An Automorphic Distance Metric and Its Application to Node Embedding for Role Mining

Role is a fundamental concept in the analysis of the behavior and function of interacting entities in complex networks. Role discovery is the task of uncovering the hidden roles of nodes within a network. Node roles are commonly defined in terms of equivalence classes. Two nodes have the same role if they fall within the same equivalence class. Automorphic equivalence, where two nodes are equivalent when they can swap their labels to form an isomorphic graph, captures this notion of role. The binary concept of equivalence is too restrictive, and nodes in real-world networks rarely belong to the same equivalence class. Instead, a relaxed definition in terms of similarity or distance is commonly used to compute the degree to which two nodes are equivalent. In this paper, we propose a novel distance metric called automorphic distance, which measures how far two nodes are from being automorphically equivalent. We also study its application to node embedding, showing how our metric can be used to generate role-preserving vector representations of nodes. Our experiments confirm that the proposed automorphic distance metric outperforms a state-of-the-art automorphic equivalence-based metric and different state-of-the-art techniques for the generation of node embeddings in different role-related tasks.

Agent-Based Semantic Role Mining for Intelligent Access Control in Multi-Domain Collaborative Applications of Smart Cities

Significance and popularity of Role-Based Access Control (RBAC) is inevitable; however, its application is highly challenging in multi-domain collaborative smart city environments. The reason is its limitations in adapting the dynamically changing information of users, tasks, access policies and resources in such applications. It also does not incorporate semantically meaningful business roles, which could have a diverse impact upon access decisions in such multi-domain collaborative business environments. We propose an Intelligent Role-based Access Control (I-RBAC) model that uses intelligent software agents for achieving intelligent access control in such highly dynamic multi-domain environments. The novelty of this model lies in using a core I-RBAC ontology that is developed using real-world semantic business roles as occupational roles provided by Standard Occupational Classification (SOC), USA. It contains around 1400 business roles, from nearly all domains, along with their detailed task descriptions as well as hierarchical relationships among them. The semantic role mining process is performed through intelligent agents that use word embedding and a bidirectional LSTM deep neural network for automated population of organizational ontology from its unstructured text policy and, subsequently, matching this ontology with core I-RBAC ontology to extract unified business roles. The experimentation was performed on a large number of collaboration case scenarios of five multi-domain organizations and promising results were obtained regarding the accuracy of automatically derived RDF triples (Subject, Predicate, Object) from organizational text policies as well as the accuracy of extracted semantically meaningful roles.

Mining and Europe’s World Heritage Cultural Landscapes

This study examines the four cases of World Heritage protected cultural landscapes in Europe that are characterized by mining in order to identify the role mining plays today in such cultural landscapes, the legal requirements for their protection, and also the exploration and exploitation in these areas and the differences that exist between the five European countries concerned. Using a qualitative comparative case study approach, the authors find that active mining is taking place in the Austrian case, and exploration is happening adjacent to the German/Czech protected cultural landscape. The legal protection of the cases is mainly based on heritage and monument protection legislation as well as environment protection legislation including the Natura 2000 network. Differences exist, as other than in Germany, exploration and mining could be allowed in protected areas, which is also contrary to the position of the United Nations Educational, Scientific, and Cultural Organization, and the International Council on Mining and Metals.

Precursors of Role-Based Access Control Design in KMS: A Conceptual Framework

Role-based access control (RBAC) continues to gain popularity in the management of authorization concerning access to knowledge assets in organizations. As a socio-technical concept, the notion of role in RBAC has been overemphasized, while very little attention is given to the precursors: role strain, role ambiguity, and role conflict. These constructs provide more significant insights into RBAC design in Knowledge Management Systems (KMS). KMS is the technology-based knowledge management tool used to acquire, store, share, and apply knowledge for improved collaboration and knowledge-value creation. In this paper, we propose eight propositions that require future research concerning the RBAC system for knowledge security. In addition, we propose a model that integrates these precursors and RBAC to deepen the understanding of these constructs. Further, we examine these precursory constructs in a socio-technical fashion relative to RBAC in the organizational context and the status–role relationship effects. We carried out conceptual analysis and synthesis of the relevant literature, and present a model that involves the three essential precursors that play crucial roles in role mining and engineering in RBAC design. Using an illustrative case study of two companies where 63 IT professionals participated in the study, the study established that the precursors positively and significantly increase the intractability of the RBAC system design. Our framework draws attention to both the management of organizations and RBAC system developers about the need to consider and analyze the precursors thoroughly before initiating the processes of policy engineering, role mining, and role engineering. The propositions stated in this study are important considerations for future work.

An Approach of Role Updating in Context-Aware Role Mining

With the rapid development of Internet of Things (IoT) and mobile technologies, the service offerings available in the IoT and mobile environments are increasing dramatically. How to provide intelligent and personalized services for users becomes a challenging issue. Several context aware service recommendation approaches have been reported to leverage roles to represent common knowledge within user communities, based on which services can be recommended for users. Prior studies on context aware role mining mainly focus on mining roles from a fixed data set of user behavior patterns, while most of them neglect the dynamic change of the input data. The frequent change of the user data will result in the change of extracted roles, and how to efficiently update extracted roles according to change of the input user data remains a challenging issue. In this paper, towards this issue, the authors introduce a novel role updating approach in context aware role mining. In the apporach, several algorithms are presented towards various scenarios such as new users and new contexts are removed from and added into the input data. Experiments show that compared with existing solutions, the proposed algorithms can guarantee the completeness of updating results while keeping good updating efficiency.