A method includes reading a plurality of historical data from a storage. The method also includes generating, using a processor, a candidate pattern list. The method also includes determining, using the points stored in the candidate pattern list, whether at least one maximal clique exists in the candidate pattern list, each maximal clique indicating a known significant pattern. The method further includes, in response to determining that at least one maximal clique exists in the candidate pattern list, for each of the at least one maximal cliques, determining a signature of the maximal clique and storing the signature of the maximal clique and an identifier identifying the known significant pattern to a library in the storage. The method further includes determining causal relationships between the identified significant patterns and storing in the library.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, comprising: reading a plurality of historical data from a storage; generating, using a processor, a candidate pattern list by: sweeping a first sliding window through the plurality of historical data from an oldest point of historical data to a most recent point of the historical data; sweeping a second sliding window through the plurality of historical data from a point of historical data at a current position of the first sliding window to the oldest point of historical data; and for each current position of the first sliding window and each current position of the second sliding window: determining, using the processor and a fuzzy logic algorithm, a correlation between at least one point in a neighborhood of the current position of the first sliding window and at least one point in a neighborhood of the current position of the second sliding window; and storing the at least one point in the neighborhood of the current position of the first sliding window, the at least one point in the neighborhood of the current position of the second sliding window, and the correlation into the candidate pattern list; determining, using the points stored in the candidate pattern list, whether at least one maximal clique exists in the candidate pattern list, each maximal clique indicating a known significant pattern; and in response to determining that at least one maximal clique exists in the candidate pattern list, for each of the at least one maximal cliques: determining a signature of the maximal clique; and storing the signature of the maximal clique and an identifier identifying the known significant pattern to a library in the storage. 2. The method of claim 1, wherein in response to determining that at least one maximal clique exists in the candidate pattern list, the method further comprises: determining a coherence, a repeatability, and a plurality of occurrences of the known significant pattern; determining a time at which the known significant pattern occurred; and storing the coherence, the repeatability, the plurality of occurrences, and the time at which the known significant pattern occurred to the library in the storage. 3. The method of claim 1, further comprising, in response to determining that at least one maximal clique exists in the candidate pattern list: determining the known significant pattern that has a high significance; marking the known significant pattern that has the high significance as a dominant known significant pattern; and storing the dominant known significant pattern to the library in the storage. 4. The method of claim 1, further comprising, for each known significant pattern: determining a significance of the known significant pattern; determining whether the significance of the known significant pattern meets or exceeds minimum acceptance criteria; and in response to determining that the significance of the known significant pattern does not meet or exceed the minimum acceptance criteria: not storing the known significant pattern to the library in the storage; and discarding the known significant pattern. 5. The method of claim 1, further comprising, for each known significant pattern: slicing the known significant pattern into at least one component; determining a significance of each of the at least one components; and storing the at least one component to the library in the storage. 6. The method of claim 1, further comprising: receiving, from an external source, expert knowledge; determining, based on the expert knowledge, whether to weight any points or correlations in the candidate pattern list or data associated with the known significant pattern; and in response to determining to weight points or correlations in the candidate pattern list or data associated with the known significant pattern, applying a mask comprising a plurality of weights to the points or correlations in the candidate pattern list or data associated with the known significant pattern that were determined to be weighted. 7. The method of claim 1, wherein the plurality of historical data read from the storage comprises data selected from the group consisting of times series data, alarms, and log file entries. 8. The method of claim 7, wherein the time series data comprises events including Quality of Service (QoS) messages. 9. The method of claim 1, further comprising: retrieving a signature, a coherence, a repeatability, and a plurality of occurrences of a maximal clique for a first known significant pattern from the library in the storage; retrieving a signature, a coherence, a repeatability, and a plurality of occurrences of a maximal clique for a second known significant pattern from the library in the storage; determining whether a relationship exists between the first known significant pattern and the second known significant pattern; and in response to determining that a relationship exists between the first known significant pattern and the second known significant pattern: determining whether the relationship between the first known significant pattern and the second known significant pattern is causal; determining a strength of the causal relationship between the first known significant pattern and the second known significant pattern; and storing data identifying the causal relationship and the strength of the causal relationship to the library in the storage. 10. The method of claim 9, further comprising: determining statistics about the causal relationship between the first known significant pattern and the second known significant pattern by: retrieving the plurality of occurrences of the first known significant pattern and the plurality of occurrences of the second known significant pattern from the library in the storage; deriving a list of causalities by associating as many of the occurrences of the first known significant pattern as possible to the occurrences of the second known significant pattern; and determining statistics about the causal relationship from the list of causalities; and storing the statistics to the library in the storage. 11. A computer configured to access a storage device, the computer comprising: a processor; and a non-transitory, computer-readable storage medium storing computer-readable instructions that when executed by the processor cause the computer to perform: reading a plurality of historical data from a storage; generating, using the processor, a candidate pattern list by: sweeping a first sliding window through the plurality of historical data from an oldest point of historical data to a most recent point of the historical data; sweeping a second sliding window through the plurality of historical data from a point of historical data at a current position of the first sliding window to the oldest point of historical data; and for each current position of the first sliding window and each current position of the second sliding window: determining, using the processor and a fuzzy logic algorithm, a correlation between at least one point in a neighborhood of the current position of the first sliding window and at least one point in a neighborhood of the current position of the second sliding window; and storing the at least one point in the neighborhood of the current position of the first sliding window, the at least one point in the neighborhood of the current position of the second sliding window, and the correlation into the candidate pattern list; determining, using the points stored in the candidate pattern list, whether at least one maximal clique exists in the candidate pattern list, each maximal clique indicating a known significant pattern; and in response to determining that at least one maximal clique exists in the candidate pattern list, for each of the at least one maximal cliques: determining a signature of the maximal clique; and storing the signature of the maximal clique and an identifier identifying the known significant pattern to a library in the storage. 12. The computer of claim 11, wherein in response to determining that at least one maximal clique exists in the candidate pattern list, the computer-readable instructions further cause the computer to perform: determining a coherence, a repeatability, and a plurality of occurrences of the known significant pattern; determining a time at which the known significant pattern occurred; and storing the coherence, the repeatability, the plurality of occurrences, and the time at which the known significant pattern occurred to the library in the storage. 13. The computer of claim 11, wherein in response to determining that at least one maximal clique exists in the candidate pattern list, the computer-readable instructions further cause the computer to perform: determining the known significant pattern that has a high significance; marking the known significant pattern that has the high significance as a dominant known significant pattern; and storing the dominant known significant pattern to the library in the storage. 14. The computer of claim 11, wherein the computer-readable instructions further cause the computer to perform, for each known significant pattern: determining a significance of the known significant pattern; determining whether the significance of the known significant pattern meets or exceeds minimum acceptance criteria; and in response to determining that the significance of the known significant pattern does not meet or exceed the minimum acceptance criteria: not storing the known significant pattern to the library in the storage; and discarding the known significant pattern. 15. The computer of claim 11, wherein the computer-readable instructions further cause the computer to perform, for each known significant pattern: slicing the known significant pattern into at least one component; determining a significance of each of the at least one components; and storing the at least one component to the library in the storage. 16. The computer of claim 11, wherein the computer-readable instructions further cause the computer to perform: receiving, from an external source, expert knowledge; determining, based on the expert knowledge, whether to weight any points or correlations in the candidate pattern list or data associated with the known significant pattern; and in response to determining to weight points or correlations in the candidate pattern list or data associated with the known significant pattern, applying a mask comprising a plurality of weights to the points or correlations in the candidate pattern list or data associated with the known significant pattern that were determined to be weighted. 17. The computer of claim 11, wherein the plurality of historical data read from the storage comprises data selected from the group consisting of times series data, alarms, and log file entries. 18. The computer of claim 11, wherein the time series data comprises events including Quality of Service (QoS) messages. 19. The computer of claim 11, wherein the computer-readable instructions further cause the computer to perform: retrieving a signature, a coherence, a repeatability, and a plurality of occurrences of a maximal clique for a first known significant pattern from the library in the storage; retrieving a signature, a coherence, a repeatability, and a plurality of occurrences of a maximal clique for a second known significant pattern from the library in the storage; determining whether a relationship exists between the first known significant pattern and the second known significant pattern; and in response to determining that a relationship exists between the first known significant pattern and the second known significant pattern: determining whether the relationship between the first known significant pattern and the second known significant pattern is causal; determining a strength of the causal relationship between the first known significant pattern and the second known significant pattern; and storing data identifying the causal relationship and the strength of the causal relationship to the library in the storage. 20. A computer program product comprising: a computer-readable storage medium having computer-readable program code embodied therewith, the computer-readable program code comprising: computer-readable program code configured to read a plurality of historical data from a storage; computer-readable program code configured to generate, using a processor, a candidate pattern list by: sweeping a first sliding window through the plurality of historical data from an oldest point of historical data to a most recent point of the historical data; sweeping a second sliding window through the plurality of historical data from a point of historical data at a current position of the first sliding window to the oldest point of historical data; and for each current position of the first sliding window and each current position of the second sliding window: determining, using the processor and a fuzzy logic algorithm, a correlation between at least one point in a neighborhood of the current position of the first sliding window and at least one point in a neighborhood of the current position of the second sliding window; and storing the at least one point in the neighborhood of the current position of the first sliding window, the at least one point in the neighborhood of the current position of the second sliding window, and the correlation into the candidate pattern list; computer-readable program code configured to determine, using the points stored in the candidate pattern list, whether at least one maximal clique exists in the candidate pattern list, each maximal clique indicating a known significant pattern; and computer-readable program code configured to, in response to determining that at least one maximal clique exists in the candidate pattern list, for each of the at least one maximal cliques: determine a signature of the maximal clique; and store the signature of the maximal clique and an identifier identifying the known significant pattern to a library in the storage.