Modeling IoT-aware Business Processes - A State of the Art Report

This research report presents an analysis of the state of the art of modeling Internet of Things (IoT)-aware business processes. IOT links the physical world to the digital world. Traditionally, we would find information about events and processes in the physical world in the digital world entered by humans and humans using this information to control the physical world. In the IoT paradigm, the physical world is equipped with sensors and actuators to create a direct link with the digital world. Business processes are used to coordinate a complex environment including multiple actors for a common goal, typically in the context of administrative work. In the past few years, we have seen research efforts on the possibilities to model IoT- aware business processes, extending process coordination to real world entities directly. This set of research efforts is relatively small when compared to the overall research effort into the IoT and much of the work is still in the early research stage. To create a basis for a bridge between IoT and BPM, the goal of this report is to collect and analyze the state of the art of existing frameworks for modeling IoT-aware business processes.Comment: 42 page

The transaction pattern through automating TrAM

Transaction Agent Modelling (TrAM) has demonstrated how the early requirements of complex enterprise systems can be captured and described in a lucid yet rigorous way. Using Geerts and McCarthy’s REA (Resource-Events-Agents) model as its basis, the TrAM process manages to capture the ‘qualitative’ dimensions of business transactions and business processes. A key part of the process is automated model-checking, which CG has revealed to be beneficial in this regard. It enables models to retain the high-level business concepts yet providing a formal structure at that high-level that is lacking in Use Cases. Using a conceptual catalogue informed by transactions, we illustrate the automation of a transaction pattern from which further specialisations impart a tested specification for system implementation, which we envisage as a multi-agent system in order to reflect the dynamic world of business activity. It would furthermore be able to interoperate across business domains as they would share the generalised TM as a pattern.</p

Predictive Monitoring of Business Processes

Modern information systems that support complex business processes generally maintain significant amounts of process execution data, particularly records of events corresponding to the execution of activities (event logs). In this paper, we present an approach to analyze such event logs in order to predictively monitor business goals during business process execution. At any point during an execution of a process, the user can define business goals in the form of linear temporal logic rules. When an activity is being executed, the framework identifies input data values that are more (or less) likely to lead to the achievement of each business goal. Unlike reactive compliance monitoring approaches that detect violations only after they have occurred, our predictive monitoring approach provides early advice so that users can steer ongoing process executions towards the achievement of business goals. In other words, violations are predicted (and potentially prevented) rather than merely detected. The approach has been implemented in the ProM process mining toolset and validated on a real-life log pertaining to the treatment of cancer patients in a large hospital

Towards a methodology for the engineering of event-driven process applications

Successful applications of the Internet of Things such as smart cities, smart logistics, and predictive maintenance, build on observing and analyzing business-related objects in the real world for business process execution and monitoring. In this context, complex event processing is increasingly used to integrate events from sensors with events stemming from business process management systems. This paper describes a methodology to combine the areas and engineer an event-driven logistics processes application. Thereby, we describe the requirements, use cases and lessons learned to design and implement such an architecture

SmartPM: An Adaptive Process Management System for Executing Processes in Cyber-Physical Domains

Nowadays, the automation of business processes not only spans classical business domains (e.g., banks and governmental agencies), but also new settings such as healthcare, smart manufacturing, domotics and emergency management [2]. Such domains are characterized by the presence of a Cyber-Physical System (CPS) coordinating heterogeneous ICT components with a large variety of architectures, sensors, actuators, computing and communication capabilities, and involving real world entities that perform complex tasks in the "physical" real world to achieve a common goal. In this context, Process Management Systems (PMSs) are used to manage the life cycle of the processes that coordinate the services offered by the CPS to the real world entities, on the basis of the contextual information collected from the specific cyber-physical domain of interest. The physical world, however, is not entirely predictable. CPSs do not necessarily and always operate in a controlled environment, and their processes must be robust to unexpected conditions and adaptable to exceptions and external exogenous events. In this paper, we tackle the above issue by introducing the SmartPM System (http://www.dis.uniroma1.it/smartpm) an adaptive PMS which combines process execution monitoring, unanticipated exception detection (without requiring an explicit definition of exception handlers), and automated resolution strategies on the basis of well-established Artificial Intelligence techniques, including the Situation Calculus and IndiGolog [1], and classical planning [3]

Adaptive Process Management in Cyber-Physical Domains

The increasing application of process-oriented approaches in new challenging cyber-physical domains beyond business computing (e.g., personalized healthcare, emergency management, factories of the future, home automation, etc.) has led to reconsider the level of flexibility and support required to manage complex processes in such domains. A cyber-physical domain is characterized by the presence of a cyber-physical system coordinating heterogeneous ICT components (PCs, smartphones, sensors, actuators) and involving real world entities (humans, machines, agents, robots, etc.) that perform complex tasks in the “physical” real world to achieve a common goal. The physical world, however, is not entirely predictable, and processes enacted in cyber-physical domains must be robust to unexpected conditions and adaptable to unanticipated exceptions. This demands a more flexible approach in process design and enactment, recognizing that in real-world environments it is not adequate to assume that all possible recovery activities can be predefined for dealing with the exceptions that can ensue. In this chapter, we tackle the above issue and we propose a general approach, a concrete framework and a process management system implementation, called SmartPM, for automatically adapting processes enacted in cyber-physical domains in case of unanticipated exceptions and exogenous events. The adaptation mechanism provided by SmartPM is based on declarative task specifications, execution monitoring for detecting failures and context changes at run-time, and automated planning techniques to self-repair the running process, without requiring to predefine any specific adaptation policy or exception handler at design-time

Risk Mitigation Strategies for The Footwear Industry During The Covid-19 Pandemic

CV. XYZ is one of the companies affected by Covid-19, which is marked by a decreased turnover of 70% and as many as 50% of workers are laid off. Risk considerations in business are a concern in the face of an increasingly competitive, unpredictable, and complex business environment. This study aims to identify, analyze and recommend priority operational risk strategies faced by CV. XYZ. The methods used are the Structured What-If Technique (SWIFT) and House of Risk (HOR). The results of this study found thatare that the operational risk events of CV. XYZ are categorized into 3 categories of risk events with a total of 12 risk events. There are 8 of 15 risk-causing agents that contribute 75% to the total ARP, and there are 5 of 17 risk prevention actions that prioritized with the highest Effectiveness Toto Difficulty (ETD) value. Keywords: house of risk, footwear industry, risk management,business processes, business strateg

A Framework for Hierarchical and Recursive Monitoring of Service Based Systems

Runtime monitoring of Service Based Systems (SBSs) usually relies on information derived from I/O messages exchanged within business processes implementing services. When service provisioning is regulated by complex Service Level Agreements (SLAs) between service requesters, (composed) services, and infrastructure providers, monitoring may require additional features, such as (i) coordination among events captured at different sources involved in service provisioning and (ii) delegation of properties monitoring to local sites. This paper discusses an architecture and engagement protocol supporting the two aforementioned requirements for monitoring complex SLA-driven service provisioning

Object-aware Process Support in Healthcare Information Systems: Requirements, Conceptual Framework and Examples

The business processes to be supported by healthcare information systems are highly complex, producing and consuming a large amount of data. Besides, the execution of these processes requires a high degree of flexibility. Despite their widespread adoption in industry, however, traditional process management systems (PrMS) have not been broadly used in healthcare environments so far. One major reason for this drawback is the missing integration of business processes and business data in existing PrMS; i.e., business objects (e.g., medical orders, medical reports) are usually maintained in specific application systems, and are hence outside the control of the PrMS. As a consequence, most existing PrMS are unable to provide integrated access to business processes and business objects in case of unexpected events, which is crucial in the healthcare domain. In this context, the PHILharmonicFlows framework offers an innovative object-aware process management approach, which tightly integrates business objects, functions, and processes. In this paper, we apply this framework to model and control the processes in the context of a breast cancer diagnosis scenario. First, we present the modeling components of PHILharmonicFlows framework applied to this scenario. Second, we give insights into the operational semantics that governs the process execution in PHILharmonicFlows. Third, we discuss the lessons learned in this case study as well as requirements from the healthcare domain that can be effectively handled when using an object-aware process management system like PHILharmonicFlows. Overall, object-aware process support will allow for a new generation of healthcare information systems treating both data and processes as first class citizens