System and method for classifying data
In one embodiment, a method includes providing an a priori classification engine, an a posteriori classification engine, and a heuristics engine. The a priori classification engine is operable to perform an a priori classification. The a posteriori classification engine is operable to perform an a posteriori classification. The heuristics engine is operable to perform a heuristics classification. In addition, the method includes accessing data from at least one source. The method further includes, responsive to an indication that the a priori classification should be performed, performing the a priori classification on the data. The method also includes, responsive to an indication that the a posteriori classification should be performed, performing the a posteriori classification on the data. Further, the method includes, responsive to an indication that the heuristics classification should be performed, performing the heuristics classification on the data.
1. A method comprising:
on a computer system comprising at least one server computer, providing a plurality of distinct data classification engines, wherein the plurality of distinct data classification engines comprising an a priori classification engine, a posteriori classification engine, and a heuristics engine;
wherein the a priori classification engine is operable to perform an a priori classification, the a priori classification comprising utilization of a set of user-specified classification rules;
wherein the a posteriori classification engine is operable to perform an a posteriori classification, the a posteriori classification comprises utilization of one or more probabilistic algorithms;
wherein the heuristics engine is operable to perform a heuristics classification, the heuristics classification comprising analysis of at least one of a number and a percentage of characteristics of particular data that match a profile of a particular classification;
accessing, by the computer system, the data from at least one source;
responsive to an indication that the a priori classification should be performed, performing, by the computer system via the priori classification engine, the a priori classification on the data to classify the data;
responsive to an indication that the a posteriori classification should be performed, performing, by the computer system via the a posteriori classification engine, the a posteriori classification on the data to classify the data;
verifying whether the previously classified data has been correctly classified;
responsive to a determination that the previously classified data has not been correctly classified, the posteriori classification reclassifies the previously classified data and updates the one or more probabilistic algorithms used for classifying the data; and
responsive to an indication that the heuristics classification should be performed, performing, by the computer system via the heuristics engine, the heuristics classification on the data to classify the data.
2. The method of claim 1 , wherein the a priori classification engine, the a posteriori classification engine, and the heuristics engine are each operable to classify the data into a plurality of classifications.
3. The method of claim 1 , wherein the a priori classification engine, the a posteriori classification engine, and the heuristics engine are each operable to classify a communication.
4. The method of claim 3 , wherein the a priori classification engine is operable to classify a type of the communication.
5. The method of claim 4 , wherein the type is selected from the group consisting of: email, instant message, voice mail, text message, and social-media message.
6. The method of claim 3 , wherein the a posteriori classification engine is operable to determine a probability that the communication is a personal message.
7. The method of claim 3 , wherein the a posteriori classification engine is operable to determine a probability that the communication is a work-related message.
8. The method of claim 3 , wherein the heuristics engine is operable to determine whether the communication should be classified as a personal message.
9. The method of claim 8 , wherein the performing of the heuristics classification comprises:
receiving new data;
identifying one or more comparison attributes;
comparing the one or more comparison attributes with one or more thresholds; and
responsive to a determination that classification criteria has been satisfied, assigning a classification to the new data.
10. The method of claim 1 , wherein the steps are performed in the order listed.
11. An information handling system comprising:
a processing unit, wherein the processing unit is operable to implement a method comprising:
providing a plurality of distinct data classification engines, wherein the plurality of distinct data classification engines comprising an a priori classification engine, a posteriori classification engine, and a heuristics engine;
wherein the a priori classification engine is operable to perform an a priori classification, the a priori classification comprising utilization of a set of user-specified classification rules;
wherein the a posteriori classification engine is operable to perform an a posteriori classification, the a posteriori classification comprises utilization of one or more probabilistic algorithms;
wherein the heuristics engine is operable to perform a heuristics classification, the heuristics classification comprising analysis of at least one of a number and a percentage of characteristics of particular data that match a profile of a particular classification;
accessing data from at least one source;
responsive to an indication that the a priori classification should be performed, performing, via the priori classification engine, the a priori classification on the data to classify the data;
responsive to an indication that the a posteriori classification should be performed, performing, via the a posteriori classification engine, the a posteriori classification on the data to classify the data;
verifying whether the previously classified data has been correctly classified;
responsive to a determination that the previously classified data has not been correctly classified, the posteriori classification reclassifies the previously classified data and updates the one or more probabilistic algorithms used for classifying the data; and
responsive to an indication that the heuristics classification should be performed, performing, via the heuristics engine, the heuristics classification on the data to classify the data.
12. The information handling system of claim 11 , wherein the a priori classification engine, the a posteriori classification engine, and the heuristics engine are each operable to classify the data into a plurality of classifications.
13. The information handling system of claim 11 , wherein the a priori classification engine, the a posteriori classification engine, and the heuristics engine are each operable to classify a communication.
14. The information handling system of claim 13 , wherein the a priori classification engine is operable to classify a type of the communication.
15. The information handling system of claim 14 , wherein the type is selected from the group consisting of: email, instant message, voice mail, text message, and social-media message.
16. The information handling system of claim 13 , wherein the a posteriori classification engine is operable to determine a probability that the communication is a personal message.
17. The information handling system of claim 13 , wherein the a posteriori classification engine is operable to determine a probability that the communication is a work-related message.
18. The information handling system of claim 13 , wherein the heuristics engine is operable to determine whether the communication should be classified as a personal message.
19. The information handling system of claim 18 , wherein the performing of the heuristics classification comprises:
receiving new data;
identifying one or more comparison attributes;
comparing the one or more comparison attributes with one or more thresholds; and
responsive to a determination that classification criteria has been satisfied, assigning a classification to the new data.
20. A computer-program product comprising a non-transitory computer-usable medium having computer-readable program code embodied therein, the computer-readable program code adapted to be executed to implement a method comprising:
providing a plurality of distinct data classification engines, wherein the plurality of distinct data classification engines comprising an a priori classification engine, a posteriori classification engine, and a heuristics engine;
wherein the a priori classification engine is operable to perform an a priori classification, the a priori classification comprising utilization of a set of user-specified classification rules;
wherein the a posteriori classification engine is operable to perform an a posteriori classification, the a posteriori classification comprises utilization of one or more probabilistic algorithms;
wherein the heuristics engine is operable to perform a heuristics classification, the heuristics classification comprising analysis of at least one of a number and a percentage of characteristics of particular data that match a profile of a particular classification;
accessing data from at least one source;
responsive to an indication that the a priori classification should be performed, performing, via the priori classification engine, the a priori classification on the data to classify the data;
responsive to an indication that the a posteriori classification should be performed, performing, via the a posteriori classification engine, the a posteriori classification on the data to classify the data;
verifying whether the previously classified data has been correctly classified;
responsive to a determination that the previously classified data has not been correctly classified, the posteriori classification reclassifies the previously classified data and updates the one or more probabilistic algorithms used for classifying the data; and
responsive to an indication that the heuristics classification should be performed, performing, via the heuristics engine, the heuristics classification on the data to classify the data.