Knowledge-Intensive Processes: Characteristics, Requirements and Analysis of Contemporary Approaches

Engineering of knowledge-intensive processes (KiPs) is far from being mastered, since they are genuinely knowledge- and data-centric, and require substantial flexibility, at both design- and run-time. In this work, starting from a scientific literature analysis in the area of KiPs and from three real-world domains and application scenarios, we provide a precise characterization of KiPs. Furthermore, we devise some general requirements related to KiPs management and execution. Such requirements contribute to the definition of an evaluation framework to assess current system support for KiPs. To this end, we present a critical analysis on a number of existing process-oriented approaches by discussing their efficacy against the requirements

Interestingness of traces in declarative process mining: The janus LTLPf Approach

Declarative process mining is the set of techniques aimed at extracting behavioural constraints from event logs. These constraints are inherently of a reactive nature, in that their activation restricts the occurrence of other activities. In this way, they are prone to the principle of ex falso quod libet: they can be satisfied even when not activated. As a consequence, constraints can be mined that are hardly interesting to users or even potentially misleading. In this paper, we build on the observation that users typically read and write temporal constraints as if-statements with an explicit indication of the activation condition. Our approach is called Janus, because it permits the specification and verification of reactive constraints that, upon activation, look forward into the future and backwards into the past of a trace. Reactive constraints are expressed using Linear-time Temporal Logic with Past on Finite Traces (LTLp f). To mine them out of event logs, we devise a time bi-directional valuation technique based on triplets of automata operating in an on-line fashion. Our solution proves efficient, being at most quadratic w.r.t. trace length, and effective in recognising interestingness of discovered constraints

Towards a process-oriented analysis of blockchain data

Blockchains sequentially store the history of transactional information, in a virtually immutable and distributed way. Moreover, second-generation blockchains such as Ethereum are programmable environments, and every operation invocation towards the smart contracts corresponds to a transaction sequentially collated in the ledgers. They thus allow for the controlled enactment of multi-party processes as well as the immutable recording of their distributed execution. Despite the verification, tracking, and monitoring of such blockchain-enabled processes appears paramount, a formal and implemented framework encompassing those aspects is still a mostly unexplored research avenue. The talk revolves around the current state of the art, as well as the opportunities and challenges that arise when it comes to conducting a process-oriented analysis on data stemming from blockchains, from a representation and modelling perspective

On the discovery of declarative control flows for artful processes

Artful processes are those processes in which the experience, intuition, and knowledge of the actors are the key factors in determining the decision making. They are typically carried out by the "knowledge workers," such as professors, managers, and researchers. They are often scarcely formalized or completely unknown a priori. Throughout this article, we discuss how we addressed the challenge of discovering declarative control flows in the context of artful processes. To this extent, we devised and implemented a two-phase algorithm, named MINERful. The first phase builds a knowledge base, where statistical information extracted from logs is represented. During the second phase, queries are evaluated on that knowledge base, in order to infer the constraints that constitute the discovered process. After outlining the overall approach and offering insight on the adopted process modeling language, we describe in detail our discovery technique. Thereupon, we analyze its performances, both from a theoretical and an experimental perspective. A user-driven evaluation of the quality of results is also reported on the basis of a real case study. Finally, a study on the fitness of discovered models with respect to synthetic and real logs is presented

A novel framework for visualizing declarative process models

The declarative approach to business process modeling has been introduced to deal with the issue of managing flexible processes. Instead of explicitly representing all the allowed enactments of a process, the approach describes the constraints that limit its behavior. However, current graphical notations for declarative processes are prone to be difficult to understand, thus hampering a widespread usage of the approach. To overcome this issue, we present a novel notation framework for visualizing declarative processes, which is devised in compliance with well-known notation design principles

Artifact-driven Process Monitoring: Dynamically Binding Real-world Objects to Running Processes

Monitoring inter-organizational business processes requires explicit knowledge about when activities start and complete. This is a challenge because no single system controls the process, activities might not be directly recorded, and the overall course of execution might only be determined at runtime. In this paper, we address these problems by integrating process monitoring with sensor data from real-world objects. We formalize our approach using the E-GSM artifact-centric language. Since the association between real-world objects and process instances is often only determined at runtime, our approach also caters for dynamic binding and unbinding at runtime

Fine-grained Data Access Control for Collaborative Process Execution on Blockchain

Multi-party business processes are based on the cooperation of different actors in a distributed setting. Blockchains can provide support for the automation of such processes, even in conditions of partial trust among the participants. On-chain data are stored in all replicas of the ledger and therefore accessible to all nodes that are in the network. Although this fosters traceability, integrity, and persistence, it undermines the adoption of public blockchains for process automation since it conflicts with typical confidentiality requirements in enterprise settings. In this paper, we propose a novel approach and software architecture that allow for fine-grained access control over process data on the level of parts of messages. In our approach, encrypted data are stored in a distributed space linked to the blockchain system backing the process execution; data owners specify access policies to control which users can read which parts of the information. To achieve the desired properties, we utilise Attribute-Based Encryption for the storage of data, and smart contracts for access control, integrity, and linking to process data. We implemented the approach in a proof-of-concept and conduct a case study in supply-chain management. From the experiments, we find our architecture to be robust while still keeping execution costs reasonably low

The Use of Ozone as an Eco-Friendly Strategy against Microbial Biofilm in Dairy Manufacturing Plants: A Review

Managing spoilage and pathogenic bacteria contaminations represents a major challenge for the food industry, especially for the dairy sector. Biofilms formed by these microorganisms in food processing environment continue to pose concerns to food manufacturers as they may impact both the safety and quality of processed foods. Bacteria inside biofilm can survive in harsh environmental conditions and represent a source of repeated food contamination in dairy manufacturing plants. Among the novel approaches proposed to control biofilm in food processing plants, the ozone treatment, in aqueous or gaseous form, may represent one of the most promising techniques due to its antimicrobial action and low environmental impact. The antimicrobial effectiveness of ozone has been well documented on a wide variety of microorganisms in planktonic forms, whereas little data on the efficacy of ozone treatment against microbial biofilms are available. In addition, ozone is recognized as an eco-friendly technology since it does not leave harmful residuals in food products or on contact surfaces. Thus, this review intends to present an overview of the current state of knowledge on the possible use of ozone as an antimicrobial agent against the most common spoilage and pathogenic microorganisms, usually organized in biofilm, in dairy manufacturing plants