Alessandro BertiRWTH Aachen University · Process and Data Science
Alessandro Berti
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21
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Citations since 2017
Publications
Publications (21)
Process mining provides a collection of techniques to gain insights into business processes by analyzing event logs.Organizations can gain various insights into their business processes by using process mining techniques.Such techniques use event logs extracted from relational databases supporting the business process as input.However, extracting e...
![Fig. 6: Object-centric directly-follows graph [5] based on the...](publication/366543133/figure/fig4/AS:11431281109253516@1671803723734/Object-centric-directly-follows-graph-5-based-on-the-object-centric-event-log-available_Q320.jpg)
![Fig. 7: Object-centric directly-follows graph [5] obtained filtering...](publication/366543133/figure/fig5/AS:11431281109244712@1671803723775/Object-centric-directly-follows-graph-5-obtained-filtering-out-the-20-of-most_Q320.jpg)
Process mining techniques are widely used to uncover performance and compliance problems. However, the traditional focus on a single object type (i.e., case) is a limiting factor when considering real-life information systems. Therefore, there is an increased interest in object-centric process mining. This paper proposes a graph-based approach for...
SAP ERP is one of the most popular information systems supporting various organizational processes, e.g., O2C and P2P. However, the amount of processes and data contained in SAP ERP is enormous. Thus, the identification of the processes that are contained in a specific SAP instance, and the creation of a list of related tables is a significant chal...
Object-centric process mining is a novel branch of process mining that aims to analyze event data from mainstream information systems (such as SAP) more naturally, without being forced to form mutually exclusive groups of events with the specification of a case notion. The development of object-centric process mining is related to exploiting object...
Open-source process mining provides many algorithms for the analysis of event data which could be used to analyze mainstream processes (e.g., O2C, P2P, CRM). However, compared to commercial tools, they lack the performance and struggle to analyze large amounts of data. This paper presents PM4Py-GPU, a Python process mining library based on the NVID...
Process mining techniques make the underlying processes in organizations transparent. Historical event data are used to perform conformance checking and performance analyses. Analyzing a single process and providing visual insights has been the focus of most process mining techniques. However, comparing two processes or a single process in differen...
Object-centric process mining provides a set of techniques for the analysis of event data where events are associated to several objects. To store Object-centric Event Logs (OCELs), the JSON-OCEL and JSON-XML formats have been recently proposed. However, the proposed implementations of the OCEL are file-based. This means that the entire file needs...
The extraction, transformation, and loading of event logs from information systems is the first and the most expensive step in process mining. In particular, extracting event logs from popular ERP systems such as SAP poses major challenges, given the size and the structure of the data. Open-source support for ETL is scarce, while commercial process...
The extraction, transformation, and loading of event logs from information systems is the first and the most expensive step in process mining. In particular, extracting event logs from popular ERP systems such as SAP poses major challenges, given the size and the structure of the data. Open-source support for ETL is scarce, while commercial process...
![Figure 7. A colored Petri net in CPN Tools [12, 41] modeling the...](profile/Wil-Aalst/publication/344486150/figure/fig5/AS:943477428846597@1601953956403/A-colored-Petri-net-in-CPN-Tools-12-41-modeling-the-process-depicted-in-Figure-4-which_Q320.jpg)
Techniques to discover Petri nets from event data assume precisely one case identifier per event. These case identifiers are used to correlate events, and the resulting discovered Petri net aims to describe the life-cycle of individual cases. In reality, there is not one possible case notion, but multiple intertwined case notions. For example, even...
Process mining provides techniques to improve the performance and compliance of operational processes. Although sometimes the term "workflow mining" is used, the application in the context of Workflow Management (WFM) and Business Process Management (BPM) systems is limited. The main reason is that WFM/BPM systems control the process, leaving less...
Token-based replay used to be the standard way to conduct conformance checking. With the uptake of more advanced techniques (e.g., alignment based), token-based replay got abandoned. However, despite decomposition approaches and heuristics to speed-up computation, the more advanced conformance checking techniques have limited scalability, especiall...
Much time in process mining projects is spent on finding and understanding data sources and extracting the event data needed. As a result, only a fraction of time is spent actually applying techniques to discover, control and predict the business process. Moreover, current process mining techniques assume a single case notion. However, in real-life...
Much time in process mining projects is spent on finding and understanding data sources and extracting the event data needed. As a result, only a fraction of time is spent actually applying techniques to discover, control and predict the business process. Moreover, current process mining techniques assume a single case notion. However, in reallife...
Process discovery algorithms discover process models on the basis of event data automatically. These techniques tend to consider the entire log to discover a process model. However, real-life event logs usually contain outlier behaviour that lead to incomprehensible, complex and inaccurate process models where correct and/or important behaviour is...
Process Mining is a branch of Data Science that aims to extract process-related information from event data contained in information systems, that is steadily increasing in amount. Many algorithms, and a general-purpose open source framework (ProM 6), have been developed in the last years for process discovery, conformance checking, machine learnin...
Process mining, i.e., a sub-field of data science focusing on the analysis of event data generated during the execution of (business) processes, has seen a tremendous change over the past two decades. Starting off in the early 2000's, with limited to no tool support, nowadays, several software tools, i.e., both open-source, e.g., ProM and Apromore,...
Much time in process mining projects is spent on finding and understanding data sources and extracting the event data needed. As a result, only a fraction of time is spent actually applying techniques to discover, control and predict the business process. Moreover, there is a lack of techniques to display relationships on top of databases without t...
In this paper I'll speak about non-spectral clustering techniques and see how
a node ordering based on centrality measures can improve the quality of
communities detected. I'll also discuss an improvement to existing techniques,
which further improves modularity.
No-Free-Lunch Theorems state, roughly speaking, that the performance of all
search algorithms is the same when averaged over all possible objective
functions. This fact was precisely formulated for the first time in a now
famous paper by Wolpert and Macready, and then subsequently refined and
extended by several authors, always in the context of a...
Projects
Projects (3)
Traditional process mining techniques assume the existence of one single process to be analyzed. However, many real-life processes are composed of different objects following individual paths through the process, e.g., in production processes or complex business processes. Object-centric process mining provides data structures and algorithms to analyze such processes accurately.
The extraction, transformation, and loading of event logs from information systems is the first and the most expensive step in process mining. In particular, extracting event logs from popular ERP systems such as SAP poses major challenges, given the size and the structure of the data.
The goal of this project is to first facilitate event data extraction from SAP ERP systems, and then discover and analyze well-known and unknown processes from such systems.
