Figure 3 - uploaded by Wil Van der Aalst
Content may be subject to copyright.
A database with three tables.

A database with three tables.

Source publication
Conference Paper
Full-text available
In order to maintain a competitive edge, enterprises are driven to improve efficiency by modeling their business processes. Existing process modeling languages often only describe the lifecycles of individual process instances in isolation. Although process models (e.g., BPMN and Data-aware Petri nets) may include data elements, explicit connection...

Contexts in source publication

Context 1
... the structure of databases should be consistent with the corresponding business processes. As mentioned, object-centric information systems store transactions of the same type in the same database table, e.g., all the orders are stored in the "order" table, as shown in Figure 3. In this paper, we use classes to represent tables and class relationships to represent the dependency relationships between tables. ...
Context 2
... ClaM is the universe of class models. Figure 3. More precisely, OC = {order, product, order line} indicates all the tables in the database while RT = {rt1, rt2} contains all the dependency relationships between tables. ...
Context 3
... object can be considered as an instance of a class, instantiating all attributes of the class. For example, a record o1 in the "order" table (the first row) in Figure 3 can be considered as an object of class "order". Each value (e.g., "c1") in the record can be considered as an attribute of the object. ...
Context 4
... object (oc, map) assigns a proper value to each attribute of its corresponding class oc, indicated by map. For instance, the first record in the "product" table in Figure 3 can be formalized as (oc, map) where oc = product , dom(map) = {name, quantity, warehouse}, and map(name) = phone, map(quantity) = 84 and map(warehouse) = Paris. An object model is a set of objects and it is valid if each object has a unique id, i.e., there do not exist two objects which have the same value for each key attribute. ...
Context 5
... object model represents the state of a database at some moment. For instance, Figure 5 shows an object model representing the state of the database in Figure 3. This object model contains 8 objects, i.e., OM = {o1, o2, ol1, ol2, ol3, ol4, phone, cup}. ...

Similar publications

Article
Full-text available
BPMN (Business Process Model and Notation) is currently the preferred standard for the representation and analysis of business processes. The elaboration of these BPMN diagrams is usually carried out in an entirely manual manner. As a result of this human-driven process, it is not uncommon to find diagrams that are not in their most simplified vers...

Citations

... In [13], the object-centric behavioral constraint models (OCBC) are proposed as declarative models with rich semantics that can describe the interaction between the different entities of a database and the activities recorded in an objectcentric event log with the features described in [14]. However, the discovery of the rich set of constraints and the proposed event log format (storing the entire state of the object model for each event) have scalability issues. ...
... https://www.promtools.org/doku.php?id=prom611 12 https://svn.win.tue.nl/repos/prom/Packages/OCELStandard/ Trunk/13 The decorations are obtained using the token-based replay technique described in[9]. ...
Preprint
Full-text available
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-centric event logs, which includes exploring and filtering the behavior contained in the logs and constructing process models which can encode the behavior of different classes of objects and their interactions (which can be discovered from object-centric event logs). This paper aims to provide a broad look at the exploration and processing of object-centric event logs to discover information related to the lifecycle of the different objects composing the event log. Also, comprehensive tool support (OC-PM) implementing the proposed techniques is described in the paper.
... In [13], the object-centric behavioral constraint models (OCBC) are proposed as declarative models with rich semantics that can describe the interaction between the different entities of a database and the activities recorded in an objectcentric event log with the features described in [14]. However, the discovery of the rich set of constraints and the proposed event log format (storing the entire state of the object model for each event) have scalability issues. ...
... https://www.promtools.org/doku.php?id=prom611 12 https://svn.win.tue.nl/repos/prom/Packages/OCELStandard/ Trunk/13 The decorations are obtained using the token-based replay technique described in[9]. ...
Article
Full-text available
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-centric event logs, which includes exploring and filtering the behavior contained in the logs and constructing process models which can encode the behavior of different classes of objects and their interactions (which can be discovered from object-centric event logs). This paper aims to provide a broad look at the exploration and processing of object-centric event logs to discover information related to the lifecycle of the different objects composing the event log. Also, comprehensive tool support (OC-PM) implementing the proposed techniques is described in the paper.
... Additionally, there have been techniques proposed in extracting objectcentric event logs from information systems [41,42] and automated event log building [43]. Furthermore, visualizing and modeling the processes has been researched [44][45][46][47]. However, the previously discussed prediction methods rely on traditional event logs with a single case notion to calculate a trace for a case and train the predictive models. ...
Preprint
Full-text available
The automation and digitalization of business processes has resulted in large amounts of data captured in information systems, which can aid businesses in understanding their processes better, improve workflows, or provide operational support. By making predictions about ongoing processes, bottlenecks can be identified and resources reallocated, as well as insights gained into the state of a process instance (case). Traditionally, data is extracted from systems in the form of an event log with a single identifying case notion, such as an order id for an Order to Cash (O2C) process. However, real processes often have multiple object types, for example, order, item, and package, so a format that forces the use of a single case notion does not reflect the underlying relations in the data. The Object-Centric Event Log (OCEL) format was introduced to correctly capture this information. The state-of-the-art predictive methods have been tailored to only traditional event logs. This thesis shows that a prediction method utilizing Generative Adversarial Networks (GAN), Long Short-Term Memory (LSTM) architectures, and Sequence to Sequence models (Seq2seq), can be augmented with the rich data contained in OCEL. Objects in OCEL can have attributes that are useful in predicting the next event and timestamp, such as a priority class attribute for an object type package indicating slower or faster processing. In the metrics of sequence similarity of predicted remaining events and mean absolute error (MAE) of the timestamp, the approach in this thesis matches or exceeds previous research, depending on whether selected object attributes are useful features for the model. Additionally, this thesis provides a web interface to predict the next sequence of activities from user input.
... • π vmap : E → (AN → AV ) is the function associating every event identifier e ∈ E to a variable-to-value assignment function val such that, for each attribute a ∈ AN in the domain of val, val(a) indicates the value assigned to a by e, 1 • π omap : E → 2 O is the function associating an event identifier to a set of related object identifiers, ...
... • < ⊆ (E × E) is a partial order of events. 2 1 The notation → indicates a partial function. 2 Typically, the partial order is induced by the timestamp, i.e., e < e ⇐⇒ π time (e ) < π time (e ). ...
Preprint
Full-text available
Object-centric processes (a.k.a. Artifact-centric processes) are implementations of a paradigm where an instance of one process is not executed in isolation but interacts with other instances of the same or other processes. Interactions take place through bridging events where instances exchange data. Object-centric processes are recently gaining popularity in academia and industry, because their nature is observed in many application scenarios. This poses significant challenges in predictive analytics due to the complex intricacy of the process instances that relate to each other via many-to-many associations. Existing research is unable to directly exploit the benefits of these interactions, thus limiting the prediction quality. This paper proposes an approach to incorporate the information about the object interactions into the predictive models. The approach is assessed on real-life object-centric process event data, using different KPIs. The results are compared with a naive approach that overlooks the object interactions, thus illustrating the benefits of their use on the prediction quality.
... The techniques proposed based on the artifact-centric process modeling do not show the process as a whole. Therefore, in [27] a discovery algorithm was proposed to discover Object-Centric Behavioral Constraints (OCBC) models from object-centric event logs. These models show interactions between the data and behavioral perspectives on the attribute level in one diagram. ...
Preprint
Full-text available
Operational processes in production, logistics, material handling, maintenance, etc., are supported by cyber-physical systems combining hardware and software components. As a result, the digital and the physical world are closely aligned, and it is possible to track operational processes in detail (e.g., using sensors). The abundance of event data generated by today's operational processes provides opportunities and challenges for process mining techniques supporting process discovery, performance analysis, and conformance checking. Using existing process mining tools, it is already possible to automatically discover process models and uncover performance and compliance problems. In the DFG-funded Cluster of Excellence "Internet of Production" (IoP), process mining is used to create "digital shadows" to improve a wide variety of operational processes. However, operational processes are dynamic, distributed, and complex. Driven by the challenges identified in the IoP cluster, we work on novel techniques for comparative process mining (comparing process variants for different products at different locations at different times), object-centric process mining (to handle processes involving different types of objects that interact), and forward-looking process mining (to explore "What if?" questions). By addressing these challenges, we aim to develop valuable "digital shadows" that can be used to remove operational friction.
... The techniques proposed based on the artifactcentric process modeling do not show the process as a whole. Therefore, in [27] a discovery algorithm was proposed to discover Object-Centric Behavioral Constraints (OCBC) models from object-centric event logs. These models show interactions between the data and behavioral perspectives on the attribute level in one diagram. ...
Chapter
Operational processes in production, logistics, material handling, maintenance, etc., are supported by cyber-physical systems combining hardware and software components. As a result, the digital and the physical world are closely aligned, and it is possible to track operational processes in detail (e.g., using sensors). The abundance of event data generated by today’s operational processes provides opportunities and challenges for process mining techniques supporting process discovery, performance analysis, and conformance checking. Using existing process mining tools, it is already possible to automatically discover process models and uncover performance and compliance problems. In the DFG-funded Cluster of Excellence “Internet of Production” (IoP), process mining is used to create “digital shadows” to improve a wide variety of operational processes. However, operational processes are dynamic, distributed, and complex. Driven by the challenges identified in the IoP cluster, we work on novel techniques for comparative process mining (comparing process variants for different products at different locations at different times), object-centric process mining (to handle processes involving different types of objects that interact), and forward-looking process mining (to explore “What if?” questions). By addressing these challenges, we aim to develop valuable “digital shadows” that can be used to remove operational friction.
... A few researchers have also attempted to address the problems caused by manyto-many relations and have proposed novel metamodels for storing data for process mining [13,17] and novel modeling notations and process mining techniques for discovering and analyzing artifact-centric processes [18,19] to achieve this; however, our proposed metamodel is aimed at providing the basis for a data extraction model to create event logs in the XES format from transaction logs. Once event logs are obtained, i.e., XES files, it is possible to perform existing process mining techniques which users are already familiar with. ...
Article
Full-text available
Enterprise resource planning (ERP) systems are often seen as viable sources of data for process mining analysis. To perform most of the existing process mining techniques, it is necessary to obtain a valid event log that is fully compliant with the eXtensible Event Stream (XES) standard. In ERP systems, such event logs are not available as the concept of business activity is missing. Extracting event data from an ERP database is not a trivial task and requires in-depth knowledge of the business processes and underlying data structure. Therefore, domain experts require proper techniques and tools for extracting event data from ERP databases. In this paper, we present the full specification of a domain-specific modeling language for facilitating the extraction of appropriate event data from transactional databases by domain experts. The modeling language has been developed to support complex ambiguous cases when using ERP systems. We demonstrate its applicability using a case study with real data and show that the language includes constructs that enable a domain expert to easily model data of interest in the log extraction step. The language provides sufficient information to extract and transform data from transactional ERP databases to the XES format.
Article
Object-centric processes (also known as Artifact-centric processes) are implementations of a paradigm where an instance of one process is not executed in isolation but interacts with other instances of the same or other processes. Interactions take place through bridging events where instances exchange data. Object-centric processes are recently gaining popularity in academia and industry, because their nature is observed in many application scenarios. This poses significant challenges in predictive analytics due to the complex intricacy of the process instances that relate to each other via many-to-many associations. Existing research is unable to directly exploit the benefits of these interactions, thus limiting the prediction quality. This paper proposes an approach to incorporate the information about the object interactions into the predictive models. The approach is assessed on real-life object-centric process event data, using different Key Performance Indicators (KPIs). The results are compared with a naïve approach that overlooks the object interactions, thus illustrating the benefits of their use on the prediction quality.