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Method and System for Providing Recommendation to a User About Objects for Ad-Hoc Reporting Purposes

IP.com Disclosure Number: IPCOM000202429D
Publication Date: 2010-Dec-15
Document File: 6 page(s) / 331K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed is a method and system for providing recommendation to a user about objects for ad-hoc reporting purposes. Such recommendation helps a user to know which objects need more analysis on a given day, or needs more analysis for business decision.

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Method and System for Providing Recommendation to a User About Objects for Ad-Hoc Reporting Purposes

Method and system is disclosed for providing recommendation to a user about objects for ad-hoc reporting purposes. Such recommendation helps a user to know which objects need more analysis on a given day, or needs more analysis for business decision. At the same time the user can provide comments on any objects and other users can also view the same. These comments also help other user to concentrate on deeper analysis.

The method provides automated recommendation and flags the following objects:
Dimension/ Attribute Objects

Measures
Prompts / Prompt list of Values
Filters

These recommendations are done based on Structured data and Text data analysis. Text data analysis mean, Email content, IM chat content, Company news letter , and Voice data analysis.

Fig. 1 describes how a user can view recommendation, comments on any objects. These recommendations can be Flags, and Dashboard Icons. These recommendations can be provided at attribute group level. Upon expanding the group the user can find the recommendation at attribute level.

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Figure 1

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Fig. 1 illustrates the comments and recommendation as shown at Object group level.

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Figure 2

Fig. 3 illustrates a high-level flow diagram of how recommendations are provided in ad-hoc reporting model. An analysis engine gathers data from different sources and based on data mining and text mining technique it identifies objects which are required to be recommended, and then accordingly flags these objects in the ad-hoc report model. Subsequently, a user can provide comments over any object and share with other users in a team.

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Figure 3

Fig. 4 describes a data collection method to show the recommendation icon on ad-hoc report model. Different types of structured data, Textual data, Voice data, and Image data are analyzed to find the rate of change, frequency of occurrence and positive/ negative statement as shown in fig.5. Every user can define a personalized threshold value or there can be a standard threshold value to show the recommendation icon on the ad-hoc report model objects. In this case object mean, Measure/ Dimension/ attribute/ Prompts/ Filters/ Lookup value of any Prompt. This information helps a user to find which business attributes needs deeper analysis.

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Figure 4


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Figure 5

As shown in fig. 5, the rate of change of any measure is calculated. System gathers data for last few loads and based on that it creates a regression model. Accordingly, it identifies the rate of change.

For exemplary purpose, consider the following as equation of the regression...