Semantic Data Ingestion for Intelligent, Value-Driven Big Data Analytics

Debattista, Jeremy; Attard, Judie; Brennan, Rob

doi:10.1109/innovate-data.2018.00008

Cited by 4 publications

(1 citation statement)

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“…In this model, the raw data is collected from heterogeneous sources in the form of a result. This data is governance by thevalue-driven AI technique to extract useful information [18].The following table shows the various algorithms and results of the existing system. We have studied different types of algorithm and some protocol in which we analyzed the LEOS, MapReduce, JOSE and SVM algorithm is a much better algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Examination of Big Dataset using LEOS, JOSE, SVM on Map Reduce

2019

IJITEE

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Data analytics (DA) is the job of reviewing datasets in order to frame conclusions about the information they have, increasingly using specialized systems and software. As with the emergence of Big Data, data analytics was needed. The problems that we are considering are going to be in a fraud detection application. Where we'll considering major aspects such application-independent format(XML/JSON) for the clusterization process based on the no label classification algorithm where we will focusing on the clusters to enhance the oversampling process and utilize the merits of parallel computing to speed up our system. We aim to use MapReduce functionality in our application and deploy it on Amazon AWS. Datasets gathered for studies often comprise millions of records and can carry hard-to-detect concealed pitfalls. In this paper, we are working on two datasets. The first one is a medical dataset and the second one is a customer dataset. Big Data Analytics is the suggested solution in this day and age, with growing demands for analyzing huge information sets and performing the required processing on complicated data structures. The problem faced at the moment is mainly, how to store and analyze the large amount of data which is generated from heterogeneous sources like social media and what to use to make data fast accessible as well as in pocket budget. To resolve all problems Map-Reduce framework is useful-by offering an integrated technique towards machine learning, it speeds up processing. In this, we will explore the LEOS algorithm, SVM, MapReduce and JOSE algorithm, their requirements, their benefits, their disadvantages, difficulties, and their corresponding solutions.

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Section: Literature Reviewmentioning

confidence: 99%

Examination of Big Dataset using LEOS, JOSE, SVM on Map Reduce

2019

IJITEE

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Towards a taxonomy for business capabilities determining data value

Hafner,

Mira da Silva

2023

Knowl Inf Syst

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Data and its valuation have gained vital significance in academia and enterprises, coinciding with diverse data valuation approaches encompassing various layers, dimensions, and characteristics. This paper assesses data value determination through a business capability lens based on the TOGAF standard. The paper encompasses (a) constructing a Data Valuation Business Capability (DVBC) taxonomy and (b) validating the taxonomy using two existing data valuation concepts from academia. The methodology involves information systems taxonomy development techniques backed by a previously conducted systematic literature review of 64 articles. The resultant taxonomy comprises four business capability layers, nine dimensions, and 36 characteristics. These layers and dimensions offer business, technology, and organizational perspectives, reflecting the interdisciplinary nature of data valuation alongside an enterprise architecture. Characteristics within these layers and dimensions are either exclusive or non-exclusive based on their contents. The compiled findings meet both objective and subjective quality criteria. The implications of the DVBC are multifaceted, influencing scholars and professionals alike. Scholars gain a cohesive tool enhancing transparency in the extensively debated data value domain, fostering linkages among information systems, enterprise architecture management, and data management. This empowers the progress in developing comprehensive data valuation concepts. Additionally, professionals may employ the DVBC taxonomy as a lighthouse and guiding tool, fostering internal dialog on data valuation. This entails elevating data valuation to a pivotal business capability, necessitating collaborative, regular assessment, and enhancement involving business and technological stakeholders. By adopting this taxonomy, the challenge of consistently determining data value can be effectively addressed in both academia and enterprises.

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