Order Independent Data Categorization, Indication, and Remediation Across Realtime Datasets of Live Service Environments

A computer-readable medium comprising computer executable instructions stored thereon which, when executed by one or more processors cause the processors to:

• perform a method of finding one or more data items that match one or more defined criteria in a dataset, the method comprising:
• identifying data snippets of the dataset using a set of attention rules, wherein each identified data snippet includes either a true positive data item or a false positive data item, wherein a true positive data item is a data item that matches one or more defined criteria, and a false positive data item is a data item that does not match the one or more defined criteria;
• categorizing the identified data snippets using fuzzy matching by assigning them to buckets such that each bucket contains the data snippets that are similar to another according to a similarity measure;
• classifying the buckets containing data snippets having more than a threshold number of the true positive data items as true positive buckets and remaining buckets as false positive buckets;
• calculating culling rules based on the true positive buckets and the false positive buckets;
• and
• using the culling rules to remove the false positive data items from the true positive buckets.

A computer system, comprising

• one or more processors;
• one or more computer-readable media having computer-executable instructions stored on the one or more processors, wherein the one or more computer-executable instructions are configured to cause the one or more processors to perform a method of finding one or more data items that match one or more defined criteria in a dataset, the method comprising:
• identifying data snippets of the dataset using a set of one or more attention rules, wherein each identified data snippet includes either a true positive data item or a false positive data item, wherein a true positive data item is a data item that matches one or more defined criteria, and a false positive data item is a data item that does not match the one or more defined criteria;
• categorizing the identified data snippets using fuzzy matching by assigning them to buckets such that each bucket contains data snippets that are similar to another according to a similarity measure;
• classifying the buckets containing data snippets having more than a threshold number of true positive data items as true positive buckets and remaining buckets as false positive buckets;
• calculating culling rules based on the true positive buckets and the false positive buckets;
• and
• using the culling rules to remove the false positive data items from the true positive buckets.