This article looks at the knowledge graphs of five diverse tech companies, comparing the similarities and differences in their respective experiences of building and using the graphs, and discussing the challenges that all knowledge-driven enterprises face today. The collection of knowledge graphs discussed here covers the breadth of applications, from search, to product descriptions, to social networks.

Five diverse technology companies show how it's done.

Abstract. Service process orchestration using workflow technologies have led to significant improvements in generating predicable outcomes by automating tedious manual tasks but suffer from challenges related to the flexibility required in work especially when humans are involved. Recently emerging trends in enterprises to explore social computing concepts have realized value in more agile work process orchestrations but tend to be less predictable with respect to outcomes. In this paper we use IT services management, specifically, incident management for large scale systems, to investigate the interplay of workflow systems and social computing. We apply a recently introduced concept of Social Compute Units, and flexible teams sourced based on various parameters such as skills, availability, incident urgency, etc. in the context of resolution of incidents in an IT service provider organization. Results from simulationbased experiments indicate that the combination of SCUs and workflow based processes can lead to significant improvement in key service delivery outcomes, with average resolution time per incident and number of SLO violations being at times as low as 52.7% and 27.3% respectively of the corresponding values for pure workflow based incident management.

In the services domain, the customers raise issues and service requests in the form of tickets. There is a pool of personnel who work on these tickets and resolve them. The problem at hand is to dispatch these tickets to the most appropriate personnel. Optimality is applied to metrics like the mean service time taken to resolve a ticket, the fair sharing of workload among the personnel, and the size and configuration of the pool. The current state of the art involves a human dispatcher for assigning incoming service requests. Though intelligent, a human dispatcher can be suboptimal with respect to the abovementioned objectives due to the large space of parameter values to be considered. Further, there exists an opportunity to achieve high-level goals such as on-the-job training, eliminating overproduction, and workload balancing among personnel through smarter dispatch decisions. For example, target skill levels of personnel can be achieved by assigning them tickets requiring those skills increasingly. Also, overproduction can be controlled by dispatching only those tickets that otherwise would be in the danger of missing deadline (SLO) constraints. Our work involves the design and implementation of an automated dispatcher which would take various characteristics of the tickets and the pool state as input and recommend an intelligent dispatching decision for the tickets, based on the above-mentioned goals and constraints.

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