US 10,705,256 B2Granted

Aggregation and analytics for application-specific optimization based on multiple data sources

AssigneeInternational Business Machines Corporation

InventorsHans-Jurgen Eickelman, Ying Liu, Thomas H. Osiecki, Lucas Correia Villa Real

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Abstract

Aggregating and transforming data, and performing analytics thereupon, for application-specific optimization based on multiple data sources. The data is preferably ingressed automatically, and may originate from various public and/or private data sources. Data transformation preferably aligns the data aggregated from the various sources, to thereby allow meaningful referencing. Complex and non-aligned data can therefore be consolidated, such that it is readily digestible by simulation (or other) software. In an embodiment, risk of flooding for a supply chain is computed from the aggregated and transformed data, using data analytics based on physical computation for flood risk assessment, allowing the supply chain to be optimized with regard to threat of flooding and/or actual flooding. In another embodiment, risk of wild fire may be assessed. Other types of risk may also be assessed.

Claims17

Dependent claims

A method for assessing risk of flooding by aggregating and transforming data from a plurality of data sources, comprising:

determining, for a supply chain spanning a plurality of geographical locations, a location of interest within the supply chain for flooding risk analysis;
determining a plurality of data sources that describe a physical environment of the location;
automatically ingressing, from selected ones of the plurality of sources, the data that describes the physical environment;
programmatically transforming the ingressed data into data maps that are aligned to one another to allow referencing therebetween;
evaluating risk of flooding pertaining to the location by using the aligned data maps as input to an evaluator selected from the group consisting of a simulation model and an analytic process;
generating an annotated map that includes a geographical area surrounding the geographical locations in the supply chain, wherein annotations of the annotated map show portions of the geographical area that are predicted, by the evaluator, to be at risk of flooding;
and
using the annotated map to prepare the predicted risk of flooding, comprising:
selecting, from the annotated map, at least one annotation showing a portion of the geographical area where the risk of flooding exceeds a predetermined threshold;
and
for each of the plurality of geographical locations that are physically located within the portion, determining an alternative geographical location to thereby avoid using, at least temporarily, the geographical location physically located in the portion, wherein the alternative geographical location is determined by repeating the processing performed by the evaluator to evaluate risk of flooding pertaining to the alternative geographical location and determining that the risk of flooding pertaining to the alternative geographical location does not exceed the predetermined threshold.

8 more elements

A method for assessing risk of natural disaster by aggregating and transforming data from a plurality of data sources, comprising:

determining, for a supply chain spanning a plurality of geographical locations, a location of interest within the supply chain for natural disaster risk analysis;
determining a plurality of data sources that describe a physical environment of the location;
automatically ingressing, from selected ones of the plurality of sources, the data that describes the physical environment;
programmatically transforming the ingressed data into data maps that are aligned to one another to allow referencing therebetween;
evaluating risk of natural disaster pertaining to the location by using the aligned data maps as input to an evaluator selected from the group consisting of a simulation model and an analytic process;
generating an annotated map that includes a geographical area surrounding the geographical locations in the supply chain, wherein annotations of the annotated map show portions of the geographical area that are predicted, by the evaluator, to be at risk of natural disaster;
and
using the annotated map to prepare the predicted risk of natural disaster, comprising:
selecting, from the annotated map, at least one annotation showing a portion of the geographical area where the risk of natural disaster exceeds a predetermined threshold;
and
for each of the plurality of geographical locations that are physically located within the portion, determining an alternative geographical location to thereby avoid using, at least temporarily, the geographical location physically located in the portion, wherein the alternative geographical location is determined by repeating the processing performed by the evaluator to evaluate risk of natural disaster pertaining to the alternative geographical location and determining that the risk of natural disaster pertaining to the alternative geographical location does not exceed the predetermined threshold.

8 more elements

Dependencies are inferred from the claim text.

Facts

Application: 15/801,786Filed 2 Nov 2017 · continuation
Priority: 13 Sep 2014
Published: US 2018/0059285 A11 Mar 2018
Granted: 7 Jul 2020
Term ends: Not estimatedThe source records don’t support an estimate.

Classification10 codes

G01W 1/14
Meteorology: rainfall or precipitation gauges
G06F 16/244
Query languages: grouping and aggregation
G06F 16/248
Querying: presentation of query results
G06F 16/254
Integrating or interfacing systems involving database management systems: extract, transform and load [ETL] procedures, e.g. ETL data flows in…

Family

Application14/485,669Parent of this continuation · filed 13 Sep 2014
Application14/512,415Parent of this continuation · filed 11 Oct 2014
US 2018/0059285 A1Pre-grant publication · published 1 Mar 2018
US 9,945,981 B2Parent of this continuation
US 9,952,353 B2Parent of this continuation
US 2016/0077238 A1Published application
US 2016/0078112 A1Published application
US 10,705,256 B2This patent

From the US filing history. Foreign family members aren’t listed.