US 11,144,575 B2Granted

Assisting a user to identify outliers and anomalies in a dataset

AssigneeKyvos Insights, Inc.

InventorsLeigh Chapman, Kavindra Singh, Dharmendra Singh Chouhan, Pratik Jain

Life of this patent

Estimating the term…

Loading patent…

Abstract

The present disclosure relates to system(s) and method(s) for data processing and visualization. The system is configured to enable processing and visually representing a dataset specified by a user. Initially, the system accepts a rule corresponding to at least one dimension associated with the dataset, wherein the rule is associated with a lower level of abstraction. When a rollup operation is performed along the specified dimension of the dataset for generating higher level graphical representation, the system is configured to preserve the rule associated with the dataset, regardless of any mathematical or logical cancellation of values due to the rollup operation and reflects the lower level details at a higher level of abstraction.

Claims14

Dependent claims

A system for assisting a user to identify a region of interest in a dataset, the system comprising:

a memory;
and
a processor coupled to the memory, wherein the processor is configured to execute program instructions stored in the memory for:
processing raw data to generate a processed dataset, wherein the processed dataset comprises a set of abstraction levels, wherein the set of abstraction levels are hierarchal arranged, and wherein each abstraction level comprises a set of data nodes, wherein each data node at an abstraction level is associated with a subset of the set of data nodes in a preceding abstraction level, wherein each data node is configured to maintain aggregated data corresponding to the subset of the set of data nodes in a preceding abstraction level;
accepting a rule corresponding to a target abstraction level from the set of abstraction levels, wherein the rule comprises one or more conditions defined by the user to identify a region of interest;
identifying a set of target data nodes corresponding to the rule from the target abstraction level, wherein the set of target data nodes are identified based on comparison of the aggregated data stored at each data node in the target abstraction level with the one or more conditions specified in the rule;
identifying a set of intermediate data nodes connecting a source data node at a first abstraction level and the set of target data nodes;
generating a multi-layer graphical representation corresponding to the set of abstraction levels with a set of visual indicators, wherein each visual indicator is associated with at least one of the source data node, the set of intermediate data nodes, and the set of target data nodes, wherein the set of visual indicators indicate the region of interest using a remarkable marker and an unremarkable marker, wherein a region point of interest is found by the user, within the multi-layer graphical representation by an remarkable marker or unremarkable marker displayed by the visualization module for an individual node;
and wherein the remarkable marker is displayed at the data node when the underlying graph of a data node comprises one or more points of interest, and wherein the unremarkable marker is displayed at the data node when the underlying graph of a data node does not have one or more points of interest;
and wherein the remarkable marker has a different appearance from the unremarkable marker;
and
displaying the multi-layer graphical representation over an output device, thereby assisting a user to identify a region of interest in the processed dataset, in the multi-layer graphical representation, corresponding to the set of target data nodes;
wherein the output device enables graph by graph navigation in the multi-layer graphical representation using rollup and drilldown operations, and wherein the region of interest corresponds to a region representing the target data nodes on a graph associated with the target level of abstraction.

10 more elements

A method for assisting a user to identify a region of interest in a dataset, the method comprising steps of:

processing, by a processor, raw data to generate a processed dataset, wherein the processed dataset comprises a set of abstraction levels, wherein the set of abstraction levels are hierarchal arranged, and wherein each abstraction level comprises a set of data nodes, wherein each data node at an abstraction level is associated with a subset of the set of data nodes in a preceding abstraction level wherein each data nodes is configured to maintain aggregated data corresponding to the subset of the set of data nodes in a preceding abstraction level;
accepting, by the processor, a rule corresponding to a target abstraction level from the set of abstraction levels, wherein the rule comprises one or more conditions defined by the user to identify a region of interest;
identifying, by the processor, a set of target data nodes corresponding to the rule from the target abstraction level, wherein the set of target data nodes are identified based on comparison of the aggregated data stored at each data node in the target abstraction level with the one or more conditions specified in the rule;
identifying, by the processor, a set of intermediate data nodes connecting a source data node at a first abstraction level and the set of target data nodes;
generating, by the processor, a multi-layer graphical representation corresponding to the set of abstraction levels with a set of visual indicators, wherein each visual indicator is associated with at least one of the source data node, the set of intermediate data nodes, and the set of target data nodes, wherein the set of visual indicators indicate the region of interest using a remarkable marker and an unremarkable marker, wherein a region point of interest is found by the user, within the multi-layer graphical representation by an remarkable marker or unremarkable marker displayed by the visualization module 220 for an individual node;
and wherein the remarkable marker is displayed at the data node when the underlying graph of a data node comprises one or more points of interest, and wherein the unremarkable marker is displayed at the data node when the underlying graph of a data node does not have one or more points of interest;
and wherein a remarkable marker has a different appearance from an unremarkable marker;
and
displaying, by the processor, the multi-layer graphical representation over an output device, thereby assisting a user identify a region of interest in the processed dataset, in the multi-layer graphical representation, corresponding to the set of target data nodes, wherein the output device enables graph by graph navigation in the multi-layer graphical representation using rollup and drilldown operations, and wherein the region of interest corresponds to a region representing the target data nodes on a graph associated with the target level of abstraction.

6 more elements

A non-transitory computer readable medium embodying a program executable in a computing device for assisting a user to identify a region of interest in a dataset, the computer program product comprising:

a program code for processing raw data to generate a processed dataset, wherein the processed dataset comprises a set of abstraction levels, wherein the set of abstraction levels are hierarchal arranged, and wherein each abstraction level comprises a set of data nodes, wherein each data node at an abstraction level is associated with a subset of the set of data nodes in a preceding abstraction level, wherein each data nodes is configured to maintain aggregated data corresponding to the subset of the set of data nodes in a preceding abstraction level;
a program code for accepting a rule corresponding to a target abstraction level from the set of abstraction levels, wherein the rule comprises one or more conditions defined by the user to identify a region of interest;
a program code for identifying a set of target data nodes corresponding to the rule from the target abstraction level, wherein the set of target data nodes are identified based on comparison of the aggregated data stored at each data node in the target abstraction level with the one or more conditions specified in the rule;
a program code for identifying a set of intermediate data nodes connecting a source data node at a first abstraction level and the set of target data nodes;
a program code for generating a multi-layer graphical representation corresponding to the set of abstraction levels with a set of visual indicators, wherein each visual indicator is associated with at least one of the source data node, the set of intermediate data nodes, and the set of target data nodes, wherein the set of visual indicators indicate the region of interest using a remarkable marker and an unremarkable marker, wherein a region point of interest is found by the user, within the multi-layer graphical representation by an remarkable marker or unremarkable marker displayed by the visualization module 220 for an individual node;
and wherein the remarkable marker is displayed at the data node when the underlying graph of a data node comprises one or more points of interest, and wherein the unremarkable marker is displayed at the data node when the underlying graph of a data node does not have one or more points of interest;
and wherein the remarkable marker has a different appearance from the unremarkable marker;
and
a program code for displaying the multi-layer graphical representation over an output device, thereby assisting a user to identify a region of interest in the processed dataset, in the multi-layer graphical representation, corresponding to the set of target data nodes, wherein the output device enables graph by graph navigation in the multi-layer graphical representation using rollup and drilldown operations, and wherein the region of interest corresponds to a region representing the target data nodes on a graph associated with the target level of abstraction.

6 more elements

Dependencies are inferred from the claim text.