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

A Privacy Risk Model for Trajectory Data

Part 2: Full PapersInternational audienceTime sequence data relating to users, such as medical histories and mobility data, are good candidates for data mining, but often contain highly sensitive information. Different methods in privacy-preserving data publishing are utilised to release such private data so that individual records in the released data cannot be re-linked to specific users with a high degree of certainty. These methods provide theoretical worst-case privacy risks as measures of the privacy protection that they offer. However, often with many real-world data the worst-case scenario is too pessimistic and does not provide a realistic view of the privacy risks: the real probability of re-identification is often much lower than the theoretical worst-case risk. In this paper we propose a novel empirical risk model for privacy which, in relation to the cost of privacy attacks, demonstrates better the practical risks associated with a privacy preserving data release. We show detailed evaluation of the proposed risk model by using k-anonymised real-world mobility data

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

Towards transparency-encouraging partial software disclosure to enable trust in data usage

Whenever software components process personal or private data, appropriate data protection mechanisms are mandatory. An essential factor in achieving trust and transparency is not to give preference to a single party but to make it possible to audit the data usage in an unbiased way. The scenario in mind for this contribution contains (i) users bringing in sensitive data they want to be safe, (ii) developers building software-based services whose Intellectual Properties (IPs) they desire to protect, and (iii) platform providers wanting to be trusted and to be able to rely on the developers integrity. The authors see these interests as an insufficiently solved field of tension that can be relaxed by a suitable level of transparently represented software components to give insights without exposing every detail

Impact of ICT on Home Healthcare

Innovation in information and communication technology has a great potential to create large impact on modern healthcare. However, for the new technologies to be adopted, the innovations have to be meaningful and timely, taking into account user needs and addressing societal and ethical concerns. In this paper, we focus on ICT innovations related to home healthcare domain, in which patient safety and security, but also trust and privacy are of utmost importance. To ensure the adoption of new healthcare services, the new innovative technologies need to be complemented with new methods that can help patients to establish trust in healthcare service providers in terms of privacy, reliability, integrity of the data chain and techniques that help service providers to assess the reliability of information and data contributed by patients. This paper sketches various lines of research for the development of trusted healthcare services namely, patient compliance, reliability of information in healthcare, and user-friendly access control

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