Data Migration Technique Options on Reforming Jiwasraya's Existing Database

PT Asuransi Jiwasraya has been facing a crisis since 2020, which later probably demand them to reconstruct their insurance policy. Nothing is decided aside from the reconstruction idea. This is a hard task to deal with as there is a high risk to be borne along. In the worst case, bankruptcy awaits. As technology has taken over most industries, including insurance, it is only normal for the company to take advantage of the applied technology. However, it is still unknown whether the database used could help fulfill the mission. Considering loads of data might be higher by year, it will be more efficient to use the integrated database to transfer the whole data into a new-adapted database rather than creating a new one and manually adapt then add the data. This could be done by doing a Bottom-up approach that occurs in two big steps. This is the safest choice now which is handy and possible.

A Prescriptive Model for Migration to Microservices Based on SDLC Artifacts

Microservices architectural style is gaining popularity in industry and is being widely adopted by large corporations like Amazon, Netflix, Spotify, eBay, and many more. Several other organizations are also preferring to migrate their existing enterprise scale applications to microservices architecture. Researchers have proposed various approaches for microservices decomposition to be used in migrating or rebuilding a monolithic application to microservices. Applying any available approach to an existing monolithic application is not a straightforward decision; thus, there is a need for guidelines that assist in the migration process. There are various challenges in a migration process because different migration approaches use different sets of input data to identify microservices. Since the available migration techniques are not structured, logically, selection of an appropriate migration strategy is a difficult decision for any system architect. So, it is a recurrent open research question – which migration technique should be adopted to get microservices for a legacy monolithic application? This paper addresses this research challenge by examining existing approaches for microservices migration and groups them based on software development life cycle (SDLC) artifacts. Our research also proposes a microservices prescriptive model (MPM) from the existing prominent microservice migration techniques. This model provides recommendation (1) for refactoring an existing legacy system to microservices, and (2) for new microservices development projects. Our study also helps in gaining more insight about greenfield and brownfield development approaches in microservices applications. Moreover, researchers and practitioners of the field can benefit from this model to further validate their migration approaches based on the available system artifacts.

Data migration: relational RDBMS to non-relational NoSQL

As a part of achieving specific targets, business decision making involves processing and analyzing large volumes of data that leads to growing enterprise databases day by day. Considering the size and complexity of the databases used in today’s enterprises, it is a major challenge for enterprises to re-engineering their applications that can handle large amounts of data. Compared to traditional relational databases, non-relational NoSQL databases are better suited for dynamic provisioning, horizontal scaling, significant performance, distributed architecture and developer agility benefits. Based on the concept of Object Relational Mapping (ORM) and traditional ETL data migration technique this thesis proposes a methodology for migrating data from RDBMS to NoSQL. The performance of the proposed solution is evaluated through a comparative analysis of RDBMS and NoSQL implementations based on query performance evaluation, query structure and developmental agility.

Data migration: relational RDBMS to non-relational NoSQL

As a part of achieving specific targets, business decision making involves processing and analyzing large volumes of data that leads to growing enterprise databases day by day. Considering the size and complexity of the databases used in today’s enterprises, it is a major challenge for enterprises to re-engineering their applications that can handle large amounts of data. Compared to traditional relational databases, non-relational NoSQL databases are better suited for dynamic provisioning, horizontal scaling, significant performance, distributed architecture and developer agility benefits. Based on the concept of Object Relational Mapping (ORM) and traditional ETL data migration technique this thesis proposes a methodology for migrating data from RDBMS to NoSQL. The performance of the proposed solution is evaluated through a comparative analysis of RDBMS and NoSQL implementations based on query performance evaluation, query structure and developmental agility.

Prevalence of Alaria alata mesocercariae in wild boars from Brandenburg, Germany

Since 2002, Alaria (A.) alata mesocercariae (AM) have been found during routine Trichinella inspection of wild boars in many European countries. To date, human infection with AM through consumption of undercooked or raw AM infested wild boar meat cannot be excluded. In Germany, data on the parasite’s prevalence in wild boars are scarce. To better understand temporal and spatial fluctuations of this parasite, this study investigated the prevalence of AM in wild boars in the German federal state of Brandenburg during three hunting seasons from 2017 to 2020. In total, 28.3% (100/354, 95% CI: 23.3–33.3%) of all wild boars sampled in eight counties of Brandenburg were tested positive for AM by Alaria alata mesocercariae migration technique (AMT). AM were detected in wild boars from seven different counties. Samples from one county (Havelland) tested completely negative for AM (0/16). Prevalences of the seven AM positive counties of Brandenburg ranged from 11.5 (3/26, 95% CI: 2.5–30.1%) in Märkisch-Oderland to 64.1% (25/39, 95% CI: 47.2–78.8%) in Uckermark. An association between sex and A. alata positivity could not be determined. A statistically significant increase in frequency of older AM positive wild boars was observed (p = 0.001). For a nationwide assessment of the prevalence of A. alata in wild boars and the risk for consumers of ingesting viable AM by consumption of raw or undercooked AM infested wild boar meat, further long-term studies in different regions of Germany are needed.

Cauchy Particle Swarm Optimization (CPSO) based Migrations of tasks in a virtual machine

Cloud computing technology helps to resolve the problem in storage management by providing virtual resources to the end users. But, the overloading of virtual machines results in degradation of performances as well as it increases in the energy consumption of the virtual machines. Several techniques were used to determine the workloads of the cloud and then apply the migration algorithm for efficient utilization of resources. But, the process depends on the past outputs and only few step ahead predictions. Most of the techniques allocate the resources based on all the attributes. This results in higher processing time for the allocation. Hence, in this, an attribute based resource allocation is proposed to allocate and utilize the resources effectively based on the user demands. The modified Principal component analysis and relief is used for the attribute selection. Then, the selected attribute is processed with the hybrid Cauchy particle swarm algorithm for the allocation of resources. The proposed method is tested google cluster dataset and its performance is evaluated in terms of migration count and power consumption. The proposed method performance is compared with the automated migration technique (ALM) and forecast based migration technique (CF-LA). The proposed method outperforms both the existing technique by reducing the power consumption and the migration count between the virtual machines. Hence, the proposed MPCA and relief basedCPSO is best for allocating the resources dynamically in the cloud.