DESC:FIELD OF THE INVENTION
The present invention relates to a method of data management. Particularly, the present invention relates to a method of data management by using an information retrieval system for accessing visual data, automating the identification and organizing of data from different visualization sources.

BACKGROUND OF THE INVENTION
In our increasingly data-driven world, there are growing challenges faced by numerous companies to rely on data visualization tools and dashboards for critical decision-making. As the volume of data and visualizations continues to increase, it becomes increasingly difficult to efficiently track, manage, and locate specific data points and graphs. This led to a significant problem within the industry that required an innovative solution.

With the proliferation of dashboards and data sources within companies, it has become challenging to keep track of where specific data is being captured and where particular visualizations or graphs are located. The complexity of data management has escalated, making it cumbersome to navigate and access the necessary information. To address this issue, many companies have resorted to hiring personnel dedicated to mapping data sources and locating relevant visual content. This manual approach is not only time-consuming but also incurs considerable human resource costs. It demands extensive effort and consumes valuable time that could be better utilized for more strategic tasks.

As the volume of visual content continues to grow, along with the addition of new visualizations daily, the process of manually mapping and locating data and visualizations has become increasingly time-consuming. This has a direct impact on productivity and efficiency within organizations.

To sum up, existing visual data management methods have many limitations, such as, they are time-consuming, resource intensive, human error-prone, inconsistent, providing duplicate records, liable to security breaches, and decentralized visualizations.
Therefore, there is an essential need of a comprehensive, automated, and scalable solution that addresses the limitations of existing data management methods.

OBJECTIVES OF THE INVENTION
The main objective of the present invention is to provide a visual data management method enhancing the accessibility and retrieval of visual content from various visualization tools, reducing the time and effort required for manual data entry, and minimizing human errors.

Another objective of the present invention is to provide a centralized repository of visual data extracted from various sources such as dashboards, graphs, and charts.

Another objective of the present invention is to allow users to search, retrieve, and view visual data efficiently via an intuitive user interface.

Another objective of the present invention is to automate the extraction, cataloging, and storage of visual content along with its associated metadata.

Yet another objective of the present invention is to reduce redundancy, improve data consistency, and strengthen security by consolidating visual data management.

BRIEF DESCRIPTION OF THE DRAWINGS
The drawings described herein are for illustrative purposes only of selected embodiments and not all possible implementations and are not intended to limit the scope of the present disclosure. The invention itself, however, both as to organization and method of operation, may best be understood by reference to the detailed description which follows taken in conjunction with accompanying drawings in which

Fig. 1 depicts an information retrieval system 100 for a visual data management, according to one or more embodiments of the present disclosure; and

Fig. 2 depicts a method 200 for visual data management by using the information retrieval system 100, according to one or more embodiments of the present disclosure.

SUMMARY OF THE INVENTION
The present disclosure discloses an information retrieval system for a visual data management and a method thereof. The information retrieval system includes a visualization module configured to analyze and visualize a raw dataset received from multiple data sources for converting into a digital files by using a processor based on a various visualization tools stored in a memory and store in a database, a scanning module configured to identify the digital files having visual contents received from the database to generate a visual dataset by using the processor and store the visual dataset in the database, a visual identifier module configured to identify the visuals present in received digital file opened by the a reading module using the processor based on instructions stored in the memory for extracting a metadata of each identified visual of the digital file stored in the database, a visual classifying module configured to classify the all visuals of the digital file in the various classes by using the processor based on the classifiers stored in the memory and store the classified visuals in the database in structured manner, a meta-information identifier module configured to identify a user metadata of the inputs entered by the users through a user interface corresponding to each visual/graph required to be retrieved by using the processor based on the instructions stored in the memory, and a mapping module configured to map the user metadata with the metadata stored in the database by using the processor and send the visuals of the digital file for metadata matched with the user metadata to the user interface.

DETAILED DESCRIPTION OF THE INVENTION
The exemplary embodiments are provided to illustrate aspects of the invention, but the invention is not limited to any embodiment. The scope of the invention encompasses numerous alternatives, modifications and equivalents; it is limited only by the claims.

Numerous specific details are set forth in the following description in order to provide a thorough understanding of the invention. However, the invention may be practiced according to the claims without some or all of these specific details. For the purpose of clarity, technical material that is known in the technical fields related to the invention has not been described in detail so that the invention is not unnecessarily obscured. As used herein, the singular forms “a”, “an” and “the” designate both the singular and the plural, unless expressly stated to designate the singular only.

The present disclosure discloses an information retrieval system for providing a data management by accessing visual data, automating the identification and organizing of data from different visualization sources and a method thereof. The method for a visual data management utilizes a chart directory processed by the information retrieval system. The method for a visual data management provides a systematic and structured approach to access and utilize visual content stored in a plurality of folders, allowing users to easily locate and retrieve information as needed from different ecosystems. The method for a visual data management involves the extraction of data from charts, graphs, diagrams, and any other visual representations. Users can provide the inputs to the user interface. The information retrieval system refines user inputs by specifying criteria such as creation date, last edit date, file location, last update date for graphs, and more.

Fig. 1 depicts an information retrieval system 100 for a visual data management, according to one or more embodiments of the present disclosure. The information retrieval system 100 is configured to provide an automated data retrieval, cataloging, and access to visual content from multiple sources such as databases, spreadsheets, APIs, or cloud-based repositories, web-pages/sub-pages, etc. for a visual data management. The information retrieval system 100 includes a visualization module 102, a database 104, a processor 108, a memory 110, a scanning module 112, a reading module 116, a visual identifier module 118, a visual classifying module 122, a meta-information identifier module 126, a user interface 128, and a mapping module 132 work together seamlessly.

The visualization module 102 is configured to receive raw dataset from multiple data sources such as databases, spreadsheets, APIs, or cloud-based repositories, web-pages/sub-pages, etc. The visualization module 102 is configured to receive raw dataset such as numerical or textual data needed to be analyzed and visualized for storing in the database 104. The visualization module 102 is configured to convert the raw dataset stored in database 104 into the corresponding digital files 106 for visualizing raw data, typically in the form of dashboard files such as multiple interactive charts, graphs, tables, etc., chart and graph files such as bar charts, pie charts, line graphs, scatter plots, etc., infographics files such as data visualization with graphic design to present data in a more engaging, visually appealing way, diagram files such as flowcharts, network diagrams, organizational charts, etc. geospatial visualization files such as geographic data, often in maps, plots, and spatial analyses, interactive data visualization files such as web-based visualization or custom interactive charts, etc. statistical graph files such as histograms, box plots, and other distribution-based visualizations, etc. by using the processor 108 based on a various visualization tools stored in the memory 110 such as business intelligence (bi) tools, data visualization libraries and frameworks, geospatial visualization tools, infographic design tools, scientific visualization tools, presentation and reporting tools, etc. The visualization module 102 is configured to allow users to create, manipulate, and present data of raw dataset stored in the database 104 visually in the form of digital files 106 such as graphs, charts, maps, diagrams, or dashboards etc. The visualization module 102 is configured to update the digital files 106 of the visual representations of the raw data in real-time, reflecting changes in the underlying data as they occur. The visualization module 102 is configured to be used for creating interactive dashboards, statistical plots, geospatial maps, or simple business charts, etc. The visualization module 102 is configured to store the generated digital files 106 of the visual representations of the raw data in the database 104.

The scanning module 112 is configured to receive the digital file 106 from the database 104. The scanning module 112 is configured to identify the digital files 106 having visual contents from the visual representations stored in database 104 by using the processor 108. In an embodiment, the scanning module 112 is configured to identify the digital files 106 such as dashboard/visualization files, PowerPoint files etc. having visual contents from the visual representations stored in database 104 by using the processor 108. The scanning module 112 is configured to generate a visual dataset 114 of the digital file 106 having visual contents by using the processor 108. The scanning module 112 is configured to store the generated visual dataset 114 in the database 104.

The reading module 116 is configured to receive the visual dataset 114 from the database 104. The reading module 116 is configured to open each type digital file 106 of the received visual dataset 114 by using the processor 108 based on the instructions stored in the memory 110.

The visual identifier module 118 is configured to receive each opened digital file 106 from the reading module 116. The visual identifier module 118 is configured to identify the visuals present in received opened digital file 106 by using the processor 108 based on instructions stored in the memory 110. The visual identifier module 118 is configured to extract a metadata 120 of each identified visual such as File name, File size, Permissions (read/write/execute), File location (address or cluster on disk), Timestamp (created, modified), web-URL(in case of web file) etc. by using the processor 108. The visual identifier module 118 is configured to store the metadata 102 of the corresponding digital file 106 in the database 104.

The visual classifying module 122 is configured to categorize the digital file 106 based on how content can be represented or interpreted in a visual manner such type of media, visual data that digital file 106 contain, which is particularly useful in contexts where images, videos, or other graphical representations are involved. The visual classifying module 122 is configured to receive the metadata 120 of the each digital file 106 containing visuals from the database 104. The visual classifying module 122 is configured to classify the all visuals of the digital file 106 in the various classes such as graph/visual type (bar graph, pie graph, doughnut chart, etc.) by using the processor 108 based on the classifiers 124 stored in the memory 110 such as Rule-Based Classifiers, Machine Learning Classifiers, Deep Learning Classifiers, etc. The visual classifying module 122 is configured to train the classifier 124 based on the metadata 120 for categorizing the visuals of the digital file 106.
The trained classifier 124 is configured to categorize data into different classes or categories based on metadata 120. The trained classifier 124 is configured to organize the visuals of the digital file 106 by sorting the digital file 106 into pre-defines classes such as bar graph, pie graph, doughnut chart, etc. assisting by the processor 108. The visual classifying module 122 is configured to store the classified visuals of the digital file 106 in the database 104 in structured manner.

The meta-information identifier module 126 is configured to receive the relevant inputs entered by the users from the user interface 128. The meta-information identifier module 126 is configured to identify the user metadata 130 of the inputs entered by the users corresponding to each visual/graph required to be retrieved by using the processor 108 based on the instructions stored in the memory 110.

The mapping module 132 is configured to receive the metadata 128 of the inputs from the meta-information identifier module 126. The mapping module 132 is configured to map the received the user metadata 130 with the metadata 120 stored in the database 104 by using the processor 108. The mapping module 132 is configured to send the visuals of the digital file 106 of metadata 120 matched with the user metadata 130 to the user interface 128.

The user interface 128 is configured to allow the user to retrieve the information of visuals of the digital file 106 stored in the database 104. The user interface 128 is configured to receive the relevant inputs entered by the users for fetching the relevant graphs/visuals for them from the database 104. The user interface 128 is configured to show the fetched visual results with an option to either copy the graph/visual directly or provide the button to reach that directory where the graph/visual is stored.

Fig. 2 depicts a method 200 for visual data management by using the information retrieval system 100, according to one or more embodiments of the present disclosure. The method 200 for visual data management by using the information retrieval system 100 provides a comprehensive and efficient method for managing, accessing, and retrieving data from visualization tool files. The method 200 provides a systematically processing and storing content in a structured database 104, along with the user metadata 130, the metadata 120 and user-defined filters, for enhanceing the accessibility and utility of visual content for users. The method 200 for the visual data management by using the information retrieval system 100 offers a robust and highly efficient solution for accessing visual data, automating the identification and organizing of data from different visualization sources includes but not limited to the following steps:

At step 202, the information retrieval system 100 is configured to receive the raw dataset needed to be analyzed and visualized from multiple data sources such as databases, spreadsheets, APIs, relevant data repository elements within on-prem/cloud data repositories (e.g. storage blobs, data lakes etc.), widgets, web-page elements, etc. The information retrieval system 100 is configured to store the raw dataset in the database 104.

At step 204, the visualization module 102 of the information retrieval system 100 is configured to convert the raw dataset stored in database 104 into the corresponding digital files 106 for visualizing raw data, typically in the form of dashboard files such as multiple interactive charts, graphs, tables, etc., chart and graph files such as bar charts, pie charts, line graphs, scatter plots, etc., infographics files such as data visualization with graphic design to present data in a more engaging, visually appealing way, diagram files such as flowcharts, network diagrams, organizational charts, etc. geospatial visualization files such as geographic data, often in maps, plots, and spatial analyses, interactive data visualization files such as web-based visualization or custom interactive charts, etc. statistical graph files such as histograms, box plots, and other distribution-based visualizations, etc. by using the processor 108 based on a various visualization tools stored in the memory 110 such as business intelligence (bi) tools, data visualization libraries and frameworks, geospatial visualization tools, infographic design tools, scientific visualization tools, presentation and reporting tools, etc. The visualization module 102 is configured to store the generated digital files 106 of the visual representations of the raw data in the database 104.

At step 206, the information retrieval system 100 is configured to scan the received digital file 106 generated at step 204 from the database 104 by using the scanning module 112. In an embodiment, the information retrieval system 100 is configured to systematically scan all folders containing digital file 106 generated at step 204 from the database 104 by using the scanning module 112. The scanning module 112 of the information retrieval system 100 is configured to identify the digital files 106 having visual contents from the visual representations stored in database 104 by using the processor 108. In an embodiment, the scanning module 112 of the information retrieval system 100 is configured to identify the digital files 106 such as dashboard/visualization files, PowerPoint files etc. having visual contents from the visual representations stored in database 104 or through a web browser interface by using the processor 108.

At step 208, The scanning module 112 of the information retrieval system 100 is configured to generate a visual dataset 114 of the digital file 106 having visual contents by using the processor 108 and store in the database 104.

At step 210, the reading module 116 of the information retrieval system 100 is configured to open each type digital file 106 of the received visual dataset 114 from the database 104 by using the processor 108 based on the instructions stored in the memory 110.

At step 212, the visual identifier module 118 of the information retrieval system 100 is configured to receive each opened digital file 106 from the reading module 116. The visual identifier module 118 is configured to identify the visuals present in received opened digital file 106 by using the processor 108 based on instructions stored in the memory 110. The visual identifier module 118 is configured to extract a metadata 120 of each identified visual such as File name, File size, Permissions (read/write/execute), File location (address or cluster on disk), Timestamp (created, modified), web-URL(in case of web file) etc. by using the processor 108. The visual identifier module 118 is configured to store the metadata 102 of the corresponding digital file 106 in the database 104.

At step 214, the information retrieval system 100 is configured to categorize the digital file 106 based on how content can be represented or interpreted in a visual manner such type of media, visual data that digital file 106 contain, which is particularly useful in contexts where images, videos, or other graphical representations are involved by using the visual classifying module 122. The information retrieval system 100 is configured to receive the metadata 120 of the each digital file 106 containing visuals from the database 104 by using the visual classifying module 122. The visual classifying module 122 of the information retrieval system 100 is configured to classify the all visuals of the digital file 106 in the various classes such as graph/visual type (bar graph, pie graph, doughnut chart, etc.) by using the processor 108 based on the classifiers 124 stored in the memory 110 such as Rule-Based Classifiers, Machine Learning Classifiers, Deep Learning Classifiers, etc.

At step 216, the classifier 124 is configured to be trained based on the metadata 120 for categorizing the visuals of the digital file 106 by using the visual classifying module 122. The trained classifier 124 is configured to categorize data into different classes or categories based on metadata 120. The trained classifier 124 is configured to organize the visuals of the digital file 106 by sorting the digital file 106 into pre-defines classes such as bar graph, pie graph, doughnut chart, etc. assisting by the processor 108. The visual classifying module 122 is configured to process and organize the metadata 120 based on the trained classifier 124 in a structured manner. The visual classifying module 122 is configured to store the classified visuals of the digital file 106 in the database 104 in structured manner for ensuring that the metadata 120 is organized, making it readily accessible for subsequent queries and searches.

At step 218, the meta-information identifier module 126 of the information retrieval system 100 is configured to receive the relevant inputs entered by the users from the user interface 128. In an embodiment, the meta-information identifier module 126 of the information retrieval system 100 is configured to receive query/search for retrieving specific information by providing relevant keywords from the user interface 128. The meta-information identifier module 126 is configured to identify the user metadata 130 of the inputs entered by the users using the user interface 128 corresponding to each visual/graph required to be retrieved by using the processor 108 based on the instructions stored in the memory 110. In an embodiment, the meta-information identifier module 126 is configured to identify the user metadata 130 associated each keyword required to be searched for file and graph and the information of any change in the currently displayed visual of the digital file 104. In an embodiment, the user metadata 130 includes information such as the creation timestamp of digital file 104, the last edit timestamp, the user who made the last edit, and the location of digital file 104.

At step 220, the mapping module 132 of the information retrieval system 100 is configured to receive the metadata 128 of the inputs from the meta-information identifier module 126. The mapping module 132 of the information retrieval system 100 is configured to map the received user metadata 130 with the metadata 120 stored in the database 104 by using the processor 108. The mapping module 132 is configured to send the visuals of the digital file 106 of metadata 120 matched with the user metadata 130 to the user interface 128.

At step 222, the user interface 128 of the information retrieval system 100 is configured to show the fetched visual results with an option to either copy the graph/visual directly or provide the button to reach that directory where the graph/visual is stored.

In an embodiment, the information retrieval system 100 is configured to provide ability to the user for applying filters based on user metadata 128 to enhance the precision of search results.

ADVANTAGES OF THE PRESENT INVENTION
1. The information retrieval system 100 for visual data management and the method 200 thereof reduces the complexity of data management, making it cumbersome to navigate and access the necessary information.
2. The information retrieval system 100 for visual data management and the method 200 thereof provides an automated retrieving and cataloguing data from various sources and easily retrieving and accessing relevant information through user-friendly search queries, making all enterprise dashboards easily searchable and accessible.
3. The information retrieval system 100 for visual data management and the method 200 thereof helps to consolidate all relevant information onto a single platform, creating a centralized repository for visual content from different sources. This centralization simplifies data management and promotes a more organized approach to handling diverse visual data.
4. The information retrieval system 100 for visual data management and the method 200 thereof offers users a searchable resource, and significantly promotes easier, quicker, and more secure access to dashboards and visualizations. Users can find the information they need efficiently, reducing the time and effort required to navigate and extract data from multiple sources.
5. The information retrieval system 100 for visual data management and the method 200 thereof automates the cataloguing method and eliminates the need for manual data entry, thus results in substantial cost savings for enterprises. It reduces labour costs associated with data management and enhances cost-effectiveness.
6. The information retrieval system 100 for visual data management and the method 200 thereof fundamentally transforms and streamlines the cataloguing data from a multitude of visual technologies and dashboards, bringing together diverse sources of data into a single, easily accessible platform, simplifying the overall process and making it more efficient.

Although a preferred embodiment of the invention has been illustrated and described, it will at once be apparent to those skilled in the art that the invention includes advantages and features over and beyond the specific illustrated construction. Accordingly, it is indented that the scope of the invention be limited solely by the scope of the hereinafter appended claims, and not by the forgoing specification, when interpreted in light of the relevant prior art.
,CLAIMS:We claim:
1. An information retrieval system (100) for a visual data management, comprising:
a. a visualization module (102) configured to analyze and visualize a raw dataset received from multiple data sources for converting into a digital files (106) by using a processor (108) based on a various visualization tools stored in a memory (110) and store in a database (104);
b. a scanning module (112) configured to identify the digital files (106) having visual contents received from the database (104) to generate a visual dataset (114) by using the processor (108) and store the visual dataset (114) in the database (104);
c. a visual identifier module (118) configured to identify the visuals present in received digital file (106) opened by the a reading module (116) using the processor (108) based on instructions stored in the memory (110) for extracting a metadata (120) of each identified visual of the digital file (106) stored in the database (104);
d. a visual classifying module (122) configured to classify the all visuals of the digital file (106) in the various classes by using the processor (108) based on the classifiers (124) stored in the memory (110) and store the classified visuals in the database (104) in structured manner;
e. a meta-information identifier module (126) configured to identify a user metadata (130) of the inputs entered by the users through a user interface (128) corresponding to each visual/graph required to be retrieved by using the processor (108) based on the instructions stored in the memory (110); and
f. a mapping module (132) configured to map the user metadata (130) with the metadata (120) stored in the database (104) by using the processor (108) and send the visuals of the digital file (106) for metadata (120) matched with the user metadata (130) to the user interface (128).

2. The information retrieval system (100) as claimed in claim 1, wherein visualization module (102) configured to allow users to create, manipulate, and present data of raw dataset stored in the database (104) visually in the form of digital files (106).

3. The information retrieval system (100) as claimed in claim 1, wherein the reading module (116) configured to open each type digital file (106) of the visual dataset (114) received from the database (104) by using the processor (108) based on the instructions stored in the memory (110).

4. The information retrieval system (100) as claimed in claim 1, wherein the classifiers (124) configured to be trained based on the metadata (120) for categorizing the visuals of the digital file (106) by using the processor (108).

5. The information retrieval system (100) as claimed in claim 1, wherein the trained classifier (124) configured to organize the visuals of the digital file (106) by sorting the digital file (106) into pre-defines classes.

6. The information retrieval system (100) as claimed in claim 1, wherein the user interface 128 configured to allow the user to retrieve the information of visuals of the digital file 106 stored in the database 104, by entering relevant inputs and show the fetched visual results with an option to either copy the graph/visual directly or to reach the directory where the graph/visual is stored.

7. A method (200) for visual data management, comprising:
a. receiving the raw dataset needed to be analyzed and visualized from multiple data sources;
b. converting the raw dataset into the corresponding digital files (106) by using the visualization module (102);
c. scanning the digital file (106) to identify the digital files (106) having visual contents by using the scanning module (112);
d. generating the visual dataset (114) of the digital file (106) having visual contents by using the scanning module (112);
e. extracting the metadata (120) of each identified visual present in received digital file (106) by using the visual identifier module (118);
f. classifying the visuals of the digital file (106) in the various classes based on the classifiers (124) trained on the metadata (120) by the visual classifying module (122);
g. identifying the user metadata (130) of the inputs entered by the users using the user interface (128) corresponding to each visual/graph required to be retrieved by using the meta-information identifier module (126);
h. mapping the user metadata (130) with the metadata (120) by using the mapping module (132);
i. sending the visuals of the digital file (106) of metadata (120) matched with the user metadata (130) to the user interface (128); and
j. showing the fetched visual results to the user on the user interface (128).