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ACKNOWLEDGMENTS 15

1.5.6 BITCOIN AS A SERVICE (BIaaS)
Cryptocurrencies are a very popular technology used to provide secure and reliable service for a huge
number of financial transactions. Bitcoin as a service (BiaaS) performs real-time data extraction from
the blockchain ledger and stores the big data in an efficient manner for bio-inspired algorithm-based big
data analytics. BiaaS-based big data analytics provides interesting benefits such as trend prediction,
theft prevention, and identification of malicious users.

1.5.7 QUANTUM COMPUTING AS A SERVICE (QCaaS)
The new trend of quantum computing helps bio-inspired algorithm-based big data analytics to solve
complex problems by handling massive digital datasets in an efficient and quick manner. Quantum com-
puting as a service (QCaaS) allows for quick detection, analysis, integration, and diagnosis from large
scattered datasets. Further, QCaaS can search extensive, unsorted datasets to quickly uncover patterns.

1.6 SUMMARY AND CONCLUSIONS
This chapter presents a review of bio-inspired algorithms for big data analytics. The comparison of bio-
inspired algorithms has been presented based on taxonomy, focus of study, and identified demerits.
Bio-inspired algorithms are categorized into three different categories and we investigated the existing
literature on big data analytics towards finding the open issues. Further, promising research directions
are proposed for future research.

GLOSSARY
this is the set of huge datasets, which contains different types of data such as video, audio,
Big data
text, social etc.
there are five types of dimensions of data management for big data analytics: volume, va-
Data management
riety, velocity, veracity, and variability.
the process of an extraction of required data from unstructured data. There are five types of
Big data analytics
analytics for big data management using bio-inspired algorithms: text analytics, audio an-
alytics, video analytics, social media analytics, and predictive analytics.
the bio-inspired algorithms are used for big data analytics, which can be ecological,
Bio-inspired
swarm-based, and evolutionary algorithms.
optimization
cloud computing offers three types of main service models: software, platform, and infra-
Cloud computing
structure. At the software level, the cloud user can utilize the application in a flexible man-
ner, which is running on cloud datacenters. The cloud user can access infrastructure to
develop and deploy cloud applications at platform level. Infrastructure as a service offers
access to computing resources such as a processor, networking, and storage and enables
virtualization-based computing.

ACKNOWLEDGMENTS
One of the authors, Dr. Sukhpal Singh Gill [Postdoctoral Research Fellow], gratefully acknowledges the Cloud
Computing and Distributed Systems (CLOUDS) Laboratory, School of Computing and Information Systems,
The University of Melbourne, Australia, for awarding him the Fellowship to carry out this research work. This
research work is supported by Discovery Project of Australian Research Council (ARC), Grant/Award Number:
DP160102414.