A New Approach for Fast Processing of SPARQL Queries on RDF Quadruples

Title from PDF of title page, viewed on July 7, 2015Dissertation advisor: Praveen R. RaoVitaIncludes bibliographic references (pages 87-92)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2015The Resource Description Framework (RDF) is a standard model for representing data on the Web. It enables the interchange and machine processing of data by considering its semantics. While RDF was first proposed with the vision of enabling the Semantic Web, it has now become popular in domain-specific applications and the Web. Through advanced RDF technologies, one can perform semantic reasoning over data and extract knowledge in domains such as healthcare, biopharmaceuticals, defense, and intelligence. Popular approaches like RDF-3X perform poorly on RDF datasets containing billions of triples when the queries are large and complex. This is because of the large number of join operations that must be performed during query processing. Moreover, most of the scalable approaches were designed to operate on RDF triples instead of quads. To address these issues, we propose to develop a new approach for fast and cost-effective processing of SPARQL queries on large RDF datasets containing RDF quadruples (or quads). Our approach employs a decrease-and-conquer strategy: Rather than indexing the entire RDF dataset, it identifies groups of similar RDF graphs and indexes each group separately. During query processing, it uses a novel filtering index to first identify candidate groups that may contain matches for the query. On these candidates, it executes queries using a conventional SPARQL processor to produce the final results. A query optimization strategy using the candidate groups to further improve the query processing performance is also used.Introduction -- Background and motivations -- The design of RIQ -- Implementation of RIQ -- Evaluation -- Conclusion and future work -- Appendix A. Queries -- Appendix B. SPARQL gramma

Cyber-Physical Threat Intelligence for Critical Infrastructures Security

Modern critical infrastructures comprise of many interconnected cyber and physical assets, and as such are large scale cyber-physical systems. Hence, the conventional approach of securing these infrastructures by addressing cyber security and physical security separately is no longer effective. Rather more integrated approaches that address the security of cyber and physical assets at the same time are required. This book presents integrated (i.e. cyber and physical) security approaches and technologies for the critical infrastructures that underpin our societies. Specifically, it introduces advanced techniques for threat detection, risk assessment and security information sharing, based on leading edge technologies like machine learning, security knowledge modelling, IoT security and distributed ledger infrastructures. Likewise, it presets how established security technologies like Security Information and Event Management (SIEM), pen-testing, vulnerability assessment and security data analytics can be used in the context of integrated Critical Infrastructure Protection. The novel methods and techniques of the book are exemplified in case studies involving critical infrastructures in four industrial sectors, namely finance, healthcare, energy and communications. The peculiarities of critical infrastructure protection in each one of these sectors is discussed and addressed based on sector-specific solutions. The advent of the fourth industrial revolution (Industry 4.0) is expected to increase the cyber-physical nature of critical infrastructures as well as their interconnection in the scope of sectorial and cross-sector value chains. Therefore, the demand for solutions that foster the interplay between cyber and physical security, and enable Cyber-Physical Threat Intelligence is likely to explode. In this book, we have shed light on the structure of such integrated security systems, as well as on the technologies that will underpin their operation. We hope that Security and Critical Infrastructure Protection stakeholders will find the book useful when planning their future security strategies

Cyber-Physical Threat Intelligence for Critical Infrastructures Security

Modern critical infrastructures can be considered as large scale Cyber Physical Systems (CPS). Therefore, when designing, implementing, and operating systems for Critical Infrastructure Protection (CIP), the boundaries between physical security and cybersecurity are blurred. Emerging systems for Critical Infrastructures Security and Protection must therefore consider integrated approaches that emphasize the interplay between cybersecurity and physical security techniques. Hence, there is a need for a new type of integrated security intelligence i.e., Cyber-Physical Threat Intelligence (CPTI). This book presents novel solutions for integrated Cyber-Physical Threat Intelligence for infrastructures in various sectors, such as Industrial Sites and Plants, Air Transport, Gas, Healthcare, and Finance. The solutions rely on novel methods and technologies, such as integrated modelling for cyber-physical systems, novel reliance indicators, and data driven approaches including BigData analytics and Artificial Intelligence (AI). Some of the presented approaches are sector agnostic i.e., applicable to different sectors with a fair customization effort. Nevertheless, the book presents also peculiar challenges of specific sectors and how they can be addressed. The presented solutions consider the European policy context for Security, Cyber security, and Critical Infrastructure protection, as laid out by the European Commission (EC) to support its Member States to protect and ensure the resilience of their critical infrastructures. Most of the co-authors and contributors are from European Research and Technology Organizations, as well as from European Critical Infrastructure Operators. Hence, the presented solutions respect the European approach to CIP, as reflected in the pillars of the European policy framework. The latter includes for example the Directive on security of network and information systems (NIS Directive), the Directive on protecting European Critical Infrastructures, the General Data Protection Regulation (GDPR), and the Cybersecurity Act Regulation. The sector specific solutions that are described in the book have been developed and validated in the scope of several European Commission (EC) co-funded projects on Critical Infrastructure Protection (CIP), which focus on the listed sectors. Overall, the book illustrates a rich set of systems, technologies, and applications that critical infrastructure operators could consult to shape their future strategies. It also provides a catalogue of CPTI case studies in different sectors, which could be useful for security consultants and practitioners as well

Industrial Applications: New Solutions for the New Era

This book reprints articles from the Special Issue "Industrial Applications: New Solutions for the New Age" published online in the open-access journal Machines (ISSN 2075-1702). This book consists of twelve published articles. This special edition belongs to the "Mechatronic and Intelligent Machines" section

Cyber-Physical Threat Intelligence for Critical Infrastructures Security

Modern critical infrastructures can be considered as large scale Cyber Physical Systems (CPS). Therefore, when designing, implementing, and operating systems for Critical Infrastructure Protection (CIP), the boundaries between physical security and cybersecurity are blurred. Emerging systems for Critical Infrastructures Security and Protection must therefore consider integrated approaches that emphasize the interplay between cybersecurity and physical security techniques. Hence, there is a need for a new type of integrated security intelligence i.e., Cyber-Physical Threat Intelligence (CPTI). This book presents novel solutions for integrated Cyber-Physical Threat Intelligence for infrastructures in various sectors, such as Industrial Sites and Plants, Air Transport, Gas, Healthcare, and Finance. The solutions rely on novel methods and technologies, such as integrated modelling for cyber-physical systems, novel reliance indicators, and data driven approaches including BigData analytics and Artificial Intelligence (AI). Some of the presented approaches are sector agnostic i.e., applicable to different sectors with a fair customization effort. Nevertheless, the book presents also peculiar challenges of specific sectors and how they can be addressed. The presented solutions consider the European policy context for Security, Cyber security, and Critical Infrastructure protection, as laid out by the European Commission (EC) to support its Member States to protect and ensure the resilience of their critical infrastructures. Most of the co-authors and contributors are from European Research and Technology Organizations, as well as from European Critical Infrastructure Operators. Hence, the presented solutions respect the European approach to CIP, as reflected in the pillars of the European policy framework. The latter includes for example the Directive on security of network and information systems (NIS Directive), the Directive on protecting European Critical Infrastructures, the General Data Protection Regulation (GDPR), and the Cybersecurity Act Regulation. The sector specific solutions that are described in the book have been developed and validated in the scope of several European Commission (EC) co-funded projects on Critical Infrastructure Protection (CIP), which focus on the listed sectors. Overall, the book illustrates a rich set of systems, technologies, and applications that critical infrastructure operators could consult to shape their future strategies. It also provides a catalogue of CPTI case studies in different sectors, which could be useful for security consultants and practitioners as well

Investigación sobre la flexibilidad de la demanda en redes eléctricas inteligentes: control directo de cargas

In recent decades, the European Union has made decisive efforts to maintain its global leadership in renewable energies to meet climate change targets resulting from international agreements. There is a deliberate intention to reduce the usage of non-renewable energy sources and promote the exploitation of renewable generation at all levels as shown by energy production data within the Eurozone. The electricity sector illustrates a successful implementation of these energy policies: The electricity coming from combustible fuels was at historical lows in 2018, accounting for 83.6 % of the electricity generated from this source in 2008. By contrast, the pool of renewables reached almost 170 % of the 2008 production. Against this background, power systems worldwide are undergoing deep-seated changes due to the increasing penetration of these variable renewable energy sources and distributed energy resources that are intermittent and stochastic in nature. Under these conditions, achieving a continuous balance between generation and consumption becomes a challenge and may jeopardize the system stability, which points out the need of making the power system flexible enough as a response measure to this trend. This Ph.D. thesis researches one of the principal mechanisms providing flexibility to the power system: The demand-side management, seen from both the demand response and the energy efficiency perspectives. Power quality issues as a non-negligible part of energy efficiency are also addressed. To do so, several strategies have been deployed at a double level. In the residential sector, a direct load control strategy for smart appliances has been developed under a real-time pricing demand response scheme. This strategy seeks to minimize the daily cost of energy in presence of diverse energy resources and appliances. Furthermore, a spread spectrum technique has also been applied to mitigate the highfrequency distortion derived from the usage of LED technology lighting systems instead of traditional ones when energy efficiency needs to be improved. In the industrial sector, a load scheduling strategy to control the AC-AC power electronic converter in charge of supporting the electric-boosted glass melting furnaces has been developed. The benefit is two-fold: While it contributes to demand flexibility by shaving the peaks found under conventional control schemes, the power quality issues related to the emission of subharmonics are also kept to a minimum. Concerning the technologies, this Ph.D. thesis provides smart solutions, platforms, and devices to carry out these strategies: From the application of the internet of things paradigm to the development of the required electronics and the implementation of international standards within the energy industry.En las últimas décadas, la Unión Europea ha realizado esfuerzos decisivos para mantener su liderazgo mundial en energías renovables con el fin de cumplir los objetivos de cambio climático resultantes de los acuerdos internacionales. Muestra una intención deliberada de reducir el uso de fuentes de energía no renovable y promover la explotación de la generación renovable a todos los niveles, como demuestran los datos de producción de energía en la eurozona. El sector de la electricidad ilustra un caso de éxito de estas políticas energéticas: la electricidad procedente de combustibles fósiles estaba en mínimos históricos en 2018, representando el 83,6 % de la electricidad generada a partir de esta fuente en 2008; en cambio, el grupo de renovables alcanzó casi el 170 % de la producción de 2008. En este contexto, los sistemas eléctricos de todo el mundo están experimentando profundos cambios debido a la creciente penetración de estas fuentes de energía renovable y de recursos energéticos distribuidos que son de naturaleza variable, intermitente y estocástica. En estas condiciones, lograr un equilibrio continuo entre generación y consumo se convierte en un reto y puede poner en peligro la estabilidad del sistema, lo que señala la necesidad de flexibilizar el sistema eléctrico como medida de respuesta a esta tendencia. Esta tesis doctoral investiga uno de los principales mecanismos que proporcionan flexibilidad al sistema eléctrico: la gestión de la demanda vista tanto desde la perspectiva de la respuesta a la demanda como de la eficiencia energética. También se abordan los problemas de calidad de suministro entendidos como parte no despreciable de la eficiencia energética. Para ello, se han desplegado varias estrategias a un doble nivel. En el sector residencial, se ha desarrollado una estrategia basada en el control directo de cargas para los electrodomésticos inteligentes siguiendo un esquema de respuesta a la demanda con precios en tiempo real. Esta estrategia busca minimizar el coste diario de la energía en presencia de diversos recursos energéticos y electrodomésticos. Además, también se ha aplicado una técnica de espectro ensanchado para mitigar la distorsión de alta frecuencia derivada del uso de sistemas de iluminación con tecnología LED, empleados para la mejora de la eficiencia energética frente a las tecnologías convencionales. En el sector industrial, se ha desarrollado una estrategia de planificación de cargas para controlar el convertidor AC-AC de los hornos de fundición de vidrio con soporte eléctrico. El beneficio es doble: mientras que se contribuye a la flexibilidad de la demanda al eliminar los picos encontrados en los esquemas de control convencionales, también se reducen al mínimo los problemas de calidad de suministro relacionados con la emisión de subarmónicos. En cuanto a las tecnologías, esta tesis doctoral aporta soluciones, plataformas y dispositivos inteligentes para llevar a cabo estas estrategias: desde la aplicación del paradigma del internet de las cosas hasta el desarrollo de la electrónica necesaria y la implementación de estándares internacionales dentro de la industria energética

A Novel Approach to Determining Real-Time Risk Probabilities in Critical Infrastructure Industrial Control Systems

Critical Infrastructure Industrial Control Systems are substantially different from their more common and ubiquitous information technology system counterparts. Industrial control systems, such as distributed control systems and supervisory control and data acquisition systems that are used for controlling the power grid, were not originally designed with security in mind. Geographically dispersed distribution, an unfortunate reliance on legacy systems and stringent availability requirements raise significant cybersecurity concerns regarding electric reliability while constricting the feasibility of many security controls. Recent North American Electric Reliability Corporation Critical Infrastructure Protection standards heavily emphasize cybersecurity concerns and specifically require entities to categorize and identify their Bulk Electric System cyber systems; and, have periodic vulnerability assessments performed on those systems. These concerns have produced an increase in the need for more Critical Infrastructure Industrial Control Systems specific cybersecurity research. Industry stakeholders have embraced the development of a large-scale test environment through the Department of Energy’s National Supervisory Control and Data Acquisition Test-bed program; however, few individuals have access to this program. This research developed a physical industrial control system test-bed on a smaller-scale that provided an environment for modeling a simulated critical infrastructure sector performing a set of automated processes for the purpose of exploring solutions and studying concepts related to compromising control systems by way of process-tampering through code exploitation, as well as, the ability to passively and subsequently identify any risks resulting from such an event. Relative to the specific step being performed within a production cycle, at a moment in time when sensory data samples were captured and analyzed, it was possible to determine the probability of a real-time risk to a mock Critical Infrastructure Industrial Control System by comparing the sample values to those derived from a previously established baseline. This research achieved such a goal by implementing a passive, spatial and task-based segregated sensor network, running in parallel to the active control system process for monitoring and detecting risk, and effectively identified a real-time risk probability within a Critical Infrastructure Industrial Control System Test-bed. The practicality of this research ranges from determining on-demand real-time risk probabilities during an automated process, to employing baseline monitoring techniques for discovering systems, or components thereof, exploited along the supply chain

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words