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Method and System for Proactive Report Processing

IP.com Disclosure Number: IPCOM000248307D
Publication Date: 2016-Nov-15
Document File: 3 page(s) / 108K

Publishing Venue

The IP.com Prior Art Database

Abstract

A method and system is disclosed to enable the detection of a likely data provider/ source of data of reports by backtracking a report created by an end user, over a studied period of time. The method and system further, optionally facilitates visualization of the report, thereby saving time for the end-user and creators of the report.

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Method and System for Proactive Report Processing

Highly extended timelines are employed by end users of reports, especially those who create the reports. The large amount of time is often spent in finding the source data, customizing the data filters/data functions in order to create the required report. The multiple users spend valuable hours in an organization, creating similar reports, thereby making it a laborious, error-prone and expensive affair for the individuals and organizations likewise. Moreover, due to the highly secure nature of these reports, the only information made available is a graph or bar chart with basic details like chart title and legend. There exists a need for a system that facilitates the ascertaining of source of data/information, over time.

Disclosed is a system and method that enables the detection of a likely data provider/ source of data of reports by backtracking a report created by an end user, over a studied period of time. The method system further, optionally facilitates visualization of the report, thereby saving time for end-user and creators of the report.

In accordance with the method and system, the system includes three analyzer engines, knowledge base and associated system for ascertaining data source. The end users of reports, often share their reports to other individuals and organizations via mail or wikis or some other channel and more often than not, the report only includes a basic chart title and a basic legend.

The first analyzer, namely the message analyzer analyses the available reports/graphs in messages/websites (both internet and intranet), over a period of time, to extract labels and identifiers from the graphs shared by the end users. Further, the extracted labels and identifiers are annotated and added to the knowledge base with a statistical likelihood of the data source. In a scenario where no titles or legend information is accessible by the system, the system extracts words proximate to the graph and scores the proximate words as possible labels and identifiers, to be further added to the knowledge base. The other analyzer engines also processes available images to build ranked pairs of data objects and the name parts with labels and descriptions, wherein the data objects may be, but not limited to data sets, data fields, columns and tables.

Consider an example, as illustrated in Figure 1, wherein based on an image screenshot accessed from an e-mail, the system can ascertain numeric data values and likely data labels as shown below:

1


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Figure 1

Further, if multiple end users exist with the same numeric values in th...