Systems, methods and apparatuses for identifying person of interest

1. A computer-implemented method for identifying a person of interest, comprising:

storing external data received from a plurality of external sources into a staging table having a first format;

transferring validated external data from the staging table to a parameterized table having a second format;

creating a first set of category views on the parameterized table, wherein the first set of category views are based on data sources associated with a traveler;

receiving personal information of the traveler generated by a biometrics and/or other input device;

creating a second set of category views in real-time based at least in part on data derived from the traveler's received personal information, wherein the second set of category views are non-overlapping from the first set of category views;

creating a plurality of pre-defined profiles, wherein each pre-defined profile comprises a plurality of characteristics associated with a particular category of behavior, each pre-defined profile defines a different type of risk, using the first and second sets of category views, and each pre-defined profile comprises one or more rules and a corresponding threshold value;

comparing the traveler's received personal information against the plurality of characteristics contained within each of the pre-defined profiles;

generating a score of likelihood for the traveler in each of the pre-defined profiles based on the one or more corresponding rules of each of the pre-defined profiles, and determining whether the traveler is a person of interest by determining if each score of likelihood exceeds the corresponding threshold value, wherein the traveler may be associated with multiple categories of behaviors;

inserting a row into a person of interest table based on the score of likelihood for the traveler exceeding the corresponding threshold for at least one of the pre-defined profiles;

outputting a message to a terminal of the traveler; and

providing an alert to a person of interest monitor.

2. The method of claim 1 , wherein each of the one or more rules comprises one or more sub-rules, and each rule and each sub-rule has a pre-determined weight, and a final score for a likelihood for the traveler to be matched is generated by summing sub scores for each rule, each sub score for a respective rule is generated by multiplying a total weight percentage of satisfied sub-rules of the respective rule and the respective rule's pre-determined weight.

3. The method of claim 1 , wherein at least one of the one or more rules comprises one or more criteria that each defines one or more conditions to identify a selected subset of data based on traveler's information.

4. The method of claim 3 , wherein the at least one of the one or more rules comprises one or more sub-rules, and the one or more sub-rules are based on data source(s) identified in the one or more criteria.

5. The method of claim 1 , wherein the second set of category views are created on combined data including extracted statistical data and the data derived from the received traveler's personal information.

6. The method of claim 1 , wherein the external data is first received in a staging table having a same structure as the parameterized table and transferred to the parameterized table if there is no error.

7. The method of claim 1 , wherein the other input device is one of a passport scanner, a digital camera, and a keyboard.

8. A system for identifying a person of interest, comprising:

a processor, and

a storage memory coupled to the processor, the processor configured to:

store external data received from a plurality of external sources into a staging table having a first format;

transfer validated external data from the staging table to a parameterized table having a second format;

create a first set of category views on the parameterized table, wherein the first set of category views are based on data sources associated with a traveler;

receive personal information of the traveler generated by a biometrics and/or other input device;

create a second set of category views in real-time based at least in part on data derived from the traveler's received personal information, wherein the second set of category views are non-overlapping from the first set of category views;

create a plurality of pre-defined profiles, wherein each pre-defined profile comprises a plurality of characteristics associated with a particular category of behavior, each pre-defined profile defining a different type of risk, using the first and second sets of category views, and each pre-defined profile comprising one or more rules and a corresponding threshold value;

compare the traveler's received personal information against the plurality of characteristics contained within each of the pre-defined profiles; and

generate a score of likelihood for the traveler in each of the pre-defined profiles based on the one or more corresponding rules of each of the pre-defined profiles, and determine whether the traveler is a person of interest by determining if each score of likelihood exceeds the corresponding threshold, wherein the traveler may be associated with multiple categories of behaviors;

insert a row into a person of interest table based on the score of likelihood for the traveler exceeding the corresponding threshold for at least one of the pre-defined profiles;

output a message to a terminal of the traveler; and

provide an alert to a person of interest monitor.

9. The system of claim 8 , wherein each of the one or more rules comprises one or more sub-rules, and each rule and each sub-rule has a pre-determined weight, and a final score for a likelihood for the traveler to be matched is generated by summing sub scores for each rule, each sub score for a respective rule is generated by multiplying a total weight percentage of satisfied sub-rules of the respective rule and the respective rule's pre-determined weight.

10. The system of claim 8 , wherein at least one of the one or more rules comprises one or more criteria that each defines one or more conditions to identify a selected subset of data based on traveler's information.

11. The system of claim 10 , wherein the at least one of the one or more rules comprises one or more sub-rules, and the one or more sub-rules are based on data source(s) identified in the one or more criteria.

12. The system of claim 8 , wherein the second set of category views are created on combined data including extracted statistical data and the data derived from the received traveler's personal information.

13. The system of claim 8 , wherein the data sources are first received in a staging table having a same structure as the parameterized table and transferred to the parameterized table if there is no error.

14. The system of claim 8 , wherein the other input device is one of a passport scanner, a digital camera, and a keyboard.

15. A non-transitory computer-readable storage medium stored thereon executable code that when executed, performs a method for identifying a person of interest comprising:

storing external data received from a plurality of external sources into a staging table having a first format;

transferring validated external data from the staging table to a parameterized table;

creating a first set of category views on the parameterized table having a second format, wherein the first set of category views are based on data sources associated with a traveler;

receiving personal information of the traveler at an enrollment generated by a biometrics and/or other input device;

creating a second set of category views in real-time based at least in part on data derived from the traveler's received personal information, wherein the second set of category views are non-overlapping from the first set of category views;

creating a plurality of pre-defined profiles, wherein each pre-defined profile comprises a plurality of characteristics associated with a particular category of behavior, each pre-defined profile defines a different type of risk, using the first and second sets of category views, and each pre-defined profile comprises one or more rules and a corresponding threshold value;

comparing the traveler's received personal information against the plurality of characteristics contained within each of the pre-defined profiles; and

generating a score of likelihood for the traveler in each of the pre-defined profiles based on the one or more corresponding rules of each of the pre-defined profiles, and determining whether the traveler is a person of interest by determining if each score of likelihood exceeds the corresponding threshold value, wherein the traveler may be associated with multiple categories of behaviors;

inserting a row into a person of interest table based on the score of likelihood for the traveler exceeding the corresponding threshold for at least one of the pre-defined profiles;

outputting a message to a terminal of the traveler; and

providing an alert to a person of interest monitor.

16. The non-transitory computer-readable storage medium of claim 15 , wherein each of the one or more rules comprises one or more sub-rules, and each rule and each sub-rule has a pre-determined weight, and a final score for a likelihood for the traveler to be matched is generated by summing sub scores for each rule, each sub score for a respective rule is generated by multiplying a total weight percentage of satisfied sub-rules of the respective rule and the respective rule's pre-determined weight.

17. The non-transitory computer-readable storage medium of claim 15 , wherein at least one of the one or more rules comprises one or more criteria that each defines one or more conditions to identify a selected subset of data based on traveler's information.

18. The non-transitory computer-readable storage medium of claim 17 , wherein the at least one of the one or more rules comprises one or more sub-rules, and the one or more sub-rules are based on data source(s) identified in the one or more criteria.

19. The non-transitory computer-readable storage medium of claim 15 , wherein the second set of category views are created on combined data including extracted statistical data and the data derived from the received personal information.

20. The non-transitory computer-readable storage medium of claim 15 , wherein the external data is first received in a staging table having a same structure as the parameterized table and transferred to the parameterized table if there is no error.

21. The method of claim 1 , wherein the first format of the staging table is unindexed or unpartitioned, and the second format of the parameterized tables is indexed or partitioned, both the first format and the second format being configured to preserve a mapping to the external sources.

22. The system of claim 8 , wherein the first format of the staging table is unindexed or unpartitioned, and the second format of the parameterized tables is indexed or partitioned, both the first format and the second format being configured to preserve a mapping to the external sources.

23. The non-transitory computer-readable storage medium of claim 15 , wherein the first format of the staging table is unindexed or unpartitioned, and the second format of the parameterized tables is indexed or partitioned, both the first format and the second format being configured to preserve a mapping to the external sources.