Leveraging Anomaly Detection in Business Process with Data Stream Mining

Identifying fraudulent or anomalous business procedures is today a key challenge for organisations of any dimension. Nonetheless, the continuous nature of business activities conveys to the continuous acquisition of data in support of business process monitoring. In light of this, we propose a method for online anomaly detection in business processes. From a stream of events, our approach extract cases descriptors and applies a density-based clustering technique to detect outliers. We applied our method to a real-life dataset, and we used streaming clustering measures for evaluating performances. Exploring different combinations of parameters, we obtained promising performance metrics, showing that our method is capable of finding anomalous process instances in a vast complexity of scenarios

S.O.B (Save Our Budget) - A Simulation-Based Method for Prediction of Acquisition Costs of Constituents of a System-of-Systems

Software economics, acquisition, and pricing are important concerns for Systems-of-Systems (SoS). SoS are alliances of independent software-intensive systems combined to offer holistic functionalities as a result of the constituents interoperability. SoS engineering involves separately acquiring constituents and combining them to form the SoS. Despite the existence of cost prediction techniques, predicting SoS acquisition costs at design-time should also include the analysis of different suppliers of constituents, their respective prices and quality. However, known methods cover only two out of these three parameters.  The main contribution of this article is to present the S.O.B. (Save Our Budget) method, a novel simulation-based method to predict, at design-time, the acquisition cost of constituents, while still considering quality attributes and different suppliers. Results of a case study in the Smart Building domain revealed that S.O.B. method supports a precise prediction of acquisition cost of constituents to build a SoS for that domain. Furthermore, it also contributes to estimate the cost based on a pre-established quality attribute (functional suitability), as well as to support the selection of coalition that exhibits better results through the analysis of cost-benefit ratio.Software economics, acquisition, and pricing are important concerns for Systems-of-Systems (SoS). SoS are alliances of independent software-intensive systems combined to offer holistic functionalities as a result of the constituents interoperability. SoS engineering involves separately acquiring constituents and combining them to form the SoS. Despite the existence of cost prediction techniques, predicting SoS acquisition costs at design-time should also include the analysis of different suppliers of constituents, their respective prices and quality. However, known methods cover only two out of these three parameters. The main contribution of this article is to present the S.O.B. (Save Our Budget) method, a novel simulation-based method to predict, at design-time, the acquisition cost of constituents, while still considering quality attributes and different suppliers. Results of a case study in the Smart Building domain revealed that S.O.B. method supports a precise prediction of acquisition cost of constituents to build a SoS for that domain. Furthermore, it also contributes to estimate the cost based on a pre-established quality attribute (functional suitability), as well as to support the selection of coalition that exhibits better results through the analysis of cost-benefit ratio