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 outcomes 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.

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.

The business process (BP) resource perspective deals with the management of human as well as non-human resources throughout the process lifecycle. Although it has received increasing attention recently, there exists no graphical notation for it up until now that is both expressive enough to cover well-known resource selection conditions and independent of any BP modelling language. In this paper, we introduce RALph, a graphical notation for the assignment of human resources to BP activities. We define its semantics by mapping this notation to a language that has been formally defined in description logics, which enables its automated analysis. Although we show how RALph can be seamlessly integrated with BPMN, it is noteworthy that the notation is independent of the BP modelling language. Altogether, RALph will foster the visual modelling of the resource perspective in BPs.