Cloud-based Decision Support System Research Articles

A multiscale orchestrated computational framework to reveal emergent phenomena in neuroblastoma

Neuroblastoma is a complex and aggressive type of cancer that affects children. Current treatments involve a combination of surgery, chemotherapy, radiotherapy, and stem cell transplantation. However, treatment outcomes vary due to the heterogeneous nature of the disease. Computational models have been used to analyse data, simulate biological processes, and predict disease progression and treatment outcomes. While continuum cancer models capture the overall behaviour of tumours, and agent-based models represent the complex behaviour of individual cells, multiscale models represent interactions at different organisational levels, providing a more comprehensive understanding of the system. In 2018, the PRIMAGE consortium was formed to build a cloud-based decision support system for neuroblastoma, including a multi-scale model for patient-specific simulations of disease progression. In this work we have developed this multi-scale model that includes data such as patient's tumour geometry, cellularity, vascularization, genetics and type of chemotherapy treatment, and integrated it into an online platform that runs the simulations on a high-performance computation cluster using Onedata and Kubernetes technologies. This infrastructure will allow clinicians to optimise treatment regimens and reduce the number of costly and time-consuming clinical trials. This manuscript outlines the challenging framework's model architecture, data workflow, hypothesis, and resources employed in its development.

An intelligent energy minimization algorithm with virtual machine consolidation for sensor-based decision support system

Cloud computing has many benefits for businesses because of its distinctive qualities, including scalability, flexibility, on-demand service, and security. A good task scheduler is required to increase the efficiency of a cloud system, which performs numerous jobs simultaneously. On-demand access to resources that have been virtualized is made available as a service without the need for further waiting. For task scheduling issues based on a makespan limitation, energy consumption is decreased, significantly reducing energy cost. Additionally, the complexity of scheduling issues has increased primarily due to the application's lack of a makespan constraint. Unfortunately, reducing the energy cloud services use presents special research issues and difficulties. More precisely, because of the diversity of servers found in cloud centers, it is challenging to choose the best servers for cloud-based decision support systems to reduce energy usage. The presented approach is innovative and could be applied for complex applications while maintaining an average energy consumption for the running resources, which is a big challenge. The current process finds an optimized energy minimization in the cloud with a combination of Virtual Machine consolidation. The outputs were considered in terms of Energy, Virtual Machine Migration, Performance Degradation, Aggregated Ideal Time Factor, and Aggregated Overload Time Fraction. The obtained results in terms of the existing state of art approaches are far better than the competing approaches.

The development of a decision support system for the infant food chain

In a time where the awareness of food safety and quality increases among the general population, it is vital that consumers are enabled to make informed decisions on risks involving the safety of their food. The SAFFI (Safe Food for Infants in EU and China) project aims to build an integrated decision support system (DSS) for the infant food chain that will enable stakeholders at all levels to make informed decisions regarding infant food. The infant food chain was selected due to its strict regulatory requirements, its vulnerabilities as highlighted by different food safety crises, the economic importance of the infant food sector in the EU and China and the focus on this particular food chain by food safety authorities. The SAFFI project will incorporate data and models from work packages dealing with hazard identification (HI), hazard detection (HD), hazard control (HC) and risk ranking (RR). The models will be integrated into a user-friendly and upgradeable cloud-based decision support system application. A multi-actor cost-benefit analysis of the project will be carried out, enabling the stakeholders in the project to assess the relevance of implementing the project technologies by integrating food safety, regulatory and economic criteria. The decision support system will be validated on four specific case studies, and tested on end-users, with the aim of extending this approach to other food chains.

Cloud-Based Decision Support System for Air Quality Management

Air quality is important for the protection of human health, the environment and our cultural heritage and it is an issue that will acquire increased significance in the future due to the adverse effects of climate change. Thus, it is important to not simply monitor air quality, but to make information immediately available to those responsible for monitoring the networks, to policy/decision makers, but also to the general population. Moreover, the development of information technologies over the last couple of decades has allowed the proliferation of real-time pollution monitoring. The work presented herein concerns the development of an effective way of monitoring environmental parameters using dedicated software. It offers a complete suite of applications that support environmental data collection management and reporting for air quality and associated meteorology. It combines modern technologies for the proper monitoring of air quality networks, which can consist of one or more measuring stations. Innovatively, it also focuses on how to effectively present the relevant information, utilizing modern technologies, such as cloud and mobile applications, to network engineers, policy/decision managers, and to the general public at large. It also has the capability of notifying appropriate personnel in the event of failures, overruns or abnormal values. The system, in its current configuration, handles information from six networks that include over 55 air pollution monitoring stations that are located throughout Greece. This practical application has shown that the system can achieve high data availability rates, even higher than 99% during the year.

A cloud‐based and web‐based group decision support system in multilingual environment with hesitant fuzzy linguistic preference relations

Due to the growing needs in decision-making under uncertainty, existing studies introduced consistency and consensus-driven algorithms for group decision-making (GDM) problems with hesitant fuzzy linguistic preference relations (HFLPRs). A decision support system (DSS) that can host these GDM algorithms to provide decision-support services or tools for practical use is urgently needed. However, the state-of-the-art architectures cannot organize these algorithms and related data to run within one framework. This is mainly due to the running environments for these GDM algorithms are different since these algorithms were not originally designed to be compatible. Given the multilingual consistency and consensus-based decision support algorithms, how to design and implement a cloud-based DSS in a multilingual environment is still an open question. To fill this gap, this paper provides a web-based and cloud-based DSS with a novel architecture that utilizes the advantages of microservices. The proposed system implements a multilingual support framework to dynamically upload, manage and run multilingual consistency and consensus-based decision support algorithms. An algorithm recommendation module is developed to help users choose suitable decision support algorithms. Tokenization is applied to deal with regulatory issues of knowledge protection, data privacy, and security while storing, analyzing, and transforming data into different algorithms for effective decision-making. An expert feedback study verified that our web and cloud-based DSS is a right artifact to fulfill the objective claimed in this paper.

Can Precise Irrigation Support the Sustainability of Protected Cultivation? A Life-Cycle Assessment and Life-Cycle Cost Analysis

To address sustainability challenges, agricultural advances in Mediterranean horticultural systems will necessitate a paradigmatic shift toward smart technologies, the impacts of which from a life cycle perspective have to be explored. Using life cycle thinking approaches, this study evaluated the synergistic environmental and economic performance of precise irrigation in greenhouse Zucchini production following a cradle-to-farm gate perspective. A cloud-based decision support system and a sensor-based irrigation management system (both referred to as “smart irrigation” approaches) were analyzed and compared to the farmer’s experience-based irrigation. The potential environmental indicators were quantified using life cycle assessment (LCA) with the ReCiPe 2016 method. For the economic analysis, life cycle costing (LCC) was applied, accounting not only for private product costs but also for so-called “hidden” or “external” environmental costs by monetizing LCA results. Smart irrigation practices exhibited similar performance, consuming on average 38.2% less irrigation water and energy, thus generating environmental benefits ranging from 0.17% to 62%. Single score results indicated that life cycle environmental benefits are up to 13% per ton of product. The cost-benefit analysis results showed that even though the implementation of smart irrigation imposes upfront investment costs, these costs are offset by the benefits to water and energy conservation associated with these practices. The reduction of investment costs and higher water costs in future, and lower internal rate of return can further enhance the profitability of smart irrigation strategies. The overall results of this study highlight that smart and innovative irrigation practices can enhance water-energy efficiency, gaining an economic advantage while also reducing the environmental burdens of greenhouse cultivation in a Mediterranean context.

A Cloud-based Decision Support System for Crop Management: Techniques and Applications

The purpose of this study is to provide an introduction to cloud-based decision support systems for crop management. In recent years, cloud computing, the Internet of Things (IoT), and machine learning have seen significant advancements, which has contributed to the rise in popularity of these kinds of systems. These technologies make it possible for farmers to gather, process, and analyse data in real time, which provides them with invaluable insights about the growth, yield, and overall health of their crops. This, in turn, assists farmers in making decisions that are better informed on the timing of when to sow, irrigate, and fertilise their crops, as well as when to harvest their produce. In this work, we draw on current research conducted in both the academic and industrial spheres to describe the many methods and uses of cloud-based decision support systems for crop management. In conclusion, we will discuss the many advantages and disadvantages of these systems, as well as the possible directions that future study may go.

Transport analytics in action: A cloud-based decision support system for efficient city bus transportation

Optimising city bus transport operations helps conserve fuel by providing the urban transport service as efficiently as possible. This study develops a Cloud-based Decision Support System (C-DSS) for transport analytics. The C-DSS is based on an intelligent model on location of depots for opening new depots and/or closing a few existing depots and allocation of city-buses to depots. The C-DSS is built on the Cloud Computing architecture with three layers and includes an efficient and simple greedy heuristic algorithm. Using modern information and communications technology tools, the proposed C-DSS minimizes the cost of city bus transport operations and in turn to reduce fuel consumption and CO2 emissions in urban passenger transport. The proposed C-DSS is demonstrated for its workability and evaluated for its performance on 25 large scale pseudo data generated based on the observation from Bangalore Metropolitan Transport Corporation (BMTC) in India.

Evidence-based Public Health Policy Models Development and Evaluation using Big Data Analytics and Web Technologies.

Introduction.According to WHO, “health policy refers to decisions, plans, and actions that are undertaken to achieve specific health care goals within a society”. Although policymaking is important to be based on scientific evidence, in many countries, evidence-informed decision-making remains the exception rather than the rule.Aim:This work presents a cloud-based Decision Support System for public health decision-making.Methods:In CrowdHEALTH, the concept of a Public Health Policy (PHP) is directly connected with one or more Key Performance Indexes (KPIs). The design and technical details of the system implementations are reported, along with use case scenarios.Results:The Policy Development Toolkit presents a unique interface and point of reference for policymakers, allowing them to create policy models and obtain analytical results for evidence-based decisions and evaluations.Conclusions:The hierarchical structure of the Public Health Policy Model offers versatility in the creation and handling of the policies, resulting in Health Analytics Tools Results Objects which offer quantitative policy support and provide the basis for meta-analytic operations.

Cloud-based decision support system for the detection and classification of malignant cells in breast cancer using breast cytology images.

The advancement of computer- and internet-based technologies has transformed the nature of services in healthcare by using mobile devices in conjunction with cloud computing. The classical phenomenon of patient-doctor diagnostics is extended to a more robust advanced concept of E-health, where remote online/offline treatment and diagnostics can be performed. In this article, we propose a framework which incorporates a cloud-based decision support system for the detection and classification of malignant cells in breast cancer, while using breast cytology images. In the proposed approach, shape-based features are used for the detection of tumor cells. Furthermore, these features are used for the classification of cells into malignant and benign categories using Naive Bayesian and Artificial Neural Network. Moreover, an important phase addressed in the proposed framework is the grading of the affected cells, which could help in grade level necessary medical procedures for patients during the diagnostic process. For demonstrating the e effectiveness of the proposed approach, experiments are performed on real data sets comprising of patients data, which has been collected from the pathology department of Lady Reading Hospital of Pakistan. Moreover, a cross-validation technique has been performed for the evaluation of the classification accuracy, which shows performance accuracy of 98% as compared to physical methods used by a pathologist for the detection and classification of the malignant cell. Experimental results show that the proposed approach has significantly improved the detection and classification of the malignant cells in breast cytology images.

A Cloud-Based Decision Support System for Self-Healing in Distributed Automation Systems Using Fault Tree Analysis

Downtime is a key performance index for industrial automation systems. An industrial automation system achieves maximum productivity when its downtime is reduced to the minimum. One approach to minimize downtime is to predict system faults and recover from them automatically. A cloud-based decision support system is proposed for rapid problem identifications and to assist the self-management processes. By running multiple parallel simulations of control software with real-time inputs ahead of system time, faults could be detected and corrected automatically using autonomous industrial software agents. Fault trees, as well as control algorithms, are modeled using IEC 61499 function blocks that can be directly executed on both physical controllers and cloud services. A case study of water heating process is used to demonstrate the self-healing process supported by the cloud-based decision support system.

Feasibility of using existing web services for on-demand data access within distributed environmental decision support systems

Abstract Web services providing machine-accessible interfaces to environmental data are now commonplace. Building on this, a current trend is to expand these web services to provide on-demand access to model and analysis services. This progression suggests the future possibility of cloud-based decision support systems (DSSs) integrating distributed data and analysis services delivered through a host of providers. Such distributed environmental DSSs have many potential benefits, but would require highly scalable and responsive web services. The objective of this study is to assess the current feasibility of building distributed environmental DSSs from existing web services in the United States. Results show that, of the many available web services providing information about soils, river network topology, watersheds, streamflow, etc., response times are often only a few seconds for a small project area, but can grow exponentially as the project area increases. On-demand watershed delineation remains a slow-to-respond service relative to the other services tested. Also, the results suggest the need to better co-locate servers near client applications to speed up response times. Collectively, these results provide specific areas where future research is needed in order to achieve the vision of on-demand distributed environmental DSSs.

Multi-objective inter-terminal truck routing

A tremendous growth in containerized trade has led to severe traffic problems in urban port areas associated with negative effects on both efficiency and pollution. In this paper, we address the multi-objective inter-terminal truck routing problem by specifically considering truck emissions. We present an efficient multi-objective archived simulated annealing approach and a visualization technique. Managerial insights are discussed based on an analysis of trade-offs. The algorithm is embedded in a cloud-based decision support system to consider contextual data, including traffic data and the current positions of trucks. This allows ports to utilize potentials of digitalization and optimization while addressing environmental issues.

Design Model for Cloud based Waste-Water Management System

Cloud computing is a network of remote servers hosted on the Internet to store, manage, and process large scale data, rather than a local server or a personal computer. Specially, the problems of big volume of spatial data in some of the emergency cases which make the needs for elastic way for storing and analysis and computing all of these resources. The combination of GIS (geographic information system) and cloud computing can provide new prospects for the development of information storage, processing and its application for the GIS. The work presented in this research project highlights the development of a cloud based decision support system for waste water management for the city. The GIS system has been designed to enable local authorities in Municipal Corporation to identify solutions for waste water management. Keywords: Cloud Computing, Pressure, pH, BOD, CO

Conceptual framework of a cloud-based decision support system for arsenic health risk assessment

Because of the lack of sustainable arsenic mitigation technologies and awareness of the possible health risks due to arsenic consumption, nearly 200 million people are potentially exposed to elevated levels of arsenic through drinking water in over 100 countries. Cloud computing (CC) could help bridge the gaps between wireless communication and data generated through environmental and/or health interventions. This study offers a conceptual framework of a cloud-based decision support system for arsenic health risk assessment (CC-AHRA). It also explains how the CC-AHRA could help assist exposed communities in assessing cancer risk due to arsenic exposure through drinking water. The study also discusses how the CC-AHRA can help water management authorities, professionals, researchers, and private enterprises in making informed decisions toward reducing the likely health risks due to arsenic consumption.

Adoption of Cloud Based Decision Support System for Building Information Modeling Software Selection

An increasing numbers of BIM software available in market has led to selection problem among the construction companies. Furthermore, an adoption of BIM is not only involved numerous of attributes, but also required high investment. Thus, selection of appropriate BIM software that fit project need has becoming more apparent. Therefore, this study aims to provide an analytical tool to evaluate and select the most suitable BIM software in fuzzy environment through adoption of contemporary Information Technology (IT) which is Web 2.0. The proposed Web DSS namely topsis4BIM has based on a real construction project in Malaysia.An analytical methodology in topsis4BIM was based on Multi Attributes Decision Making method using fuzzy TOPSIS.The software were evaluated and selected by BIM expert based on seven attributes and five alternatives software. In addition, this study has introduced a new architecture of DSS development through the current Web technology such as Web 2.0 and cloud computing.

Implementing a Cloud-Based Decision Support System in a Private Cloud

This paper introduces the architecture and the deployment of a Cloud based decision support system (DSS). The DSS the authors proposed is deployed on a platform Cloud (CloudBees) and managed by the OpenStack infrastructure. The DSS is built on the Cloud Computing architecture with three layers and includes the multiple criteria decision making (MCDM) method PROMETHEE II as well as the procedure of negotiation Hare in order to help the decision makers to select the best urban project. The Cloud based DSS reduces the deployment and processing time, ameliorates the communication and the cooperation between the decision makers, facilitates the accessibility and decrease the cost. The Cloud based multiple criteria DSS the authors designed and implemented has significant advantages. It reduces the deployment and processing time, ameliorates the communication and the cooperation between the decision makers, facilitates the accessibility, and decrease the cost.

RECOVER: An Automated, Cloud-Based Decision Support System for Post-Fire Rehabilitation Planning

Abstract. RECOVER is a site-specific decision support system that automatically brings together in a single analysis environment the information necessary for post-fire rehabilitation decision-making. After a major wildfire, law requires that the federal land management agencies certify a comprehensive plan for public safety, burned area stabilization, resource protection, and site recovery. These burned area emergency response (BAER) plans are a crucial part of our national response to wildfire disasters and depend heavily on data acquired from a variety of sources. Final plans are due within 21 days of control of a major wildfire and become the guiding document for managing the activities and budgets for all subsequent remediation efforts. There are few instances in the federal government where plans of such wide-ranging scope and importance are assembled on such short notice and translated into action more quickly. RECOVER has been designed in close collaboration with our agency partners and directly addresses their high-priority decision-making requirements. In response to a fire detection event, RECOVER uses the rapid resource allocation capabilities of cloud computing to automatically collect Earth observational data, derived decision products, and historic biophysical data so that when the fire is contained, BAER teams will have a complete and ready-to-use RECOVER dataset and GIS analysis environment customized for the target wildfire. Initial studies suggest that RECOVER can transform this information-intensive process by reducing from days to a matter of minutes the time required to assemble and deliver crucial wildfire-related data.

Cloud-based decision support systems and availability context: the probability of successful decision outcomes

In an age of cloud computing, mobile users, and wireless networks, the availability of decision support related computing resources can no longer guarantee five-nines (99.999%) availability. Since the dependence on decision support systems is ever increasing, obtaining accurate deterministic advice from these systems will become critical. This study proposes a probabilistic model that maps decision resource availability to correct decision outcomes. Grounded in system reliability theory, the probability functions are given and developed. The model is evaluated with a simulated decision opportunity and the outcome of the experimentation is quantified using a goodness of fit measure and ANOVA testing.