1. An abnormal pattern analysis method, the method comprising: determining, by a processor, a service application associated with analysis data; selecting, by the processor, at least one abnormal pattern analysis module among a plurality of different types of modules based on the determined service application, wherein the plurality of different types of abnormal pattern analysis modules are categorized in a plurality of groups in an abnormal pattern analysis framework, and the selecting, by the processor, at least one abnormal pattern analysis module including determining out of the plurality of groups, a selected categorized group; and performing, by the processor, an analysis for the analysis data through the selected at least one abnormal pattern analysis module to detect an abnormal pattern in the analysis data, wherein selecting the at least one abnormal pattern analysis module includes selecting a main abnormal pattern analysis module and a plurality of sub-abnormal pattern analysis modules based on the selected categorized group related to the determined service application, the plurality of sub-abnormal pattern analysis modules configured to detect a probability of an abnormal pattern in the analysis data when the main abnormal pattern analysis module does not detect an abnormal pattern, the plurality of categorized groups of the abnormal pattern analysis framework includes an abnormal pattern analysis group, a relationship analysis group, and an effectiveness analysis group, and the at least one abnormal pattern analysis module is selected according to the abnormal pattern analysis framework, from among the plurality of categorized groups, in which the abnormal pattern analysis group includes two or more main abnormal pattern analysis modules that analyze disparateness within the analysis data, the relationship analysis group includes two or more main abnormal pattern analysis modules that analyze a relationship between entities represented in the analysis data, and the effectiveness analysis group includes two or more main abnormal pattern analysis modules that analyze an effectiveness of an object associated with the analysis data, and performing the analysis for the analysis data includes: performing a determination, by the main abnormal pattern analysis module, to determine if an abnormality exists in the analysis data, when the abnormal pattern is detected in an analysis result, such performing the determination, by the main abnormal pattern analysis module, not detecting an abnormality; the plurality of sub-abnormal pattern analysis modules including a first sub-abnormal pattern analysis module and a second sub-abnormal pattern analysis module; based on such not detecting, by the main abnormal pattern analysis module, the plurality of sub-abnormal pattern analysis modules determining a possibility of an abnormality in the analysis data including: the first sub-abnormal pattern analysis module performing a first determination of whether an abnormal pattern is detected to yield a first result, the first result included in aggregated results; the second sub-abnormal pattern analysis module performing a second determination of whether an abnormal pattern is detected to yield a second result, the second result included in the aggregated results; the processor performing an assessment to determine an existence possibility to the abnormality including: determining a value based on at least the first result and the second result; comparing the value against the aggregated results to yield, by the processor, a numerical result; determining an existence possibility based on the numerical result; the determining the value based on at least the first result and the second result is constituted by a number of positive determinations in the first result and the second result, the numerical result is based on the number of positive determinations, reflecting an abnormality, compared to the aggregated results; and the aggregated results is a total number of determinations performed by the plurality of sub-abnormal pattern analysis modules.