Ricerc@Sapienza

Models of human habits in smart spaces can be expressed by using a multitude of formalisms, whose readability influences the possibility of being validated by human experts. Given the growing availability of low-cost sensing devices promoted by the emerging Internet-of-Things, the analysis of huge amount of data produced by these systems will assume an utmost importance in the near future. But most of them are designed for single user cases. Moving forward in their development, often they hardly fit a realistic environment with many users.

Process mining allows analysts to exploit logs of historical executions of business processes to extract insights regarding the actual performance of these processes. One of the most widely studied process mining operations is automated process discovery. An automated process discovery method takes as input an event log, and produces as output a business process model that captures the control-flow relations between tasks that are observed in or implied by the event log.

Robotic Process Automation (RPA) operates on the user interface (UI) of software applications and automates - by means of a software (SW) robot - mouse and keyboard interactions to remove intensive routine tasks (or simply routines). With the recent advances in Artificial Intelligence, the automation of routines is expected to undergo a radical transformation.

Robotic Process Automation (RPA) is a fast-emerging automation technology
that sits between the fields of Business Process Management (BPM) and
Artificial Intelligence (AI), and allows organizations to automate high volume
routines. RPA tools are able to capture the execution of such routines
previously performed by a human users on the interface of a computer system,
and then emulate their enactment in place of the user by means of a software
robot. Nowadays, in the BPM domain, only simple, predictable business processes

Business Process Management (BPM) is a central element of today’s organizations. Over the years, its main focus has been the support of business processes (BPs) in highly controlled domains. However—in the current era of Big Data and Internet-of-Things—several real-world domains are becoming cyber-physical (e.g., consider the shift from traditional manufacturing to Industry 4.0), characterized by ever-changing requirements, unpredictable environments and increasing amounts of data and events that influence the enactment of BPs.

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.

The aim of process discovery is to build a process model from an event log without prior information about the process. The discovery of declarative process models is useful when a process works in an unpredictable and unstable environment since several allowed paths can be represented as a compact set of rules. One of the tools available in the literature for discovering declarative models from logs is the Declare Miner, a plug-in of the process mining tool ProM.

Nowadays, business processes are increasingly supported by IT services that produce massive amounts of event data during the execution of a process. These event data can be used to analyze the process using process mining techniques to discover the real process, measure conformance to a given process model, or to enhance existing models with performance information. Mapping the produced events to activities of a given process model is essential for conformance checking, annotation and understanding of process mining results.

This paper presents a command-line tool, called Entropia, that implements a family of conformance checking measures for process mining founded on the notion of entropy from information theory. The measures allow quantifying classical non-deterministic and stochastic precision and recall quality criteria for process models automatically discovered from traces executed by IT-systems and recorded in their event logs.

Research on concept drift detection has inspired recent advancements of process mining and expanding the growing arsenal of process analysis tools. What has so far been missing in this new research stream are techniques that support comprehensive process drift analysis in terms of localizing, drillingdown, quantifying, and visualizing process drifts. In our research, we built on ideas from concept drift, process mining, and visualization research and present a novel web-based software tool to analyze process drifts, called Visual Drift Detection (VDD).