Abstract: A system (100) for detecting and preventing cyber-threats is disclosed. The system (100) can include an online-analytical-processing (OLAP) resource (102) coupled to a data mining engine (104), a reporting resource (106) and a processor (108). The processor (108) can run instructions stored within an extract-transform-load (ETL) module (112). The ETL module (112) can enable the processor (108) to extract one or more data tuples various data sources (110). The ETL module (112) can enable the processor to transform the extracted tuple(s).

Abstract: A method for linking records (related to an entity) from separate databases may include extracting a first record from a first database as a first vector, extracting a second record from a second database as a second vector, generating first and second sub-vectors for the first and second vectors, where each sub-vector includes quality features from the respective vector, pre-processing the first and second sub-vectors using domain knowledge, calculating a distance assessment classifier based on the first and second sub-vectors, and determining whether the distance represented by the distance assessment classifier is greater than a threshold. If the distance is greater than the threshold, the records may be linked; if not, the method extracts additional records and repeats after generating first and second sub-vectors until the distance is greater than the threshold. A system for linking records is also disclosed.

Abstract: A system (100) for detecting and preventing cyber-threats is disclosed. The system (100) can include an online-analytical-processing (OLAP) resource (102) coupled to a data mining engine (104), a reporting resource (106) and a processor (108). The processor (108) can run instructions stored within an extract-transform-load (ETL) module (112). The ETL module (112) can enable the processor (108) to extract one or more data tuples various data sources (110). The ETL module (112) can enable the processor to transform the extracted tuple(s).

Abstract: A system (100) for detecting and preventing cyber-threats is disclosed. The system (100) can include an online-analytical-processing (OLAP) resource (102) coupled to a data mining engine (104), a reporting resource (106) and a processor (108). The processor (108) can run instructions stored within an extract-transform-load (ETL) module (112). The ETL module (112) can enable the processor (108) to extract one or more data tuples various data sources (110). The ETL module (112) can enable the processor to transform the extracted tuple(s).

Abstract: A method for linking records (related to an entity) from separate databases may include extracting a first record from a first database as a first vector, extracting a second record from a second database as a second vector, generating first and second sub-vectors for the first and second vectors, where each sub-vector includes quality features from the respective vector, pre-processing the first and second sub-vectors using domain knowledge, calculating a distance assessment classifier based on the first and second sub-vectors, and determining whether the distance represented by the distance assessment classifier is greater than a threshold. If the distance is greater than the threshold, the records may be linked; if not, the method extracts additional records and repeats after generating first and second sub-vectors until the distance is greater than the threshold. A system for linking records is also disclosed.

Abstract: A system (100) for detecting and preventing cyber-threats is disclosed. The system (100) can include an online-analytical-processing (OLAP) resource (102) coupled to a data mining engine (104), a reporting resource (106) and a processor (108). The processor (108) can run instructions stored within an extract-transform-load (ETL) module (112). The ETL module (112) can enable the processor (108) to extract one or more data tuples various data sources (110). The ETL module (112) can enable the processor to transform the extracted tuple(s).