Aggregating financial services data without assumptions: A semantic data reference architecture

Gollapudi, Sunila

doi:10.1109/icosc.2015.7050825

Cited by 9 publications

(3 citation statements)

References 4 publications

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“…Table VII lists the tools cited more than once. Of all the tools, Apache Hadoop was the most mentioned, having 37 citations ( [1], [6], [8], [9], [12], [18], [23]- [25], [27], [28], [30], [31], [40], [42]- [44], [47], [48], [50], [52], [55], [61], [65], [67]- [71], [73], [76], [80], [82]- [86], [88]). Hadoop is a platform that allows distributed processing of large data sets in computer clusters.…”

Section: Resultsmentioning

confidence: 99%

A Mapping Study about Data Lakes: An Improved Definition and Possible Architectures

Couto¹,

Borges²,

Ruiz³

et al. 2019

International Conferences on Software Engineering and Knowledge Engineering

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In the past few years, data lakes emerged as a trending topic in big data technologies. Although literature presents different points of view related to its functionalities, it serves mainly to store a variety of data in a big data context. In this paper, we aim to identify and analyze data lake definitions and possible architectures. Our methodology was composed of a systematic literature mapping based on PRISMA, software engineering best practices to perform reviews, and Kappa method to assess results' quality. We performed the search in eight different electronic databases to achieve a wide variety of publishers in Computer Science. We first identified 662 papers matching our search criteria; after filtering, we selected 87 papers for review. We found that the term data lakes was first defined by James Dixon in 2010. We also found that the term is often related to raw data repositories. From the identified definitions, we propose a new one as a means to better state what data lakes refer to and improve how the community use them. Moreover, we foind that Hadoop and its ecosystem compose the most used toolset to create data lakes, revealing that this is the mainstream in architectures for data lakes as of today's available technologies.

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Section: Resultsmentioning

confidence: 99%

A Mapping Study about Data Lakes: An Improved Definition and Possible Architectures

Couto¹,

Borges²,

Ruiz³

et al. 2019

International Conferences on Software Engineering and Knowledge Engineering

View full text Add to dashboard Cite

show abstract

“…According to [40], a change is taking place in the financial services domain, where the main focus is on the commoditizing of data. The work concentrates on moving from traditional data management (such as Extract-Transform-Load (ETL) technologies) to a new approach supporting semantic technologies, which will give the opportunity to support dynamic data formats and structures and is scalable to encounter upcoming and new consumption purposes.…”

Section: Positioning Of the Researchmentioning

confidence: 99%

“…Various approaches and data sources are used trying to achieve better results and/or tools solving the issues of complex formats, structures and meanings. Yet, the majority of the works used limited and basic semantic ontologies and did not fully imply the Big Data for example [35,40]. Currently, the enhancement of Big Data using ontologies and semantic techniques to solve the variety challenge is at the early stages and still a research challenge that requires further work especially in the business and financial domain, which creates an opportunity to have a good contribution to the knowledge.…”

Section: Positioning Of the Researchmentioning

confidence: 99%

Creating Intelligent Business Systems by Utilising Big Data and Semantics

Quboa

Mehandjiev

2017

2017 IEEE 19th Conference on Business Informatics (CBI)

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Increasing volumes of data are generated from a variety of industries, organizations and people. Such data is now seen as a key business asset, since analysing it allows business decision makers to make choices with confidence. This data is often "big data"-a data of such volume, velocity and variety that conventional processing techniques are not suitable. We research the specific challenge of variety, where data comprises a number of incompatible formats. Semantic technology, or the use of ontologies, is seen as a core approach to resolving this challenge and aligning the data generated from heterogeneous data sources. This paper lays down the issues which should be addressed and reviews the work which has been done in the area of integrating structured and unstructured data sources, with a special focus on the financial domain. We then chart a research plan in the area following Design Science research.

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Big-Data Aggregating, Linking, Integrating and Representing Using Semantic Web Technologies

Saber

Al-Zoghby

Elmougy

2018

The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018)

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Aggregating financial services data without assumptions: A semantic data reference architecture

Cited by 9 publications

References 4 publications

A Mapping Study about Data Lakes: An Improved Definition and Possible Architectures

A Mapping Study about Data Lakes: An Improved Definition and Possible Architectures

Creating Intelligent Business Systems by Utilising Big Data and Semantics

Big-Data Aggregating, Linking, Integrating and Representing Using Semantic Web Technologies

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