Big Data Tools-An Overview
---
paper_title: Networking for Big Data: A Survey

paper_content:

Complementary to the fancy big data applications, networking for big data is an indispensable supporting platform for these applications in practice. This emerging research branch has gained extensive attention from both academia and industry in recent years. In this new territory, researchers are facing many unprecedented theoretical and practical challenges. We are therefore motivated to solicit the latest works in this area, aiming to pave a comprehensive and solid starting ground for interested readers. We first clarify the definition of networking for big data based on the cross disciplinary nature and integrated needs of the domain. Second, we present the current understanding of big data from different levels, including its formation, networking features, mathematical representations, and the networking technologies. Third, we discuss the challenges and opportunities from various perspectives in this hopeful field. We further summarize the lessons we learned based on the survey. We humbly hope this paper will shed light for forthcoming researchers to further explore the uncharted part of this promising land.
---
paper_title: Survey of pre-processing techniques for mining big data

paper_content:

Big Data analytics has become important as many administrations, organizations, and companies both public and private have been collecting and analyzing huge amounts of domain-specific information, which can contain useful information about problems such as national intelligence, cyber security, fraud detection, marketing, and medical informatics. With more and more data being generated the ever dynamic size, scale, diversity, and complexity has made the requirement for newer architectures, techniques, algorithms, and analytics to manage it and extract value from the data collected. The progress and innovation is no longer hindered by the ability to collect data but, by the ability to manage, analyze, summarize, visualize, and discover knowledge from the collected data in a timely manner and in a scalable fashion as well as a credible clean and noise free data sets. This paper mainly makes an attempt to understand the different problems to solve in the processes of data preprocessing, to also familiarize with the problems related to cleaning data, know the problems to apply data cleaning and noise removal techniques for big data analytics and to mitigate the imperfect data, together with some techniques to solve them and also to identify the shortcomings in the existing methods of the reduction techniques in the necessary respective areas of application and also to identify the current big data preprocessing proposal's effectiveness to various data sets.
---
paper_title: Critical analysis of Big Data challenges and analytical methods

paper_content:

Big Data (BD), with their potential to ascertain valued insights for enhanced decision-making process, have recently attracted substantial interest from both academics and practitioners. Big Data Analytics (BDA) is increasingly becoming a trending practice that many organizations are adopting with the purpose of constructing valuable information from BD. The analytics process, including the deployment and use of BDA tools, is seen by organizations as a tool to improve operational efficiency though it has strategic potential, drive new revenue streams and gain competitive advantages over business rivals. However, there are different types of analytic applications to consider. Therefore, prior to hasty use and buying costly BD tools, there is a need for organizations to first understand the BDA landscape. Given the significant nature of the BD and BDA, this paper presents a state-of-the-art review that presents a holistic view of the BD challenges and BDA methods theorized/proposed/employed by organizations to help others understand this landscape with the objective of making robust investment decisions. In doing so, systematically analysing and synthesizing the extant research published on BD and BDA area. More specifically, the authors seek to answer the following two principal questions: Q1 – What are the different types of BD challenges theorized/proposed/confronted by organizations? and Q2 – What are the different types of BDA methods theorized/proposed/employed to overcome BD challenges?. This systematic literature review (SLR) is carried out through observing and understanding the past trends and extant patterns/themes in the BDA research area, evaluating contributions, summarizing knowledge, thereby identifying limitations, implications and potential further research avenues to support the academic community in exploring research themes/patterns. Thus, to trace the implementation of BD strategies, a profiling method is employed to analyze articles (published in English-speaking peer-reviewed journals between 1996 and 2015) extracted from the Scopus database. The analysis presented in this paper has identified relevant BD research studies that have contributed both conceptually and empirically to the expansion and accrual of intellectual wealth to the BDA in technology and organizational resource management discipline.
---
paper_title: Defining architecture components of the Big Data Ecosystem

paper_content:

Big Data are becoming a new technology focus both in science and in industry and motivate technology shift to data centric architecture and operational models. There is a vital need to define the basic information/semantic models, architecture components and operational models that together comprise a so-called Big Data Ecosystem. This paper discusses a nature of Big Data that may originate from different scientific, industry and social activity domains and proposes improved Big Data definition that includes the following parts: Big Data properties (also called Big Data 5V: Volume, Velocity, Variety, Value and Veracity), data models and structures, data analytics, infrastructure and security. The paper discusses paradigm change from traditional host or service based to data centric architecture and operational models in Big Data. The Big Data Architecture Framework (BDAF) is proposed to address all aspects of the Big Data Ecosystem and includes the following components: Big Data Infrastructure, Big Data Analytics, Data structures and models, Big Data Lifecycle Management, Big Data Security. The paper analyses requirements to and provides suggestions how the mentioned above components can address the main Big Data challenges. The presented work intends to provide a consolidated view of the Big Data phenomena and related challenges to modern technologies, and initiate wide discussion.
---
paper_title: Comparative Study of Big Data Computing and Storage Tools: A Review

paper_content:

As a result of tremendous rise in internet usage like social media and forums, mail systems, scholarly and research articles, daily online transactions from multiple sources like health care systems, meteorological and environmental organizations etc., the data collected has shoot up exponentially. This vast collection of data, called Big Data, has caused the traditional tools incompetent for managing it from either of storage, computing or analytical perspective. There is an immense need of architectures, platforms, tools, techniques and algorithms to handle Big Data. The available technologies deal with two broad aspects related to Big Data that are Big Data Storage Management and Big Data Computing, focused to overcome various challenges such as scalability, faster processing speed, multiple format data processing, availability, faster response time and analytics etc. This paper reviews recent trends of storage and computing tools with their relative capabilities, limitations and environment they are suitable to work with.
---
paper_title: Big Data Challenges : Society, Security, Innovation and Ethics

paper_content:

This book brings together an impressive range of academic and intelligence professional perspectives to interrogate the social, ethical and security upheavals in a world increasingly driven by data. Written in a clear and accessible style, it offers fresh insights to the deep reaching implications of Big Data for communication, privacy and organisational decision-making. It seeks to demystify developments around Big Data before evaluating their current and likely future implications for areas as diverse as corporate innovation, law enforcement, data science, journalism, and food security. The contributors call for a rethinking of the legal, ethical and philosophical frameworks that inform the responsibilities and behaviours of state, corporate, institutional and individual actors in a more networked, data-centric society. In doing so, the book addresses the real world risks, opportunities and potentialities of Big Data.
---

Title: Big Data Tools-An Overview
Section 1: Introduction
Description 1: Outline the current state and significance of big data, historical perspectives, and traditional data systems.

Section 2: Value
Description 2: Discuss the importance of data value and the challenges related to data analysis in terms of cost and time.

Section 3: Veracity
Description 3: Address the quality of data, focusing on data noise, accuracy, and the implications for analysis and decision-making.

Section 4: Big data challenges
Description 4: Categorize and explain the various challenges of big data including data, process, and management challenges.

Section 5: Uncertainty of the Data Management Landscape
Description 5: Evaluate the challenges of selecting appropriate tools due to the vast number of available options and technical specificity.

Section 6: The Big Data Talent Gap
Description 6: Identify the difficulties in finding and retaining experts proficient in big data due to its novelty.

Section 7: Getting Data into the Big Data Platform
Description 7: Discuss the challenges involved in data integration, accessibility, and preparation for big data platforms.

Section 8: Synchronization across Data Sources
Description 8: Explain the issues related to synchronizing data from various sources with differing rates and times.

Section 9: Getting Useful Information out of the Big Data Platform
Description 9: Highlight the importance of adequately providing different applications with necessary data from the big data platform.

Section 10: Data storage and management tools
Description 10: Review various tools available for big data storage and management, including their features and use cases.

Section 11: Big Data Analytics Platforms
Description 11: Describe the key platforms for big data analytics, their functions, and characteristics.

Section 12: Big Data Analysis Tools
Description 12: Explain the tools available for data analysis and their specific functionalities and uses.

Section 13: Big Data Visualization Tools
Description 13: Detail the most notable visualization tools used for big data, their features, and their benefits and drawbacks.

Section 14: Discussion and Conclusion
Description 14: Summarize the findings, discuss the future directions, and present the vision for the evolution of big data tools.