Designing Human-Centered Collective Intelligence

Human-Centered Collective Intelligence (HCCI) is an emergent research area that seeks to bring together major research areas like machine learning, statistical modeling, information retrieval, market research, and software engineering to address challenges pertaining to deriving intelligent insights and solutions through the collaboration of several intelligent sensors, devices and data sources. An archetypal contextual CI scenario might be concerned with deriving affect-driven intelligence through multimodal emotion detection sources in a bid to determine the likability of one movie trailer over another. On the other hand, the key tenets to designing robust and evolutionary software and infrastructure architecture models to address cross-cutting quality concerns is of keen interest in the “Cloud” age of today. Some of the key quality concerns of interest in CI scenarios span the gamut of security and privacy, scalability, performance, fault-tolerance, and reliability. I present recent advances in CI system design with a focus on highlighting optimal solutions for the aforementioned cross-cutting concerns. I also describe a number of design challenges and a framework that I have determined to be critical to designing CI systems. With inspiration from machine learning, computational advertising, ubiquitous computing, and sociable robotics, this literature incorporates theories and concepts from various viewpoints to empower the collective intelligence engine, ZOEI, to discover affective state and emotional intent across multiple mediums. The discerned affective state is used in recommender systems among others to support content personalization. I dive into the design of optimal architectures that allow humans and intelligent systems to work collectively to solve complex problems. I present an evaluation of various studies that leverage the ZOEI framework to design collective intelligence

Endless Data

Small and Medium Enterprises (SMEs), as well as micro teams, face an uphill task when delivering software to the Cloud. While rapid release methods such as Continuous Delivery can speed up the delivery cycle: software quality, application uptime and information management remain key concerns. This work looks at four aspects of software delivery: crowdsourced testing, Cloud outage modelling, collaborative chat discourse modelling, and collaborative chat discourse segmentation. For each aspect, we consider business related questions around how to improve software quality and gain more significant insights into collaborative data while respecting the rapid release paradigm

Improving System Reliability for Cyber-Physical Systems

Cyber-physical systems (CPS) are systems featuring a tight combination of, and coordination between, the system's computational and physical elements. Cyber-physical systems include systems ranging from critical infrastructure such as a power grid and transportation system to health and biomedical devices. System reliability, i.e., the ability of a system to perform its intended function under a given set of environmental and operational conditions for a given period of time, is a fundamental requirement of cyber-physical systems. An unreliable system often leads to disruption of service, financial cost and even loss of human life. An important and prevalent type of cyber-physical system meets the following criteria: processing large amounts of data; employing software as a system component; running online continuously; having operator-in-the-loop because of human judgment and an accountability requirement for safety critical systems. This thesis aims to improve system reliability for this type of cyber-physical system. To improve system reliability for this type of cyber-physical system, I present a system evaluation approach entitled automated online evaluation (AOE), which is a data-centric runtime monitoring and reliability evaluation approach that works in parallel with the cyber-physical system to conduct automated evaluation along the workflow of the system continuously using computational intelligence and self-tuning techniques and provide operator-in-the-loop feedback on reliability improvement. For example, abnormal input and output data at or between the multiple stages of the system can be detected and flagged through data quality analysis. As a result, alerts can be sent to the operator-in-the-loop. The operator can then take actions and make changes to the system based on the alerts in order to achieve minimal system downtime and increased system reliability. One technique used by the approach is data quality analysis using computational intelligence, which applies computational intelligence in evaluating data quality in an automated and efficient way in order to make sure the running system perform reliably as expected. Another technique used by the approach is self-tuning which automatically self-manages and self-configures the evaluation system to ensure that it adapts itself based on the changes in the system and feedback from the operator. To implement the proposed approach, I further present a system architecture called autonomic reliability improvement system (ARIS). This thesis investigates three hypotheses. First, I claim that the automated online evaluation empowered by data quality analysis using computational intelligence can effectively improve system reliability for cyber-physical systems in the domain of interest as indicated above. In order to prove this hypothesis, a prototype system needs to be developed and deployed in various cyber-physical systems while certain reliability metrics are required to measure the system reliability improvement quantitatively. Second, I claim that the self-tuning can effectively self-manage and self-configure the evaluation system based on the changes in the system and feedback from the operator-in-the-loop to improve system reliability. Third, I claim that the approach is efficient. It should not have a large impact on the overall system performance and introduce only minimal extra overhead to the cyberphysical system. Some performance metrics should be used to measure the efficiency and added overhead quantitatively. Additionally, in order to conduct efficient and cost-effective automated online evaluation for data-intensive CPS, which requires large volumes of data and devotes much of its processing time to I/O and data manipulation, this thesis presents COBRA, a cloud-based reliability assurance framework. COBRA provides automated multi-stage runtime reliability evaluation along the CPS workflow using data relocation services, a cloud data store, data quality analysis and process scheduling with self-tuning to achieve scalability, elasticity and efficiency. Finally, in order to provide a generic way to compare and benchmark system reliability for CPS and to extend the approach described above, this thesis presents FARE, a reliability benchmark framework that employs a CPS reliability model, a set of methods and metrics on evaluation environment selection, failure analysis, and reliability estimation. The main contributions of this thesis include validation of the above hypotheses and empirical studies of ARIS automated online evaluation system, COBRA cloud-based reliability assurance framework for data-intensive CPS, and FARE framework for benchmarking reliability of cyber-physical systems. This work has advanced the state of the art in the CPS reliability research, expanded the body of knowledge in this field, and provided some useful studies for further research

Visualization & Automation of Shams Dubai Report

Dubai’s Smart Grid strategy includes the implementation of Distributed Energy Resources and Distribution Automation (DA) facilities to continuous monitoring and remote control from DEWA’s Distribution Control Center (DCC) , and, in some cases, automatic control of electric distribution assets operated at 33kV or lower. The increase level of telemetry and automation in the field imposes a greater challenge in monitoring and live data visualization for establishing a decision support system that empowers distribution system operators and enables optimal control of existing and planned assets. This challenge can be overcome through introducing data science tools in the sector of energy. Through imposing certain reporting and visualization tool, the data generated utilization level is improved which will lead to an increase in reliability and efficiency, rise asset utilization, workforce productivity, decision making, thus, increase customer satisfaction. The use case covered during this capstone proposal is one of the daily reports generated by distribution operation department manually on the daily bases. During this project, data science tools will be benchmarked accordingly to distribution power utility needs of reporting and anticipating certain parameters such as distribution solar generation and key performance indicators (SAIDI, SAIFI, CML, MTTR, MTBF etc.). The selected tool will be utilized to generate live reports/ dashboards and to decrease the level of manpower intervention. This proposal will highlights the background of the project, problem statement, project definition and goals and explains project methodology and evaluation followed by project deliverables & timeline

Serverless Cloud Computing: A Comparative Analysis of Performance, Cost, and Developer Experiences in Container-Level Services

Serverless cloud computing is a subset of cloud computing considerably adopted to build modern web applications, while the underlying server and infrastructure management duties are abstracted from customers to the cloud vendors. In serverless computing, customers must pay for the runtime consumed by their services, but they are exempt from paying for the idle time. Prior to serverless containers, customers needed to provision, scale, and manage servers, which was a bottleneck for rapidly growing customer-facing applications where latency and scaling were a concern. The viability of adopting a serverless platform for a web application regarding performance, cost, and developer experiences is studied in this thesis. Three serverless container-level services are employed in this study from AWS and GCP. The services include GCP Cloud Run, GKE AutoPilot, and AWS EKS with AWS Fargate. Platform as a Service (PaaS) underpins the former, and Container as a Service (CaaS) the remainder. A single-page web application was created to perform incremental and spike load tests on those services to assess the performance differences. Furthermore, the cost differences are compared and analyzed. Lastly, the final element considered while evaluating the developer experiences is the complexity of using the services during the project implementation. Based on the results of this research, it was determined that PaaS-based solutions are a high-performing, affordable alternative for CaaS-based solutions in circumstances where high levels of traffic are periodically anticipated, but sporadic latency is never a concern. Given that this study has limitations, the author recommends additional research to strengthen it

Cloud Computing: TOE Adoption Factors By Service Model In Manufacturing

Organizations are adopting cloud technologies for two primary reasons: to reduce costs and to enhance business agility. The pressure to innovate, reduce costs and respond quickly to changes in market demand brought about by intense global competition has U.S. manufacturing firms turning to cloud computing as an enabling strategy. Cloud computing is a service based information technology model that enables on-demand access to a shared pool of computing services provisioned over a broadband network. Cloud is categorized across three primary service models, Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS), differentiated by the cloud provider’s level of responsibility for managing hardware services, development platforms and application services. While prior research in cloud computing has sought to define the concept and explore the business value, empirical studies in the Information Systems literature stream are sparse, limited to exploratory case studies and SaaS research. Using the Technology, Organization, and Environment framework as a theoretical foundation, this research provides a holistic cloud adoption model inclusive of all cloud service layers. The study analyzes factors influencing organizational cloud adoption utilizing survey data from 150 U.S. manufacturing firms. The results find organizational innovativeness as a crucial factor to cloud computing adoption in manufacturing. An inverse factor relationship suggests the more innovative the firm culture, the less likely it is to adopt cloud. Other significant adoption factors include trust and technical competency. Findings also suggest variations in adoption influences based on the cloud service model deployed. The study has strategic implications for both researchers and managers seeking to understand the antecedents to adoption, and for practitioners developing an organizational cloud strategy spanning multiple cloud service models. For vendors, the study provides insights that can be leveraged to inform product design, solution strategy, and value proposition creation for future cloud service offerings