Challenges and Research Disputes and Tools in Big Data Analytics

Venkatesh, K.; Ali, Mohamed; Nithiyanandam, N.; Rajesh, M.

doi:10.35940/ijeat.f1376.0986s319

Cited by 11 publications

(2 citation statements)

References 6 publications

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“…This makes it difficult to process using traditional data processing methods, which are often designed to handle only specific types of data. To handle the scale, velocity, and variety of big data, organizations are turning to new technologies and techniques, such as Hadoop, Spark, and NoSQL databases (Storey and Song, 2017 ; Oussous et al, 2018 ; Venkatesh et al, 2019 ). These technologies are designed to handle large, complex sets of data and make it possible to perform advanced analytics, such as machine learning and natural language processing.…”

Section: Review Of Important Literaturementioning

confidence: 99%

The role of big data in financial technology toward financial inclusion

Mhlanga

2024

Front. Big Data

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In the rapidly evolving landscape of financial technology (FinTech), big data stands as a cornerstone, driving significant transformations. This study delves into the pivotal role of big data in FinTech and its implications for financial inclusion. Employing a comprehensive literature review methodology, we analyze diverse sources including academic journals, industry reports, and online articles. Our findings illuminate how big data catalyzes the development of novel financial products and services, enhances risk management, and boosts operational efficiency, thereby fostering financial inclusion. Particularly, big data's capability to offer insightful customer behavior analytics is highlighted as a key driver for creating inclusive financial services. However, challenges such as data privacy and security, and the need for ethical algorithmic practices are also identified. This research contributes valuable insights for policymakers, regulators, and industry practitioners, suggesting a need for balanced regulatory frameworks to harness big data's potential ethically and responsibly. The outcomes of this study underscore the transformative power of big data in FinTech, indicating a pathway toward a more inclusive financial ecosystem.

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Section: Review Of Important Literaturementioning

confidence: 99%

The role of big data in financial technology toward financial inclusion

Mhlanga

2024

Front. Big Data

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“…Apache Lucene is the basis for famous and widely used search engines like Solr and Elasticsearch (VENKATESH et al, 2019).…”

Section: Data Search and Discoverymentioning

confidence: 99%

Data management in digital twins: a systematic literature review

2023

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The Internet of Things (IoT), personal and wearable devices, and continuous advances in data-gathering techniques have significantly increased the amount of relevant data that can be leveraged for innovative real-time, data-driven applications. Digital Twins (DTs) are virtual representations of physical objects which are fully integrated and in which the automatic data exchange occurs in a bidirectional way. DTs and big data are mutually reinforcing technologies since huge volumes of data representing the physical/virtual worlds are collected, transformed, and generated through models to aggregate value to the business. Modern DTs follow a five-component architecture, which includes a Data Management (DM) component that bridges a physical system, a mirrored virtual one, and services components. However, there is no clarity on the functionality required for the DM component. This work presents a Systematic Literature Review on DM issues and proposed solutions in the DT context. We analyzed DM under the big data value chain activities, highlighting key issues to be addressed: data heterogeneity, interoperability, integration, data search/discovery, and quality. In addition to surveying existing solutions for handling these issues, we contextualized them in the domain and function for which the DT was proposed, the type of data dealt with, and the technical infrastructure. The compilation of these solutions sheds light on the functionality of the DM component in a DT, trends, and opportunities. Our main findings revealed that the maturity level assumed for the DM component is at an early stage. The most mature solutions were proposed for the industry domain, and many of them assume humans as the ultimate information consumers. Data integration is the prevalent DM issue addressed due to the bridging role of the DM component, and cloud computing is the key implementation technology. Among the research opportunities are reference data management architectures, adoption of industry standards and ontologies, interoperability among distinct DTs, the development of agnostic standard implementations, and data provenance mechanisms.

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