Claudio Di Ciccio

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.

A two-step fast algorithm for the automated discovery of declarative workflows

Declarative approaches are particularly suitable for modeling highly flexible processes. They especially apply to artful processes, i.e., rapid informal processes that are typically carried out by those people whose work is mental rather than physical (managers, professors, researchers, engineers, etc.), the so called “knowledge workers”. This paper describes MINERful++, a two-step algorithm for an efficient discovery of constraints that constitute declarative workflow models. As a first step, a knowledge base is built, with information about temporal statistics gathered from execution traces. Then, the statistical support of constraints is computed, by querying that knowledge base. MINERful++ is fast, modular, independent of the specific formalism adopted for representing constraints, based on a probabilistic approach and capable of eliminating the redundancy of subsumed constraints.

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.

Predictive Task Monitoring for Business Processes

Information sources providing real-time status of physical objects have drastically increased in recent times. So far, research in business process monitoring has mainly focused on checking the completion of tasks. However, the availability of real-time information allows for a more detailed tracking of individual business tasks. This paper describes a framework for controlling the safe execution of tasks and signalling possible misbehaviours at runtime. It outlines a real use case on smart logistics and the preliminary results of its application.

Mining Constraints for Artful Processes

Artful processes are informal processes typically carried out by those people whose work is mental rather than physical (managers, professors, researchers, engineers, etc.), the so called “knowledge workers”. MailOfMine is a tool, the aim of which is to automatically build, on top of a collection of email messages, a set of workflow models that represent the artful processes laying behind the knowledge workers activities. After an outline of the approach and the tool, this paper focuses on the mining algorithm, able to efficiently compute the set of constraints describing the artful process. Finally, an experimental evaluation of it is reported.

Blockchains for Business Process Management - Challenges and Opportunities

Blockchain technology offers a sizable promise to rethink the way interorganizational business processes are managed because of its potential to realize execution without a central party serving as a single point of trust (and failure). To stimulate research on this promise and the limits thereof, in this article, we outline the challenges and opportunities of blockchain for business process management (BPM). We first reflect how blockchains could be used in the context of the established BPM lifecycle and second how they might become relevant beyond. We conclude our discourse with a summary of seven research directions for investigating the application of blockchain technology in the context of BPM.

MailOfMine - Analyzing Mail Messages for Mining Artful Collaborative Processes

Artful processes are informal processes typically carried out by those people whose work is mental rather than physical (managers, professors, researchers, engineers, etc.), the so called “knowledge workers”. In this paper we propose the MailOfMine approach, to automatically build, on top of a collection of email messages, a set of workflow models that represent the artful processes laying behind the knowledge workers activities.

Representing and visualizing mined artful processes in mailofmine

Artful processes are informal processes typically carried out by those people whose work is mental rather than physical (managers, professors, researchers, engineers, etc.), the so called “knowledge workers”. mailofmine is a tool, the aim of which is to automatically build, on top of a collection of e-mail messages, a set of workflow models that represent the artful processes laying behind the knowledge workers activities. This paper presents its innovative graphical syntax proposal and the interface for representing and showing such mined processes to users.

Lecture Notes in Business Information Processing

The act of retrieving process models from event-based data logs can offer valuable information to business owners. Many approaches have been proposed for this purpose, mining for either a procedural or declarative outcome. A blended approach that combines both process model paradigms exists and offers a great way to deal with process environments which consist of different layers of flexibility. In this paper, it will be shown how to check such models for correctness, and how this checking can contribute to retrieving the models as well. The approach is based on intersecting both parts of the model and provides an effective way to check (i) whether the behavior is aligned, and (ii) where the model can be improved according to errors that arise along the respective paradigms. To this end, we extend the functionality of Fusion Miner, a mixed-paradigm process miner, in a way to inspect which amount of flexibility is right for the event log. The procedure is demonstrated with an implemented model checker and verified on real-life event logs.

Detecting flight trajectory anomalies and predicting diversions in freight transportation

Abstract Timely identifying flight diversions is a crucial aspect of efficient multi-modal transportation. When an airplane diverts, logistics providers must promptly adapt their transportation plans in order to ensure proper delivery despite such an unexpected event. In practice, the different parties in a logistics chain do not exchange real-time information related to flights. This calls for a means to detect diversions that just requires publicly available data, thus being independent of the communication between different parties. The dependence on public data results in a challenge to detect anomalous behavior without knowing the planned flight trajectory. Our work addresses this challenge by introducing a prediction model that just requires information on an airplanes position, velocity, and intended destination. This information is used to distinguish between regular and anomalous behavior. When an airplane displays anomalous behavior for an extended period of time, the model predicts a diversion. A quantitative evaluation shows that this approach is able to detect diverting airplanes with excellent precision and recall even without knowing planned trajectories as required by related research. By utilizing the proposed prediction model, logistics companies gain a significant amount of response time for these cases.