IEEE Access Special Session Editorial: Big Data Services and Computational Intelligence for Industrial Systems

The availability of a huge amount of heterogeneous data from different sources to the Internet has been termed as the problem of Big Data. Clustering is widely used as a knowledge discovery tool that separate the data into manageable parts. There is a need of clustering algorithms that scale on big databases. In this chapter we have explored various schemes that have been used to tackle the big databases. Statistical features have been extracted and most important and relevant features have been extracted from the given dataset. Reduce and irrelevant features have been eliminated and most important features have been selected by genetic algorithms (GA).Clustering with reduced feature sets requires lower computational time and resources. Experiments have been performed at standard datasets and results indicate that the proposed scheme based clustering offers high clustering accuracy. To check the clustering quality various quality measures have been computed and it has been observed that the proposed methodology results improved significantly. It has been observed that the proposed technique offers high quality clustering.

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Big Data Mining Based on Computational Intelligence and Fuzzy Clustering

Web Services ◽

10.4018/978-1-5225-7501-6.ch024 ◽

2019 ◽

pp. 413-430

Author(s):

Usman Akhtar ◽

Mehdi Hassan

Keyword(s):

Big Data ◽

Computational Intelligence ◽

Clustering Algorithms ◽

Heterogeneous Data ◽

Computational Time ◽

Statistical Features ◽

Feature Sets ◽

Clustering Quality ◽

The Given ◽

Different Sources

The availability of a huge amount of heterogeneous data from different sources to the Internet has been termed as the problem of Big Data. Clustering is widely used as a knowledge discovery tool that separate the data into manageable parts. There is a need of clustering algorithms that scale on big databases. In this chapter we have explored various schemes that have been used to tackle the big databases. Statistical features have been extracted and most important and relevant features have been extracted from the given dataset. Reduce and irrelevant features have been eliminated and most important features have been selected by genetic algorithms (GA). Clustering with reduced feature sets requires lower computational time and resources. Experiments have been performed at standard datasets and results indicate that the proposed scheme based clustering offers high clustering accuracy. To check the clustering quality various quality measures have been computed and it has been observed that the proposed methodology results improved significantly. It has been observed that the proposed technique offers high quality clustering.

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Architecture for Big Data Storage in Different Cloud Deployment Models

Research Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing ◽

10.4018/978-1-7998-5339-8.ch009 ◽

2021 ◽

pp. 178-208

Author(s):

Chandu Thota ◽

Gunasekaran Manogaran ◽

Daphne Lopez ◽

Revathi Sundarasekar

Keyword(s):

Cloud Computing ◽

Big Data ◽

Data Storage ◽

High Performance ◽

Data Services ◽

Big Data Applications ◽

Nosql Database ◽

Amazon Web Services ◽

Product Domains ◽

Scalable Database

Cloud Computing is a new computing model that distributes the computation on a resource pool. The need for a scalable database capable of expanding to accommodate growth has increased with the growing data in web world. More familiar Cloud Computing vendors such as Amazon Web Services, Microsoft, Google, IBM and Rackspace offer cloud based Hadoop and NoSQL database platforms to process Big Data applications. Variety of services are available that run on top of cloud platforms freeing users from the need to deploy their own systems. Nowadays, integrating Big Data and various cloud deployment models is major concern for Internet companies especially software and data services vendors that are just getting started themselves. This chapter proposes an efficient architecture for integration with comprehensive capabilities including real time and bulk data movement, bi-directional replication, metadata management, high performance transformation, data services and data quality for customer and product domains.

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Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications

10.1016/c2016-0-03919-0 ◽

2018 ◽

Keyword(s):

Big Data ◽

Computational Intelligence ◽

Multimedia Big Data ◽

Engineering Applications

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Integrating Big Data Services Into an Undergraduate MIS Curriculum

International Journal of Systems and Service-Oriented Engineering ◽

10.4018/ijssoe.2017040104 ◽

2017 ◽

Vol 7 (2) ◽

pp. 58-73 ◽

Cited By ~ 1

Author(s):

Scott Jensen

Keyword(s):

Big Data ◽

Real World ◽

Data Science ◽

Graduate Programs ◽

Advanced Degree ◽

Data Services ◽

Hands On ◽

Data Problem

There is an insatiable demand in industry for data scientists, and graduate programs and certificates are gearing up to meet this demand. However, there is agreement in the industry that 80% of a data scientist's work consists of the transformation and profiling aspects of wrangling Big Data; work that may not require an advanced degree. In this paper, the authors present hands-on exercises to introduce Big Data to undergraduate MIS students using the CoNVO Framework and Big Data tools to scope a data problem and then wrangle the data to answer questions using a real-world dataset. This can provide undergraduates with a single course introduction to an important aspect of data science.

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