Applying data mining techniques to address critical process optimization needs in advanced manufacturing

Cloud Computing IT infrastructure has the potential to be particularly suitable for collaborative commerce (c-commerce) applications; because it generally requires less efforts and interferences for development, customization, integration, operation, and maintenance than other traditional IT infrastructures (e.g., on-premises and data centers). However, upgrading c-commerce applications running on traditional IT infrastructures, to run efficiently on cloud computing infrastructure, faces a number of challenges, mainly, lack of effective and reliable architectural model. This paper presents a description of a new architectural model for developing cloud computing based c-commerce applications; which is denoted as cc-commerce model. The model is an basically based on the standard cloud computing model, and it consists of six main components; these are: client, provider, auditor, broker, security and privacy, and communications network. The new model is implemented in a simple and flexible Web-based test tool, namely, the cc-commerce test (3CT) tool, which is used to evaluate the performance of the model through measuring the response times for four different configurations. The analysis of the obtained results demonstrates that the cc-commerce model can provide better response time than equivalent c-commerce models.

The trend of merging telecommunication infrastructures with traditional IT infrastructures is becoming increasingly more common. The driver behind this development is partly the strong need for enhanced services from the telecom operator?s side aiming for value added services provision to the customer. The effect of this new trend is that existing value chains in the Telco infrastructure will disappear and the merge between the Telco infrastructure and the IT infrastructure can bring added value to the Telco?s network, which is needed in order to increase the willingness to pay for the services. Thus, Telecom operators are very keen on participating in the overall service delivery chain being placed on top of their transport infrastructure. This paper proposes a method, to help to propel the previously mentioned trend by enabling the user to query for aggregated composed services directly from the network edge. A network selection is therefore achived based on the services provided by the access network and their capabilities and costs, parallel to other traditional pure network-related parameters such as signal strength and network QoS.

The increasing proliferation of independent application service providers is making traditional IT infrastructure insufficient when dealing with new issues that appear in a dynamic outsourcing business model. Quality of service (QoS) gradually becomes an essential benchmark to differentiate diverse service providers during service selection process. In this paper, we argue service selection can be deemed as a decision making process -to decide which services providers should be selected within the specified service provision context during a definitive timeslot. Thus, existing decision support approaches can be leveraged if applicable. Hence, we propose a service selection solution which utilizes the decision support systems module (DSS Module) to select the most appropriate service. In DSS module we introduce AHP model to carry out the service QoS measurement based on the Context-specific Quality Aspects. The contributions of this paper are two folds. Firstly, we provide a novel and feasible solution for QoS-based service selection and secondly, we apply DSS module into web services, thus opening a new, fertile ground for DSS research in service ecosystem literature.

Data analytics is the science of extracting patterns, trends, and actionable information from large sets of data. The growing nature of data in from different servers with in consistent data formats like structured, semi-structured and unstructured data. Traditional IT infrastructure is simply not able to meet the demands of this new “Data Analytics” landscape. The secondary problem for data analytics is security, the rapid increase in usage of Internet, and consequent applications that allow users to create contents freely, amplify the already huge web volume. In today's data world, there are some vital things to keep in mind while beginning big data and analytics innovation projects. Hence the need of secured data analytics tool is mandatory.

Cloud is a large pool of easily usable and accessible virtualized resources (such as hardware, development platforms and/or services). It provides an on-demand, pay-as-you-go computing resources and had become an alternative to traditional IT Infrastructure. As more and more consumers delegate their task to cloud providers, Service Level Agreement (SLA) between consumer and provider becomes an important aspect. Due to the dynamic nature of cloud the matching of SLA templates need to be dynamic and continuous monitoring of Quality of Service (QoS) is necessary to enforce SLAs. SLA template contains many parameters like cloud's resources (physical memory, main memory, processor speed etc.) and properties (availability, response time etc.). This work addresses the issue of matching SLA parameters to find suitable cloud provider for particular application.

Copyright: © 2013 Deka GC. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. As governments around the world continue to grapple with slow-moving economies, cloud computing is emerging as a viable and valuable answer to the persistent demands for reducing public sector spending. Recent attention has shifted to a new initiative called Cloud Government (G-Cloud) incorporating cloud computing into e-Government services, particularly aimed at communicating directly with citizens and implementing cloud computing services in order to cut costs and improve upon the IT services infrastructure. G-Cloud is an Internet based communications which enable public bodies to host, select and use computing services from secure, resilient and cost-effective IT service environment. The ambition of the journey from traditional IT infrastructures towards cloud computing and IaaS is a trusted Cloud with the network as the logical platform to bring the existing assets in the Datacenter and new Cloud computing approaches with Virtualization, Governance, Security, Information and Application. The G-Cloud program’s core benefits are cost reduction, improved services, faster delivery and progressive sustainability agenda. The citizen-centric data, services and applications will be stored, maintained and delivered from G-Cloud and a comprehensive government portal will be a part and parcel of this cloud-based service delivery. Government applications store will provide a “portal” to purchase G-Cloud services. The latest trend on G-Cloud is following the agency-by-agency implementation scenario. Various Ministries/Departments may use cloud services provided by various Cloud Service Provider (CSP) based on the availability in a particular locality as well as the specific requirement of the particular Department/Ministry. It is a fact that all the CSPs are not equally competent in all types of services like accounting, ERP, CRM etc. But for the smooth function of the Government machinery these clouds must be interoperable. Current cloud frameworks are not designed to interact with each other, which make it more difficult for organizations to consolidate their applications and fully realize the savings and productivity benefits of cloud solutions. The dynamic location of service facilities, Virtualization of hardware and software elements is stressing the communication network & protocols, when datacenters are interconnected through the Internet. Although the “Computing” aspects of Cloud have been largely investigated, lower attention has been devoted to the “Networking of Clouds”.

Recent years have shown an increase in the number and complexity of cyberattacks not only on traditional IT infrastructures, but also on smart energy supply networks. Cybercriminals and cyberspies are becoming more sophisticated and persistent in their actions, which requires increased cyber security measures and the development of new technologies to protect infrastructure. In this work we present a framework for detection of Man-in-the-Middle cyberattacks in smart grids based on the application of the ensemble process for feature selection. The workflow of the framework consists of two main stages: model creation and performance evaluation. To create the model, a study was carried out, which involved the selection of the optimal set of features by applying various methods of feature selection. In addition to the sets of features that were formed by feature selection methods, another set of features was formed, which is the result of the process of assembling features that were formed by feature selection methods. A feature score was proposed, which allows weighting of each feature depending on its frequency of selection in all selection methods and the relative number of selected features in the entire set. To check the effectiveness of detecting MitM cyberattacks, SVM and Decision Tree classifiers were used, the test data for which were generated according to the obtained sets of features.

Hybrid cloud is an integrated cloud computing environment utilizing a mix of public cloud, private cloud, and on-premise traditional IT infrastructures. Workload awareness, defined as a detailed full range understanding of each individual workload, is essential in implementing the hybrid cloud. While it is critical to perform an accurate analysis to determine which workloads are appropriate for on-premise deployment versus which workloads can be migrated to a cloud off-premise, the assessment is mainly performed by rule or policy based approaches. In this paper, we introduce StackInsights, a novel cognitive system to automatically analyze and predict the cloud readiness of workloads for an enterprise. Our system harnesses the critical metrics across the entire stack: 1) infrastructure metrics, 2) data relevance metrics, and 3) application taxonomy, to identify workloads that have characteristics of a) low sensitivity with respect to business security, criticality and compliance, and b) low response time requirements and access patterns. Since the capture of the data relevance metrics involves an intrusive and in-depth scanning of the content of storage objects, a machine learning model is applied to perform the business relevance classification by learning from the meta level metrics harnessed across stack. In contrast to traditional methods, StackInsights significantly reduces the total time for hybrid cloud readiness assessment by orders of magnitude.

The rapid adoption of cloud computing technologies has transformed the way organizations design, develop, and manage complex projects. Cloud-based environments, characterized by their distributed architectures, dynamic scaling, and multi-tenancy features, offer enhanced flexibility and scalability. However, these benefits also introduce new layers of complexity and vulnerability, making risk assessment and mitigation more challenging than in traditional IT infrastructures. This explores the latest advances in risk assessment and mitigation strategies specifically tailored for complex cloud-based project environments. Modern risk landscapes in the cloud are shaped by evolving cybersecurity threats, compliance demands, integration challenges, and operational uncertainties. Traditional risk assessment models often fall short in capturing the dynamic and interconnected nature of cloud ecosystems. In response, recent developments have introduced advanced techniques such as AI-driven threat detection, real-time risk monitoring, and scenario-based probabilistic modeling. These innovations enable proactive identification and prioritization of risks, allowing for more agile and adaptive mitigation responses. Mitigation strategies have also evolved significantly, with the rise of cloud-native security controls, DevSecOps practices, and resilience engineering methods like chaos testing. Organizations are increasingly adopting Zero Trust architectures and automated incident response frameworks to enhance their defensive posture. Additionally, there is a growing focus on vendor risk management and compliance automation to address regulatory requirements across jurisdictions. This highlights several case studies that demonstrate the practical application of these advanced methodologies in various sectors, including healthcare, finance, and enterprise IT. Despite these advancements, challenges such as data sensitivity, high implementation costs, and skill gaps persist. This concludes by outlining future directions for research and practice, emphasizing the need for continuous innovation, cross-disciplinary collaboration, and the integration of emerging technologies like quantum-safe cryptography to ensure robust risk management in cloud environments. These efforts are essential to sustaining the security, resilience, and trustworthiness of modern cloud-based project infrastructures.

Traditional IT infrastructure monitoring systems do not account for contextual relationships between events in log files, which leads to a high rate of false positives (up to 60–80%). This work proposes an innovative hybrid architecture that combines semantic understanding of event sequences (LSTM) with the classification accuracy of tabular models (XGBoost). The main idea is to create a “semantic fingerprint” of the event history for each service. The expected experimental results are anticipated to demonstrate an improvement in the F1 score by 15–25% while maintaining a low latency of less than 50 ms when running exclusively on CPU.

Cloud computing has transformed traditional IT infrastructure, offering dynamic, configurable resources on demand.On the other hand, it brings unique cybersecurity challenges, particularly in protecting sensitive data from unauthorized access.Traditional single factor authentication does not address these risks.By implementing multi-factor authentication (MFA), which combines several factors for verification including password, tokens, and biometrics, this cloud security method has become increasingly popular.Existing authentication methods fall short in the cloud environment, so this paper presents a novel multi-factor, multi-layered cloud access authentication framework.The methodology for MFA implementation uses risk-adaptive authentication based on real-time risk assessment, adaptive policy for MFA selection, and seamless verification workflows for end-users.Moreover, the implementation of the advanced technologies that cover all aspects such as encryption, SSO, and adaptive authentication further safeguards it against evolving threats.Through experimental analyses, we show that the framework is effective to substantially reduce the vulnerabilities without compromising usability and scalability.This will help cloud service providers and organizations alike better equip themselves with an effective and agile security environment through the adoption of next generation MFA strategies.

The ongoing implementation of Industry 4.0 in all segments of society will improve many aspects of human life and initiate many changes in business, production processes, and supply chains from raw materials to finished products. The implementation of new business methods leads to the transformation of production models and production processes, consumption, transport, and supply. Industry 4.0 enables the adaptation of production processes in the industry to full intelligent automation. In other words, it enables the introduction of self-automation, self-configuration, self-diagnosis and solution, knowledge, and intelligent decision-making, owing to the innovations and their implementation in robotics, smart sensors, 3D printers, Internet of Things (IoT), and Big Data technology. The essential quality shift in the implementation of Industry 4.0 in production processes is the implementation of AGV service robots (automatically guided vehicles) in the automation of transport operations in the production process itself (such as transport of raw materials, semi-products, and finished products), on assembly lines, storage of finished products and supply of finished products. The implementation of service robots in the production process is amortized very quickly if they are in operation for 24 h, while the investments can pay off in 2–3 years, and the system operates for about 15 years. The availability of work itself is about 98.5%. The implementation of service robots optimizes production time and costs, thus providing high productivity. The paper presents the annual application of service robots in logistics and the application of AGV service robots with different structures in production processes.

This paper presents a novel design of a smart and efficient Manufacturing Execution System (MES) to acquire data from the shop floor in a factory. With increasing complexities in the production process, improving industrial productivity using production information systems is a challenge for all industries in the world. Manufacturing Execution Systems (MES), since their introduction in the factory environment, are gaining popularity due to their ability to control and monitor production process using integrated data. However, employers/managers are still confronting with many complications in understanding huge information on the production process and reacting quickly based on such information. In this paper, we propose the design of NIRMAN Factory Information System (FIS)-A Smart MES to enable the control and monitoring of multiple factors of the production process like assembly line, personnel, quality control, safety, etc. NIRMAN FIS, a MES based on Android Smartphone, presents data about factory environment and factory operations on the manager's smartphone screen and helps him to react and take important decisions quickly and flexibly. Index Terms: Sectional Efficiency, Factory Efficiency, Assembly Line, Threshold Limit, Productivity, Morale, Factory Environment, Data Acquisition, Analysis, Automatic and Manual Control, Bluetooth, Arduino, Android, Remote Monitoring.

Predictable and fast production launch of new products (product ramp-up) is a crucial success factor in the production industry in general, and for the production of integrated circuits (ICs) in particular. During the ramp-up phase of the product there is, inter alia, the need for product-specific configuration of a wide range of software systems that control the production process in a fully automated manner. This collection of software systems is sourced from several vendors and has therefore to be configured in different ways. Moreover, configuration has to be orchestrated along the whole production process, in accordance with the needs of the new product. This is a complicated, error-prone, and time-consuming task for product engineers, process engineers, and application engineers. The approach described in this chapter avoids such efforts and risks through a semiautomated generation of configurations of software systems. It uses a knowledge base which provides a unified configuration schema across all involved software systems. The approach applies automated reasoning of new configuration content based on the knowledge about new products’ characteristics and knowledge about the existing production environment. The knowledge base is described by ontology models and based on Semantic Web technologies. The described approach is the basis for IT professionals of IC factories and of factories with comparable IT infrastructure to standardize the configuration of software systems being in charge for production control and process control. It contributes to accelerate the launch of new products and to make their ramp-up phase more deterministic.

Digital twin technology plays a crucial role in completing the virtual mapping of the physical workshop, enabling production monitoring and process optimization. New energy batteries are a form of green energy with advantages such as high energy density, environmental protection, and beneficial safety. The assembly line for new energy batteries is essential in efficiently obtaining battery packs of diverse varieties and high quality. This paper presents three assembly design schemes for square lithium iron phosphate battery packs as an example. By comparing the characteristics of different schemes, scheme C was selected as the preferred option for conducting three-dimensional modeling of the assembly line. This paper utilizes digital twin technology to create a digital representation of the battery pack assembly process. The process flow of the designed production line can be tested. The loading time, equipment idle time, fault time, blocking waiting time, material shortage waiting time, etc., of each workstation in the production line can be accurately calculated. Optimize the production process flow, reduce resource occupation, and save a large amount of human and material resources.