Knowledge intensive data management system for business process and case management

Patent metadata

Field	Value
Publication number	US-9330119-B2
Application number	US-201314109651-A
Country	US
Kind code	B2
Filing date	Dec 17, 2013
Priority date	Apr 11, 2013
Publication date	May 3, 2016
Grant date	May 3, 2016

Abstract

Data can be categorized into facts, information, hypothesis, and directives. Activities that generate certain categories of data based on other categories of data through the application of knowledge which can be categorized into classifications, assessments, resolutions, and enactments. Activities can be driven by a Classification-Assessment-Resolution-Enactment (CARE) control engine. The CARE control and these categorizations can be used to enhance a multitude of systems, for example diagnostic system, such as through historical record keeping, machine learning, and automation. Such a diagnostic system can include a system that forecasts computing system failures based on the application of knowledge to system vital signs such as thread or stack segment intensity and memory heap usage. These vital signs are facts that can be classified to produce information such as memory leaks, convoy effects, or other problems. Classification can involve the automatic generation of classes, states, observations, predictions, norms, objectives, and the processing of sample intervals having irregular durations.

First claim

What is claimed is:

1. A computer-implemented method comprising:
identifying a first partition of data with a fact category, a second partition of data with an information category, a third partition of data with a hypothesis category, and a fourth partition of data with a directive category;
identifying a first partition of transformative actions with a classification category, a second partition of transformative actions with an assessment category, a third partition of transformative actions with a resolution category, and a fourth partition of transformative actions with an enactment category;
invoking a first action to produce a second set of data from the information category;
invoking a second action to produce a third set of data from the hypothesis category;
invoking a third action to produce a fourth set of data from the directive category;
invoking a fourth action to produce a fifth set of data from the fact category;
invoking a classification action on a first set of facts to produce a first set of information;
invoking an assessment action on the first set of information to produce a first set of hypotheses;
invoking a resolution action on the first set of hypotheses to produce a first set of directives;
invoking an enactment action on the first set of directives to modify system behavior and to produce a second set of facts that differs from the first set of facts;
invoking the classification action on the second set of facts to produce a second set of information that differs from the first set of information;
invoking the assessment action on the second set of information to produce a second set of hypotheses that differs from the first set of hypotheses;
invoking the resolution action on the second set of hypotheses to produce a second set of directives that differs from the first set of directives; and
invoking the enactment action on the second set of directives to modify system behavior and to produce a third set of facts that differs from the first and second set of facts;
wherein the method is performed by one or more computing processors.

2. The method of claim 1 , wherein the first set of information specifies at least an observation, a prediction, a norm, an objective.

3. The method of claim 2 , further comprising:
inferring influence of any new set of data from the fact category on a first action from the classification category;
invoking the first action to produce a second new set of data from the information category, the second new set of data including an objective;
determining whether the second new set of data including the objective satisfies a condition; and
iterating the foregoing operations until the second new set of data including the objective satisfies the condition.

4. The method of claim 2 , wherein:
classifying a set of fact is performed based at least in part on a feature object that relates a value, a valid time, a figure of merit, an entity, and a feature type;
assessing a set of information is performed based at least in part on a feature object that relates a value, a valid time, a figure of merit, an entity, and a feature type;
resolving a set of fact, information, or hypothesis is performed based at least in part on a feature object that relates a value, a valid time, a figure of merit, an entity, and a feature type; and
enacting a set of directive is performed based at least in part on a feature object that relates a value, a valid time, a figure of merit, an entity, and a feature type.

5. The method of claim 4 , wherein the valid time is a time interval indicating an interval of time during which the feature object is valid.

6. The method of claim 4 , wherein the figure of merit represents at least one of a confidence level, a confidence interval, a probability, or a root mean square error.

7. The method of claim 4 , wherein the observation relates a feature vector, the valid time, and the figure of merit; and wherein the feature vector is a vector that includes the feature object.

8. The method of claim 4 , wherein the prediction relates a feature vector, the valid time, and the figure of merit; and wherein the feature vector is a vector that includes the feature object.

9. The method of claim 4 , wherein the norm relates a feature vector, the valid time, and the figure of merit; and wherein the feature vector is a vector that includes the feature object.

10. The method of claim 4 , wherein the objective relates a feature vector, the valid time, and the figure of merit; and wherein the feature vector is a vector that includes the feature object.

11. The method of claim 4 , wherein the first set of hypotheses relates a feature vector, the valid time, and the figure of merit; and wherein the feature vector is a vector that includes the feature object.

12. The method of claim 4 , wherein the first set of directives relates a feature vector, the valid time, and the figure of merit; and wherein the feature vector is a vector that includes the feature object.

13. The method of claim 4 , wherein the classifying comprises applying a set of functions that maps a tuple of feature vectors to at least one of an observation, a prediction, a norm, or an objective; wherein the observation relates a feature vector, the valid time, and the figure of merit; wherein the prediction relates a feature vector, the valid time, and the figure of merit; wherein the norm relates a feature vector, the valid time, and the figure of merit; wherein the objective relates a feature vector, the valid time, and the figure of merit; and
wherein the feature vector is a vector that includes the feature object.

14. The method of claim 4 , further comprising:
identifying a set of feature vectors;
maintaining a separate valid time for each feature vector in the set of feature vectors;
maintaining a separate transaction time for reification of relations recording information changes;
identifying a state transition event based on an enactment of a directive;
assigning a valid time to the state transition;
assigning a transaction time to a reification of an enactment of a directive on an entity; and
applying a bi-temporal calculus based at least in part on provenance information specified by the relations.

15. A computer-implemented method comprising:
identifying a first partition of data with a fact category, a second partition of data with an information category, a third partition of data with a hypothesis category, and a fourth partition of data with a directive category;
identifying a first partition of transformative actions with a classification category, a second partition of transformative actions with an assessment category, a third partition of transformative actions with a resolution category, and a fourth partition of transformative actions with an enactment category;
invoking a first action to produce a second set of data from the information category;
invoking a second action to produce a third set of data from the hypothesis category;
invoking a third action to produce a fourth set of data from the directive category;
invoking a fourth action to produce a fifth set of data from the fact category;
invoking a classification action on a first set of facts to produce a first set of information;
invoking an assessment action on the first set of information to produce a first set of hypotheses;
invoking a resolution action on the first set of hypotheses to produce a first set of directives;
invoking an enactment action on the first set of directives to modify system behavior and to produce a second set of facts that differs from the first set

Assignees

• Oracle Int Corp

Inventors

• Chan Eric S
• Gawlick Dieter
• Ghoneimy Adel
• Liu Zhen Hua

Classifications

• G06F11/3471

  Address tracing · CPC title

• G06F2201/88

  Monitoring involving counting · CPC title

• G06F11/3409

  for performance assessment · CPC title

• G06F11/079

  Root cause analysis, i.e. error or fault diagnosis (in a hardware test environment G06F11/22; in a software test environment G06F11/36) · CPC title

• G06F16/1824

  implemented using Network-attached Storage [NAS] architecture (distributed or networked storage systems G06F3/067; protocols for distributed storage of data in a network H04L67/1097) · CPC title