Big Data Preprocessing for Modern World: Opportunities and Challenges

2018 ◽  
pp. 335-343 ◽  
Author(s):  
Andrea Prakash ◽  
Narem Navya ◽  
Jayapandian Natarajan
Keyword(s):  
Big Data ◽  
Data Preprocessing ◽  
Modern World

Big Data Analytics and Models

2020 ◽  
pp. 10-33
Author(s):  
Ferdi Sönmez ◽  
Ziya Nazım Perdahçı ◽  
Mehmet Nafiz Aydın
Keyword(s):  
Big Data ◽  
Important Implication ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Financial Econometrics ◽  
Optimization Models ◽  
Modern World ◽  
Financial Instability ◽  
The Future ◽  
Social Environmental

When uncertainty is regarded as a surprise and an event in the minds, it can be said that individuals can change the future view. Market, financial, operational, social, environmental, institutional and humanitarian risks and uncertainties are the inherent realities of the modern world. Life is suffused with randomness and volatility; everything momentous that occurs in the illustrious sweep of history, or in our individual lives, is an outcome of uncertainty. An important implication of such uncertainty is the financial instability engendered to the victims of different sorts of perils. This chapter is intended to explore big data analytics as a comprehensive technique for processing large amounts of data to uncover insights. Several techniques before big data analytics like financial econometrics and optimization models have been used. Therefore, initially these techniques are mentioned. Then, how big data analytics has altered the methods of analysis is mentioned. Lastly, cases promoting big data analytics are mentioned.

scholarly journals Big Data and Business Analytics: Trends, Platforms, Success Factors and Applications

2019 ◽  
Vol 3 (2) ◽  
pp. 32 ◽  
Author(s):  
Ifeyinwa Angela Ajah ◽  
Henry Friday Nweke
Keyword(s):  
Big Data ◽  
Success Factors ◽  
Database Management System ◽  
Unstructured Data ◽  
Digital Data ◽  
Business Strategies ◽  
Modern World ◽  
Successful Implementation ◽  
Business Analytics ◽  
Business World

Big data and business analytics are trends that are positively impacting the business world. Past researches show that data generated in the modern world is huge and growing exponentially. These include structured and unstructured data that flood organizations daily. Unstructured data constitute the majority of the world’s digital data and these include text files, web, and social media posts, emails, images, audio, movies, etc. The unstructured data cannot be managed in the traditional relational database management system (RDBMS). Therefore, data proliferation requires a rethinking of techniques for capturing, storing, and processing the data. This is the role big data has come to play. This paper, therefore, is aimed at increasing the attention of organizations and researchers to various applications and benefits of big data technology. The paper reviews and discusses, the recent trends, opportunities and pitfalls of big data and how it has enabled organizations to create successful business strategies and remain competitive, based on available literature. Furthermore, the review presents the various applications of big data and business analytics, data sources generated in these applications and their key characteristics. Finally, the review not only outlines the challenges for successful implementation of big data projects but also highlights the current open research directions of big data analytics that require further consideration. The reviewed areas of big data suggest that good management and manipulation of the large data sets using the techniques and tools of big data can deliver actionable insights that create business values.

scholarly journals Application of Big Data in the Financial Audit Procedures

2021 ◽  
pp. 4
Author(s):  
Kotryna Nagytė ◽  
Lina Dagilienė
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Big Data Analytics ◽  
Graphical Analysis ◽  
Statistical Correlation ◽  
Modern World ◽  
Audit Process ◽  
Systematic Analysis ◽  
Financial Audit ◽  
Jel Code

Annotation. Big Data (BD) is one of the most commonly used terms in the modern world of business and information technology. The main features of BD (quantity, speed, and variety) introduce to unique processing of large information amounts, regardless of their scale, storage and computational complexity, analytical and statistical correlation. The significant emergence and potential use of BD has affected business accounting and financial auditing by replacing the long-used mechanical data collection and completion processes with automatic ones, comparing and searching for correlations between different structure and nature data. According to analysis, the main advantages of applying the BDA in the audit process are related to faster and more efficient execution of procedures, obtaining more detailed results, grouping and comparing data according to selected criteria. In the meantime, cons of BD application are related to the additional professional supervision requirements and the proper data analysis in order for the correct results interpretation. The paper presents the conceptual model, which shows the relationships between BDA tools and financial audit procedures. In addition, the model shows factors and risks, which have impacts on internal and external environment of clients, the applicability of specific audit procedures. It was found that the application of the model in the procedures includes testing of 5 relationships, i. e. classification, clustering, regression and time series analyses, the method of association rules and text research, visualization tool. The Aim of the Study is to identify the application of DDA tools in financial audit procedures. Research Methods: comparative and systematic analysis of the literature; content analysis; statistical data analysis; graphical analysis. Keywords: Big data, Big data Analytics, Financial Audit, Financial Audit Procedures. JEL Code: M15, M40, M42.

scholarly journals The Analysis of Opportunities of the Application of Big Data and Artificial Intelligence Technologies in Public Governance and Social Policy

2021 ◽  
Vol 22 ◽  
pp. 88-100
Author(s):  
Adomas Vincas Rakšnys ◽  
Dangis Gudelis ◽  
Arvydas Guogis
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Social Policy ◽  
Ethical Issues ◽  
Policy Formation ◽  
Modern World ◽  
Business Systems ◽  
Public Governance ◽  
Advantages And Disadvantages ◽  
Data Fragmentation

This interdisciplinary article presents a concept of the 21st century and phenomena that are products of the 4th industrial revolution – big data and Artificial Intelligence technologies – as well as the opportunities of their application in public governance and social policy. This paper examines the advantages and disadvantages of big data, problems of data collection, its reliability and use. Big data can be used for the analysis and modeling of phenomena relevant to public governance and social policy. Big data consist of three main types: a) historical data, b) present data with little delay, c) prognostic data for future forecasting. The following categories of big data can be defined as: a) data from social networks, b) traditional data from business systems, c) machine-generated data, such as water extraction, pollution, satellite information. The article analyzes the advantages and disadvantages of big data. There are big data challenges such as data security, lack of cooperation in civil service and social work, in rare situations – data fragmentation, incompleteness and erroneous issues, as well as ethical issues regarding the analysis of data and its use in social policy and social administration. Big data, covered by Artificial Intelligence, can be used in public governance and social policy by identifying “the hot spots” of various phenomena, by prognosing the meanings of variables in the future on the basis of past time rows, and by calculating the optimal motion of actions in the situations where there are possible various alternatives. The technologies of Artificial Intelligence are used more profoundly in many spheres of public policy, and in the governance of COVID-19 pandemics too. The substantial advantages of the provided big data and Artificial Intelligence are a holistic improvement of public services, possibilities of personalization, the enhancement of citizen satisfaction, the diminishing of the costs of processing expenditure, the targeting of adopted and implemented decisions, more active involvement of citizens, the feedback of the preferences of policy formation and implementation, the observation of social phenomenas in real time, and possibilities for more detailed prognosing. Challenges to security of data, necessary resources and competences, the lack of cooperation in public service, especially rare instances of data fragmentation, roughness, falseness, and ethical questions regarding data analysis and application can be evaluated as the most significant problems of using big data and Artificial Intelligence technologies. Big data and their analytics conducted using Artificial Intelligence technologies can contribute to the adequacy and objectivity of decisions in public governance and social policy, effectively curbing corruption and nepotism by raising the authority and confidence of public sector organizations in governance, which is so lacking in the modern world.

scholarly journals Text and Data Formatting for Machine Learning

2019 ◽  
Vol 9 (1) ◽  
pp. 2756-2760
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Data Preprocessing ◽  
Learning Model ◽  
Poor Quality ◽  
Big Data Analysis ◽  
Model Quality ◽  
Machine Learning Model ◽  
Learning Analysis

Machine learning is a prominent tool for getting data from large amounts of information. Whereas a good amount of machine learning analysis has targeted on increasing the accuracy and potency of coaching and reasoning algorithms, there is less attention within the equally vital issues of observing the standard of information fed into the machine learning model. The standard of huge information is far away from good. Recent studies have shown that poor quality will bring serious errors to the result of big data analysis and this could have an effect on in making additional precise results from the information. Advantages of data preprocessing within the context of ML are advanced detection of errors, model-quality improves by the usage of better data, savings in engineering hours to debug issues

scholarly journals ACOCA: Ant Colony Optimization Based Clustering Algorithm for Big Data Preprocessing

2019 ◽  
Vol 4 (5) ◽  
pp. 1239-1250
Author(s):  
Neelam Singh ◽  
Devesh Pratap Singh ◽  
Bhasker Pant
Keyword(s):  
Big Data ◽  
Ant Colony Optimization ◽  
Clustering Algorithm ◽  
Optimization Problems ◽  
Data Preprocessing ◽  
Ant Colony ◽  
Raw Material ◽  
Search Spaces ◽  
Domain Specific Knowledge ◽  
Search Speed

Big Data is rapidly gaining impetus and is attracting a community of researchers and organization from varying sectors due to its tremendous potential. Big Data is considered as a prospective raw material to acquire domain specific knowledge to gain insights related to management, planning, forecasting and security etc. Due to its inherent characteristics like capacity, swiftness, genuineness and diversity Big Data hampers the efficiency and effectiveness of search and leads to optimization problems. In this paper we explore the complexity imposed by big search spaces leading to optimization issues. In order to overcome the above mentioned issues we propose a hybrid algorithm for Big Data preprocessing ACO-clustering algorithm approach. The proposed algorithm can help to increase search speed by optimizing the process. As the proposed method using ant colony optimization with clustering algorithm it will also contribute to reducing pre-processing time and increasing analytical accuracy and efficiency.

Implementation of Data Preprocessing Techniques on Distributed Big Data Platforms

2019 ◽  
Author(s):  
Oguz Celik ◽  
Muruvvet Hasanbasoglu ◽  
Mehmet S. Aktas ◽  
Oya Kalipsiz ◽  
Alper Nebi Kanli
Keyword(s):  
Big Data ◽  
Data Preprocessing
Sign in / Sign up

Export Citation Format

Share Document