Toward Problem Solving Support Based on Big Data and Domain Knowledge: Interactive Granular Computing and Adaptive Judgement

Nowadays efficient methods for dealing with Big Data are urgently needed for many real-life applications. Big Data is often distributed over networks of agents involved in complex interactions. Decision support for users, to solve problems using Big Data, requires to develop relevant computation models for the agents as well as methods for incorporating changes in the reasoning of the computation models themselves; these requirements would enable agents to control computations for achieving the target goals. It is to be noted that users are also agents. Agents are performing computations on complex objects of very different natures (e.g., (behavioral) patterns, classifiers, clusters, structural objects, sets of rules, aggregation operations, reasoning schemes etc.). One of the challenges for systems based on Big Data is to provide the systems with high-level primitives of users for composing and building complex analytical pipelines over Big Data. Such primitives are very often expressed in natural language, and they should be approximated using low-level primitives, accessible from raw data. In Granular Computing (GrC), all such constructed and/or induced objects are called granules. To model interactive computations, performed by the agent in complex systems based on Big Data, we extend the existing approach to GrC by introducing complex granules (c-granules or granules, for short). Many advanced tasks, concerning complex systems based on Big Data may be classified as control tasks performed by agents aiming at achieving the high quality trajectories (defined by computations) relative to the considered target tasks and quality measures. Here, new challenges are to develop strategies to control, predict, and bound the behavior of the system based on Big Data at scale. We propose to investigate these challenges using the GrC framework. The reasoning, which aims at controlling the computational schemes from time-to-time, in order to achieve the required target, is called an adaptive judgement. This reasoning deals with granules and computations over them. Adaptive judgement is more than a mixture of reasoning based on deduction, induction and abduction. Due to the uncertainty the agents generally cannot predict exactly the results of actions (or plans). Moreover, the approximations of the complex vague concepts initiating actions (or plans) are drifting with time. Hence, adaptive strategies for evolving approximation of concepts with respect to time are needed. In particular, the adaptive judgement is very much needed in the efficiency management of granular computations, carried out by agents, for risk assessment, risk treatment, cost/benefit analysis. The approach, discussed in this paper, is a step towards realization of the Wisdom Technology (WisTech) program [2, 3], and is developed over years of experiences, based on the work on different real-life projects.