Augmented exploration for big data and beyond

• US 10,482,088 B2
• Filed: 05/02/2017
• Issued: 11/19/2019
• Est. Priority Date: 05/04/2016
• Status: Active Grant

• Current Assignee: Eugene Santos Jr, Eugene S. Santos, Eunice E. Santos, Evelyn W. Santos
• Original Assignee: Eugene Santos Jr, Eugene S. Santos, Eunice E. Santos, Evelyn W. Santos
• Inventors: Santos, Eugene S., Santos, Eunice E., Santos, Evelyn W., Santos, Jr., Eugene
• Primary Examiner(s): Ly, Anh
• Application Number:
  US15/584,874
• Publication Number:
  US 20170322979A1
• Time in Patent Office: 931 Days
• CPC Class Codes: G06F 16/24568 Data stream processing; Continuous queries
• CPC Class Codes: G06F 16/24573 using data annotations, e.g. user-defined metadata
• CPC Class Codes: G06F 16/254 Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
• CPC Class Codes: G06F 16/27 Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
• CPC Class Codes: G06F 16/28 Databases characterised by their database models, e.g. relational or object models
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/9024 Graphs; Linked lists G06F16/9027 takes precedence
• CPC Class Codes: G06F 16/9027 Trees
• CPC Class Codes: G06F 16/9038 Presentation of query results

Abstract

According to an aspect of an embodiment, a computer system including at least one computer is configured to generate, specification concept graphs of nodes spec₁, spec₂, . . . , spec_m including concepts node and relation nodes according to at least one of a plurality of digitized data from user input from a plurality of computerized data sources d₁, d₂, . . . , d_l forming a first set of evidences U; generate concept graphs of nodes cα₁, cα₂, . . . , cα_n including concept nodes and relation nodes for corresponding obtained plurality of IKs α₁, α₂, . . . , α_n forming a second set of evidences U; select a subset of concept graphs of nodes cα_i1, cα_i2, . . . , cα_ih from cα₁, cα₂, . . . , cα_n according to a computable measure of any one or combinations of consistency, inconsistency or priority threshold between cα_j in cα₁, cα₂, . . . , cα_n to specification concept graph spec_k in spec₁, spec₂, . . . , spec_m. Generate knowledge fragments for corresponding obtained subset of concept graphs cα_i1, cα_i2, . . . cα_ih. Create or add into at least one among a plurality of knowledge-bases (KBs) for the corresponding knowledge fragments obtained by creating objects in form ω=E→A. Determine relationship constraints _κ in form of set relations among a plurality of subsets of evidences E. And execute any one or combination of knowledge completion, augmentation, and analysis.

Claims

1. A method, comprising:

executing, by at least one processor, instructions stored in at least one memory coupled to the at least one processor to;

generate specification concept graphs of nodes spec₁,spec₂, . . . ,spec_m including concept nodes of concepts and relation nodes according to at least one of a plurality of digitized data input from a plurality of computerized data sources d₁, d₂, . . . , d_l forming a first set of evidences U to represent a first knowledge base (KB) among a plurality of KBs;

generate concept graphs of nodes cα

₁, cα

₂, . . . , cα

_n including concept nodes and relation nodes for corresponding obtained plurality of information and knowledge (IKs) α

₁, α

₂, . . . , α

_n forming a second set of evidences U to represent a second knowledge base among the plurality of KBs;

select a subset of concept graphs of nodes cα

_i1, cα

_i2, . . . , cα

_ih from cα

₁, cα

₂, . . . , cα

_n according to a computable measure of consistency threshold between each ca_j in cα

₁, cα

₂, . . . , cα

_n to each specification concept graph spec_k in spec₁,spec₂, . . . , spec_m;

generate knowledge fragment objects of concept fragments obtained for corresponding subset of concept graphs cα

_i1, cα

_i2, . . . , cα

_ih,

a knowledge fragment object among the knowledge fragment objects to store a mapping of values to first and second sets of evidences U, where A is a rule among rules A′

in at least the first and second KBs among the plurality of KBs, and E is a subset of the first and second sets of evidences U from the at least first and second KBs that supports the rule A, so that the rule A is supportable by the subset of evidences E, according to the concept fragments;

generate a new KB, add into at least one KB among the plurality of KBs, and/or add into the first and/or second KBs for the concept fragments, to include augmenting information objects of augmenting information by,

creating objects in form ω

=E→

A from the concept fragments;

computing for each object co a validity (v) and a plausibility (p) based upon atomic propositions among the rules A′

;

obtaining relationship constraints _κ

in form of a plurality of set relations among a plurality of the subsets of evidences E for a plurality of the concept fragments;

obtaining propositions _κ

for the plurality of fragment concepts in form of logical relations from among the rules A′

in the at least first and second KBs and/or from the atomic propositions;

computing a validity (v) and a plausibility (p) for a combination of the relationship constraints _κ

and the propositions _κ

; and

generating information tags to identify each object ω

, each relationship constraint in _κ

, and each proposition in _κ

,

to cause extending, by the augmenting information objects, at least a forecasting and/or an abduction based upon the concepts to a higher-order prosection and/or abduction deductively and/or inductively in conjunction with the generated information tags.

2. The method according to claim 1, wherein the executing of the instructions by the at least one processor further causes to:

generate and rank new objects in the form ω

=E→

A based on validity and plausability using a computerized process of forecasting according to the spec₁, spec₂, . . . , spec_m.

3. The method according to claim 1, wherein the executing of the instructions by the at least one processor further causes to:

generate and rank a plurality of the objects in the form ω

=E→

A based on validity and plausibility that support at least one object ω

=E→

A from a plurality of objects in the KBs corresponding to spec₁, spec₂, . . . , spec_m using a computerized process of abduction.

4. The method according to claim 1, wherein the executing of the instructions by the at least one processor further causes to:

execute one or more processes according to spec₁, spec₂, . . . , spec_m of;

compute a plurality of sequences of how a plurality of the objects in the form ω

=E→

A evolved and/or progressed using deductive and/or inductive reasoning corresponding to a plurality of IKs α

₁, α

₂, . . . , α

_n;

compute a plurality of alternative outcomes on a plurality of the objects in the form ω

=E→

A using deductive projection forecasting and/or inductive projection forecasting;

compute emergence on a plurality of the objects in the form ω

=E→

A;

compute a plurality of new objects in the form ω

=E→

A that serve as unknown/missing links in the computerized reasoning processes for determining validity and plausibility;

generates results of the one or more corresponding processes executed.

5. The method according to claim 1, wherein the executing of the instructions by the at least one processor further causes to:

address current problems/questions based upon the spec₁, spec₂, . . . , spec_m as specified in support of the user.

6. The method according to claim 1, wherein the KBs include Bayesian Knowledge Bases (BKBs), Compound BKBs (CBKBs), Relational Databases (RDbs), Deductive Databases (DDbs), Augment Knowledge-Bases (AKBs).

7. The method according to claim 1, wherein the concept fragments include concept fragments obtained from authorized access to proprietary information.

8. The method according to claim 1, wherein the concepts are according to user input to control generation of the specification concept graphs.

9. The method according to claim 1, wherein the information tags include one or a combination of information to indicate an audit of generation of the knowledge fragments, or provide an explanation of the computing.

10. An apparatus, comprising:

at least one memory; and

at least one processor coupled to the at least one memory to execute instructions stored in the at least one memory to cause the apparatus to;

generate specification concept graphs of nodes spec₁, spec₂, . . . , spec_m including concept nodes of concepts and relation nodes according to at least one of a plurality of digitized data input from a plurality of computerized data sources d₁, d₂, . . . , d_l forming a first set of evidences U to represent a first knowledge base (KB) among a plurality of KBs;

generate specification concept graphs of nodes spec₁,spec₂, . . . ,spec_m including concept nodes of concepts and relation nodes according to at least one of a plurality of digitized data input from a plurality of computerized data sources d₁, d₂, . . . , d_l forming a first set of evidences U to represent a first knowledge base (KB) among a plurality of KBs;

select a subset of concept graphs of nodes cα

_i1, cα

_i2, . . . , cα

_ih from cα

₁, cα

₂, . . . , cα

_n according to a computable measure of consistency threshold between each ca_j in cα

₁, cα

₂, . . . , cα

_n to each specification concept graph spec_k in spec₁, spec₂, . . . , spec_m;

generate knowledge fragment objects of concept fragments obtained for corresponding subset of concept graphs cα

_i1, cα

_i2, . . . , cα

_ih,

a knowledge fragment object among the knowledge fragment objects to store a mapping of values to first and second sets of evidences U, where A is a rule among rules A′

in at least the first and second KBs among the plurality of KBs, and E is a subset of the first and second sets of evidences U from the at least first and second KBs that supports the rule A, so that the rule A is supportable by the subset of evidences E, according to the concept fragments;

generate a new KB, add into at least one KB among the plurality of KBs, and/or add into the first and/or second KBs for the concept fragments, to include augmenting information objects of augmenting information by,

creating objects in form ω

=E→

A from the concept fragments;

computing for each object co a validity (v) and a plausibility (p) based upon atomic propositions among the rules A′

;

obtaining relationship constraints _κ

in form of a plurality of set relations among a plurality of the subsets of evidences E for a plurality of the concept fragments;

obtaining propositions _κ

for the plurality of fragment concepts in form of logical relations from among the rules A′

in the at least first and second KBs and/or from the atomic propositions;

computing a validity (v) and a plausibility (p) for a combination of the relationship constraints _κ

and the propositions _κ

; and

generating information tags to identify each object ω

, each relationship constraint in _κ

, and each proposition in _κ

,

to cause extending, by the augmenting information objects, at least a forecasting and/or an abduction based upon the concepts to a higher-order projection and/or abduction deductively and/or inductively in conjunction with the generated information tags.

11. The apparatus according to claim 10, wherein the at least one processor is to receive the concepts according to user input to control generation of the specification concept graphs.

12. The apparatus according to claim 10, wherein the information tags include one or a combination of information to indicate an audit of generation of the knowledge fragments, or provide an explanation of the computing.