Data Life Cycle Management in Big Data Analytics

In this digital and computing world, data formation and collection rate are growing very rapidly. With these improved proficiencies of data storage and fast computation along with the real-time distribution of data through the internet, the usual everyday ingestion of data is mounting exponentially. With the continuous advancement in data storage and accessibility of smart devices, the impact of big data will continue to develop. This chapter provides the fundamental concepts of big data, its benefits, probable pitfalls, big data analytics and its impact in Bioinformatics. With the generation of the deluge of biological data through next generation sequencing projects, there is a need to handle this data trough big data techniques. The chapter also presents a discussion of the tools for analytics, development of a novel data life cycle on big data, details of the problems and challenges connected with big data with special relevance to bioinformatics.

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Data Mining, Big Data, Data Analytics

Library and Information Services for Bioinformatics Education and Research - Advances in Library and Information Science ◽

10.4018/978-1-5225-1871-6.ch005 ◽

2017 ◽

pp. 91-111

Author(s):

Priya P. Panigrahi ◽

Tiratha Raj Singh

Keyword(s):

Big Data ◽

Data Storage ◽

Data Analytics ◽

Big Data Analytics ◽

Biological Data ◽

Smart Devices ◽

Data Life Cycle ◽

The Impact ◽

Special Relevance ◽

Generation Sequencing

In this digital and computing world, data formation and collection rate are growing very rapidly. With these improved proficiencies of data storage and fast computation along with the real-time distribution of data through the internet, the usual everyday ingestion of data is mounting exponentially. With the continuous advancement in data storage and accessibility of smart devices, the impact of big data will continue to develop. This chapter provides the fundamental concepts of big data, its benefits, probable pitfalls, big data analytics and its impact in Bioinformatics. With the generation of the deluge of biological data through next generation sequencing projects, there is a need to handle this data trough big data techniques. The chapter also presents a discussion of the tools for analytics, development of a novel data life cycle on big data, details of the problems and challenges connected with big data with special relevance to bioinformatics.

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Usefulness and Impact of Big Data in Libraries: An Opportunity to Implement Embedded Librarianship

10.31235/osf.io/fsau9 ◽

2021 ◽

Author(s):

Subhajit Panda

Keyword(s):

Big Data ◽

Data Analytics ◽

Information Overload ◽

Big Data Analytics ◽

Library Services ◽

Data Practices ◽

Data Life Cycle ◽

Data Volume ◽

Primary Focus

The concept of Big Data has been extensively considered as a technological modernisation in Library & Information centres. According to IDC, data volume is set to increase exponentially and envisages a data volume of over 160 zettabytes by the year 2025. Size is the first, and at times, the only dimension that leaps out at the mention of Big Data. Big Data is defined as information overload due to the volume, velocity, variety, variability & veracity of the data which must be processed to get value and better visualisation. Big Data contains the answer to several valuable questions related to patterns, trends & associations of user behaviour. It plays a major role in helping libraries to clearly understand the changing user needs, accordingly, reshape & restructure their services & procedures. The primary focus of this study was to explore the concept of Big Data in a library environment, steps to introduce Big Data in libraries and the use of Big Data in providing library services using the concept of data life cycle developed by DataONE. The main influential components to perform this study was the capabilities of Big Data analytics, the need & usefulness of Big Data practices, its significant utilisation in libraries and discuss some globally taken practical initiatives. The study highlights the important role of Big Data analytics capabilities to uncover new challenges of information utilisation, consequently helps a librarian to fulfil his role as an Embedded Librarian, both in theoretical & practical terms.

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Big Data Analytics Life cycle for Social Networks’ Posts

Advances in Intelligent Systems and Computing - Information Systems Design and Intelligent Applications ◽

10.1007/978-981-13-3338-5_35 ◽

2019 ◽

pp. 379-388

Author(s):

Sadiyah Muthy ◽

Thomas Sookram ◽

Baby Gobin-Rahimbux

Keyword(s):

Social Networks ◽

Big Data ◽

Life Cycle ◽

Data Analytics ◽

Big Data Analytics

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Security and Privacy in Big Data Life Cycle: A Survey and Open Challenges

Sustainability ◽

10.3390/su122410571 ◽

2020 ◽

Vol 12 (24) ◽

pp. 10571

Author(s):

Jahoon Koo ◽

Giluk Kang ◽

Young-Gab Kim

Keyword(s):

Big Data ◽

Life Cycle ◽

Big Data Analytics ◽

Life Cycles ◽

Security And Privacy ◽

Personal Privacy ◽

Security Issues ◽

New Information ◽

Data Life Cycle ◽

Data Market

The use of big data in various fields has led to a rapid increase in a wide variety of data resources, and various data analysis technologies such as standardized data mining and statistical analysis techniques are accelerating the continuous expansion of the big data market. An important characteristic of big data is that data from various sources have life cycles from collection to destruction, and new information can be derived through analysis, combination, and utilization. However, each phase of the life cycle presents data security and reliability issues, making the protection of personally identifiable information a critical objective. In particular, user tendencies can be analyzed using various big data analytics, and this information leads to the invasion of personal privacy. Therefore, this paper identifies threats and security issues that occur in the life cycle of big data by confirming the current standards developed by international standardization organizations and analyzing related studies. In addition, we divide a big data life cycle into five phases (i.e., collection, storage, analytics, utilization, and destruction), and define the security taxonomy of the big data life cycle based on the identified threats and security issues.

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Big Data Analytics: The Next Big Thing

The Management Accountant Journal ◽

10.33516/maj.v54i5.20-24p ◽

2019 ◽

Vol 54 (5) ◽

pp. 20

Author(s):

Dheeraj Kumar Pradhan

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics

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Strategy in Discovery Mode - Wie Big Data & Analytics strategisches Denken verdrängen kann

WiSt - Wirtschaftswissenschaftliches Studium ◽

10.15358/0340-1650-2020-5-11 ◽

2020 ◽

Vol 49 (5) ◽

pp. 11-17

Author(s):

Thomas Wrona ◽

Pauline Reinecke

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics

Big Data & Analytics (BDA) ist zu einer kaum hinterfragten Institution für Effizienz und Wettbewerbsvorteil von Unternehmen geworden. Zu viele prominente Beispiele, wie der Erfolg von Google oder Amazon, scheinen die Bedeutung zu bestätigen, die Daten und Algorithmen zur Erlangung von langfristigen Wettbewerbsvorteilen zukommt. Sowohl die Praxis als auch die Wissenschaft scheinen geradezu euphorisch auf den „Datenzug“ aufzuspringen. Wenn Risiken thematisiert werden, dann handelt es sich meist um ethische Fragen. Dabei wird häufig übersehen, dass die diskutierten Vorteile sich primär aus einer operativen Effizienzperspektive ergeben. Strategische Wirkungen werden allenfalls in Bezug auf Geschäftsmodellinnovationen diskutiert, deren tatsächlicher Innovationsgrad noch zu beurteilen ist. Im Folgenden soll gezeigt werden, dass durch BDA zwar Wettbewerbsvorteile erzeugt werden können, dass aber hiermit auch große strategische Risiken verbunden sind, die derzeit kaum beachtet werden.

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