Organizations increasingly rely on Process Performance Indicators (PPIs) to evaluate how effectively their business processes achieve strategic and operational goals. Recent approaches compute PPIs directly from event logs leveraging the increasing availability of event data in organizations, but they assume that PPIs have been previously defined using well-established reference models and that their linkage to the event log has been explicitly specified. However, this definition of appropriate PPIs remains challenging, as managers often lack detailed insights into real process executions and must adapt generic frameworks to organizational contexts. To address this gap, this paper introduces PPIPilot, an approach and tool that supports managers in defining measurable PPIs by suggesting a set of relevant PPIs and their computations directly from a business process description, an associated event log, and a specified organizational goal. PPIPilot leverages large language models for contextual PPI suggestion and structured translation, operating through a three-component pipeline of suggestion, translation, and computation, with a fallback mechanism to address computation errors. The quantitative evaluation shows that PPIPilot has a strong ability to suggest and compute PPIs, and that the fallback mechanism reduces failures, increasing the share of PPIs that can be computed without errors. Complementing these findings, an in-depth qualitative study indicates that users find the tool intuitive, transparent and helpful for easing both the conceptual and technical challenges of defining PPIs.

Abstract Knowledge-intensive processes represent a particularly challenging scenario for process mining. The flexibility that such processes allow constitutes a hurdle as they are hard to capture in a single model. To tackle this problem, multiple visual representations of the same processes could be beneficial, each addressing different information dimensions according to the specific needs and background knowledge of the concrete process workers and stakeholders. In this paper, we propose, describe, and evaluate a framework, named , that leverages visual analytics for the interactive visualization of multi-faceted process information, aimed at supporting the investigation and insight generation of users in their process analysis tasks. is based on a multi-layer visualization methodology that includes a visual backdrop that provides context and an arbitrary number of superimposed and on-demand dimension layers. This arrangement allows our framework to display process information from different perspectives and to project this information onto a domain-friendly representation of the context in which the process unfolds. We provide an in-depth description of the approach’s founding principles, deeply rooted in visualization research, that justify our design choices for the whole framework. We demonstrate the feasibility of the framework through its application in two use-case scenarios in the context of healthcare and personal information management. Plus, we conducted qualitative evaluations with potential end users of both scenarios, gathering precious insights about the efficacy and applicability of our framework to various application domains.

Business process compliance ensures that the business processes of an organisation are designed and executed according to the rules that enforce the compliance controls that govern the company. We faced the challenge of building a Business Process Compliance Management System (BPCMS) for a process-aware organisation that had to provide support for several needs that, despite having been identified in the literature, were only partially satisfied by existing approaches. The variability in the types of rules and their interpretation generally restricts the existing support for compliance checking to specific types of rules (e.g., rules affecting the control flow of the process), a specific phase of the business process management (BPM) lifecycle (e.g., design time or run time), or certain information systems (ISs) for data retrieval (e.g., process event logs). Motivated by this, we designed a conceptual framework for design-time and run-time compliance checking that relies on the use ofmashupsfor rule specification and checking. It presents the following advantages: (i) an open-ended set of types rules can be specified by designing and connecting mashup components; (ii) (parts of) the definitions of the rules can be reused as needed; and (iii) the mashup-based compliance checking (MCC) system can be integrated with ISs of the organisation, enabling the verification of actual facts on actions performed during the execution of a process (e.g., the existence of a specific document in a concrete location). Design-time and run-time implementations of the framework were conducted and tested in a real setting.

A Service Level Agreement (SLA) regulates the provisioning of a service by defining a set of guarantees. Each guarantee sets a Service Level Objective (SLO) on some service metrics, and optionally a compensation that is applied when the SLO is unfulfilled or overfulfilled. Currently, there are software tools and research proposals that use the information about compensations to automate and optimise certain parts of the service management. However, they assume that compensations are well defined, which is too optimistic in some circumstances and can lead to undesirable situations. In this article we discuss about the notion of validity of guarantees with a compensation, which we refer to as compensable guarantees (CG). We describe an abstract model of CGs and we provide a technique that leverages constraint satisfaction problem solvers to automatically validate them. We also present a materialisation of the model of CGs in iAgree, a language to specify SLAs and a tooling support that implements our whole approach. An assessment over 319 CGs taken from 24 real-world SLAs suggests that the expressiveness and effectiveness of our proposal can pave the way for using CGs in a safer and more reliable way.

Knowledge-intensive Processes (KIPs) are processes whose execution is heavily dependent on knowledge workers performing various interconnected knowledge-intensive decision-making tasks. Among other characteristics, KIPs are usually non-repeatable, collaboration-oriented, unpredictable, and, in many cases, driven by implicit knowledge, derived from the capabilities and previous experiences of participants. Despite the growing body of research focused on understanding KIPs and on proposing systems to support these KIPs, the research question on how to define performance measures thereon remains open. In this article, we address this issue with a proposal to enable the performance management of KIPs. Our approach comprises an ontology that allows us to define process performance indicators (PPIs) in the context of KIPs, and a methodology that builds on the ontology and the concepts of lead and lag indicators to provide process participants with actionable guidelines that help them conduct the KIP in a way that fulfills a set of performance goals. Both the ontology and the methodology have been applied to a case study of a real organization in Brazil to manage the performance of an Incident Troubleshooting Process within an ICT (Information and Communications Technology) Outsourcing Company.

Nowadays, process mining is becoming a growing area of interest in business process management (BPM). Process mining consists in the extraction of information from the event logs of a business process. From this information, we can discover process models, monitor and improve our processes. One of the applications of process mining, is the predictive monitoring of business process. The aim of these techniques is the prediction of quantifiable metrics of a running process instance with the generation of predictive models. The most representative approaches for the runtime prediction of business process are summarized in this paper. The different types of computational predictive methods, such as statistical techniques or machine learning approaches, and certain aspects as the type of predicted values and quality evaluation metrics, have been considered for the categorization of these methods. This paper also includes a summary of the basic concepts, as well as a global overview of the process predictive monitoring area, that can be used to support future efforts of researchers and practitioners in this research field.

Nowadays, service trading over the Web is gaining momentum. In this highly dynamic scenario, both providers and consumers need to formalize their contractual and legal relationship, creating service level agreements. Although there exist some proposals that provide models to describe that relationship, they usually only cover technical aspects, not providing explicit semantics to the agreement terms. Furthermore, these models cannot be effectively shared on the Web, since they do not actually follow Web principles. These drawbacks hamper take-up and automatic analysis. In this article, we introduce Linked USDL Agreement, a semantic model to specify, manage and share service level agreement descriptions on the Web. This model is part of the Linked USDL family of ontologies that can describe not only technical but also business related aspects of services, incorporating Web principles. We validate our proposal by describing agreements in computational and non-computational scenarios, namely cloud computing and business process outsourcing services. Moreover, we evaluate the actual coverage and expressiveness of Linked USDL Agreement comparing it with existing models. In order to foster its adoption and effectively manage the service level agreement lifecycle, we present an implemented tool that supports creation, automatic analysis, and publication on the Web of agreement descriptions.

As the use of services available on the Web is becoming mainstream, contracts and legal aspects of the relationship between providers and consumers need to be formalized. However, current proposals to model service level agreements are mostly focused on technical aspects, do not explicitly provide semantics to agreement terms, and do not follow Web principles. These limitations prevent take-up, automatic processing, and effective sharing of agreements. Linked USDL Agreement is a Linked Data based semantic model to describe and share service agreements that extends Linked USDL, which offers a family of languages to describe various technical and business aspects of services. We followed a use case driven approach, evaluating the applicability of our proposal in a cloud computing scenario, and comparing its expressiveness with existing models. Finally, we show a concrete tool that helps to model and check the validity of agreements.

Plenty of activities in many business contexts must be performed collaboratively, e.g., in a hospital or when organising a conference. Tasks such as team composition and allocation are usually performed manually and on the ground of limited criteria such as individual skills, a.o. because adequate automatic support is missing. This paper addresses this shortcoming. We present an approach for team selection and compliance checking in business processes, which includes (i) a language for describing teams; (ii) a way to define team selection conditions and policies related to team composition; and (iii) a mechanism for the automatic resolution of the team selection conditions and for team-related compliance checking based on formal ontologies.

Service Level Agreements (SLAs) establish the Quality of Service (QoS) agreed between service-based systems consumers and providers. Since the violation of such SLAs may involve penalties, quality assurance techniques have been developed to supervise the SLAs fulfillment at runtime. However, existing proposals present some drawbacks: 1) the SLAs they support are not expressive enough to model real-world scenarios, 2) they couple the monitoring configuration to a given SLA specification, 3) the explanations of the violations are difficult to understand and even potentially inaccurate, 4) some proposals either do not provide an architecture, or present low cohesion within their elements. In this paper, we propose a comprehensive solution, from a conceptual reference model to its design and implementation, that overcomes these drawbacks. The resulting platform, SALMonADA, receives the SLA agreed between the parties as input and reports timely and comprehensive explanations of SLA violations. SALMonADA performs an automated monitoring configuration and it analyses highly expressive SLAs by means of a constraint satisfaction problems based technique. We have evaluated the impact of SALMonADA over the resulting service consumption time performance. The results are satisfactory enough to consider SALMonADA for SLA supervision because of its low intrusiveness.

WS-Agreement is one of the most widely used SLA specifications. An advantage of WS-Agreement over other agreement metamodels is that it allows one to define conditional and optional term sets inside an agreement document, which are commonly found features in real-world agreements. Unfortunately, they increase the complexity of the automated detection and explanation of conflicts between SLA terms, leading to new kinds of conflicts that are not supported by current techniques. Furthermore, creating a general-purpose conflict analyser in WS-Agreement is a hard task since it should understand the semantics of an unbounded number of languages that can be used in the eight extension points that WS-Agreement includes for the sake of flexibility. In this article, we address these issues by providing a conflict classification for SLAs that includes new conflicts derived from the use of conditional and optional term sets; and a novel language-agnostic technique based on constraint satisfaction problems to automatically detect and explain these conflicts. In pursuing these results, we defined some WS-Agreement concepts as well as a fully-fledged WS-Agreement-compliant language. The developed technique and its reference implementation have been thoroughly validated.

Autonomic computing (AC), self-management based on high level guidance from humans, is increasingly gaining momentum as the way forward in designing reliable systems that hide complexity and conquer IT management costs. Effectively, AC may be viewed as policy-based self-management. The model driven architecture (MDA) approach focuses on building models that can be transformed into code in an automatic manner. In this paper, we look at ways to implement policy-based self-management by means of models that can be converted to code using transformations that follow the MDA philosophy. We propose a set of UML-based models to specify autonomic and autonomous features along with the necessary procedures, based on modification and composition of models, to deploy a policy as an executing system