Optimization of business processes is a persistent topic and a key goal in business process management (BPM). In this work, we investigate how a given resource allocation in business processes can drive optimizations in an underlying BPMN diagram. More specifically, the main contribution is a proposal to leverage a variant of representation of processes as Refined Process Structure Trees (RPSTs) with a view to enabling novel resource allocation-driven task re-ordering in a principled manner. The re-orderings targeted enforce the parallelism redesign heuristic that allows for multiple resources operating concurrently, which yields improvements in the process cycle time.

Modern techniques in intrusion and DoS (Denial of Service) detection tend to be either supervised or semi-supervised, i.e., they require training and labelled data. In this work, we study the problem of correlating security attacks with anomalies reported at runtime by a fully unsupervised outlier detection module, i.e., a component that does not require any training at all. Through a concrete proof-of-concept case study, we demonstrate that unsupervised anomaly detection is both efficient and effective, but still, it needs to be combined with additional mechanisms to yield a complete intrusion detection and prevention solution.

Health sector is becoming more and more dependent on digital information every day. This fact can be exploited by cyber criminals who may obtain very lucrative benefits from stolen data. Moreover, a breach of integrity of health data can have terrible consequences for the patients. CUREX project aims to protect the confidentiality of health data and to maintain its integrity by producing a novel, flexible and scalable situational awareness-oriented platform. CUREX has been conceived as GDPR compliant by design. This design has been thought as a decentralised architecture enhanced with a private blockchain infrastructure. Thus, it ensures the integrity of the risk assessment process and of all data transactions.

Spark has become one of the main options for large-scale analytics running on top of shared-nothing clusters. This work aims to make a deep dive into the parallelism configuration and shed light on the behavior of parallel spark jobs. It is motivated by the fact that running a Spark application on all the available processors does not necessarily imply lower running time, while may entail waste of resources. We first propose analytical models for expressing the running time as a function of the number of machines employed. We then take another step, namely to present novel algorithms for configuring dynamic partitioning with a view to minimizing resource consumption without sacrificing running time beyond a user-defined limit. The problem we target is NP-hard. To tackle it, we propose a greedy approach after introducing the notions of dependency graphs and of the benefit from modifying the degree of partitioning at a stage; complementarily, we investigate a randomized approach. Our polynomial solutions are capable of judiciously use the resources that are potentially at user's disposal and strike interesting trade-offs between running time and resource consumption. Their efficiency is thoroughly investigated through experiments based on real execution data.

Modern data analysis is increasingly employing data-intensive flows for processing very large volumes of data. As the data flows become more and more complex and operate in a highly dynamic environment, we argue that we need to resort to automated cost-based optimization solutions rather than relying on efficient designs by human experts. We further demonstrate that the current state-of-the-art in flow optimizations needs to be extended and we propose a promising direction for optimizing flows at the logical level, and more specifically, for deciding the sequence of flow tasks.