AU2021106044A4 - A visualization of uncertainties and noise in dark data - Google Patents
A visualization of uncertainties and noise in dark data Download PDFInfo
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- AU2021106044A4 AU2021106044A4 AU2021106044A AU2021106044A AU2021106044A4 AU 2021106044 A4 AU2021106044 A4 AU 2021106044A4 AU 2021106044 A AU2021106044 A AU 2021106044A AU 2021106044 A AU2021106044 A AU 2021106044A AU 2021106044 A4 AU2021106044 A4 AU 2021106044A4
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- 238000012800 visualization Methods 0.000 title claims abstract description 10
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Abstract
A VISUALIZATION OF UNCERTAINTIES AND NOISE IN DARK DATA
The present invention relates to a visualization of uncertainties and noise in dark data.
The present invention provides identification of the duplicate data from the big data.
Present invention improve spatial-temporal efficiency by identifying uncertainty in the
data and injecting intelligent insights into diverse and continuously evolving data silos by
uncovering and transforming the value of unanalyzed dark data through application of
cognitive visualytics and intelligent process automation techniques. The uncertainties and
noise in the dark data affects the results leaving behind the techniques to resolve it.
Description
Technical field of invention:
Present invention, in general, relates to the field of computer science and more specifically to avisualization of uncertainties and noise in dark datawhich includes identification of the duplicate data from the big data.
Background of the invention:
The background information herein below relates to the present disclosure but is not necessarily prior art.
There are several SQL based customized queries to retrieve information from the big data. Scientists tried to extract information from the unused data; they followed the approach of converting unstructured data to structured data with the dependencies. Also many visualization tools and techniques are available to represent data in different forms.
The unstructured data affects the processing performance and increases the difficulty of the '0 user. The methods presented by Wael M.S. Yafooz et al; are often used to manage unstructured data in relational databases using query structure. Relational data contain a huge amount of untouched data, and if these data are not organized properly, they will be meaningless and not useful. The important challenge faced is to extract the knowledge from the unstructured data. Researcher have used internal database schema to handled unstructured data.
ShunanGuo et al; discuss and resulted that in event sequence prediction most of the data analysts apply statistical method and machine learning techniques while travelling through the result phase. In event sequence prediction the most difficult part is to convey uncertainty and finding alternative paths or possible outcomes.
The data is the most important asset for any organization, 70 percent of the growth and decision making strategy of the companies depends on the analysis carried out on the data.
According to NjeruMwiti Kevin et al; insight data leads should be understand well with cost cutting techniques which will help in avenues to the organization.
J. Liu et al; discloses the analysis on Scholarly information usually contains millions of raw data such as authors, papers, citations, as well as scholarly networks. For them, the challenge was in the visual representation of data. Nowadays, various visualization techniques can be easily applied on scholarly data visualization and visual analysis to obtain required information, it also enables scientists to have a better way to represent the structure of scholarly datasets and reveal hidden patterns in the data. In this work, researchers first introduce the basic concepts and the methods of collection of scholarly data. Then they have provided a comprehensive overview of tools in data visualization, existing techniques as well as systems for analysing volumes of diverse scholarly data.
S. Su et al., discloses VR enabled scientific visualization applications and tools which has proven as the best techniques. In archeological research, VR-enabled 3-D immersive visualization technique was used. Further it was modified and a fully immersive visualization tool for geosciences was developed. Due to the custom-built nature of the hardware and software, virtual reality (VR) and augmented reality (AR) technologies have remained absent from study of science and engineering workflows. In addition to characteristics, high cost '0 creates a critical entry barrier for most clients to invest in VR and AR applications.
There exist many drawbacks in the existing unit or system. Hence the present invention provides visualization of uncertainties and noise in dark data.
Objective of the invention
An objective of the present invention is to attempt to overcome the problems of prior art and provide avisualization of uncertainties and noise in dark data.
In a preferred embodiment, the present invention providesidentification of the duplicate data from the big data.
These and other objects and characteristics of the present invention will become apparent from the further disclosure to be made in the detailed description given below.
Summary of the invention:
Accordingly, the following invention provides avisualization of uncertainties and noise in dark data.The present inventionprovides identification of the duplicate data from the big data. Present invention improves spatial-temporal efficiency by identifying uncertainty in the data and injecting intelligent insights into diverse and continuously evolving data silos by uncovering and transforming the value of unanalyzed dark data through application of cognitive visualytisc and intelligent process automation techniques. Invention deals with the uncertainties and noise in the dark data for organization and provides them a solution to reduce these uncertainties and noise for better results. The lack of automation tools to improve the productivity and dark data utilization leads to expensive storage and security issues and leaves the increasing dark data volume still unanalyzed and disvalued. The uncertainties and noise in the dark data affects the results leaving behind the techniques to resolve it.
Detailed description of the invention:
'0 The present invention relates a visualization of uncertainties and noise in dark data.More specificallypresent inventionprovides identification of the duplicate data from the big data.
Proposed invention minimizes expensive storage and security issues and reduces dark data volume which is still unanalyzed and disvalued. The methodology proposed to improve spatial-temporal efficiency by injecting intelligent insights into diverse and continuously evolving data silos by uncovering and transforming the value of unanalyzed dark data through application of cognitive visualytics and intelligent process automation (IPA) techniques.
Present invention deal with the uncertainties and noise in the dark data for organization and provide them a solution to reduce these uncertainties and noise for better results. The lack of automation tools to improve the productivity and dark data utilization leads to expensive storage and security issues and leaves the increasing dark data volume still unanalyzed and disvalued.
The uncertainties and noise in the dark data affects the results leaving behind the techniques to resolve it.
The data preparation, gathering and data observation and validation process is followed by data analysis phase. In data analysis, the analysis of the dark data in the big data is calculated. The dependencies of actual data with dark data is also be identified; descriptive analytics is basically uses data aggregation and the concept of data mining to show what has happen, predictive analyticsis based on statistical model too understand the future. Prediction and forecasting techniques will be used, prescriptive analytics; Decision making based on the comparative results.
Automated rule-based workflows are further enhanced with decision-making capabilities. As a result, forward-thinking organizations that have already adopted intelligent process automation technology are realizing greater efficiencylevels, improved staff performance, less risk, better response times and ultimately more positive customer experiences. Intelligent process automation (IPA) refers to the application of artificial intelligence and related new technologies, including computer vision, cognitive automation and machine learning to robotic process automation.
'0 Additionally, it provides an end-to-end automation of dark-to-smart data diagnosis and decision-making based on the historical practices for continuous refinement of its value assessment encompassing the analytics requirements of diverse stakeholders. The work and implementation will try to achieve this by researching IPA methodologies to automatically identify, classify and extract multilateral unstructured dark data and implement intelligent data-to-function-aware cognitive agents to effectively scan the data landscape of an organization, label and annotate dark data, analyses the degree of darkness based on multi dimensional explicit and implicit features and diagnose its cause to control volume growth through automated retention and defensible removal.
Further, the present invention is lead to provide analyzing uncertainty and mapping its variables to visualization for providing more appropriate results to support decision making rule based identification of the unused data and injecting the same with intelligent process automation to remove uncertainty and noise from the visual variable will help the user to compare results with and without uncertainties. The proposed method will help to add intelligent automation for calculating results.
The many features and advantages of the invention are apparent from the detailed specification, and thus, it is intended by the appended claims to cover all such features and advantages of the invention which fall within the true spirit and scope of the invention. Further, since numerous modifications and variations will readily occur to those skilled in the art, it is not desired to limit the invention to the exact construction and operation illustrated and described, and accordingly, all suitable modifications and equivalents may be resorted to, falling within the scope of the invention.
Claims (4)
1. A visualization of uncertainties and noise in dark data wherein minimization of expensive storage and security issues and reduces dark data volume which is still unanalyzed and disvalued characterized in that; said method improve spatial-temporal efficiency by injecting intelligent insights into diverse and continuously evolving data silos by uncovering and transforming the value of unanalyzed dark data through application of cognitive visualytics and intelligent process automation (IPA) techniques.
2. The method as claimed in claim 1 deal with the uncertainties and noise in the dark data for organization and provide them a solution to reduce these uncertainties and noise for better results; the lack of automation tools to improve the productivity and dark data utilization leads to expensive storage and security issues and leaves the increasing dark data volume still unanalyzed and disvalued.
3. The method as claimed in claim 1 wherein the data preparation, gathering and data observation and validation process is followed by data analysis phase; in data analysis, the analysis of the dark data in the big data is calculated; the dependencies of actual data with dark data is also be identified; descriptive analytics is basically uses data aggregation and the concept of data mining to show what has happen, predictive analytics is based on statistical model too understand the future.
4. The method as claimed in claim 1 wherein automated rule-based workflows are further enhanced with decision-making capabilities; as a result, forward-thinking organizations that have already adopted intelligent process automation technology are realizing greater efficiency levels, improved staff performance, less risk, better response times and ultimately more positive customer experiences; intelligent process automation (IPA) refers to the application of artificial intelligence and related new technologies, including computer vision, cognitive automation and machine learning to robotic process automation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| AU2021106044A AU2021106044A4 (en) | 2021-08-19 | 2021-08-19 | A visualization of uncertainties and noise in dark data |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| AU2021106044A AU2021106044A4 (en) | 2021-08-19 | 2021-08-19 | A visualization of uncertainties and noise in dark data |
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| AU2021106044A4 true AU2021106044A4 (en) | 2021-11-18 |
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| AU2021106044A Ceased AU2021106044A4 (en) | 2021-08-19 | 2021-08-19 | A visualization of uncertainties and noise in dark data |
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2021
- 2021-08-19 AU AU2021106044A patent/AU2021106044A4/en not_active Ceased
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