Events happening at defined moments in time and involving specific entities from a social or physical system can be organized in networks or graphs. The study of such event graphs may reveal causal relations between subsequent events or compound events that we define as “typed events”. Moreover, characteristic sequences of events or patterns can arise in consequence of phenomena affecting the system. Methods to build the event graph and to search for the typed events and their significance are described in detail. An embedding strategy to encode typed events in low dimensional vectors is defined, and both supervised and unsupervised learning is applied to search for meaningful patterns. Experiments have been conducted using data from a real investigation and some synthetic data.

Assuring anomaly-free business process executions is a key challenge for many organizations. Traditional techniques address this challenge using prior knowledge about anomalous cases that is seldom available in real-life. In this work, we propose the usage of word2vec encoding and One-Class Classification algorithms to detect anomalies by relying on normal behavior only. We investigated 6 different types of anomalies over 38 real and synthetics event logs, comparing the predictive performance of Support Vector Machine, One-Class Support Vector Machine, and Local Outlier Factor. Results show that our technique is viable for real-life scenarios, overcoming traditional machine learning for a wide variety of settings where only the normal behavior can be labeled.

The advent of Big Data has revolutionized the way in which data are collected, analyzed, and processed, becoming a pre-requisite for each enterprise that competes in the global market. In this respect, the commodization of Big Data analytics is an essential goal to be faced in the near future. Recently, some preliminary approaches have been presented mostly focusing on distributing Big Data platforms as a service, while less has been done on cross-platform Big Data analytics. In this paper, we propose a model-based methodology for Big Data Analytics-as-a-Service that extends existing techniques by supporting cross-communication between batch and stream processing, deployment on multiple platforms, and end-to-end verification against users' requirements.

Big Data domain is one of the most promising ICT sectors with substantial expectations both on the side of market growing and design shift in the area of data storage managment and analytics. However, today, the level of complexity achieved and the lack of standardisation of Big Data management architectures represent a huge barrier towards the adoption and execution of analytics especially for those organizations and SMEs not including a sufficient amount of competences and knowledge. The full potential of Big Data Analytics (BDA) can be unleashed only through the definition of approaches that accomplish Big Data users' expectations and requirements, also when the latter are fuzzy and ambiguous. Under these premises, we propose Big Data Analytics-as-a-Service (BDAaaS) as the next-generation Big Data Analytics paradigm and we discuss issues and challenges from the BDAaaS design and development perspective.

Organizational risk management should not only rely on protecting data and information but also on protecting knowledge which is underdeveloped in many cases or measures are applied in an uncoordinated, dispersed way. Therefore, we propose a consistent top-down translation from the organizational risk management goals to implemented controls to overcome these shortcomings. Our approach adopted from the domain of IT security management allows to measure how well knowledge protection is actually pursued in organizations. This affects organizations' abilities to prove compliance to risk management standards, laws, guidelines, or frameworks and creates transparency throughout the whole knowledge protection processes. After introducing our integrated risk management framework, we demonstrate how the technical part of the framework can be implemented by using process mining in a case study of an Italian aerospace company.

We present a way of building ontologies that proceeds in a bottom-up fashion, defining concepts as clusters of concrete XML objects. Our rough bottom-up ontologies are based on simple relations like association and inheritance, as well as on value restrictions, and can be used to enrich and update existing upper ontologies. Then, we show how automatically generated assertions based on our bottom-up ontologies can be associated with a flexible degree of trust by nonintrusively collecting user feedback in the form of implicit and explicit votes. Dynamic trust-based views on assertions automatically filter out imprecisions and substantially improve metadata quality in the long run