1. A machine-implemented method for automatically identifying anomalies within system servers and/or other system resources of a data processing system and generating alerts therefor which progressively home in on locations and/or causes of the identified anomalies, thereby enabling mitigation of adverse consequences of the identified anomalies on operations of the data processing system, the method comprising: automatically first identifying a finite set of event records generated by a data processing system having system servers and/or other system resources, the generated event records corresponding to a predetermined time duration and operations of a corresponding one or more of the system servers and/or the other system resources; automatically second identifying one or more groups of alike ones of the first identified event records for thereby identifying event records more likely to have relevant features, the second identifying being based on horizontal and vertical analysis of data content in the first identified event records when the first identified event records are organized as a matrix; automatically designating a subset of features of the second identified groups among the first identified event records as features to try to compound together; for each second identified group, testing respective permutations of two or more features identified by said designating and present within the respective second identified group for ability to distinguish between anomalies present in the respective second identified group when the present anomalies are mapped into a corresponding feature space having the two or more designated features as axes of that feature space; ranking the abilities of the tested permutations to distinguish between the present anomalies within the corresponding feature space; and using at least one of the better ranked and tested permutations for detecting later anomalies within later recorded event records of the data processing system and generating corresponding alerts, where the used better ranked and tested permutations provide progressively improved homing in on locations and/or causes of the identified anomalies, thereby enabling mitigation of adverse consequences of the identified anomalies on operations of the data processing system.