A Big Data Analytics Framework for Enterprise Service Ecosystems in an e-Governance Scenario

Shrivastava, Swapnil; Pal, Supriya N.

doi:10.1145/3047273.3047274

Cited by 11 publications

(5 citation statements)

References 11 publications

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“…Large-scale social media data is successfully tackled by Lemieux (2016) with comprehensive framework accompanied by set of data mining tools and interface. Real time data analytics was addressed by Shrivastava & Pal (2017) in the domain of enterprise service ecosystem. Images data was addressed in Huang et al (2002) by proposing multimedia data mining framework and its implementation with user relevance feedback integration and instance learning.…”

Section: Purposementioning

confidence: 99%

Adaptations of data mining methodologies: a systematic literature review

Plotnikova¹,

Dumas²,

Milani³

2020

PeerJ Computer Science

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The use of end-to-end data mining methodologies such as CRISP-DM, KDD process, and SEMMA has grown substantially over the past decade. However, little is known as to how these methodologies are used in practice. In particular, the question of whether data mining methodologies are used ‘as-is’ or adapted for specific purposes, has not been thoroughly investigated. This article addresses this gap via a systematic literature review focused on the context in which data mining methodologies are used and the adaptations they undergo. The literature review covers 207 peer-reviewed and ‘grey’ publications. We find that data mining methodologies are primarily applied ‘as-is’. At the same time, we also identify various adaptations of data mining methodologies and we note that their number is growing rapidly. The dominant adaptations pattern is related to methodology adjustments at a granular level (modifications) followed by extensions of existing methodologies with additional elements. Further, we identify two recurrent purposes for adaptation: (1) adaptations to handle Big Data technologies, tools and environments (technological adaptations); and (2) adaptations for context-awareness and for integrating data mining solutions into business processes and IT systems (organizational adaptations). The study suggests that standard data mining methodologies do not pay sufficient attention to deployment issues, which play a prominent role when turning data mining models into software products that are integrated into the IT architectures and business processes of organizations. We conclude that refinements of existing methodologies aimed at combining data, technological, and organizational aspects, could help to mitigate these gaps.

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Section: Purposementioning

confidence: 99%

Adaptations of data mining methodologies: a systematic literature review

Plotnikova¹,

Dumas²,

Milani³

2020

PeerJ Computer Science

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“…Phase 1: (Caesarita et al, 2017;Ganesha et al, 2017b;Meincheim et al, 2017;Abo-Hamad, 2017;Belo et al, 2017;Ganesha et al, 2017a;Mahendrawathi et al, 2017;Shrivastava and Pal, 2017) 8 2018…”

Section: Demonstrationmentioning

confidence: 99%

A Classification of Process Mining Bottleneck Analysis Techniques for Operational Support

Bemthuis

Slooten

Arachchige

et al. 2021

Proceedings of the 18th International Conference on E-Business

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A bottleneck usually is a sub-process in the main process which delays the process. The performance of a process can be increased by eliminating the bottlenecks. To this end, opportunities to analyze and mitigate bottlenecks by using process mining techniques can be an interesting direction to utilize. This paper aims to classify literature on process mining bottleneck analysis techniques and propose a model for operational support regarding bottleneck analysis utilizing process mining. To this end, we first propose a model for classifying bottleneck analysis techniques. Then, we conduct a systematic literature review to identify existing papers that address bottleneck analysis by utilizing process mining techniques. The results indicate that many researchers are focusing on detecting bottlenecks, while limited attention is paid to predicting bottlenecks or recommending actions on what to do with bottlenecks. The proposed classification model is validated through a demonstration, showing how process mining bottleneck analysis techniques can be applied to a logistics case study.

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“…The biggest example of the success of e-governance is Aadhar (UDAI) portal run by Indian government. This portal stores variety of data like text, images, PDF, retina scan, name, age, address and other related information of their stakeholders [2]. As of 2018, India has a population of over 1.355 billion people, and its growth is expected to continue through at least 2050 [11].…”

Section: A E-governancementioning

confidence: 99%

Integrating E-Governance with Big Data Analytics using Apache Spark

Salwan*¹,

Maan²

2020

IJRTE

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The constant innovations and rapid developments in the IT industry have revolutionized the thinking and mindset of the people throughout the world. Government departments have also been computerized to provide transparent, efficient and responsible government through e-governance. The government have been providing access to various websites or portal for filing complaints, uploading or downloading forms, pictures, data or PDFs to avail the government services. Enlightened citizens are frequently using the portal to access government services. Thus, the size and volume of data that need to be managed by government departments have been increasing drastically under e-governance. The traditional database management system is not designed to deal with such mix type of data. Moreover, the speed at which the e-governance generated data need to be processed is another big challenge being faced by traditional database system. All the abovesaid concerns can be solved by using the emerging technology - Big Data Analytics techniques. Big data analytic techniques can make the government more efficient and transparent by processing structured, unstructured or mixed types data at a great speed. In this paper, we shall understand the scenario for the need or the emergence of big data analytics in egovernance and knowhow of Apache Spark. This paper proposes a practical approach to integrate big data analytics with egovernance using Apache Spark. This paper also reflects how major issues of traditional database management system (mixed type datasets, speed and accuracy) can be resolved through the integration of big data analytics and e-governance.

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A Big Data Analytics Framework for Enterprise Service Ecosystems in an e-Governance Scenario

Cited by 11 publications

References 11 publications

Adaptations of data mining methodologies: a systematic literature review

Adaptations of data mining methodologies: a systematic literature review

A Classification of Process Mining Bottleneck Analysis Techniques for Operational Support

Integrating E-Governance with Big Data Analytics using Apache Spark

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