examines the evolving relationship between financial technology (FinTech) and banking, specifically addressing the credit risk aspects within the domains of Supply Chain Finance (SCF) and peer-to-peer (P2P) lending. FinTech has experienced rapid growth and innovation over the past decade. It encompasses a wide range of technologies and services that aim to enhance and streamline financial processes, disrupt traditional banking models, and offer new solutions to consumers and businesses. The status of FinTech and banking is assessed through an extensive review of the current literature and empirical data. Accordingly, FinTech development has significantly impacted the financial landscape, driving innovation, competition, and customer expectations while it has exposed inefficiencies within traditional banking, it has also compelled banks to evolve and embrace technological advancements. The impact of FinTech on traditional banking models, customer behaviours, and market competition is aimed to be explored. This investigation highlights the challenges and opportunities that arise as FinTech disrupts and reshapes the banking sector, emphasizing its potential to enhance efficiency, accessibility, and customer experiences. As Chapter 3 focuses on an empirical analysis of the impact of FinTech on the operating efficiency of commercial banks in China. Further, in the context of credit risk, the thesis focuses on SCF and P2P lending, two prominent areas influenced by FinTech innovation. SCF has witnessed substantial transformation with the infusion of FinTech solutions. Digital platforms have streamlined the flow of funds within complex supply networks, enhancing the liquidity of suppliers and optimizing working capital for buyers. However, this transformation introduces new credit risk challenges. As suppliers' financial data becomes more accessible, the need for accurate risk assessment and predictive modelling becomes paramount. The integration of big data analytics, machine learning, and artificial intelligence (AI) holds the promise of refining credit risk evaluation by offering real-time insights into supplier financial health, thereby improving lending decisions and reducing defaults. Similarly, P2P lending has redefined the borrowing and lending landscape, enabling direct connections between individual borrowers and lenders. While P2P lending platforms offer speed, convenience, and access to credit for previously underserved segments, they also grapple with credit risk concerns. Evaluating the creditworthiness of individual borrowers without sufficient credit history demands innovative risk assessment methodologies. The emergence of data issues, such as imbalanced data issues, feature selection, and data processing, presents challenges in building accurate credit risk profiles for P2P lending participants. FinTech solutions play a pivotal role in creating and implementing these alternative risk assessment models. Note that, few studies in the literature investigate the benchmark of the advanced method of solving the credit risk assessment in emerging financial services. This thesis aims to address this research gap by evaluating the effectiveness of credit risk assessment models in these FinTech-driven contexts, considering both traditional methodologies and novel data-driven approaches. Chapter 4 investigates the credit risk assessment issue in Digital Supply Chain Finance (DSCF) with the Machine Learning approach and Chapter 5 emphasises the issue of data imbalance of credit risk assessment in P2P Lending. By addressing these gaps and issues, this thesis aims to contribute to the broader discourse on FinTech's role in shaping the future of banking. The findings have implications for financial institutions, policymakers, and regulators seeking to harness the benefits of FinTech while mitigating associated risks. Ultimately, this study offers insights into navigating the evolving landscape of credit risk in SCF and P2P lending within the context of an increasingly technology-driven financial ecosystem

Australian federalism and the use of tied grants: case studies of public hospitals and schools

Tied grants are a contentious feature of Australian federalism. This study evaluates the policy making dynamics and performance of tied grants using case study evidence on public hospital and school grants from 1975 to 2008. The study finds that policy control has wavered between the Commonwealth and States. Further, the study argues that provided the Commonwealth acts as a strategic and refined player, the tied grant and co-operative federalism can offer distinct performance advantages

An Exploratory Study of Patient Falls

Debate continues between the contribution of education level and clinical expertise in the nursing practice environment. Research suggests a link between Baccalaureate of Science in Nursing (BSN) nurses and positive patient outcomes such as lower mortality, decreased falls, and fewer medication errors. Purpose: To examine if there a negative correlation between patient falls and the level of nurse education at an urban hospital located in Midwest Illinois during the years 2010-2014? Methods: A retrospective crosssectional cohort analysis was conducted using data from the National Database of Nursing Quality Indicators (NDNQI) from the years 2010-2014. Sample: Inpatients aged ≥ 18 years who experienced a unintentional sudden descent, with or without injury that resulted in the patient striking the floor or object and occurred on inpatient nursing units. Results: The regression model was constructed with annual patient falls as the dependent variable and formal education and a log transformed variable for percentage of certified nurses as the independent variables. The model overall is a good fit, F (2,22) = 9.014, p = .001, adj. R2 = .40. Conclusion: Annual patient falls will decrease by increasing the number of nurses with baccalaureate degrees and/or certifications from a professional nursing board-governing body

12th EASN International Conference on "Innovation in Aviation & Space for opening New Horizons"

Epoxy resins show a combination of thermal stability, good mechanical performance, and durability, which make these materials suitable for many applications in the Aerospace industry. Different types of curing agents can be utilized for curing epoxy systems. The use of aliphatic amines as curing agent is preferable over the toxic aromatic ones, though their incorporation increases the flammability of the resin. Recently, we have developed different hybrid strategies, where the sol-gel technique has been exploited in combination with two DOPO-based flame retardants and other synergists or the use of humic acid and ammonium polyphosphate to achieve non-dripping V-0 classification in UL 94 vertical flame spread tests, with low phosphorous loadings (e.g., 1-2 wt%). These strategies improved the flame retardancy of the epoxy matrix, without any detrimental impact on the mechanical and thermal properties of the composites. Finally, the formation of a hybrid silica-epoxy network accounted for the establishment of tailored interphases, due to a better dispersion of more polar additives in the hydrophobic resin