Browsing by Author "Khan, Muhammad"

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  • A Cloud based Remote Diagnostics service for Industrial Paper Mill
    (2020-12-14) Khan, Muhammad
     Perustieteiden korkeakoulu | Master's thesis
    This is an era where data is being generated by several devices around the clock which accumulates to petabytes. Industrial equipment are also generating telemetry data which is used to gain insights of industrial processes. Telemetry data also help perform remote diagnostics, early failure detection and incident cause discovery. This leads to development of big data processing systems which help answer how the data should be stored and processed. There are numerous existing architectures for big data processing systems, one of which is Lambda Architecture which provides a way to implement a distributed data processing system which can be customized according to the business needs. Lambda Architecture can also be complex to implement and maintain due to the combination of two processing systems in a single architecture. In this thesis, we propose and implement a robust and simplified approach for developing the data processing system using Lambda Architecture. The proposed approach strives to use minimum number of services limiting to Azure CosmosDb, Apache Spark, Azure EventHubs and Azure Functions to implement this complex architecture. We show that our approach makes the data processing system maintainable and reduces infrastructure management.
  • Coupling the fire simulation and structural analysis through adiabatic surface temperature
    (2022-10-10) Khan, Muhammad
     Insinööritieteiden korkeakoulu | Master's thesis
    This study presents the results of one-way coupling between a fire simulation performed in computational fluid dynamics (CFD) and the structural response in finite element (FE) analysis. A built-in tool of adiabatic surface temperature (AST) in Fire Dynamics Simulator (FDS) is utilized for transferring the boundary conditions into SAFIR, a Finite Element package. A validation study is done using the experimental data where an unprotected steel column is subjected to a localized fire. The results from FDS-SAFIR coupling indicate good agreement with the experimental data however, some discrepancies at different time intervals are noticed. The thermal response is checked based on steel temperatures on all faces of the column while the structural response is in terms of horizontal and vertical deflections. A sensitivity analysis highlights that the material properties of steel and convective coefficients are assumed to have an influence on both thermal and structural responses. A comparison between AST surface and AST gas device indicates that a structure in close proximity to fire has an influence on the development of temperature and therefore it should be modelled as an obstruction in fire analysis. The coupling methodology is then used to investigate the forces redistribution and progressive collapse mechanism in a planar steel frame subjected to a localized fire. It is observed that the fire-induced stresses in a member are redistributed to the adjacent members, and this redistribution of forces continues even during the cooling phase. A comparison of different fire locations in the frame has highlighted that a fire on the central ground floor column and first-floor column is more detrimental to the overall stability of the structure. The analysis shows that a progressive collapse occurs due to a variety of phenomena like high load ratio, cantilever beam and pull-in force mechanism. Further investigation highlights that the addition of one bay to the frame does not significantly improve the forces redistribution however, increasing the stiffness of ground floor columns does improve the overall stability and performance.
  • Distributed and scalable parsing solution for telecom network data
    (2020-01-20) Khan, Muhammad
     Perustieteiden korkeakoulu | Master's thesis
    The growing usage of mobile devices and the introduction of 5G networks have increased the significance of network data for the telecom business. The success of telecom organizations can depend on employing efficient data engineering techniques for transforming raw network data into useful information by analytics and machine learning (ML). Elisa Oyj., a Finnish telecommunications company, receives massive amounts of network data from network equipment manufactured by various vendors. The effectiveness of data analytics depends on efficient data engineering processes. This thesis presents a scalable data parsing solution that leverages Spark, a distributed programming framework, for parallelizing parsing routines from an existing parsing solution. We design and deploy this solution as a component of the organization's data engineering pipeline to enable automation of data-centric operations. Experimental results indicate that the efficiency of the proposed solution is heavily dependent on the individual file size distribution. The proposed parsing solution demonstrates reliability, scalability, and speed during empirical evaluation and processes a 24-hour network data within 3 hours. The main outcome of the project is an optimized setup with the minimum number of data partitions to ensure zero failures and thus minimum execution time. A smaller execution time leads to lower costs of the continuously running infrastructure provisioned on the cloud.
  • Drug side-effect prediction using machine learning methods
    (2017-12-11) Khan, Muhammad
     Perustieteiden korkeakoulu | Master's thesis
    Drug toxicity (or adverse side effects) is a pressing health problem which is also an impediment to the development of therapeutically effective drugs. Despite many on-going efforts to determine the toxicity beforehand, computational prediction of drug side-effects remains a challenging task. This thesis presents an approach to predict side-effects by utilizing side-information sources for the drugs, while simultaneously comparing state-of-the-art machine learning methods to improve accuracy. Specifically, the thesis implements a data-analysis pipeline for obtaining side-information that are useful for the prediction task. This thesis then formulates the drug side-effect prediction as a machine learning problem: Given disease indications and structural features (as side-information sources) of drugs, for which some measurements of side-effect exist, predict sideeffect for a new drug. As case studies, the prediction accuracies are compared for ten different side-effects using linear as well as non-linear machine learning methods. The thesis summarizes three key findings. First, the drug side-information sources are predictive of the side-effects. Second, non-linear methods show improved prediction accuracies as compared to their linear analogs. Third, the integration of disease indications and structural features with a principled machine learning approach further improves the drug side-effect predictions. However, the current study limits the analysis assuming side-effects are independent. In future, modeling the joint relationships of several side-effects could yield more strong predictions and better help to understand the underlying biological mechanism.
  • Scalable invoice-based B2B payments with microservices
    (2020-01-20) Khan, Muhammad
     Perustieteiden korkeakoulu | Master's thesis
    Paying by invoice has several advantages for businesses over conventional payment methods such as Debit/Credit cards. An invoice not only allows a buyer to make the purchase on credit but also contains the tax information for each purchased item. Businesses need to save this information in their financial records and report it to the authorities. The core challenge in an invoice-based payment method is the ability to make an accurate credit decision for a given purchase. Such a credit decision requires information about the buying company such as their credit rating. The company information is gathered in real time from different third-party sources. In this context, Enterpay Oy provides an invoiced-based B2B payment solution and is growing its payment service to European countries. In order to support this expansion, Enterpay needs to develop new capabilities such as the ability to detect fraudulent purchases. These new features require the application architecture to be flexible in terms of technology. For example, different components of the service should be built with the most suited programming language, libraries, and frameworks. The goal of this thesis is to enable efficient scaling and high availability for Enterpay's payment service. Thus, we have migrated from a monolithic a microservice-based architecture. This transition allows us to choose the best suited technology for the business case of the given microservice. We extracted various modules from the original monolithic application, which have different scalability criteria. We built these modules as Docker containers, which run as independent microservices. We used Kubernetes as the container orchestration framework and deployed the microservice in Amazon Web Services (AWS). Finally, we conducted experiments to measure the performance of the service with the new architecture. We found that this architecture not only scales faster but also recovers from instance failures quicker than the previous solution. Additionally, we noticed that the average response time of service request is similar in both architectures. Finally, we observed that new microservices can be built using different technology stack and deployed conveniently in the same Kubernetes cluster.