Probabilistic Sequence Modeling for Trustworthy IT Servicing by Collective Expert Networks

Within the enterprise the timely resolution of incidents that occur within complex Information Technology (IT) systems is essential for the business, yet it remains challenging to achieve. To provide incident resolution, existing research applies probabilistic models locally to reduce the transfers (links) between expert groups (nodes) in the network. This approach is inadequate for incident management that must meet IT Service Levels (SLs). We show this using an analysis of enterprise 'operational big data' and the existence of collective problem solving in which expert skills are often complementary and are applied in sequences that are meaningful. We call such a network - 'Collective Expert Network' (or CEN). We propose a probabilistic model which uses the content-base of transfer sequences to generate assistive recommendations that improves the performance of CEN by: (1) resolving incidents to meet customer time constraints and satisfaction (and not just minimize number of transfers), (2) conforming to previous transfer sequences that have already achieved their SLs, and additionally (3) address trust in order to ensure adoption of recommendations. We present a two-level classification framework that learns regular patterns first and then recommends SL-achieving sequences on a subset of tickets, and for the remaining directly recommends knowledge improvement. The experimental validation shows 34% accuracy improvement over other existing research and locally applied generative models. In addition we show 10% reduction in the volume of SL breaching incidents, and 7% reduction in MTTR of all tickets.