Abstract: A method to for real-time scoring, classification, assortment, and contextual nurturing of digital engagements using numerical, statistical, and heuristics-based techniques. Ongoing customer engagement segregation into personas based on discovered traits and collective inferences drawn from historic and ongoing engagements, clustering ongoing engagements using numerical and string manipulation techniques. Computing real time noise and focus scores for engagements, facilitating stateless nudges for increasing favorable engagements, and providing business insights on possibly undiscovered noise and focus patterns that eventually culminate as desired or non-desired outcomes. Generating interest score for engagements based on non-linear formulations of noise and focus scores, and then clustering engagements with similar interests and focus into communities to generate additional insights and facilitate secure communication among users, efficient marketing campaigns and decisioning sales lead assignment.

Abstract: A system for efficiently parsing semi-structured deep packet inspection traffic data tied to a telecommunications entity. The system is capable of parsing such records at million-records-per-second scale through use of a numerical data model, leverage on proven fundamental algebraic techniques, and shortcuts to label streaming traffic on the fly. In some embodiments, the system may perform parallel accumulation of data traffic into business grade counters using elementary techniques and subsequently identify subscribers exhibiting specific data patterns in real time for contextual targeting of promotional offers. A method of efficiently parsing the traffic data via the system of the disclosure.

Abstract: A system for efficiently parsing semi-structured deep packet inspection traffic data tied to a telecommunications entity. The system comprises a computing device having access to a user activity data source and is configured to progressively accumulate a plurality of incoming usage activity data into a plurality of hypercubes, classify streaming data on-the-fly into multiple grades, route it to an appropriate next stage of processing, numerically factorize it to enable drilldown to individual subscriber data, and organize into layouts for efficient data processing, anomaly detection, and subsequent access/investigation. A computerized method for performing the same.

Abstract: A system for efficiently parsing semi-structured deep packet inspection traffic data tied to a telecommunications entity. The system is capable of parsing such records at million-records-per-second scale through use of a numerical data model, leverage on proven fundamental algebraic techniques, and shortcuts to label streaming traffic on the fly. In some embodiments, the system may perform parallel accumulation of data traffic into business grade counters using elementary techniques and subsequently identify subscribers exhibiting specific data patterns in real time for contextual targeting of promotional offers. A method of efficiently parsing the traffic data via the system of the disclosure.