Data about process executions has witnessed a notable increase in the last decades, due to the growing adoption of Information Technology systems able to trace and store this information. Meanwhile, Semantic Web methodologies and technologies have become more and more robust and able to face the issues posed by a variety of new domains, taking advantage of reasoning services in the “big data” era. In this demo paper we present ProMo, a tool for the collaborative modeling and monitoring of Business Process executions. Specifically, by exploiting semantic modeling and reasoning, it enables the reconciliation of business and data layers as well as of static and procedural aspects, thus allowing business analysts to infer knowledge and use it to analyze process executions.
The last decades have witnessed a rapid and widespread adoption of Information
Technology (IT) to support business activities in all phases. As a side effect, IT systems
have made available huge quantities of data about process executions, thus enabling
(i) to monitor the actual execution and the progress of (instances of) Business Processes
(BPs); (ii) to provide statistical analysis; (iii) to detect deviations of process executions
from process models (e.g., [
Meanwhile, Semantic Web technologies have known an important growth and have made available powerful reasoning services able to reason on complex domains, as well as technologies able to deal with huge quantities of data. This opens the way to the use of Semantic Web technologies for process modeling and monitoring and for the analysis of processes characterizing complex scenarios as those of large organizations.
In these complex scenarios, knowledge can be classified in two orthogonal ways. First, we distinguish between a dynamic dimension, which concerns the procedures and the activities carried out by the organization for realizing specific objectives, and a static dimension, which concerns the organization structure (e.g., the role hierarchy), the data structure (e.g., the document organization), and the relationships among these and other domain entities. Then, knowledge can be ascribed to two layers: the IT layer, which concerns the actual data items processed by IT systems; and the business layer, which concerns the models of the dynamic and static aspects of the organization domain. Given this frame, two main challenges need to be faced: (i) bridging the unavoidable gap between the business and the data layer; and (ii) reconciling the static and dynamic dimensions so to make them available for monitoring and analysis purposes.
In this demo we present and showcase ProMo, a tool that exploits Semantic Web technologies to address the above challenges through an integrated representation of knowledge, enabling the collaborative modeling, monitoring and analysis of business processes. By reconciling all these different dimensions and layers, ProMo overcomes existing approaches. In the remainder we describe how ProMo reconciles the business and IT layers and the static and dynamic dimensions, introducing the ProMo main components that will be demonstrated live during the Posters and Demo session. 2
Aligning the business and IT layers is a difficult task. For example, process monitoring at the IT layer cannot observe data exchanged on paper documents or user activities not mediated by IT systems, and thus brings only partial information on which activities were executed and what data or artifacts they produced. Even when IT data exists, it is not easy to associate it to a specific process instance. Indeed, IT services can be shared by process classes and instances, and traced information can be hard to disambiguate.
ProMo solution to this problem is based on the introduction of an intermediate layer (Figure 1), which enables the communication between the business and the IT layers through an intermediate model. Such a model formalizes the relationships between business models and information extracted at the IT layer and relies on the integrated representation of all the information collected about a process execution (the IT-trace).
To accomplish its goal, ProMo integrates a modeling component and a monitoring
component. At the business level, the modeling component provides MoKi-ProMo, a
customized version of the MediaWiki-based3 tool MoKi [
and monitoring editors that allow IT Experts (taking advantage of the Domain Experts
modeling) to specify, respectively, aggregation/monitoring rules and the relationships
between business models and the information extracted at IT level; and (ii) an editor
for defining interesting Key Performance Indicators (KPIs) to be monitored.
Specifically, the input required at the intermediate layer is provided by experts by using the
DomainObject language [
At run-time, whenever an IT-level event occurs, it is captured and handled by the
monitoring component. In detail, the event is managed by the monitoring engine, which,
based on the specification and rules defined at design-time, correlates and aggregates
events, produces new control events, monitors and maps the events to the
corresponding one(s) at the business layer and eventually produces the IT-trace. The information
in the IT-trace, which in many cases is only partial with respect to a complete
execution flow of a designed process model, is hence passed to a reasoning engine. Such
an engine, by taking advantage of the business knowledge, reconstructs missing
information by applying model-driven satisfiability rules [
Although different in their nature, static and dynamic knowledge about an organization domain are strictly related and should be jointly considered in order to obtain a comprehensive view of the organization processes. Importantly, reconciliation of these two dimensions should be done both at the business layer, allowing an explicit representation of the links between static and dynamic model elements (e.g., the fact that a process activity operates on a certain document), and at the data layer, allowing the collection, integration and comprehensive querying of static and procedural data.
At the business layer, ProMo solution is represented by the modeling component of MoKi-ProMo, which allows different experts (e.g., Business Designers, Knowledge Engineers and Domain Experts) to collaboratively model the different static and dynamic aspects describing the domain (see Figure 1). Specifically, MoKi-ProMo allows Domain Experts and Knowledge Engineers to collaboratively model the static aspects of the domain in form of OWL 2 ontologies. Concerning the dynamic aspects, MoKiProMo customizes the Oryx editor4 for the BPMN modeling of business processes by introducing symbol variations (e.g., special data objects for explicitly capturing data structures). Moreover, MoKi-ProMo also provides an interface allowing Business Analysts and Domain Experts to edit KPIs of interest, thus enabling them to access IT data from a (static and dynamic) business perspective.
At the IT layer, ProMo solution consists in exploiting a Domain ontology [