Systems, methods, and apparatus for monitoring and alerting on large sensory data sets for improved safety, security, and business productivity

• US 8,730,040 B2
• Filed: 01/14/2013
• Issued: 05/20/2014
• Est. Priority Date: 10/04/2007
• Status: Active Grant

• Current Assignee: SecureNet Solutions Group, LLC
• Original Assignee: KD Secure LLC
• Inventors: Donovan, John J, Hussain, Daniar
• Primary Examiner(s): La, Anh V
• Application Number:
  US13/740,810
• Publication Number:
  US 20130222600A1
• Time in Patent Office: 491 Days
• Field of Search: 340/540, 340/506, 340/539.25, 340/521, 340/541, 340/937, 340/522, 340/517, 340/3.1, 340/933, 340/936, 340/942, 340/286.07, 340/545.2, 340/573.4, 348/143, 348/148, 348/159
• US Class Current: 340/540
• CPC Class Codes: G06F 3/0481 based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
• CPC Class Codes: G06F 3/0605 by facilitating the interaction with a user or administrator
• CPC Class Codes: G06F 3/0649 Lifecycle management
• CPC Class Codes: G06F 3/0685 Hybrid storage combining heterogeneous device types, e.g. hierarchical storage, hybrid arrays
• CPC Class Codes: G08B 13/19645 Multiple cameras, each having view on one of a plurality of scenes, e.g. multiple cameras for multi-room surveillance or for tracking an object by view hand-over
• CPC Class Codes: G08B 13/19656 Network used to communicate with a camera, e.g. WAN, LAN, Internet
• CPC Class Codes: G08B 13/19671 Addition of non-video data, i.e. metadata, to video stream
• CPC Class Codes: G08B 13/19693 using multiple video sources viewed on a single or compound screen
• CPC Class Codes: G08B 13/19697 Arrangements wherein non-video detectors generate an alarm themselves
• CPC Class Codes: H04L 41/0631 using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
• CPC Class Codes: H04L 41/0654 using network fault recovery ring fault isolation or reconfiguration in loop networks without recovery actions by a network management system H04L12/437
• CPC Class Codes: H04L 67/12 specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04N 23/661 Transmitting camera control signals through networks, e.g. control via the Internet
• CPC Class Codes: H04N 23/80 Camera processing pipelines; Components thereof
• CPC Class Codes: H04N 23/90 Arrangement of cameras or camera modules, e.g. multiple cameras in TV studios or sports stadiums
• CPC Class Codes: H04N 7/181 for receiving images from a plurality of remote sources
• CPC Class Codes: H04N 7/183 for receiving images from a single remote source

Abstract

The present invention is a process monitoring method, comprising the steps of capturing sensory data from one or more sensors; storing the sensory data from the one or more sensors in a data storage device; processing the sensory data from the sensors to detect primitive events in the sensory data; correlating two or more primitive events to determine one or more correlated events; and performing one or more actions based on the correlation performed in the correlating step, including sending alerts to recipients in a company'"'"'s hierarchy. The process monitoring method can be used with a variety of sensors, including temperature, pressure, revolutions per minute, electrical meters, altitude meters, and speedometers, and can accept input from a variety of legacy systems, including financial systems, inventory systems, personnel systems, currency systems, and law enforcement databases. The invention can be used to optimize business processes and ensure safety and security procedures are followed.

Claims

1. A monitoring system, comprising:

one or more sensors for capturing sensory data about a physical environment;

one or more communication links to one or more legacy systems external to the sensors;

one or more data storage devices for storing the sensory data from the one or more sensors;

one or more processors, operatively coupled to the one or more sensors; and

one or more memories, operatively coupled to the one or more processors, the one or more memories comprising program code which when executed causes the one or more processors to;

capture the sensory data from the one or more sensors;

store the sensory data from the sensors in the one or more data storage devices;

process the sensory data from the sensors to detect one or more primitive events, comprising primitive sensory events, in the sensory data, wherein the primitive sensory events are weighted based at least on a data quality of the sensors used to capture the sensory data;

process information from the legacy systems to detect one or more primitive legacy events;

perform one or more historical correlations by automatically analyzing said primitive sensory and legacy events across time or space for one or more historical correlations between primitive sensory and legacy events;

monitor continuously and in real-time sensory data from the one or more sensors for exceeding a threshold determined from the one or more historical correlations to determine one or more critical events; and

initiate one or more actions based on said one or more critical events.

2. The system of claim 1, wherein the one or more actions are performed to increase security.

3. The system of claim 1, wherein the one or more actions are performed to improve safety.

4. The system of claim 1, wherein the one or more actions are performed to increase business productivity.

5. The system of claim 1, wherein the sensors comprise at least a temperature sensor, a speedometer, a revolutions per minute sensor, and an electrical meter, and wherein the memory comprises additional program code which when executed causes the processors to perform a process to:

correlate the sensory data from the temperature sensor, the pressure sensor, the speedometer, the revolutions per minute sensor, and the electrical meter.

6. The system of claim 5, further comprising one or more additional sensors selected from the group consisting of an altitude sensor, a chlorine meter, a radon meter, a dust particle meter, a CO₂ meter, and a water meter, and wherein the memory comprises additional program code which when executed causes the processors to perform a process to:

correlate the sensory data from the revolutions per minute sensor, the speedometer, the electrical meter, the legacy systems, and the at least one additional sensor selected from the group consisting of the altitude sensor, the chlorine meter, the radon meter, the dust particle meter, the CO₂ meter, and the water meter.

7. The system of claim 5, further comprising one or more additional sensors selected from the group consisting of a video camera and a microphone, and wherein the memory comprises additional program code which when executed causes the processors to perform a process to:

correlate the sensory data from the temperature sensor, the speedometer, the revolutions per minute sensor, the electrical meter, and the at least one additional sensor selected from the group consisting of the video camera and the microphone.

8. The system of claim 1, wherein the memory comprises additional program code which when executed causes the processors to perform a process to:

store the sensory data and the primitive events corresponding to the sensory data in the data storage devices, wherein the primitive events are stored with an index back to the sensory data corresponding to locations of occurrence of the primitive events within the sensory data.

9. The system of claim 1, wherein the actions comprising at least an alert to one or more recipients in a company'"'"'s hierarchy.

10. The system of claim 1, wherein the data quality is determined based on attribute data of the sensors used to capture the data.

11. The system of claim 1, wherein one of the legacy systems is selected from the group consisting of a financial system, a currency system, a personnel system, and combinations thereof.

12. The system of claim 1, wherein the legacy systems include at least a personnel system, and wherein the memory further comprises program code, which when executed causes the processors to:

retrieve experience levels of personnel from the personnel system;

correlate the experience levels of personnel with the primitive sensory events; and

generate one or more alerts based on the correlation between the experience levels of personnel and the primitive sensory events.

13. The system of claim 1, wherein the legacy systems include at least a personnel system and an access control system, and wherein the memory further comprises program code, which when executed causes the processors to:

retrieve experience levels of personnel from the personnel system based on a badge swiped in the access control system;

correlate the experience levels of personnel with the primitive sensory events; and

generate one or more alerts based on the correlation between the experience levels of personnel and the primitive sensory events.

14. The system of claim 1, wherein the legacy systems include at least a financial system, and wherein the memory further comprises program code, which when executed causes the processors to:

retrieve financial system information from the financial system;

process the financial system information to generate one or more primitive financial events;

correlate the primitive financial events with the primitive sensory events; and

generate one or more alerts based on the correlation between the primitive financial events and the primitive sensory events.

15. The system of claim 1, wherein the legacy systems include at least a security legacy system selected from the group consisting of an FBI Most Wanted system, an Interpol Wanted Fugitives system, a law enforcement system, a warrants database, a stolen vehicles database, and a stolen plates database, and wherein the memory further comprises program code, which when executed causes the processors to:

retrieve security information from the security legacy system;

process the security information to generate one or more primitive security events;

correlate the primitive security events with the primitive sensory events; and

generate one or more alerts based on the correlation between the primitive security events and the primitive sensory events.

16. The system of claim 1, wherein the memory further comprises program code, which when executed causes the processors to:

perform one or more actions based on a system-wide correlation level exceeding a predetermined threshold,

wherein the system-wide correlation level is determined based on the equation;

17. The system of claim 1, further comprising a service component allowing a human operator to detect additional primitive events, and wherein the memory further comprises program code, which when executed causes the processors to:

receive meta-data from the human operator corresponding to sensory data analyzed by the human operator;

store the meta-data received from the human operator along with the corresponding sensory data;

correlate the stored meta-data from the human operator and the primitive sensory events; and

generate one or more alerts based on the correlation between the meta-data from the human operator and the primitive sensory events.

18. The system of claim 1, wherein the primitive sensory events, the primitive legacy events, and the critical events are set based on a workshop with users of an organization using the system.

19. The system of claim 1, wherein the memory further comprises program code, which when executed causes the processors to:

detect compound events composed of two or more primitive events;

correlate one or more compound events with one or more primitive events; and

generate one or more alerts based on the correlation between the compound events and the primitive events.

20. The system of claim 1, wherein the memory further comprises program code, which when executed causes the processors to:

generate one or more alerts based on the correlation between events detected now and events that occurred historically.

21. The system of claim 1, wherein the memory further comprises program code, which when executed causes the processors to:

correlate events across space for events occurring substantially simultaneously across multiple sensors located in different locations across space; and

generate one or more alerts based on the correlation between events occurring substantially simultaneously across multiple sensors located in different locations across space.

22. The system of claim 1, wherein the memory further comprises program code, which when executed causes the processors to:

time correlate the primitive events across time;

space correlate the primitive events across space; and

evaluate one or more rules based on the correlation performed in the time correlating step and the space correlating step; and

generate one or more alerts based at least on the time correlation and the space correlation.

23. The system of claim 1, wherein the memory further comprises program code, which when executed causes the processors to:

generate one or more new rules based on the primitive events correlated in the correlating step and the actions performed in the action step.

24. The system of claim 1, wherein the memory further comprises program code, which when executed causes the processors to:

receive tip data from one or more external sources;

determine attribute data for the tip data, the attribute data representing a reliability of a source of the tip data;

generate tip events based on the tip data and the attribute data;

correlate one or more tip events with one or more primitive sensory events; and

generate one or more alerts based on the correlation between the tip events and the primitive sensory events.

25. The system of claim 1, further comprising:

a data storage hierarchy comprising two or more data storage areas,

wherein the memory comprises additional program code, which when executed causes the processors to;

cascade the sensory data down the data storage hierarchy based on an importance of the sensory data,

wherein the importance of the sensory data is based on an attribute selected from the group consisting of a resolution of the sensor used to capture the sensory data, an age of the sensor used to capture the sensory data, a time since last maintenance of the sensor used to capture the sensory data, a location of the sensor used to capture the sensory data, and a reliability of the sensor used to capture the sensory data, and

wherein the importance (Y) of the sensory data is calculated as a weighted average based on the equation;

26. The system of claim 1, wherein the memory further comprises program code, which when executed causes the processors to:

monitor network status of the one or more sensors;

generate network events reflective of the network status of the sensors;

correlate one or more network events with one or more primitive sensory events; and

generate one or more alerts based on the correlation between the network events and the primitive sensory events.

27. The system of claim 1, wherein the memory further comprises program code, which when executed causes the processors to:

capture attribute data for at least one of the sensors, the attribute data comprising information about the sensors used to capture the sensory data; and

correlate the primitive sensory events by weighing the primitive sensory events by attribute data weights corresponding to the sensor used to capture the sensory data.

28. The system of claim 27, wherein the attribute data comprises a quality of sensory data produced by the sensor.

29. The system of claim 27, wherein the attribute data comprises an age of the sensor used to capture the sensory data.

30. The system of claim 27, wherein the attribute data comprises a time since the sensor was last maintained.

31. The system of claim 27, wherein the attribute data comprises an integrity of the sensor used to capture the sensory data.

32. The system of claim 27, wherein the attribute data comprises a reliability of the sensor used to capture the sensory data.

33. The system of claim 27, wherein the attribute data comprises reliability of power to the sensor.

34. The system of claim 27, wherein the attribute data comprises reliability of a transmission and a bandwidth of a communication link to the sensor.

35. The system of claim 27, wherein the memory further comprises program code, which when executed causes the processors to:

determine the attribute data based on past evidence of usefulness of sensory data from the sensors.

36. The system of claim 27, wherein the memory further comprises program code, which when executed causes the processors to:

determine the attribute data based on susceptibility of the sensors to noise, interference, or overexposure.

37. The system of claim 27, wherein the memory further comprises program code, which when executed causes the processors to:

determine the attribute data based on weather conditions around the sensors.

38. The system of claim 27, wherein the memory further comprises program code, which when executed causes the processors to:

determine the attribute data based on a type of the sensor.

39. The system of claim 1, wherein the memory further comprises program code, which when executed causes the processors to:

normalize the primitive events detected in the sensory data into a standardized format before correlating two or more normalized primitive events.

40. The system of claim 1, wherein the memory further comprises program code, which when executed causes the processors to:

filter out primitive events based on a set of privacy rules, wherein the set of privacy rules are designed to protect a privacy of individuals where said system is being used.

41. The system of claim 1, wherein the memory further comprises program code, which when executed causes the processors to:

filter out primitive events based on a set of business rules, wherein the set of business rules are designed to customize said system to business processes of an organization using said system.

42. The system of claim 1, wherein the one or more actions include an action to reboot a sensor upon detection of a failure of said sensor.

43. A method for correlating events in real-time, comprising:

capturing sensory data from one or more sensors, the sensors comprising at least a temperature sensor, a speedometer, a revolutions per minute sensor, and an electrical meter;

storing the sensory data from the sensors in one or more data storage devices;

processing the sensory data from the sensors to detect one or more primitive events, comprising primitive sensory events, in the sensory data;

processing information from legacy systems external to the sensors, comprising at least financial systems, to detect one or more additional primitive events, comprising primitive financial events;

correlating continuously and in real-time one or more primitive sensory events from the sensors with one or more primitive financial events from the financial system to determine one or more correlated events, by automatically analyzing the primitive sensory and financial events across time for historical correlations between primitive sensory or financial events detected now and primitive sensory or financial events that occurred historically; and

triggering one or more actions in real-time based at least on a correlation determined between primitive sensory events from the sensors and primitive financial events from the financial system.

44. The method of claim 43, further comprising:

capturing sensory data from one or more additional sensors selected from the group consisting of a pressure sensor and an altitude sensor.

45. The method of claim 43, further comprising:

capturing sensory data from one or more additional sensors selected from the group consisting of a chlorine meter, a radon meter, a dust particle meter, a CO₂ meter, and a water meter; and

correlating the sensory data from the temperature sensor, the speedometer, the revolutions per minute sensor, the electrical meter, and the at least one additional sensor selected from the group consisting of the chlorine meter, the radon meter, the dust particle meter, the CO₂ meter, and the water meter.

46. The method of claim 43, further comprising:

capturing sensory data from one or more additional sensors selected from the group consisting of a video camera and a microphone; and

correlating the sensory data from the temperature sensor, the speedometer, the revolutions per minute sensor, and the electrical meter and the at least one additional sensor selected from the group consisting of the video camera and the microphone.

47. The method of claim 43, further comprising:

storing the sensory data and the primitive sensory events corresponding to the sensory data in the data storage devices, wherein the primitive sensory events are stored with an index back to the sensory data corresponding to locations of occurrence of the primitive sensory events within the sensory data.

48. The method of claim 43, wherein the actions comprise at least an alert to one or more recipients in a company'"'"'s hierarchy.

49. The method of claim 43, wherein the legacy systems further comprise at least a personnel system.

50. The method of claim 43, wherein the legacy systems further comprise a personnel system, the method further comprising the steps of:

retrieving experience levels of personnel from the personnel system;

correlating the experience levels of personnel with the primitive sensory events; and

generating one or more alerts based on the correlation between the experience levels of personnel and the primitive sensory events.

51. The method of claim 43, wherein the legacy systems further comprise a personnel system and an access control system, and the method further comprising the steps of:

retrieving experience levels of personnel from the personnel system based on a badge swiped in the access control system;

correlating the experience levels of personnel with the primitive sensory events; and

generating one or more alerts based on the correlation between the experience levels of personnel and the primitive sensory events.

52. The method of claim 43, wherein the legacy systems further comprise a currency system, and the method further comprising the steps of:

retrieving currency information from the currency system;

processing the currency information to generate one or more primitive currency events;

correlating the primitive currency events with the primitive sensory events; and

generating one or more alerts based on the correlation between the primitive currency events and the primitive sensory events.

53. The method of claim 43, wherein the legacy systems further comprise a legacy security system selected from the group consisting of an FBI Most Wanted system, an Interpol Wanted Fugitives system, a law enforcement system, a warrants database, a stolen vehicles database, and a stolen plates database, and the method further comprises of the steps of:

retrieving security information from the legacy security system;

processing the security information to generate one or more primitive security events;

correlating the primitive security events with the primitive sensory events; and

generating one or more alerts based on the correlation between the primitive security events and the primitive sensory events.

54. The method of claim 43, further comprising:

performing one or more actions based on a system-wide correlation level exceeding a predetermined threshold,

wherein the system-wide correlation level is determined based on the equation;

55. The method of claim 43, further comprising the steps of:

receiving meta-data from a human operator corresponding to sensory data analyzed by the human operator;

storing the meta-data received from the human operator along with the corresponding sensory data;

correlating the stored meta-data from the human operator and the primitive sensory events; and

generating one or more alerts based on the correlation between the meta-data from the human operator and the primitive sensory events.

56. The method of claim 43, wherein the primitive sensory events, the primitive financial events, and the actions are set based on a workshop with users of an organization using the system.

57. The method of claim 43, further comprising the steps of:

detecting compound events composed of two or more primitive events;

correlating one or more compound events with one or more primitive events; and

generating one or more alerts based on the correlation between the compound events and the primitive events.

58. The method of claim 43, further comprising:

generating one or more alerts based on the correlation between events detected now and events that occurred historically.

59. The method of claim 43, further comprising:

correlating events across space for events occurring substantially simultaneously across multiple sensors located in different locations across space; and

generating one or more alerts based on the correlation between events occurring substantially simultaneously across multiple sensors located in different locations across space.

60. The method of claim 43, further comprising:

time correlating the primitive events across time;

space correlating the primitive events across space;

evaluating one or more rules based on the correlation performed in the time correlating step and the space correlating step; and

generating one or more alerts based at least on the time correlation and the space correlation.

61. The method of claim 43, further comprising:

generating one or more new rules based on the primitive events correlated in the correlating step and the actions performed in the action step.

62. The method of claim 43, further comprising:

receiving tip data from one or more external sources;

determining attribute data for the tip data, the attribute data representing a reliability of a source of the tip data;

generating tip events based on the tip data and the attribute data;

correlating one or more tip events with one or more primitive sensory events; and

generating one or more alerts based on the correlation between the tip events and the primitive sensory events.

63. The method of claim 43, further comprising the steps of:

cascading the sensory data down a data storage hierarchy comprising two or more data storage areas based on an importance of the sensory data,

wherein the importance of the sensory data is based on an attribute selected from the group consisting of a resolution of the sensor used to capture the sensory data, an age of the sensor used to capture the sensory data, a time since last maintenance of the sensor used to capture the sensory data, a location of the sensor used to capture the sensory data, and a reliability of the sensor used to capture the sensory data, and

wherein the importance (Y) of the sensory data is calculated as a weighted average based on the equation;

64. The method of claim 43, further comprising:

monitoring network status of the one or more sensors;

generating network events reflective of the network status of the sensors;

correlating one or more network events with one or more primitive sensory events; and

generating one or more alerts based on the correlation between the network events and the primitive sensory events.

65. The method of claim 43, further comprising:

capturing attribute data for at least one of the sensors, the attribute data comprising information about the sensors used to capture the sensory data;

correlating the primitive sensory events by weighing the primitive sensory events by attribute data weights corresponding to the sensor used to capture the sensory data.

66. The method of claim 65, wherein the attribute data comprises a quality of sensory data produced by the sensor.

67. The method of claim 65, wherein the attribute data comprises an age of the sensor used to capture the sensory data.

68. The method of claim 65, wherein the attribute data comprises a time since the sensor was last maintained.

69. The method of claim 65, wherein the attribute data comprises an integrity of the sensor used to capture the sensory data.

70. The method of claim 65, wherein the attribute data comprises a reliability of the sensor used to capture the sensory data.

71. The method of claim 65, wherein the attribute data comprises reliability of power to the sensor.

72. The method of claim 65, wherein the attribute data comprises reliability of a transmission and a bandwidth of a communication link to the sensor.

73. The method of claim 65, further comprising:

determining the attribute data based on past evidence of usefulness of sensory data from the sensors.

74. The method of claim 65, further comprising:

determining the attribute data based on susceptibility of the sensors to noise, interference, or overexposure.

75. The method of claim 65, further comprising:

determining the attribute data based on weather conditions around the sensors.

76. The method of claim 65, further comprising:

determining the attribute data based on a type of the sensor.

77. The method of claim 43, further comprising:

normalizing the primitive events into a standardized format before correlating two or more normalized primitive events.

78. The method of claim 43, further comprising:

filtering out primitive events based on a set of privacy rules, wherein the set of privacy rules are designed to protect a privacy of individuals where said system is being used.

79. The method of claim 43, further comprising:

filtering out primitive events based on a set of business rules, wherein the set of business rules are designed to customize said system to business processes of an organization using said system.

80. The method of claim 43, wherein the one or more actions include an action to reboot a sensor upon detection of a failure of said sensor.

81. A method for correlating events in real-time, comprising:

capturing sensory data from one or more sensors;

storing the sensory data from the sensors in one or more data storage devices;

processing the sensory data from the sensors to detect one or more primitive events, comprising primitive sensory events, in the sensory data;

processing information from legacy systems to detect one or more additional primitive events, comprising primitive legacy events, in the legacy systems;

correlating one or more primitive sensory events weighted by an importance of the primitive events based on attribute data of the sensors used to capture the sensory data, using a computer processor, from the sensors with one or more legacy primitive events from the legacy systems to determine one or more correlated events, wherein the correlated events are determined by correlating the primitive events across time for historical correlations between primitive events detected now and primitive events that occurred historically; and

initiating one or more actions based at least on the determined correlation between primitive sensory events from the sensors and primitive legacy events from the legacy system.

82. The method of claim 81, wherein the sensors include at least a revolutions per minute sensor, an electrical meter, an altitude meter, and a speedometer.

83. The method of claim 81, wherein the sensors are selected from the group consisting of an a temperature sensor, a pressure sensor, a speedometer, and an altitude sensor.

84. The method of claim 81, wherein the sensors are selected from the group consisting of a chlorine meter, a radon meter, and a dust particle meter.

85. The method of claim 81, further comprising a video camera, and the method further comprising the steps of:

correlating the sensory data from the sensors and video data from the video camera.

86. The method of claim 81, wherein the legacy systems further comprise a personnel system.

87. The method of claim 81, wherein the legacy systems further comprise a personnel system, and the method further comprising the steps of:

retrieving experience levels of personnel from the personnel system;

correlating the experience levels of personnel with the sensory data; and

generating one or more alerts based on the correlation between the experience levels of personnel and the sensory data.

88. The method of claim 81, wherein the legacy systems further comprise a personnel system and an access control system, and further comprising the steps of:

retrieving experience levels of personnel from the personnel system based on a badge swiped in the access control system;

correlating the experience levels of personnel with the sensory data; and

generating one or more alerts based on the correlation between the experience levels of personnel and the sensory data.

89. The method of claim 81, wherein the legacy systems further comprise a currency system, and the method comprising the steps of:

retrieving currency information from the currency system;

processing the currency information to generate one or more primitive currency events;

correlating the primitive currency events with the sensory data; and

generating one or more alerts based on the correlation between the currency system and the sensory data.

90. The method of claim 81, wherein the legacy systems further comprise a legacy security system selected from the group consisting of an FBI Most Wanted system, an Interpol Wanted Fugitives system, a law enforcement system, a warrants database, a stolen vehicles database, and a stolen plates database, and the method further comprising the steps of:

retrieving security information from the legacy security system;

processing the security information to generate one or more primitive security events;

correlating the primitive security events with the sensory data; and

generating one or more alerts based on the correlation between the legacy security system and the sensory data.

91. The method of claim 81, further comprising:

receiving tip data from one or more external sources;

determining attribute data for the tip data, the attribute data representing a reliability of a source of the tip data;

generating tip events based on the tip data and the attribute data;

correlating one or more tip events with one or more events from the sensory data; and

generating one or more alerts based on the correlation between the tip events and the sensory data.

92. The method of claim 81, further comprising:

cascading the sensory data down a data storage hierarchy comprising two or more data storage areas based on an importance of the sensory data,

wherein the importance of the sensory data is based on an attribute selected from the group consisting of a resolution of the sensor used to capture the sensory data, an age of the sensor used to capture the sensory data, a time since last maintenance of the sensor used to capture the sensory data, a location of the sensor used to capture the sensory data, and a reliability of the sensor used to capture the sensory data.

93. The method of claim 81, further comprising:

monitoring network status of the one or more sensors;

generating network events reflective of the network status of the sensors;

correlating one or more network events with one or more primitive events from the sensory data; and

generating one or more alerts based on the correlation between the network events and the sensory data.

94. The method of claim 81, wherein the attribute data comprises a quality of sensory data produced by the sensor.

95. The method of claim 81, wherein the attribute data comprises an age of the sensor used to capture the sensory data.

96. The method of claim 81, wherein the attribute data comprises a time since the sensor was last maintained.

97. The method of claim 81, wherein the attribute data comprises an integrity of the sensor used to capture the sensory data.

98. The method of claim 81, wherein the attribute data comprises a reliability of the sensor used to capture the sensory data.

99. The method of claim 81, wherein the attribute data comprises reliability of power to the sensor.

100. The method of claim 81, wherein the attribute data comprises reliability of a transmission and a bandwidth of a communication link to the sensor.

101. The method of claim 81, further comprising:

determining the attribute data based on past evidence of usefulness of sensory data from the sensors.

102. The method of claim 81, further comprising:

determining the attribute data based on susceptibility of the sensors to noise, interference, or overexposure.

103. The method of claim 81, further comprising:

determining the attribute data based on weather conditions around the sensors.

104. The method of claim 81, further comprising:

determining the attribute data based on a type of the sensor.

105. The method of claim 81, further comprising:

retrieving financial system information from a financial system;

processing the financial system information to generate one or more primitive financial events;

correlating the primitive financial events with the primitive sensory events; and

triggering one or more actions based on the correlation between the primitive financial events and the primitive sensory events.