arXiv subjects

Asjad Khan

Publications and source records attributed to Asjad Khan.

4 recordsLinked to original sources

arXiv2025

Game-Of-Goals: Using adversarial games to achieve strategic resilience

Our objective in this paper is to develop a machinery that makes a given organizational strategic plan resilient to the actions of competitor agents (adverse environmental actions). We assume that we are given a goal tree representing strategic goals (can also be seen business requirements for a software systems) with the assumption that competitor agents are behaving in a maximally adversarial fashion(opposing actions against our sub goals or goals in general). We use game tree search methods (such as minimax) to select an optimal execution strategy(at a given point in time), such that it can maximize our chances of achieving our (high level) strategic goals. Our machinery helps us determine which path to follow(strategy selection) to achieve the best end outcome. This is done by comparing alternative execution strategies available to us via an evaluation function. Our evaluation function is based on the idea that we want to make our execution plans defensible(future-proof) by selecting execution strategies that make us least vulnerable to adversarial actions by the competitor agents. i.e we want to select an execution strategy such that its leaves minimum room(or options) for the adversary to cause impediment/damage to our business goals/plans.

cs.AI↗

arXiv2023

Towards Knowledge-Centric Process Mining

Process analytic approaches play a critical role in supporting the practice of business process management and continuous process improvement by leveraging process-related data to identify performance bottlenecks, extracting insights about reducing costs and optimizing the utilization of available resources. Process analytic techniques often have to contend with real-world settings where available logs are noisy or incomplete. In this paper we present an approach that permits process analytics techniques to deliver value in the face of noisy/incomplete event logs. Our approach leverages knowledge graphs to mitigate the effects of noise in event logs while supporting process analysts in understanding variability associated with event logs.

cs.AI↗

arXiv2023

Advances in Process Optimization: A Comprehensive Survey of Process Mining, Predictive Process Monitoring, and Process-Aware Recommender Systems

Process analytics approaches allow organizations to support the practice of Business Process Management and continuous improvement by leveraging all process-related data to extract knowledge, improve process performance and support decision-making across the organization. Process execution data once collected will contain hidden insights and actionable knowledge that are of considerable business value enabling firms to take a data-driven approach for identifying performance bottlenecks, reducing costs, extracting insights and optimizing the utilization of available resources. Understanding the properties of 'current deployed process' (whose execution trace is often available in these logs), is critical to understanding the variation across the process instances, root-causes of inefficiencies and determining the areas for investing improvement efforts. In this survey, we discuss various methods that allow organizations to understand the behaviour of their processes, monitor currently running process instances, predict the future behavior of those instances and provide better support for operational decision-making across the organization.

cs.OH↗

arXiv2021

DeepProcess: Supporting business process execution using a MANN-based recommender system

Process-aware Recommender systems can provide critical decision support functionality to aid business process execution by recommending what actions to take next. Based on recent advances in the field of deep learning, we present a novel memory-augmented neural network (MANN) based approach for constructing a process-aware recommender system. We propose a novel network architecture, namely Write-Protected Dual Controller Memory-Augmented Neural Network (DCw-MANN), for building prescriptive models. To evaluate the feasibility and usefulness of our approach, we consider three real-world datasets and show that our approach leads to better performance on several baselines for the task of suffix recommendation and next task prediction.

cs.NE↗