Laura Genga

Behavioral process mining for unstructured processes

Real world applications provide many examples of unstructured processes, where process execution is mainly driven by contingent decisions taken by the actors, with the result that the process is rarely repeated exactly in the same way. In these cases, traditional Process Discovery techniques, aimed at extracting complete process models from event logs, reveal some limits. In fact, when applied to logs of unstructured processes, Process Discovery techniques usually return complex, “spaghetti-like” models, which usually provide limited support to analysts. As a remedy, in the present work we propose Behavioral Process Mining as an alternative approach to enlighten relevant subprocesses, representing meaningful collaboration work practices. The approach is based on the application of hierarchical graph clustering to the set of instance graphs generated by a process. We also describe a technique for building instance graphs from traces. We assess advantages and limits of the approach on a set of synthetic and real world experiments.

Collaborative Building of an Ontology of Key Performance Indicators

In the present paper we propose a logic model for the representation of Key Performance Indicators (KPIs) that supports the construction of a valid reference model (or KPI ontology) by enabling the integration of definitions proposed by different engineers in a minimal and consistent system. In detail, the contribution of the paper is as follows: (i) we combine the descriptive semantics of KPIs with a logical representation of the formula used to calculate a KPI, allowing to make the algebraic relationships among indicators explicit; (ii) we discuss how this representation enables reasoning over KPI formulas to check equivalence of KPIs and overall consistency of the set of indicators, and present an empirical study on the efficiency of the reasoning; (iii) we present a prototype implementing the approach to collaboratively manage a shared ontology of KPI definitions.

Discovering behavioural patterns in knowledge-intensive collaborative processes

Domains like emergency management, health care, or research and innovation development, are characterized by the execution of so-called knowledge-intensive processes. Such processes are typically highly uncertain, with little or no structure; consequently, classical process discovery techniques, aimed at extracting complete process schemas from execution logs, usually provide a limited support in analysing these processes. As a remedy, in the present work we propose a methodology aimed at extracting relevant subprocesses, representing meaningful collaboration behavioural patterns. We consider a real case study regarding the development of research activities, to test the approach and compare its results with the outcome of classical process discovery techniques.

A composite methodology for supporting collaboration pattern discovery via semantic enrichment and multidimensional analysis

Classical process discovery approaches usually investigate logs generated by processes in order to mine and discovery corresponding process schemas. When the collaboration processes case is addressed, such approaches turn to be poorly effective, due to the fact that: (i) logs of collaboration processes are usually stored in heterogeneous data storages which also expose different data types; (ii) it is not easy and direct to derive a common analysis model from such logs. As a consequence, classical methodologies usually fail. In order to fulfill this gap, in this paper we describe a composite methodology that combines semantics-based techniques and multidimensional analysis paradigms to support effective and efficient collaboration process discovery from log data.

A methodology for building log of collaboration processes

The analysis of data produced during collaborative activities allows organizations to improve collaboration management. Since people use several collaboration tools, these kind of data are difficult to obtain. Furthermore they are heterogeneous and require an important preprocessing step to be useful. In the present work we introduce a methodology aimed at obtaining a single log with all data related to team activities. To improve process analysis, such data log is semantically enriched by means of a multidimensional taxonomy capable of describing collaboration activities at various abstraction levels. We also introduce a case study to be used throughout the paper as an illustrative example.

Subgraph mining for anomalous pattern discovery in event logs

Conformance checking allows organizations to verify whether their IT system complies with the prescribed behavior by comparing process executions recorded by the IT system against a process model (representing the normative behavior). However, most of the existing techniques are only able to identify low-level deviations, which provide a scarce support to investigate what actually happened when a process execution deviates from the specification. In this work, we introduce an approach to extract recurrent deviations from historical logging data and generate anomalous patterns representing high-level deviations. These patterns provide analysts with a valuable aid for investigating nonconforming behaviors; moreover, they can be exploited to detect high-level deviations during conformance checking. To identify anomalous behaviors from historical logging data, we apply frequent subgraph mining techniques together with an ad-hoc conformance checking technique. Anomalous patterns are then derived by applying frequent items algorithms to determine highly-correlated deviations, among which ordering relations are inferred. The approach has been validated by means of a set of experiments.

Innovation pattern analysis

The evolution of innovation management in last decades was strongly influenced and led by the theory of the “Open Innovation” introduced by Chesbrough [1], and has become one of the hottest topic in business Literature. In the current economical scenario an increasingly number of organizations decide to adopt a more open approach in their innovation policy, trying to establish more or less strong relations with external partners, directly involving them in innovative projects. Consequently the collaborative work is gaining a growing importance in innovation practices of organizations, since the success or failure of innovative projects is often strictly related to results of collaborative tasks. Therefore, to support innovation processes of an organization one can investigate and improve its collaboration practices, with the aim to discover the best ones, i.e. those that maximize the success probability of organizations innovative projects. However, this kind of analysis is often prevented by the lack of real world data, mainly due to the limited diffusion of innovation management systems capable to collect innovation activities traces. Nevertheless, the daily activities of an enterprise, both internal and external, are almost completely performed by software systems. Both explicitly and implicitly, these systems keep track of users activities, e.g. ERP logs, versioning systems, list of emails, file timestamps, and so forth. In the present work we propose a methodology aimed to discover relevant collaboration patterns based on real data daily collected by enterprises, with the aim of providing business users with a better understanding of the dynamics of the interactions among members of collaborating groups. Our idea is firstly to collect any kind of data produced during the collaborative development of an innovation project, then to integrate them into a unique knowledge base storing traces of enterprise activities. Through preprocessing analysis, such traces are translated into process schemas, that can be considered as a representation of collaborative innovation processes in the organization, on which we can perform pattern discovery. To this aim we consider hierarchical clustering, which is capable to extracts frequent subprocesses representing common collaboration patterns and to arrange them in a hierarchy with different level of abstractions. The rest of this work is organized in two sections, the former aimed to describe the main ideas of the methodology, the latter to sketch out future extensions we plan to conduct.

Towards a customizable user-centered model for data analytics

Evidence-based governance and e-democracy both rely on the capability to analyze aggregated and statistical data. Recent studies report that existing analysis tools were never fully embraced by managers mainly because of their complexity for many analytical use cases. This is even more true for citizens, that do not have full control over underlying data and analysis models. In the present work, we propose an innovative user-centered approach for data analytics, that facilitates the interaction of users with statistical and aggregated measures, i.e. indicators. We provide an overview of the framework, discussing its main components and functionalities. In particular we focus on an ontology representing both atomic and compound indicators, that are provided with a calculation formula. We show how such a logic-based representation of indicators allows the implementation of powerful, automatic reasoning services, capable to provide a valuable support to users for performing analysis tasks.

Towards a Systematic Process-aware Behavioral Analysis for Security.

Nowadays, security is a key concern for organizations. An increasingly popular solution to enhance security in organizational settings is the adoption of anomaly detection systems. These systems raise an alert when an abnormal behavior is detected, upon which proper measures have to be taken. A well-known drawback of these solutions is that the underlying detection engine is a black box, i.e., the behavioral profiles used for detections are encoded in some mathematical model that is challenging to understand for human analysts or, in some cases, is not even accessible. Therefore, anomaly detection systems often fail in supporting analysts in understanding what is happening in the system and how to respond to detected security threats. In this work, we investigate the use of process analysis techniques to build behavioral models understandable by human analysts. We also delineate a systematic methodology for process-aware behaviors analysis and discuss the findings obtained by applying such a methodology to a real-world event log.

Discovering anomalous frequent patterns from partially ordered event logs

Conformance checking allows organizations to compare process executions recorded by the IT system against a process model representing the normative behavior. Most of the existing techniques, however, are only able to pinpoint where individual process executions deviate from the normative behavior, without considering neither possible correlations among occurred deviations nor their frequency. Moreover, the actual control-flow of the process is not taken into account in the analysis. Neglecting possible parallelisms among process activities can lead to inaccurate diagnostics; it also poses some challenges in interpreting the results, since deviations occurring in parallel behaviors are often instantiated in different sequential behaviors in different traces. In this work, we present an approach to extract anomalous frequent patterns from historical logging data. The extracted patterns can exhibit parallel behaviors and correlate recurrent deviations that have occurred in possibly different portions of the process, thus providing analysts with a valuable aid for investigating nonconforming behaviors. Our approach has been implemented as a plug-in of the ESub tool and evaluated using both synthetic and real-life logs.