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Non-Obvious Root Cause Analysis

IP.com Disclosure Number: IPCOM000240546D
Publication Date: 2015-Feb-09
Document File: 4 page(s) / 87K

Publishing Venue

The IP.com Prior Art Database

Abstract

The features deals with methods and systems to extract the non-obvious root cause analysis (or likeliness behavior) of entities chosen by a user and its Implementation on Visualization and BI systems via Metadata Enhancement.

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Non-Obvious Root Cause Analysis

Problem Statement and Existing Solutions:

Often it is felt to conduct a root cause analysis on some entities of business value as they are seen on a business intelligence or visualization system. At times these entities might not have an obvious relation among them in the current layout.

Sometimes even while changing the facts/ dimensions of the visualization or BI also no apparent relation can be discovered across any combination of the dimension/ facts which can describe the root cause of co-existence/ like behavior of the entities of interest.

At such occasions the need of a method is felt which can do some complex modeling to extract the non-obvious root cause of the given behavior (likeliness)

Summary:

The features deals to extract the non-obvious root cause analysis (or likeliness behavior) of entities chosen by a user and its Implementation on Visualization and BI systems via Metadata Enhancement.

Implementation Details:

Phase - I: Optimal Separation Space/ Axes Extraction:


 Updating the metadata with identification of the Selection Set (Ss) and Non Selection Set (So)


 Rotating the Selection Set orthogonally and extracting the top 3 (or lesser in case of less than 3 dimensions/ facts in the data) to form the Principal Component Matrix for the Selection Set (Ӣեp-s) with dimensions (1,n)


 Rotating the Non-Selection Set orthogonally and extracting the top 3 (or lesser in case of less than 3 dimensions/ facts in the data) to form the Principal Component Matrix for the Non-Selection Set (Ӣեp-o) with dimension (m,1)


 Obtaining the correlation matrix (Ӣեc) across the Component matrices Ӣեp-s and Ӣեp-o with dimension (m,n)


 Obtaining the Transpose (Ƭc) matrix of the correlation matrix Ӣեc


 Obtaining the Optimal Space Matrix (Ӣեos) with dimension (m,1) by the multiplication of the Correlation Transpose Matrix Ƭc and the Selection Set Component Matrix Ӣեp-s.


 Selection of the two (or n Axes) axes with the highest scalar components of the Optimal

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Space Matrix for a two (or n) dimensional layout.


 Loading the Optimal Axes data to the the existing dataset as additional facts.

Phase - II: Constrained Centroid Space Clustering in the Optimal Separation Plane:


 Identification of the space for the play of the constrained centroid by extending the boundaries of the peripheral points of the Selection set by an area/ volume etc. (circle or sphere etc . depending upon the dimensions of the optimal space) with radius 0.5 times the Euclidean distance between the peripheral point/ entity and the entity closest to it.


 Identifying the Initial positioned of the constrained space centroid by considering the centroid of the Selection Set entities, assigning equal weights to each entity.


 Iteratively choosing 2…N nos. of centroids at random positions outside the selection


o For each of the above iteration progressing and completing the clustering proces...