Key-Value stores (KVSs) are the most flexible and simplest model of NoSQL databases, which have become highly popular over the last few years due to their salient features such as availability, portability, reliability, and low operational cost. From the perspective of software engineering, the chief obstacle for KVSs is to achieve software quality attributes (consistency, throughput, latency, security, performance, load balancing, and query processing) to ensure quality. The presented research is a Systematic Literature Review (SLR) to find the state-of-the-art research in the KVS domain, and through doing so determine the major challenges and solutions. This work reviews the 45 papers between 2010–2018 that were found to be closely relevant to our study area. The results show that performance is addressed in 31% of the studies, consistency is addressed in 20% of the studies, latency and throughput are addressed in 16% of the studies, query processing is addressed in 13% of studies, security is addressed in 11% of the studies, and load balancing is addressed in 9% of the studies. Different models are used for execution. The indexing technique was used in 20% of the studies, the hashing technique was used in 13% of the studies, the caching and security techniques were used together in 9% of the studies, the batching technique was used in 5% of the studies, the encoding techniques and Paxos technique were used together in 4% of the studies, and 36% of the studies used other techniques. This systematic review will enable researchers to design key-value stores as efficient storage. Regarding future collaborations, trust and privacy are the quality attributes that can be addressed; KVS is an emerging facet due to its widespread popularity, opening the way to deploy it with proper protection.

Handling complexity in the data of information systems has emerged into a serious challenge in recent times. The typical relational databases have limited ability to manage the discrete and heterogenous nature of modern data. Additionally, the complexity of data in relational databases is so high that the efficient retrieval of information has become a bottleneck in traditional information systems. On the side, Big Data has emerged into a decent solution for heterogenous and complex data (structured, semi-structured and unstructured data) by providing architectural support to handle complex data and by providing a tool-kit for efficient analysis of complex data. For the organizations that are sticking to relational databases and are facing the challenge of handling complex data, they need to migrate their data to a Big Data solution to get benefits such as horizontal scalability, real-time interaction, handling high volume data, etc. However, such migration from relational databases to Big Data is in itself a challenge due to the complexity of data. In this paper, we introduce a novel approach that handles complexity of automatic transformation of existing relational database (MySQL) into a Big data solution (Oracle NoSQL). The used approach supports a bi-fold transformation (schema-to-schema and data-to-data) to minimize the complexity of data and to allow improved analysis of data. A software prototype for this transformation is also developed as a proof of concept. The results of the experiments show the correctness of our transformations that outperform the other similar approaches.

KSII Transactions on Internet and Information Systems TIIS Paper Details

Financial fraud poses a severe threat to the integrity of corporate financial statements and market efficiency. While auditors play a crucial role in detecting and preventing such fraud, the unique characteristics of this domain, including class imbalance, high misclassification costs, and am-biguous attributes, pose significant challenges. Traditional classification algorithms may not be optimally suited for this task. This paper proposes a novel stacking ensemble learning approach to enhanced detecting of Financial Statement Scam. By combining the strengths of multiple base learners, including Random Forest, XGBoost, and Gradient Boosting Decision Trees, our model leverages their diverse predictions to improve overall performance. A Logistic Regression me-ta-learner is employed to integrate the outputs of the base learners, capturing their collective wisdom while mitigating individual weaknesses. Extensive experimentation using a real-world dataset shown the superiority of our approach, achieving remarkable recall, precision, accuracy, and area under the curve metrics. The proposed model outperforms individual base learners, under-scoring the efficacy of ensemble learning in tackling the intricate problem of financial fraud detection. This research contributes to the creation of substantial and robust fraud detection systems, fostering trust and transparency in financial markets.

Business information required for applications and business processes is extracted using systems like business rule engines. Since the advent of Big Data, such rule engines are producing rules in a big quantity whereas more rules lead to more complexity in semantic analysis and understanding. This paper introduces a method to handle semantic complexity in rules and support automated generation of Resource Description Framework (RDF) metadata model of rules and such model is used to assist in querying and analysing Big Data. Practically, the dynamic changes in rules can be a source of conflict in rules stored in a repository. It is identified during the literature review that there is a need of a method that can semantically analyse rules and help business analysts in testing and validating the rules once a change is made in a rule. This paper presents a robust method that not only supports semantic analysis of rules but also generates RDF metadata model of rules and provide support of querying for the sake of semantic interpretation of the rules. The results of the experiments manifest that consistency checking of a set of big data rules is possible through automated tools.