Comprehensive Study of MERN Stack - Architecture, Popularity and Future Scope

The biome of JavaScript is constantly evolving and a new framework or library is launched on a regular basis claiming better features. This study focuses on giving guideline to the reader in the process of choosing the best technology by comparing the runtime performance of the MERN (MongoDB, Express, React.js and Node.js) and MEVN (V stands for Vue.js) stack as well as increasing their workability in the job market. To fulfill the purpose, an experiment was conducted to conclude how swift the said stacks perform in building a single page application. For the experiment, two easy to-do applications are built with MERN and MEVN and loading time, adding time, updating and deleting time of the tasks are measured. Moreover, to be able to reveal the software stack trend among the Swedish-based companies, a survey study was conducted. Out of approximately 70 companies contacted, 12 responded. Due to the low number of response on the survey drawing conclusions from the survey and generalizing, the result was challenging. However, the results gathered show that all the respondents use either Vue.js or React.js or both as their client-side software though they are not always combined with Node.js and MongoDB. Other preferred server-side software that are used in combination with React.js or Vue.js are Java, Go and Django. Some of the main factors that the respondents pointed out that affects their choice of software was the ease of learning, the community behind the software, clients’ need and availability of that specific software developer.

eHMCOKE: an enhanced overlapping clustering algorithm for data analysis

Improved multi-cluster overlapping k-means extension (IMCOKE) uses median absolute deviation (MAD) in detecting outliers in datasets makes the algorithm more effective with regards to overlapping clustering. Nevertheless, analysis of the applied MAD positioning was not considered. In this paper, the incorporation of MAD used to detect outliers in the datasets was analyzed to determine the appropriate position in identifying the outlier before applying it in the clustering application. And the assumption of the study was the size of the cluster and cluster that are close to each other can led to a higher runtime performance in terms of overlapping clusters. Therefore, additional parameters such as radius of clusters and distance between clusters are added measurements in the algorithm procedures. Evaluation was done through experimentations using synthetic and real datasets. The performance of the eHMCOKE was evaluated via F1-measure criterion, speed and percentage of improvement. Evaluation results revealed that the eHMCOKE takes less time to discover overlap clusters with an improvement rate of 22% and achieved the best performance of 91.5% accuracy rate via F1-measure in identifying overlapping clusters over the IMCOKE algorithm. These results proved that the eHMCOKE significantly outruns the IMCOKE algorithm on mosts of the test conducted.

Applying matrix factorization to consistency-based direct diagnosis

Configuration systems must be able to deal with inconsistencies which can occur in different contexts. Especially in interactive settings, where users specify requirements and a constraint solver has to identify solutions, inconsistencies may more often arise. In inconsistency situations, there is a need of diagnosis methods that support the identification of minimal sets of constraints that have to be adapted or deleted in order to restore consistency. A diagnosis algorithm’s performance can be evaluated in terms of time to find a diagnosis (runtime) and diagnosis quality. Runtime efficiency of diagnosis is especially crucial in real-time scenarios such as production scheduling, robot control, and communication networks. However, there is a trade off between diagnosis quality and the runtime efficiency of diagnostic reasoning. In this article, we deal with solving the quality-runtime performance trade off problem of direct diagnosis. In this context, we propose a novel learning approach based on matrix factorization for constraint ordering. We show that our approach improves runtime performance and diagnosis quality at the same time.

An empirical evaluation of cost-based federated SPARQL query processing engines

Finding a good query plan is key to the optimization of query runtime. This holds in particular for cost-based federation engines, which make use of cardinality estimations to achieve this goal. A number of studies compare SPARQL federation engines across different performance metrics, including query runtime, result set completeness and correctness, number of sources selected and number of requests sent. Albeit informative, these metrics are generic and unable to quantify and evaluate the accuracy of the cardinality estimators of cost-based federation engines. To thoroughly evaluate cost-based federation engines, the effect of estimated cardinality errors on the overall query runtime performance must be measured. In this paper, we address this challenge by presenting novel evaluation metrics targeted at a fine-grained benchmarking of cost-based federated SPARQL query engines. We evaluate five cost-based federated SPARQL query engines using existing as well as novel evaluation metrics by using LargeRDFBench queries. Our results provide a detailed analysis of the experimental outcomes that reveal novel insights, useful for the development of future cost-based federated SPARQL query processing engines.

Service-based Processing of Gigapixel Images

With the ongoing improvement of digital cameras and smartphones, more and more people can acquire high- resolution digital images. Due to their size and high performance requirements, such Gigapixel Images (GPIs) are often challenging to process and explore compared to conventional low resolution images. To address this problem, this paper presents a service-based approach for GPI processing in a device-independent way using cloud-based processing. For it, the concept, design, and implementation of GPI processing functionality into service-based architectures is presented and evaluated with respect to advantages, limitations, and runtime performance.