Migration Plan Research Articles

Data Migration Strategy and Execution for a Large-Scale ERP Implementation

Most organizations undergo rigorous Enterprise resource planning (ERP) system implementation projects along their journey towards digital transformation and business systems modernization. These ERP systems meet the business requirements of integrating data and processes across the various business domains of finance, sales, warehouse, manufacturing, global trade, human resources, etc. A critical aspect of any ERP implementation is data migration for a source or legacy system to the newly implemented target ERP system. For an organization to truly leverage the big data, machine learning and data automation capabilities offered by modern ERP systems, it is imperative that project implementation teams define and executed an enhanced data migration plan ensuring data quality, accuracy and completeness, as well as establishing data governance processes for ongoing operations. The article aims to explore the components of such an effective, all-encompassing data migration strategy and execution and the future trends around the same.

Data migration strategies in mergers and acquisitions: A case study of banking sector

Data migration is a critical component of mergers and acquisitions (M&A), particularly in the banking sector, where seamless integration of information systems is essential for operational continuity and regulatory compliance. This review examines data migration strategies employed during M&A activities, focusing on a case study in the banking sector. The study explores the challenges, methodologies, and best practices that ensure successful data transfer while minimizing risks and disruptions. In the context of banking, M&A transactions often involve integrating vast amounts of sensitive data, including customer records, financial transactions, and regulatory documents. Effective data migration is crucial to preserving data integrity, maintaining customer trust, and achieving the strategic goals of the merger or acquisition. This case study highlights the importance of a well-structured data migration plan, which includes comprehensive data mapping, validation processes, and robust security measures. The review delves into the various strategies adopted for data migration, such as phased migration, parallel run strategies, and the use of automated tools to streamline the process. It also discusses the significance of collaboration between IT and business teams to address data quality issues, ensure compliance with regulatory standards, and align migration efforts with the overall M&A objectives. Furthermore, the study examines the challenges faced during the data migration process, including legacy system integration, data redundancy, and the need for real-time data synchronization. By analyzing the outcomes of the case study, the review provides insights into the lessons learned and the critical success factors that can guide future M&A activities in the banking sector. In conclusion, this study underscores the pivotal role of effective data migration strategies in ensuring a smooth transition during mergers and acquisitions in the banking sector, ultimately contributing to the long-term success of the consolidated entity. Keywords: Data Migration, Mergers and Acquisitions, Banking Sector, Data Integration, Data Mapping, Regulatory Compliance, Phased Migration, Parallel Run Strategy, Legacy Systems, Data Synchronization.

Fifth Generation Non-Standalone to Standalone Wireless Architecture Reconstruction Solution and Implementation: South Africa Context

The wireless mobile communication industry has evolved immensely over the past five decades, starting from the voice-centric 1G technology to the current data-centric 5G technology because of the increasing data traffic and the need for higher data speed rates. To provide the initial experience of 5G technology, many network operators worldwide started their deployment with the Non-Standalone (NSA) solution to mainly reduce the cost and time. 5G NSA solution already provides lower latency, higher network capacity, and bandwidth compared to the legacy network, however, there is growing oncern about its reliance on the 4G core network, capability, and the long-term migration plan to 5G Standalone (SA) network solution. This paper discusses a brief review of 5G evolution, standardization formulation, deployment strategies, security enhancement, challenges, and presents the reconstruction of 5G NSA to SA solution implementation. Furthermore, a 5G field test will be performed to analyze the user experience under the NSA NR in South Africa, and key practical challenges facing the South African MNOs from the perspective of business and technical challenges will be discussed with recommendations.

The impact of the COVID 19 pandemic on motivation to stay: a comparison between Chinese and non-Chinese international students in Nova Scotia, Canada

ABSTRACT The COVID-19 pandemic has disproportionately impacted international students due to unexpected changes in policies and regulations regarding their visa status and immigration, travel restrictions, and heightened scrutiny against foreigners. Such changes potentially disrupt and affect international students’ post-graduation migration plan: whether they decide to go back to their home country or stay in Canada and apply for permanent residency. This may particularly be the case for Chinese international students, the 2nd largest group of international students in Canada, due to the rise of anti-Asian racism and the stigmatization that the COVID-19 is a ‘Chinese virus’. Using ‘intellectual migration’ as our analytical framework, we pay particular attention to the experiences of Chinese international students in the province of Nova Scotia, an intellectual periphery in Canada. Drawing upon data from online surveys and focus groups, this study compares the experiences of Chinese and non-Chinese international students during the pandemic and whether these experiences have impacted their post-migration plans and their motivation to stay in Nova Scotia, Canada.

Migration Misadventures: The Challenges of Creating an Inter-Institutional Migration Plan

Archival institution migration projects are planned data transfers from existing data formats to more accessible formats that assist in preserving transferred data, improving information retrieval, and preventing obsolescence. Various archival institutions are increasingly planning migration projects because current technology makes the process easier and more affordable than in the past. In this proceeding, I will walk through the planning stages and associated challenges of NASA’s inter-institutional migration plan, Archival Description Center Migration Plans Project, which is part of a larger reorganization of the archives’ functional area to an Enterprise program. For this project, we utilized the open-source, standards-based, web-based applications Access to Memory and Archivematica. At the current project stage, we have collaborated with archivists from seven of the eleven existing NASA archive centers. One of the goals of this Enterprise reorganization is to facilitate cross-collection search and discovery and establish cross-institution metadata standards.

Livelihood diversification and migration intentions among land-poor youth in Tigray, Northern Ethiopia: do they correlate with livestock assets, trust, and trustworthiness?

Youth unemployment has been prevalent in Ethiopia. Over the past decades, efforts to rehabilitate degraded communal lands have been taking place in Ethiopia. This has created the opportunity to organize landless and land-poor youth and implement a policy of allocating rehabilitated lands for youth to engage in agriculture as a livelihood option. However, whether these rural youth will remain in agriculture or choose other livelihood options including migration, and how their trusting behaviors (trust and trustworthiness) and other factors influence their choices are worth investigating and are the aims of this study. This will help our understanding of what would incentivize the youth to enhance their livelihoods. We used data collected from samples of 1,138 youth group members in the 2016 survey and from 2,427 youth group members in the 2019 survey in five districts of the Tigray region of Northern Ethiopia. Our results from panel data multinomial logit and probit models show that the number of oxen, access to land in the land rental market, and income from youth group activity significantly correlated with youth group members' choices for livelihood options and planning for migrating out of the country. A higher number of oxen owned by the youth group members are associated with a higher likelihood that the youth choose agriculture as a livelihood. Youth group members with a larger number of oxen are also less likely to plan for migration. We also found that more trusting youth group members are more likely to choose off-farm employment relative to staying in agriculture than less trusting members. More trustworthy members are less likely to migrate and more likely to stay in agriculture because trustworthiness is associated with better access to land in the rental market. Thus, improving youth group members' access to land and their asset endowments such as oxen for increasing the productivity of youth group activity and hence income would incentivize youth group members to stay in agriculture and enhance youth group activity as a sustainable livelihood.

ESCALB: An effective slave controller allocation-based load balancing scheme for multi-domain SDN-enabled-IoT networks

In software-defined networking (SDN), several controllers improve the reliability as well as the scalability of networks such as the Internet-of-Things (IoT), with the distributed control plan. To achieve optimal results in IoT networks, an SDN can be employed to reduce the complexity associated with IoT and provide an improved quality-of-service (QoS). With time, it is likely expected that the demand for IoT will rise, and a large number of sensors will be connected, which can generate huge network traffic. With these possibilities, the SDN controllers processing capacity will be surpassed by the traffic sent by the IoT sensors. To handle this kind of challenge, and achieve promising results, a dynamic slave controller allocation with a premeditated mechanism can play a pivotal role to accomplish the task management and migration plan. Following this, we proposed an efficient slave controller allocation-based load balancing approach for a multi-domain SDN-enabled IoT network, which aims to transfer switches to a controller with idle resources effectively. Among several slave controllers for selecting a target controller, a multi-criteria decision-making (MCDM) strategy, i.e., an analytical network process (ANP) has been used in our approach to enrich communication metrics and maintain high-standard QoS statistics. Moreover, switch migration is modeled with knapsack 0/1 problem to achieve maximum utilization of the slave controllers. Our proposed scheme enabled with a flexible decision-making process for selecting controllers with varying resources. The results demonstrated with emulation environment show the effectiveness of the ESCALB.

Transnational Business Services, Cultural Transformation/Identity, and Employee Performance: With Special Focus on Migration Experience and Emigration Plan

Abstract The aim of this article is to analyze the impact of cultural identity centrality on stress, burnout, probability to leave the job, and job performance in the context of cultural transformation requirements, which is common in transnational business services. The key contribution of this analysis consists of controlling for the effects of migration experience and migration plan neglected so far in the relevant literature. This study also includes the first-time use of a random sample of 200 Kosovan call center employees. Findings indicate that individual identity centrality and organizational identity centrality are positively associated with stress and burnout. National identity centrality, however, reduces stress and burnout, but it is statistically significant only in the burnout model. As expected, empirical results suggest that migration experience reduces stress and burnout, while migration plan increases employee performance. Further results indicate that the probability to leave the job is enhanced by perceived job stress and reduced by perceived job satisfaction.

Assessment of Urban Resilience as an Approach towards Urban Sustainability: A Case of Gurugram India.

Abstract: Indian cities are complex and interdependent system, extremely vulnerable to threats from both natural risks and manmade disasters caused by the growing urban population, migration, depleting resources and terrorism. There are abundant evidences on resource consumption and emissions, biodiversity and land use change to show that Indian cities are responsible for a large part of unsustainable trends which push the planet beyond its ecological boundaries. With expedited urbanization, it becomes imperative to prioritise vulnerability assessment in order to increase resilience and thus attain sustainability. Recent studies indicate that the resilience framework is not consistent; they have either been applied in a natural resource context or focused on a single factor such as socio- economic vulnerability or structural weakness of physical components. Secondly, there is lack of resilience framework for developing nations or local contexts as the applicability of results from developed countries are problematic to instrument technically or theoretically at native scale. Given the diversity of interpretations and application of the resilience assessment in complex urban systems, this paper looks at factors and parameters that influence the Indian cities with major focus on Gurugram.(the third wealthiest city in India, by per-capital income with a massive population but such phenomena growth has overwhelmed city planning even with the availability of funds).The paper further explores the resilience assessment framework based on the five defined and measurable domains including social (education equity, transportation access, health coverage, communication capacity), economic (employment, income, housing capital), institution (migration plan, flood coverage, political fragmentation by Indian institutions such as National Institute of Urban Affairs (NIUA), physical (e.g. water, sanitation, housing and land use), spatial (elevation, spatial and temporal indicators), human (e.g. literacy rate, health insurance) and Environment (e.g. ecosystem services, environmental policies).Various case examples are used to suggest that urban resilience can be conceived as a multidisciplinary framework to analyse the reactive, adaptive and transformative capacities of (and within) the complex urban system of Indian cities.

MoDEMS: Optimizing Edge Computing Migrations for User Mobility

Edge computing capabilities in 5G wireless networks promise to benefit mobile users: computing tasks can be offloaded from user devices to nearby edge servers, reducing users’ experienced latencies. Few works have addressed how this offloading should handle long-term user mobility: as devices move, they will need to offload to different edge servers, which may require migrating data or state information from one edge server to another. In this paper, we introduce MoDEMS, a system model and architecture that provides a rigorous theoretical framework and studies the challenges of such migrations to minimize the service provider cost and user latency. We show that this cost minimization problem can be expressed as an integer linear programming problem, which is hard to solve due to resource constraints at the servers and unknown user mobility patterns. We show that finding the optimal migration plan is in general NP-hard, and we propose alternative heuristic solution algorithms that perform well in both theory and practice. We finally validate our results with real user mobility traces, ns-3 simulations, and an LTE testbed experiment. Migrations reduce the latency experienced by users of edge applications by 33% compared to previously proposed migration approaches.

Scaling AI-based industry 4.0 projects in the medical device industry: An exploratory analysis

As technological advancements in artificial intelligence (AI) accelerate, the effect it has on the adoption of Industry 4.0 is significant. The adoption of AI-based Industry 4.0 projects creates a significant opportunity to generate new revenue streams and high-value customer-centric products. However, medical device manufacturing organisations are strictly scrutinised by regulatory authorities due to the critical nature of these devices and their impact on humans. This presents a challenging environment for medical device manufacturers to experiment with and implement new technologies. However, the significance and impact of AI-based projects are acknowledged by regulatory authorities such as the FDA and EMA and PMDA, allowing the incorporation of AI-based devices. This change in regulations, although limited, opens up immense opportunities for medical device manufacturers. Although extensive studies on the barriers, challenges, and failure factors of AI projects are available, very few studies explore these from a medical device manufacturing perspective. This is essential due to the significant limitations posed by the regulations governing medical device development. This study analyses the challenges and factors affecting the scaling of AI-based Industry 4.0 projects in medical device manufacturing organisations. The study found three major factors influencing the scaling of AI-based Insutry 4.0 projects. They comprise infrastructural costs, security risks and a change management migration plan. However, investing in these factors with a view to scaling may create additional revenue sources and also help in reaching a faster return on investment. Such investments can also improve the overall productivity of the industry, allowing the creation of high-value customer-centric products. The perceived security risks in the industry were found to be higher than the risks analysed in previous literature, suggesting a need to create an informed workforce. The study also found that for effective implementation of scalable AI-based industry 4.0 projects a skilled workforce with a shared long-term vision is required.

Quality care guidelines for uterine artery embolisation in women with symptomatic uterine fibroids in Australia and New Zealand: According to the AGREE-II checklist and endorsed by the Interventional Radiology Society of Australasia.

Quality care guidelines for uterine artery embolisation in women with symptomatic uterine fibroids in Australia and New Zealand: According to the AGREE-II checklist and endorsed by the Interventional Radiology Society of Australasia.

British Nationality and Borders Bill and the international protection of refugees in the light of the concept of community interest in international law

The crisis that Europe faced in 2015 has never been resolved and countries have adopted different strategies to deal with the influx of migrants . Some of them raise serious legal doubts for good reason. One of the new national solutions currently in the process of passing is the new migration plan announced by the United Kingdom in the Nationality and Borders Bill last year. The aim of the reform is to improve the British asylum system and to fight effectively illegal immigration and people smuggling.The aim of the article is to present the most important assumptions of the British reform in the field of grant- ing refugee status. The analysis would allow to assess the compliance of the designed solutions with international obligations, the fulfilment of which should form the basis of the asylum policy of each State being a party to the 1951 Convention relating to the Status of Refugees. The main aim of the article, however, is to draw attention to the fact that the international protection of refugees should be equated with community interests and referring to the individual interest of the State is an erroneous and dangerous assumption.

Evaluation of a gender synchronized family planning intervention for married couples in rural India: The CHARM2 cluster randomized control trial.

SummaryBackgroundDespite calls for gender transformative family planning interventions to increase male engagement and female reproductive agency, there is limited research involving rigorous evaluation of the integration of these approaches. CHARM2 (counseling Husbands and wives to Achieve Reproductive Health and Marital Equity) builds upon a prior three-session male engagement intervention by integrating female-focused sessions emphasizing women's choice and agency (i.e., gender synchronization). We hypothesized that CHARM2 participants will be more likely to report marital contraceptive use and communication and women's contraceptive agency, and less likely to report unintended pregnancy, relative to participants in the control condition.MethodsWe conducted a two-armed cluster randomized controlled trial evaluating the effects of CHARM2 on marital contraceptive use, communication, decision-making; women's contraceptive agency, and pregnancy among young married couples in rural Maharashtra, India (ClinicalTrials.gov #NCT03514914, complete). 40 geographic clusters, defined based on the catchment areas of subcenter health facilities (the most proximal level of community health care within India's public health system) were randomized to control (n = 20) and intervention (n = 20). We assigned all participants within that geographic cluster to the corresponding cluster treatment condition; participants, investigators, and study staff were not masked to treatment assignment. Eligibility criteria included wife aged 18–29, couple residing together for at least six months with no plan for migration, and neither spouse sterilized or infertile. The CHARM2 intervention included five provider-delivered sessions on gender equity and family planning, two delivered in parallel to husbands and wives separately by gender-matched providers, and one final joint session, delivered within the four months subsequent to baseline survey. We conducted surveys and pregnancy testing at baseline and 9-month and 18-month follow-up. We used difference-in-differences multilevel mixed effects logistic regressions to assess CHARM2 effects on marital contraceptive use and communication, and women's contraceptive agency; we used single time point mixed effects logistic regressions for pregnancy outcomes. All analyses used an intention-to-treat approach.Findings1201 couples were recruited between September 2018 and June 2019; 600 intervention and 601 control. All couples were included in outcome analyses. Full couple retention was 90·2% (n = 1083) at 9-month follow-up and 90·5% (n = 1087) at 18-month follow-up. Modern contraceptive use was higher among intervention participants at 9-month but not 18-month follow-up (9-month adjusted ratio of odds ratio [AROR] 1·5, 95% CI 1·03–2·3; 18-month AROR 0·8, 95% CI 0·4–1·4). Communication (9-month AROR 1·9, 95% CI 1·0–3·6; 18-month AROR 2·7, 95% CI 1·5–4·8) and agency (9-month AROR 5·1, 95% CI 1·2–22·4; 18-month AROR 8·1, 95% CI 1·4–48·2) both improved at both time points. There was no significant difference in pregnancy between groups over the 18-month period (AOR 0·8, 95% CI 0·7–1.1) However, for women who expressed fertility ambivalence at baseline, unintended pregnancy was marginally less likely among intervention participants (47% vs 19%) (p = 0·07). There were no reported adverse events.InterpretationThe CHARM2 intervention offers a scalable model to improve contraceptive use, communication, and agency and possibly decrease unplanned pregnancies for couples in rural India.

EosDNN: An Efficient Offloading Scheme for DNN Inference Acceleration in Local-Edge-Cloud Collaborative Environments

With the popularity of mobile devices, intelligent applications, e.g., face recognition, intelligent voice assistant, and gesture recognition, have been widely used in our daily lives. However, due to the lack of computing capacities, it is difficult for mobile devices to support complex Deep Neural Network (DNN) inference. To alleviate the pressure on these devices, traditional methods usually upload part of the DNN model to a cloud server and perform a DNN query after uploading an entire DNN model. To achieve real-time DNN query, we consider the collaboration between local, edge and cloud, and perform DNN query when uploading DNN partitions. In this paper, we propose an Efficient offloading scheme for DNN Inference Acceleration (EosDNN) in a local-edge-cloud collaborative environment, where the DNN inference acceleration is mainly embodied in the optimization of migration delay and realization of real-time DNN query. EosDNN comprehensively considers the migration plan and uploading plan, where for the former, a Particle Swarm Optimization with Genetic Algorithm (PSO-GA) is applied to obtain the distribution of DNN layers under the server with the lowest migration delay, and for the latter, a Layer Merge Uploading Algorithm (LMU) is proposed to obtain DNN partitions and their upload order with efficient DNN query performance. Experimental results demonstrate that EosDNN can be applied to large-scale DNN model migration, which can achieve an ideal migration delay and obtain a more fine-grained DNN partition uploading plan, thereby optimizing DNN query performance.

Potential of four definitions of metabolic syndrome to discriminate individuals with different 10-year cardiovascular disease risk scores: a cross-sectional analysis of an Iranian cohort

ObjectiveWe aimed to reveal the potential of four different metabolic syndrome (Mets) definitions to differentiate subjects according to 10-year risk of cardiovascular disease.DesignA cross-sectional analysis of a prospective cohort.SettingThis study...

Correlational Study on the Contextual Factors Influencing the Effectiveness of Flexible Learning: the Case of One State University in the Philippines

With the onset of the pandemic, no face-to-face instruction is seemingly the most pressing problem faced today in schools. The acquisition of required skills has been challenged by the digital divide and limited access to quality and equitable higher education. This scenario has pushed LSPU for further flexibility in rendering its services anchored on its vision of becoming a center of technological innovation that promotes interdisciplinary learning, sustainable utilization of resources, collaboration, and partnership with the community and stakeholders. Hence, this quantitative, non-experimental research with observational approach utilized predictive design explored the LSPU's delivery and assessment of flexible learning based on the outcomes-based education. The research instrument with an excellent reliability coefficient of .962. was programmed in the google form and the extracted data from 5,314 respondents were analyzed using summary statistics and regression analysis. Multiple linear regression analysis was used to treat the data. It was found out that there is a high extent of implementation of sound assessment practices that support student learning. The respondents agreed that the level of teacher’s assessment practices and support to student learning are promising and attuned to the requirements of flexible learning. The University’s migration plan really worked for the faculty and students. Majority of the respondents are motivated and engaged in flexible learning. The study found that 55.1% variance in the effectiveness of the implementation of flexible learning is positively explained by teacher’s assessment practices and support to students, student’s attainment of cognitive learning outcomes, student’s engagement and motivation, and student’s self-assessed academic performance during the new normal.

Plan for Academic Networks of the University of Montenegro migration to IPv6 with application examples

The paper presents the migration plan to theIPv6 protocol in the Academic Network of the University of Montenegro with application examples in the IT Center and in the network of Faculty of electrical engineering. The application procedures, the plan of the recommended migration model from IPv4 to IPv6 and Dual Stack technology and their advantages are explained and discussed . The concept of segmentation of the new IPv6 address range and migration of public services to the new IP protocol is also presented.

Energy-aware workflow task scheduling in clouds with virtual machine consolidation using discrete water wave optimization

The scientific workflows are high-level complex applications that demand more computing power. The cloud data center (CDC) remains one of the essential models of economic infrastructure for workflow applications. These CDCs consume a lot of electric power while running workflow applications. Hence, efficient energy-aware scheduling techniques are required to perform the task to a virtual machine (VM) mapping. The existing researches overlooked to join the workflow scheduling and VM consolidation which addresses resource utilization and energy consumption effectively. In this article, we propose an energy-aware algorithm for workflow scheduling in cloud computing with VM consolidation called EASVMC. The proposed EASVMC approach is modeled to address the multi-objectives such as energy consumption, resource utilization, and VM migrations. The EASVMC algorithm runs in two phases task scheduling and VM consolidation (VMC). In the first phase, the task with maximum execution length is mapped to the virtual machine that will perform it with the minimum energy. The second phase contains VM consolidation is a prominent NP-hard problem. The VMC phase categorizes the physical hosts into the normal load, under-loaded and overloaded hosts based on CPU utilization. Double threshold values are used for this purpose. VMs from underloaded and overloaded hosts are migrated to normally loaded hosts. For the VMC phase, we used a nature-inspired meta-heuristic approach called the Water Wave Optimization (WWO) algorithm, which finds a suitable migration plan to reduce the energy consumption by increasing the overall resource utilization and switch off idle hosts after migrating its VMs to a suitable target host. The efficiency of our proposed method evaluated using the WorkflowSim simulation tool with five different real-world scientific workloads. The experimental results show that the EASVMC approach surpassed the similar works in stated objectives irrespective of diverse workloads.

An adaptive and cost‐efficient migration to cloud approach in dynamic environments

SummaryDue to the variety in available cloud providers along with the frequent changes in strategic objectives of an enterprise, migrating existing software components to the cloud has become a challenging decision in the software maintenance phase. “Financial” and “customer satisfaction” viewpoints are two important strategic objectives of all enterprises that greatly affect the decision about migration to the cloud. Moreover immense number of target cloud services with too many configurations and cost models has made the search space of possible migrations huge. Many existing approaches of software migration to the cloud have modeled the problem as deployment optimization of software components over available platforms, while in this research following a valid migration plan is intended rather than proposing a final optimal migration solution (deployment). A migration plan is a sequence of actions to be taken by the technical team to move the software components to the cloud stepwise. Since at each stage of the migration there might be many valid alternative paths to follow, a recommender module is proposed to direct the management by recommending the best migration plan out of all valid plans in a Labeled Transition System. The recommendation is based on the current state of the enterprise which is estimated using a two‐state Hidden Markovian Model by observing ambient signals. The empirical study showed that particularly in dynamic and changing conditions the proposed adaptive and plan‐oriented method succeeded in posing lower accrued maintenance costs on the enterprise over time with confidence 90% compared to the non‐adaptive method due to its reactive and self‐balancing nature.