scholarly journals Cooperation of Business Intelligence and Big Data in one Ecosystem

2020 ◽  
Vol 83 ◽  
pp. 01008
Author(s):  
Matej Černý
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Business Intelligence ◽  
Structured Data ◽  
The Other ◽  
Unstructured Data ◽  
Data Types ◽  
Data Ecosystem

This paper is focused on the issue, how the business can analyze all data types (structured and unstructured) in one cooperative environment. With structured data handle Business Intelligence and with unstructured data on the other side Big Data. As a solution to this issue, we have suggested our Business Intelligence and Big Data ecosystem. This model - the ecosystem is based on already proven data processing processes running in Business Intelligence and in Big Data areas. Both processes are integrated into one unit. We have also described their common functioning.

scholarly journals Optimization and analysis of information using business intelligence techniques and reporting using dashboards

2018 ◽  
Vol 7 (4.5) ◽  
pp. 596
Author(s):  
Ishwarappa Kalbandi ◽  
P. P. Hakarnika ◽  
Mohana . ◽  
H. P. Khandagale
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Business Intelligence ◽  
Structured Data ◽  
Unstructured Data ◽  
Sql Server ◽  
Correct Decision ◽  
World Data ◽  
Analytical Processing ◽  
The Given

In Today’s world data is collected at an unpredictable scale from various application areas. Prior to the arrival of Big Data, all the data that was generated was handled manually. With data being produced in the range of terabytes today, that is impossible. To make the situation worse, almost 80% of the data generated by organizations is unstructured. This means that it cannot be understood in its avail- able format. It is very difficult and risky to make decisions just based on such crude data. In order to make quick, yet correct decisions, the generated data has to be optimized. This Paper discusses to create an end-to-end system to optimize approximately 6 million records of unstructured data provided as .txt files, which is in the form of strings and numbers into understandable or structured data. The next step is to analyse the structured data in order to make calculations on the given dataset. Finally, the analysed data will be represented in the form of dashboards, which are tabular reports or charts. In this Paper, unstructured data in the form of .txt files will be transformed into structured data in the form of tables through the SQL stored procedures in SQL Server Management Studio (SSMS). Along with the data, four other tables called dimensions will be created and then all five tables will then be integrated using SQL Server Integrated Ser- vices. Then an Online Analytical Processing (OLAP) cube is built over this data with product, customer, currency and time as its dimen- sions using the SQL Server Analysis Services (SSAS). At last this analysed data is then reported through dashboards through SQL Server Reporting Services (SSRS).The results of the analysed data is viewed in the form of reports and charts. These reports are customizable and a variety of operations can be performed on them as required by an organization. Since these reports are short and informative, they will be easy to understand and will provide for easier and correct decision making.  

Big Data Tools for Computing on Clouds and Grids

2018 ◽  
pp. 152-174
Author(s):  
Forest Jay Handford
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Processing ◽  
Cost Effective ◽  
The Other ◽  
Relative Cost ◽  
Scientific Communities ◽  
Academic Communities ◽  
Historic Data ◽  
The Government

The number of tools available for Big Data processing have grown exponentially as cloud providers have introduced solutions for businesses that have little or no money for capital expenditures. The chapter starts by discussing historic data tools and the evolution to those of today. With Cloud Computing, the need for upfront costs has been removed, costs are continuing to fall and costs can be negotiated. This chapter reviews the current types of Big Data tools, and how they evolved. To give readers an idea of costs, the chapter shows example costs (in today's market) for a sampling of the tools and relative cost comparisons of the other tools like the Grid tools used by the government, scientific communities and academic communities. Readers will take away from this chapter an understanding of what tools work best for several scenarios and how to select cost effective tools (even tools that are unknown today).

The Impact of Big Data on Security

Big Data ◽  
2016 ◽  
pp. 1495-1518
Author(s):  
Mohammad Alaa Hussain Al-Hamami
Keyword(s):  
Social Media ◽  
Big Data ◽  
Management System ◽  
Database Management ◽  
Database Systems ◽  
Structured Data ◽  
Database Management System ◽  
Unstructured Data ◽  
And Behavior ◽  
The Impact

Big Data is comprised systems, to remain competitive by techniques emerging due to Big Data. Big Data includes structured data, semi-structured and unstructured. Structured data are those data formatted for use in a database management system. Semi-structured and unstructured data include all types of unformatted data including multimedia and social media content. Among practitioners and applied researchers, the reaction to data available through blogs, Twitter, Facebook, or other social media can be described as a “data rush” promising new insights about consumers' choices and behavior and many other issues. In the past Big Data has been used just by very large organizations, governments and large enterprises that have the ability to create its own infrastructure for hosting and mining large amounts of data. This chapter will show the requirements for the Big Data environments to be protected using the same rigorous security strategies applied to traditional database systems.

scholarly journals Research on Unstructured Data Processing Technology in Executing Audit Based on Big Data Budget

2020 ◽  
Vol 1650 ◽  
pp. 032100
Author(s):  
Benzhong Hou ◽  
Yongqiang Zhang ◽  
Ying Shang ◽  
Xin Liang ◽  
Tiantian Liu ◽  
...  
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Processing Technology ◽  
Unstructured Data

scholarly journals Limitations of information extraction methods and techniques for heterogeneous unstructured big data

2019 ◽  
Vol 11 ◽  
pp. 184797901989077 ◽  
Author(s):  
Kiran Adnan ◽  
Rehan Akbar
Keyword(s):  
Big Data ◽  
Information Extraction ◽  
Extraction Methods ◽  
Unstructured Data ◽  
Future Research ◽  
Data Sets ◽  
Data Types ◽  
Efficiency And Effectiveness ◽  
Single Data ◽  
The Impact

During the recent era of big data, a huge volume of unstructured data are being produced in various forms of audio, video, images, text, and animation. Effective use of these unstructured big data is a laborious and tedious task. Information extraction (IE) systems help to extract useful information from this large variety of unstructured data. Several techniques and methods have been presented for IE from unstructured data. However, numerous studies conducted on IE from a variety of unstructured data are limited to single data types such as text, image, audio, or video. This article reviews the existing IE techniques along with its subtasks, limitations, and challenges for the variety of unstructured data highlighting the impact of unstructured big data on IE techniques. To the best of our knowledge, there is no comprehensive study conducted to investigate the limitations of existing IE techniques for the variety of unstructured big data. The objective of the structured review presented in this article is twofold. First, it presents the overview of IE techniques from a variety of unstructured data such as text, image, audio, and video at one platform. Second, it investigates the limitations of these existing IE techniques due to the heterogeneity, dimensionality, and volume of unstructured big data. The review finds that advanced techniques for IE, particularly for multifaceted unstructured big data sets, are the utmost requirement of the organizations to manage big data and derive strategic information. Further, potential solutions are also presented to improve the unstructured big data IE systems for future research. These solutions will help to increase the efficiency and effectiveness of the data analytics process in terms of context-aware analytics systems, data-driven decision-making, and knowledge management.

scholarly journals Big Data and MapReduce Challenges, Opportunities and Trends

2016 ◽  
Vol 6 (6) ◽  
pp. 2911
Author(s):  
Sachin Arun Thanekar ◽  
K. Subrahmanyam ◽  
A. B. Bagwan
Keyword(s):  
Big Data ◽  
High Velocity ◽  
Structured Data ◽  
Unstructured Data ◽  
Map Reduce ◽  
Huge Amount ◽  
Commodity Hardware ◽  
Survey Paper ◽  
Recent Trends ◽  
Large Clusters

<p>Nowadays we all are surrounded by Big data. The term ‘Big Data’ itself indicates huge volume, high velocity, variety and veracity i.e. uncertainty of data which gave rise to new difficulties and challenges. Big data generated may be structured data, Semi Structured data or unstructured data. For existing database and systems lot of difficulties are there to process, analyze, store and manage such a Big Data.  The Big Data challenges are Protection, Curation, Capture, Analysis, Searching, Visualization, Storage, Transfer and sharing. Map Reduce is a framework using which we can write applications to process huge amount of data, in parallel, on large clusters of commodity hardware in a reliable manner. Lot of efforts have been put by different researchers to make it simple, easy, effective and efficient. In our survey paper we emphasized on the working of Map Reduce, challenges, opportunities and recent trends so that researchers can think on further improvement.</p>

scholarly journals Factors Affecting The Usability Of Unstructured Big Data

10.31645/09 ◽  
2020 ◽  
Author(s):  
Joshua Devadason ◽  
◽  
Rehan Akbar
Keyword(s):  
Big Data ◽  
Research Work ◽  
Business Environment ◽  
Market Analysis ◽  
Unstructured Data ◽  
Future Trends ◽  
Survey Questionnaire ◽  
Data Types ◽  
Factors Affecting ◽  
Number Of Factors

Big data is a valuable asset for organisation as it analyses and help to understand the customers, changes within their business environment, market analysis and future trends. The big data is multifaceted (different data types and versatile), and mostly exists in unstructured formats. The extraction of value from this data is challenging. The usability and productivity of this multifaceted unstructured data is greatly compromised. A number of factors and associated reasons affect the usability of unstructured big data. The present research work investigates these factors and associated reasons behind the usability issues of multifaceted unstructured big data. The identification of these factors contribute to develop solutions to reduce the lack of usability of highly unstructured big data. A detailed study of existing literature followed by survey questionnaire has been conducted to identify the factors and their reasons. Descriptive statistics has been used to analyse and interpret the data and results.

scholarly journals Big Data and digital economy

2020 ◽  
pp. 187-197
Author(s):  
Sergey Belov ◽  
Daria Zrelova ◽  
Vladimir Korenkov
Keyword(s):  
Big Data ◽  
Information Systems ◽  
Data Processing ◽  
Economic Research ◽  
Distributed Information Systems ◽  
Research Teams ◽  
Distributed Information ◽  
New Knowledge ◽  
Data Ecosystem ◽  
Analytical Platforms

In this paper, Big Data is considered as an "umbrella" term that combines various concepts, technologies and methods of data processing in distributed information systems that provide a qualitatively new useful information (new knowledge). The stages of "standard" research in the Big Data approach are described. A brief description of the Big Data ecosystem, which consists of several main categories, is given. Various projects and initiatives at the national and international levels are considered, as well as examples of the use of Big Data in business, economy, and society. As concrete examples of the construction and use of analytical platforms for Big Data, successful socio-economic research carried out by the authors as part of research teams at the Plekhanov Russian University of Economics is presented. The Big data metaphor is certainly successful, since it naturally connects a complex of concepts, technologies and methods of Big data with the economy by hinting at a connection with other well-known metaphors –"Big oil", "Big ore", etc.

scholarly journals A Scalable Business Intelligence Decision-Making System in the Era of Big Data

2019 ◽  
Vol 8 (12) ◽  
pp. 2034-2042
Keyword(s):  
Big Data ◽  
Data Warehouse ◽  
Data Storage ◽  
Business Intelligence ◽  
Unstructured Data ◽  
Data Sources ◽  
Second Step ◽  
Decision Making System ◽  
Set Up

Transformation presents the second step in the ETL process that is responsible for extracting, transforming and loading data into a data warehouse. The role of transformation is to set up several operations to clean, to format and to unify types and data coming from multiple and different data sources. The goal is to get data to conform to the schema of the data warehouse to avoid any ambiguity problems during the data storage and analytical operations. Transforming data coming from structured, semi-structured and unstructured data sources need two levels of treatments: the first one is transformation schema to schema to get a unified schema for all selected data sources and the second treatment is transformation data to data to unify all types and data gathered. To ensure the setting up of these steps we propose in this paper a process switch from one database schema to another as a part of transformation schema to schema, and a meta-model based on MDA approach to describe the main operations of transformation data to data. The results of our transformations propose a data loading in one of the four schemas of NoSQL to best meet the constraints and requirements of Big Data.

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