Subgraph Mining for Anomalous Pattern Discovery in Event Logs

Conformance checking allows organizations to verify whether their IT system complies with the prescribed behavior by comparing process executions recorded by the IT system against a process model (representing the normative behavior). However, most of the existing techniques are only able to identify low-level deviations, which provide a scarce support to investigate what actually happened when a process execution deviates from the specification. In this work, we introduce an approach to extract recurrent deviations from historical logging data and generate anomalous patterns representing high-level deviations. These patterns provide analysts with a valuable aid for investigating nonconforming behaviors; moreover, they can be exploited to detect high-level deviations during conformance checking. To identify anomalous behaviors from historical logging data, we apply frequent subgraph mining techniques together with an ad-hoc conformance checking technique. Anomalous patterns are then derived by applying frequent items algorithms to determine highly-correlated deviations, among which ordering relations are inferred. The approach has been validated by means of a set of experiments

Measuring privacy compliance using fitness metrics

Nowadays, repurposing of personal data is a major privacy issue. Detection of data repurposing requires posteriori mechanisms able to determine how data have been processed. However, current a posteriori solutions for privacy compliance are often manual, leading infringements to remain undetected. In this paper, we propose a privacy compliance technique for detecting privacy infringements and measuring their severity. The approach quantifies infringements by considering a number of deviations from specifications (i.e., insertion, suppression, replacement, and re-ordering)

Measuring privacy compliance using fitness metrics

Nowadays, repurposing of personal data is a major privacy issue. Detection of data repurposing requires posteriori mechanisms able to determine how data have been processed. However, current a posteriori solutions for privacy compliance are often manual, leading infringements to remain undetected. In this paper, we propose a privacy compliance technique for detecting privacy infringements and measuring their severity. The approach quantifies infringements by considering a number of deviations from specifications (i.e., insertion, suppression, replacement, and re-ordering)

Subgraph mining for anomalous pattern discovery in event logs

Conformance checking allows organizations to verify whether their IT system complies with the prescribed behavior by comparing process executions recorded by the IT system against a process model (representing the normative behavior). However, most of the existing techniques are only able to identify low-level deviations, which provide a scarce support to investigate what actually happened when a process execution deviates from the specification. In this work, we introduce an approach to extract recurrent deviations from historical logging data and generate anomalous patterns representing high-level deviations. These patterns provide analysts with a valuable aid for investigating nonconforming behaviors; moreover, they can be exploited to detect high-level deviations during conformance checking. To identify anomalous behaviors from historical logging data, we apply frequent subgraph mining techniques together with an ad-hoc conformance checking technique. Anomalous patterns are then derived by applying frequent items algorithms to determine highly-correlated deviations, among which ordering relations are inferred. The approach has been validated by means of a set of experiments

Safety and efficacy of prothrombin complex concentrate as first-line treatment in bleeding after cardiac surgery

BACKGROUND: Bleeding after cardiac surgery requiring surgical reexploration and blood component transfusion is associated with increased morbidity and mortality. Although prothrombin complex concentrate (PCC) has been used satisfactorily in bleeding disorders, studies on its efficacy and safety after cardiopulmonary bypass are limited. METHODS: Between January 2005 and December 2013, 3454 consecutive cardiac surgery patients were included in an observational study aimed at investigating the efficacy and safety of PCC as first-line coagulopathy treatment as a replacement for fresh frozen plasma (FFP). Starting in January 2012, PCC was introduced as solely first-line treatment for bleeding following cardiac surgery. RESULTS: After one-to-one propensity score-matched analysis, 225 pairs of patients receiving PCC (median dose 1500 IU) and FFP (median dose 2 U) were included. The use of PCC was associated with significantly decreased 24-h post-operative blood loss (836 ± 1226 vs. 935 ± 583 ml, p < 0.0001). Propensity score-adjusted multivariate analysis showed that PCC was associated with significantly lower risk of red blood cell (RBC) transfusions (odds ratio [OR] 0.50; 95 % confidence interval [CI] 0.31-0.80), decreased amount of RBC units (β unstandardised coefficient -1.42, 95 % CI -2.06 to -0.77) and decreased risk of transfusion of more than 2 RBC units (OR 0.53, 95 % CI 0.38-0.73). Patients receiving PCC had an increased risk of post-operative acute kidney injury (AKI) (OR 1.44, 95 % CI 1.02-2.05) and renal replacement therapy (OR 3.35, 95 % CI 1.13-9.90). Hospital mortality was unaffected by PCC (OR 1.51, 95 % CI 0.84-2.72). CONCLUSIONS: In the cardiac surgery setting, the use of PCC compared with FFP was associated with decreased post-operative blood loss and RBC transfusion requirements. However, PCC administration may be associated with a higher risk of post-operative AKI

PERSONA : a personalized data protection framework

The European Directive on Data Protection recognizes the right of data subjects to control the usage of their information. However, to date there are no data protection solutions that involve data subjects in the definition and enforcement of data protection policies. In this paper we present the foundation of a novel approach to personalized data protection in which users play a central role in the authoring and enforcement of the policies governing the access and usage to their data. We discuss the challenges of designing a personalized data protection framework using personalized medicine as an illustrative scenario

From security-by-design to the identification of security-critical deviations in process executions

\u3cp\u3eSecurity-by-design is an emerging paradigm that aims to deal with security concerns from the early phases of the system development. Although this paradigm can provide theoretical guarantees that the designed system complies with the defined processes and security policies, in many application domains users are allowed to deviate from them to face unpredictable situations and emergencies. Some deviations can be harmless and, in some cases, necessary to ensure business continuity, whereas other deviations might threat central aspects of the system, such as its security. In this paper, we propose a tool supported method for the identification of security-critical deviations in process executions using compliance checking analysis. We implemented the approach as part of the STS-Tool and evaluated it using a real loan management process of a Dutch financial institute.\u3c/p\u3