What Automated Planning Can Do for Business Process Management

Business Process Management (BPM) is a central element of today organizations. Despite over the years its main focus has been the support of processes in highly controlled domains, nowadays many domains of interest to the BPM community are characterized by ever-changing requirements, unpredictable environments and increasing amounts of data that influence the execution of process instances. Under such dynamic conditions, BPM systems must increase their level of automation to provide the reactivity and flexibility necessary for process management. On the other hand, the Artificial Intelligence (AI) community has concentrated its efforts on investigating dynamic domains that involve active control of computational entities and physical devices (e.g., robots, software agents, etc.). In this context, Automated Planning, which is one of the oldest areas in AI, is conceived as a model-based approach to synthesize autonomous behaviours in automated way from a model. In this paper, we discuss how automated planning techniques can be leveraged to enable new levels of automation and support for business processing, and we show some concrete examples of their successful application to the different stages of the BPM life cycle

A planning approach to the automated synthesis of template-based process models

The design-time specification of flexible processes can be time-consuming and error-prone, due to the high number of tasks involved and their context-dependent nature. Such processes frequently suffer from potential interference among their constituents, since resources are usually shared by the process participants and it is difficult to foresee all the potential tasks interactions in advance. Concurrent tasks may not be independent from each other (e.g., they could operate on the same data at the same time), resulting in incorrect outcomes. To tackle these issues, we propose an approach for the automated synthesis of a library of template-based process models that achieve goals in dynamic and partially specified environments. The approach is based on a declarative problem definition and partial-order planning algorithms for template generation. The resulting templates guarantee sound concurrency in the execution of their activities and are reusable in a variety of partially specified contextual environments. As running example, a disaster response scenario is given. The approach is backed by a formal model and has been tested in experiment

Augmented Business Process Management Systems: A Research Manifesto

Augmented Business Process Management Systems (ABPMSs) are an emerging class of process-aware information systems that draws upon trustworthy AI technology. An ABPMS enhances the execution of business processes with the aim of making these processes more adaptable, proactive, explainable, and context-sensitive. This manifesto presents a vision for ABPMSs and discusses research challenges that need to be surmounted to realize this vision. To this end, we define the concept of ABPMS, we outline the lifecycle of processes within an ABPMS, we discuss core characteristics of an ABPMS, and we derive a set of challenges to realize systems with these characteristics.Comment: 19 pages, 1 figur

The Internet-of-Things Meets Business Process Management: Mutual Benefits and Challenges

The Internet of Things (IoT) refers to a network of connected devices collecting and exchanging data over the Internet. These things can be artificial or natural, and interact as autonomous agents forming a complex system. In turn, Business Process Management (BPM) was established to analyze, discover, design, implement, execute, monitor and evolve collaborative business processes within and across organizations. While the IoT and BPM have been regarded as separate topics in research and practice, we strongly believe that the management of IoT applications will strongly benefit from BPM concepts, methods and technologies on the one hand; on the other one, the IoT poses challenges that will require enhancements and extensions of the current state-of-the-art in the BPM field. In this paper, we question to what extent these two paradigms can be combined and we discuss the emerging challenges

Process mining meets model learning: Discovering deterministic finite state automata from event logs for business process analysis

Within the process mining field, Deterministic Finite State Automata (DFAs) are largely employed as foundation mechanisms to perform formal reasoning tasks over the information contained in the event logs, such as conformance checking, compliance monitoring and cross-organization process analysis, just to name a few. To support the above use cases, in this paper, we investigate how to leverage Model Learning (ML) algorithms for the automated discovery of DFAs from event logs. DFAs can be used as a fundamental building block to support not only the development of process analysis techniques, but also the implementation of instruments to support other phases of the Business Process Management (BPM) lifecycle such as business process design and enactment. The quality of the discovered DFAs is assessed wrt customized definitions of fitness, precision, generalization, and a standard notion of DFA simplicity. Finally, we use these metrics to benchmark ML algorithms against real-life and synthetically generated datasets, with the aim of studying their performance and investigate their suitability to be used for the development of BPM tools

SmartPM: automatic adaptation of dynamic processes at run-time

The research activity outlined in this thesis is devoted to define a general approach, a concrete architecture and a prototype Process Management System (PMS) for the automated adaptation of dynamic processes at run-time, on the basis of a declarative specification of process tasks and relying on well-established reasoning about actions and planning techniques. The purpose is to demonstrate that the combination of procedural and imperative models with declarative elements, along with the exploitation of techniques from the field of artificial intelligence (AI), such as Situation Calculus, IndiGolog and automated planning, can increase the ability of existing PMSs of supporting dynamic processes. To this end, a prototype PMS named SmartPM, which is specifically tailored for supporting collaborative work of process participants during pervasive scenarios, has been developed. The adaptation mechanism deployed on SmartPM is based on execution monitoring for detecting failures at run-time, which does not require the definition of the adaptation strategy in the process itself (as most of the current approaches do), and on automatic planning techniques for the synthesis of the recovery procedure

Towards human-relevant preclinical models: fluid-dynamics and three-dimensionality as key elements

The activity of research of this thesis focuses on the relevance that appropriate in vitro fully humanized models replicating physiological microenvironments and cues (e.g., mechanical and fluidic) are essential for improving human biology knowledge and boosting new compound testing. In biomedical research, the high percentage of the low rate of successful translation from bench to bedside failure is often attributed to the inability of preclinical models in generating reliable results. Indeed, it is well known that 2D models are far from being representative of human complexity and, on the other side, although animal tests are currently required by regulatory organizations, they are commonly considered unpredictive. As a matter of fact, there is a growing awareness that 3D human tissue models and fluid-dynamic scenarios are better reproducers of the in vivo context. Therefore, during this PhD, I have worked to model and validate technologically advanced fluidic platforms, where to replicate biological processes in a systemic and dynamic environment to better assess the pharmacokinetics and the pharmacodynamics of drug candidates, by considering different case studies. First, skin absorption assays have been performed accordingly to the OECD Test Guidelines 428 comparing the standard diffusive chamber (Franz Diffusion Cell) to a novel fluidic commercially available organ on chip platform (MIVO), demonstrating the importance of emulating physiological fluid flows beneath the skin to obtain in vivo-like transdermal penetration kinetics. On the other hand, after an extensive research analysis of the currently available intestinal models, which resulted insufficient in reproducing chemicals and food absorption profiles in vivo, a mathematical model of the intestinal epithelium as a novel screening strategy has been developed. Moreover, since less than 8% of new anticancer drugs are successfully translated from preclinical to clinical trials, breast, and ovarian cancer, which are among the 5 most common causes of death in women, and neuroblastoma, which has one of the lowest survival rates of all pediatric cancers, have been considered. For each, I developed and optimized 3D ECM-like tumor models, then cultured them under fluid-dynamic conditions (previously predicted by CFD simulations) by adopting different (customized or commercially available) fluidic platforms that allowed to mimic u stimuli (fluid velocity and the fluid flow-induced shear stress) and investigate their impact on tumor cells viability and drug response. I provided evidence that such an approach is pivotal to clinically reproduce the complexity and dynamics of the cancer phenomenon (onset, progression, and metastasis) as well as to develop and validate traditional (i.e., platin-based drugs, caffein active molecule) or novel treatment strategies (i.e., hydroxyapatite nanoparticles, NK cells-based immunotherapies)

Design and Realization of a Sensor-aware Task List Handler for Adaptive Processes in Cyber-Physical Environments

Protsesside juhtimise süsteemid leiavad aina enam kasutust toetamaks muutlike situatsioone ja koostööd nõudvaid protsesse. Mõned valdkonnad on väga muutlikud oma keskkonna poolest, võides muutuda protsessi jooksul ja seega mõjutada töövoogu moel, mil protsessiga pole enam võimalik jätkata. Sellistes valdkondades tegelevad näiteks hädaabi, päästekomandod, kiirabi ja teised. Taolised meeskonnad koosnevad üldjuhul vastavalt tegevuskohale opereerivatest osalejatest. Nendes valdkondades on oodamatute sündmuste sagedus ja erinevus väga suur võrreldes tavapäraste äriprotsessidega mida praegused äriprotsesside juhtimise lahendused hallata suudavad. 2011. aastal tutvustati Rooma Sapienza Ülikoolis esialgset SmartPM (Tark Protsesside Juhtija) konseptsiooni tõestavat prototüüpi ja mudelit mis suudab automaatselt kohanduda planeerimata muutustega. Pidev reaalmaailma muutujate jälgimine on vajalik taolistes valdkondades. Küber-füüsilise süsteemi loomine aitab seda automatiseerida, luues füüsilisest-digitaalseks silla. See sild võib olla tööriistade kogum mis koosneb sensoritest, mobiilsetest seadmetest ja tõlkivast kihist et võtta reaalmaailmast informatsioon ja muuta see digitaalsele süsteemile mõistetavaks. Probleem tekib sensoritelt tuleva informatsiooni tõlkimisel kuna digitaalne süsteem töötleb ainult diskreetseid väärtuseid, aga sensoritelt tulev informatsioon on üldjuhul pidev. Selle probleemi lahendamiseks pakkus autor välja ja implementeeris konkreetse lahenduse. Käesolev töö tutvustab lähemalt sensori-teadliku ülesannete juhtijat ja veebitööriista (mis loodi lahendamaks reaalmaailma väärtuste diskretiseermise probleemi) arhitektuuri ja implementatsiooni. Samuti seletatakse kuidas käesoleva töö tulemusena täiendati ja uuendati kohanevat protsesside juhtimise süsteemi, SmartPMi.Process Management Systems (PMSs) are more and more used to support highly dynamic situations and cooperative processes. Some domains have great diversity of environment variables that can change during the process and therefore affect the workflow in a way that process can not be successfully carried out. Such can be emergency management, health care and other domains involving in most cases in-field actors. In those domains, the frequency and variety of unexpected changes is really high compared to classical business domains that current Business Process Management (BPM) solutions can handle. In 2011, a model and an initial proofof-concept prototype of SmartPM (Smart Process Management) was introduced in Sapienza - Universit´a di Roma that is able to automatically cope with unplanned changes. The continuous screening of the real-world factors is suggested for such domains. A cyber-physical system can be created to automate the screening via physical-to-digital bridge. This bridge can be a set of tools consisting of sensors, mobile devices and translation layer to extract and feed the real-world information to the digital system. Challenge arises when transferring the information from sensors to the system as the system works with discrete values, but the information gathered by the sensors is continuous in most cases. To target this problem, a concrete solution is proposed and implemented by the author. This thesis explains the architecture and implementation of the sensor-aware task list handler and the web tool approach that was created to solve the discretization challenge of the real-world values. It is also explained how the adaptive PMS, SmartPM, was further developed and updated as the contribution of this thesis