The present invention relates to a method of checking data gathered from a content source comprising: receiving initial data from the content source; training a data profiler to generate a set of trusted constraint modules, said training comprising (1) selecting constraint modules having parameters that are applicable to the initial data, (2) adjusting the parameters of the applicable constraint modules to conform with new data from the content source, (3) identifying non-stable constraint modules, and (4) generating a set of trusted constraint modules by removing the non-stable constraint modules; applying the set of trusted constraint modules to subsequently received data from the content source to determine whether the subsequently received data meets the parameters of the set of trusted constraint modules; and signaling a failure upon the subsequently received data failing to meet the parameters of the set of trusted constraint modules.
Please help me write a proper abstract based on the patent claims. CLAIM: 1.-26. (canceled) 27. A method of training a data profiler to monitor a quality of data extracted from a content source, the method comprising: receiving an acceptable data element for a first characteristic of data extracted from a first content source by a data extractor; identifying, from a plurality of constraint modules, a set of one or more constraint modules applicable to the first characteristic; adjusting, for each constraint module in the identified set of one or more constraint modules, parameters of the respective constraint module to accept the received acceptable data element; determining, for each constraint module in the identified set of one or more constraint modules, based on the adjustments, whether each respective constraint module is stable or non-stable; and generating a set of trusted constraint modules selected from the stable constraint modules, omitting the constraint modules determined as non-stable. 28. The method of claim 27 comprising: receiving a second acceptable data element for the first characteristic of data extracted from the first content source; adjusting, for each constraint module in the set of trusted constraint modules, parameters of the respective constraint module to accept the received second acceptable data element; and removing constraint modules determined as non-stable from the set of trusted constraint modules. 29. The method of claim 28 comprising: receiving an extracted data element extracted from the first content source, identified as being for the first characteristic; determining, by applying the set of trusted constraint modules to the received extracted data element, that the received extracted data element does not meet the parameters of the set of trusted constraint modules; and signaling a failure condition. 30. The method of claim 29, wherein signaling the failure condition comprises sending a signal to at least one of: an email account, a database for entry into a log, a mobile device, an SMS system, a monitoring agent, or to a user at a computer terminal. 31. The method of claim 29, wherein signaling the failure comprises sending an error signal to a processor, the method further comprising the processor automatically adjusting subsequent extraction of data elements from the content source. 32. The method of claim 27 wherein the first characteristic is one of a text length range, a numeric value range, a count of data elements, a value for a data element, a pattern of characters to be matched by a data element, or a comparison between data elements. 33. The method of claim 27 wherein generating the set of trusted constraint modules comprises including, in the set of trusted constraint modules, a set of applicable constraint modules that are pre-determined to be stable constraint modules. 34. The method of claim 27 wherein the non-stable constraint modules are constraint modules identified as one of: constraint modules that have been adjusted at a frequency that exceeds a threshold value of a stability algorithm; and constraint modules that have been adjusted at a rate that is trending away from a threshold value of a stability trending algorithm. 35. The method of claim 27 wherein the stable constraint modules are constraint modules that are identified as one of: constraint modules that have been adjusted at a frequency within a threshold value of a stability algorithm; and constraint modules that have been adjusted at a rate that is trending towards a threshold value of a stability trending algorithm. 36. The method of claim 27 wherein the plurality of constraint modules are sourced from a trusted set of constraint modules for the first characteristic of data extracted from a second content source. 37. A system comprising: a data extractor configured to extract data from a first content source; a data profiler, comprising at least one computing processor and memory storing instructions that, when executed by the at least one computing processor, cause the at least one computing processor to: receive an acceptable data element for a first characteristic of data extracted from the first content source by the data extractor; identify, from a plurality of constraint modules, a set of one or more constraint modules applicable to the first characteristic; adjust, for each constraint module in the identified set of one or more constraint modules, parameters of the respective constraint module to accept the received acceptable data element; determine, for each constraint module in the identified set of one or more constraint modules, based on the adjustments, whether each respective constraint module is stable or non-stable; and generate a set of trusted constraint modules selected from the stable constraint modules, omitting the constraint modules determined as non-stable. 38. The system of claim 37, wherein the instructions further cause the at least one computing processor to: receive a second acceptable data element for the first characteristic of data extracted from the first content source; adjust, for each constraint module in the set of trusted constraint modules, parameters of the respective constraint module to accept the received second acceptable data element; and remove constraint modules determined as non-stable from the set of trusted constraint modules. 39. The system of claim 38, wherein the instructions further cause the at least one computing processor to: receive an extracted data element extracted from the first content source, identified as being for the first characteristic; determine, by applying the set of trusted constraint modules to the received extracted data element, that the received extracted data element does not meet the parameters of the set of trusted constraint modules; and signal a failure condition. 40. The system of claim 39, wherein signaling the failure condition comprises sending a signal to at least one of: an email account, a database for entry into a log, a mobile device, an SMS system, a monitoring agent, or to a user at a computer terminal. 41. The system of claim 39, wherein signaling the failure comprises sending an error signal to a processor, the method further comprising the processor automatically adjusting subsequent extraction of data elements from the content source. 42. The system of claim 37 wherein the first characteristic is one of a text length range, a numeric value range, a count of data elements, a value for a data element, a pattern of characters to be matched by a data element, or a comparison between data elements. 43. The system of claim 37 wherein generating the set of trusted constraint modules comprises including, in the set of trusted constraint modules, a set of applicable constraint modules that are pre-determined to be stable constraint modules. 44. The system of claim 37 wherein the non-stable constraint modules are constraint modules identified as one of: constraint modules that have been adjusted at a frequency that exceeds a threshold value of a stability algorithm; and constraint modules that have been adjusted at a rate that is trending away from a threshold value of a stability trending algorithm. 45. The system of claim 37 wherein the stable constraint modules are constraint modules that are identified as one of: constraint modules that have been adjusted at a frequency within a threshold value of a stability algorithm; and constraint modules that have been adjusted at a rate that is trending towards a threshold value of a stability trending algorithm. 46. The system of claim 37 wherein the plurality of constraint modules are sourced from a trusted set of constraint modules for the first characteristic of data extracted from a second content source.