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Probabilistic relational models (PRMs) extend Bayesian networks (BNs) to a relational data mining context. Even though a panoply of works have focused, separately, on Bayesian networks and relational databases random generation, no work has been identified for PRMs on that track. This paper provides an algorithmic approach allowing to generate random PRMs from scratch to cover the absence of generation process. The proposed method allows to generate PRMs as well as synthetic relational data from a randomly generated relational schema and a random set of probabilistic dependencies. This can be of interest for machine learning researchers to evaluate their proposals in a common framework, as for databases designers to evaluate the effectiveness of the components of a database management system.

A Unified Recommendation Framework Based on Probabilistic Relational Models

Probabilistic relational models (PRMs) extend Bayesian networks to multi-relational domains and represent the dependencies between attributes within a table and across multiple tables. This paper presents a method of integrating and learning with concept hierarchies with PRMs, in order to retrieve richer object and relational information from multi-relational databases. A concept hierarchy defines a partially ordered sequence of mappings from a set of concepts to their higher-level correspondences. Natural concept hierarchies are often associated with some attributes in databases and can be used to discover knowledge. We first introduce concept hierarchies to PRMs by using background knowledge. A score-based search algorithm is then investigated for learning with concept hierarchies in PRMs parameter estimation procedure. The method can learn the most appropriate concepts from the data and use them to update the parameters. Experimental results on both real and synthetic data are discussed.KeywordsMinimum Description LengthConcept HierarchyStatistical Relational LearningProbabilistic Relational ModelAggregate RuleThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Most asset deterioration tools are designed for a specific application, as a consequence, a small change of the specification may result in a complete change of the tool. Inspired by the model-based approach of separating problem specification from analysis technique, we propose a model-based asset deterioration assessment framework using probabilistic relational models. The probabilistic relational models express abstract probabilistic dependency covers a range of deterioration modelling assumptions. An expert in the domain of asset deterioration can then use his knowledge of the factors that affect deterioration to customise the abstract models to a specific application, without requiring a detailed understanding the underlying computational framework. We illustrate the use of the framework with multiple variants of deterioration models.

There is a dearth of academic research literature on the practices and commitments of information security governance in organizations. Despite the existence of referential and standards of the security governance, the research literature remains limited regarding the practices of organizations and, on the other hand, the lack of a strategy and practical model to follow in adopting an effective information security governance. This chapter aims to explore the engagement processes and the practices of organizations involved in a strategy of information security governance via a statistical and econometric analysis of data from a survey of 1000 participants (with a participation rate of 83.67%) from large and medium companies belonging to various industries. Based on the results of the survey regarding practices of information security management and governance, a practical maturity framework for the information security governance and management in organizations is presented.

A Personalized Recommender System from Probabilistic Relational Model and Users’ Preferences

There is currently a large interest in relational probabilistic models. While the concept of context-specific independence (CSI) has been well-studied for models such as Bayesian networks, this is not the case for relational probabilistic models. In this paper we show that directed relational probabilistic models often exhibit CSI by identifying three different sources of CSI in such models (two of which are inherent to the relational character of the models).

Information as one of the important assets in an organization has a strategic value in particular conditions should fulfill confidentiality, integrity, availability, compliance, and reliability. In performing its functions, most organizations have a security threats, therefore it is necessary to implement the governance of information security to protect information that has strategic value. In managing information, information security aspects such as confidentiality, integrity and availability is a very important aspect to be considered to business process sustainability and to achieve the objectives of the organization. To ensure information security in an organization, information security governance could be one of the strategies actions that can be implemented. COBIT 5 provides a comprehensive framework that helps companies in achieving their goals for the governance and management of enterprise IT. COBIT 5 for Information Security is built based on COBIT 5 framework that focuses on information security and provides more detailed guidance and more practical for security information. This paper discusses how to design an information security governance using COBIT 5 for Information Security and Analytic Network Process to select the priority objectives of the organization.

This chapter focuses mainly to proactive means in information security and more specifically governance of information security in civil aviation. The reason is that, to find sustainable, coherent and holistic way to implement information security through the complete civil aviation ecosystem, the governance plays a key role when creating sufficient framework enabling information security by design environment. The study will help aviation and other critical infrastructure sectors to consider, understand and coordinate the information security governance. The study will test how to apply information security governance with ISO27014 through such a safety critical, interconnected infrastructure sector like civil aviation.KeywordsCybersecurity governanceInformation security governanceAviation cybersecurityAviation information security

In this paper, a probabilistic relational model is presented which combines relational algebra with probabilistic retrieval. Based on certain independence assumptions, the operators of the relational algebra are redefined such that the probabilistic algebra is a generalization of the standard relational algebra. Furthermore, a special join operator implementing probabilistic retrieval is proposed. When applied to typical document databases, queries can not only ask for documents, but for any kind of object in the database. In addition, an implicit ranking of these objects is provided in case the query relates to probabilistic indexing or uses the probabilistic join operator. The proposed algebra is intended as a standard interface to combined database and IR systems, as a basis for implementing user-friendly interfaces.

This paper presents a workflow for the design of transformation processes using different kinds of expert’s knowledge. It introduces POND (Process and observation ONtology Discovery), a workflow dedicated to answer expert’s questions about processes. It addresses two main issues: (1) how to represent the processes inner complexity, and (2) how to reason about processes taking into account uncertainty and causality. First, we show how to use a semantic model, an ontology, and its associated data to answer some of the expert’s questions concerning the processes, using semantic web languages and technologies. Then, we describe how to learn a predictive model, to discover new knowledge and provide explicative models by integrating the semantic model into a probabilistic relational model. The result is a complete workflow able to extensively analyze transformation processes through all their granularity levels and answer expert’s questions about their domains. An example of this workflow is given on biocomposites manufacturing for food packaging.

Information Security Management System (ISMS) implementation in Institution is an effort to minimize information security risks and threats such as information leakage, application damage, data loss and declining IT network performance. The several incidents related to information security have occurred in the implementation of the Academic System application in Indonesian higher education. This research was conducted to determine the maturity level of information security practices in Academic Information Systems at universities in Indonesia. The number of universities used as research samples were 35 institutions. Compliance with the application of ISO 27001:2013 standard is used as a reference to determine the maturity level of information system security practices. Meanwhile, to measure and calculate the level of maturity using the SSE-CMM model. In this research, the Information System Security Index obtained from the analysis results can be used as a tool to measure the maturity of information security that has been applied. There are six key areas examined in this study, namely the role and importance of ICT, information security governance, information security risk management, information security management framework, information asset management, and information security technology. The results showed the level of information security maturity at 35 universities was at level 2 Managed Process and level 3 Established Process. The composition is that 40% of universities are at level 3, and 60% are out of level 3. The value of the gap between the value of the current maturity level and the expected level of maturity is varied for each clause (domain). The smallest gap (1 level) is in clause A5: Information Security Policy, clause A9: Access Control, and clause A11: Physical and environmental security. The biggest gap (4 levels) is in clause A14: System acquisition, development and maintenance and clause A18: compliance.  Â

Understanding genome-wide protein-DNA interaction signals forms the basis for further focused studies in gene regulation. In particular, the chromatin immunoprecipitation with massively parallel DNA sequencing technology (ChIP-Seq) can enable us to measure the in vivo genome-wide occupancy of the DNA-binding protein of interest in a single run. Multiple ChIP-Seq runs thus inherent the potential for us to decipher the combinatorial occupancies of multiple DNA-binding proteins. To handle the genome-wide signal profiles from those multiple runs, we propose to integrate regularized regression functions (i.e., LASSO, Elastic Net, and Ridge Regression) into the well-established SignalRanker and FullSignalRanker frameworks, resulting in six additional probabilistic models for inference on multiple normalized genome-wide signal profiles. The corresponding model training algorithms are devised with computational complexity analysis. Comprehensive benchmarking is conducted to demonstrate and compare the performance of nine related probabilistic models on the ENCODE ChIP-Seq datasets. The results indicate that the regularized SignalRanker models, in contrast to the original SignalRanker models, can demonstrate excellent inference performance comparable to the FullSignalRanker models with low model complexities and time complexities. Such a feature is especially valuable in the context of the rapidly growing genome-wide signal profile data in the recent years.

Identity verification is essential in our mission to identify potential terrorists and criminals. It is not a trivial task because terrorists reportedly assume multiple identities using either fraudulent or legitimate means. A national identification card and biometrics technologies have been proposed as solutions to the identity problem. However, several studies show their inability to tackle the complex problem. We aim to develop data mining alternatives that can match identities referring to the same individual. Existing identity matching techniques based on data mining primarily rely on personal identity features. In this research, we propose a new identity matching technique that considers both personal identity features and social identity features. We define two groups of social identity features including social activities and social relations. The proposed technique is built upon a probabilistic relational model that utilizes a relational database structure to extract social identity features. Experiments show that the social activity features significantly improve the matching performance while the social relation features effectively reduce false positive and false negative decisions.

: Natural language (supplemented with diagrams and some mathematical notations) is convenient for succinct communication of technical descriptions between the various stakeholders (e.g., customers, designers, implementers) involved in the design of software systems. However, natural language descriptions can be informal, incomplete, imprecise and ambiguous, and cannot be processed easily by design and analysis tools. Formal languages, on the other hand, formulate design requirements in a precise and unambiguous mathematical notation, but are more difficult to master and use. We propose a methodology for connecting semi-formal requirements with formal descriptions through an intermediate representation. We have implemented this methodology in a research prototype called Automatic Requirements Specification Extraction from Natural Language (ARSENAL). The main novelty of ARSENAL lies in its ability to generate a fully-specified complete formal model automatically from natural language requirements. ARSENAL extracts relations from text using semantic parsing and progressively refines them over multiple stages to create a final composite model. Currently, ARSENAL generates formal models in linear-time temporal logic (LTL), but the approach can be adapted for other models, e.g., probabilistic relational models like Markov Logic Networks (MLN). The formal models of the requirements can be used to check important design and system properties, e.g., consistency, satisfiability, realizability. ARSENAL has a modular and flexible architecture that facilitates porting it from one domain to another. We evaluated ARSENAL on complex requirements from two real-world case studies: the Time-Triggered Ethernet (TTEthernet) communication platform used in space, and FAA-Isolette infant incubators used in NICU. We systematically evaluated various aspects of ARSENAL -- the accuracy of the natural language processing stage, the degree of automation, and robustness to noise.