3. The method according to claim 1 , wherein selecting at least some of the search engines based on the search engine weights includes the step of selecting all of the search engines having a search engine weight above a defined value.
1. A method of intelligent event-based data mining, comprising: receiving an event from an application, said event having a set of properties; assigning each of said properties a respective property weight; building a query from said properties based on the property weights; assigning each of a set of search engines a respective search engine weight; selecting at least some of the search engines based on the search engine weights; sending the query to the selected search engines; receiving query results from the selected search engines; storing the query results in a knowledge repository; and adjusting the property weights and the search engine weights based on the query results, wherein the adjusting of said property weights and the search engine weights includes calculating a search result relevance for each query result and using said search result relevance to adjust the property weights and the search engine weights; wherein the calculating of said search result relevance includes, for each of the query results, identifying the number of common text substrings that occur in both the query and said each query result, and using said number of common text substrings to calculate the search result relevance for said each query result; and wherein using said number of common text substrings to calculate the search result relevance for said each query result includes calculating the result relevant using the equation: ( m 1 × m 2 ⁢ ⁢ … ⁢ ⁢ m n ) ( N × n ) × 100 = Result ⁢ ⁢ Relevance where: m n is the number of words in the nth match N is the number of words in the query n is the number of matched subsequences.