Navegación

Búsqueda

Búsqueda avanzada

A Hybrid Reliability Metric for SLA Predictive Monitoring

Modern SLA management includes SLA prediction based on data collected during service operations. Besides overall accuracy of a prediction model, decision makers should be able to measure the reliability of individual predictions before taking important decisions, such as whether to renegotiate an SLA. Measures of reliability of individual predictions provided by machine learning techniques tend to depend strictly on the technique chosen and to neglect the features of the system generating the data used to learn a model, i.e., the service provisioning landscape in this case. In this paper, we define a hybrid measure of reliability of an individual SLA prediction for classification models, which accounts for both the reliability of the chosen prediction technique, if available, and features capturing the variability of the service provisioning scenario. The metric is evaluated empirically using SLAs and event logs of a real world case.

This paper was presented in ACM Symposium on Applied Computing (SAC) in April 2019 (GGS Class 2).

Tactical Business-Process-Decision Support based on KPIs Monitoring and Validation

Key Performance Indicators (KPIs) can be used to evaluate the success of an organization, facilitating the detection of the deviations and unexpected evolution of the behaviour of a company. The difficulty for enterprises is to ascertain what to do when a deviation is detected. In this paper, we propose a modelling approach to improve the operational business-level and to ascertain the possible actions that can be executed to maintain the right direction in a company. For business process-oriented companies, it entails knowing how KPIs can be affected by the business processes. It implies not only pointing out that a system malfunction exists, but also to know what to do when a deviation is detected. Our proposal presents a methodology that covers: (1) an extension of the existing models in order to combine KPIs, goals of the companies, and the decision variables together with business processes; (2) a methodology based on data mining analysis to verify the correctness of the enriched proposed model according to the data stored during business evolution, and; (3) a framework to simulate the evolution of the business according to the decisions taken in the governance process, thereby supporting governance activities to achieve the defined objectives by exploiting goals and KPIs from the proposed model.

Towards a general architecture for predictive monitoring of business processes

Process mining allows the extraction of useful information from event logs and historical data of business processes. This information will improve the performance of these processes and is generally obtained after they have finished. Therefore, predictive monitoring of business process running instances is needed, in order to provide proactive and corrective actions to improve the process performance and mitigate the possible risks in real time. This monitoring allows the prediction of evaluation metrics for a runtime process. In this context, this work describes a general methodology for a business process monitoring system for the prediction of process performance indicators and their stages, such as, the processing and encoding of log events, the calculation of aggregated attributes or the application of a data mining algorithm.

A Family of Case Studies on Business Process Mining

Business processes, most of which are automated by information systems, have become a key asset in organizations. Unfortunately, uncontrolled maintenance implies that information systems age over time until they need to be modernized. During software modernization, ageing systems cannot be entirely discarded because they gradually embed meaningful business knowledge, which is not present in any other artifact. This paper presents a technique for recovering business processes from legacy systems in order to preserve that knowledge. The technique statically analyzes source code and generates a code model, which is later transformed by pattern matching into a business process model. This technique has been validated over a two year period in several industrial modernization projects. This paper reports the results of a family of case studies that were performed to empirically validate the technique using analysis and meta-analysis techniques. The study demonstrates the effectiveness and efficiency of the technique.

Timed Automata and Model Checking to Verify Business Processes

Currently, the use of Software Engineering methods has been shown to be useful in improving business modelling techniques as a result of their application to Business Process (BP)­modelling initiatives. Nowadays when the business process needs to be supported by Information Technology, the Unified Modeling Language (UML) is widely used for modelling. This paper presents the use of Timed Automata (TA) and Model Checking (MC) by their application to an example of a BP enterprise­project related to the Customer Relationship Management (CRM) business. The Uppaal tool is used in this work. With this proposal, the analysts and designers are supported in the development of the BP­task model associated with a BP design by using UML as the main modelling language. The application of the proposal is aimed at ensuring the correctness of the BP­task model with respect to the initial property specification derived from the business rules.