Semantic-aware Anomaly Detection in Distributed Storage Systems
| Author | : Ghanbari Saeed |
| Publisher | : |
| Total Pages | : |
| Release | : 2012 |
| Genre | : |
| ISBN | : |
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Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Semantic Aware Anomaly Detection In Distributed Storage Systems PDF full book. Access full book title Semantic Aware Anomaly Detection In Distributed Storage Systems.
Situation Awareness with Systems of Systems
| Author | : Piërre van de Laar |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 273 |
| Release | : 2013-02-03 |
| Genre | : Technology & Engineering |
| ISBN | : 1461462304 |
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This book discusses various aspects, challenges, and solutions for developing systems-of-systems for situation awareness, using applications in the domain of maritime safety and security. Topics include advanced, multi-objective visualization methods for situation awareness, stochastic outlier selection, rule-based anomaly detection, an ontology-based event model for semantic reasoning, new methods for semi-automatic generation of adapters bridging communication gaps, security policies for systems-of-systems, trust assessment, and methods to deal with the dynamics of systems-of-systems in run-time monitoring, testing, and diagnosis. Architectural considerations for designing information-centric systems-of-systems such as situation awareness systems, and an integrated demonstrator implementing many of the investigated aspects, complete the book.
Towards Semantically Enabled Complex Event Processing
| Author | : Robin Keskisärkkä |
| Publisher | : Linköping University Electronic Press |
| Total Pages | : 169 |
| Release | : 2017-10-30 |
| Genre | : |
| ISBN | : 9176854795 |
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The Semantic Web provides a framework for semantically annotating data on the web, and the Resource Description Framework (RDF) supports the integration of structured data represented in heterogeneous formats. Traditionally, the Semantic Web has focused primarily on more or less static data, but information on the web today is becoming increasingly dynamic. RDF Stream Processing (RSP) systems address this issue by adding support for streaming data and continuous query processing. To some extent, RSP systems can be used to perform complex event processing (CEP), where meaningful high-level events are generated based on low-level events from multiple sources; however, there are several challenges with respect to using RSP in this context. Event models designed to represent static event information lack several features required for CEP, and are typically not well suited for stream reasoning. The dynamic nature of streaming data also greatly complicates the development and validation of RSP queries. Therefore, reusing queries that have been prepared ahead of time is important to be able to support real-time decision-making. Additionally, there are limitations in existing RSP implementations in terms of both scalability and expressiveness, where some features required in CEP are not supported by any of the current systems. The goal of this thesis work has been to address some of these challenges and the main contributions of the thesis are: (1) an event model ontology targeted at supporting CEP; (2) a model for representing parameterized RSP queries as reusable templates; and (3) an architecture that allows RSP systems to be integrated for use in CEP. The proposed event model tackles issues specifically related to event modeling in CEP that have not been sufficiently covered by other event models, includes support for event encapsulation and event payloads, and can easily be extended to fit specific use-cases. The model for representing RSP query templates was designed as an extension to SPIN, a vocabulary that supports modeling of SPARQL queries as RDF. The extended model supports the current version of the RSP Query Language (RSP-QL) developed by the RDF Stream Processing Community Group, along with some of the most popular RSP query languages. Finally, the proposed architecture views RSP queries as individual event processing agents in a more general CEP framework. Additional event processing components can be integrated to provide support for operations that are not supported in RSP, or to provide more efficient processing for specific tasks. We demonstrate the architecture in implementations for scenarios related to traffic-incident monitoring, criminal-activity monitoring, and electronic healthcare monitoring.
Anomaly Detection and Complex Event Processing Over IoT Data Streams
| Author | : Patrick Schneider |
| Publisher | : Academic Press |
| Total Pages | : 408 |
| Release | : 2022-01-07 |
| Genre | : Computers |
| ISBN | : 0128238194 |
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Anomaly Detection and Complex Event Processing over IoT Data Streams: With Application to eHealth and Patient Data Monitoring presents advanced processing techniques for IoT data streams and the anomaly detection algorithms over them. The book brings new advances and generalized techniques for processing IoT data streams, semantic data enrichment with contextual information at Edge, Fog and Cloud as well as complex event processing in IoT applications. The book comprises fundamental models, concepts and algorithms, architectures and technological solutions as well as their application to eHealth. Case studies, such as the bio-metric signals stream processing are presented –the massive amount of raw ECG signals from the sensors are processed dynamically across the data pipeline and classified with modern machine learning approaches including the Hierarchical Temporal Memory and Deep Learning algorithms. The book discusses adaptive solutions to IoT stream processing that can be extended to different use cases from different fields of eHealth, to enable a complex analysis of patient data in a historical, predictive and even prescriptive application scenarios. The book ends with a discussion on ethics, emerging research trends, issues and challenges of IoT data stream processing. Provides the state-of-the-art in IoT Data Stream Processing, Semantic Data Enrichment, Reasoning and Knowledge Covers extraction (Anomaly Detection) Illustrates new, scalable and reliable processing techniques based on IoT stream technologies Offers applications to new, real-time anomaly detection scenarios in the health domain
Latency-aware Resource Management at the Edge
| Author | : Klervie Toczé |
| Publisher | : Linköping University Electronic Press |
| Total Pages | : 126 |
| Release | : 2020-02-19 |
| Genre | : |
| ISBN | : 9179299040 |
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The increasing diversity of connected devices leads to new application domains being envisioned. Some of these need ultra low latency or have privacy requirements that cannot be satisfied by the current cloud. By bringing resources closer to the end user, the recent edge computing paradigm aims to enable such applications. One critical aspect to ensure the successful deployment of the edge computing paradigm is efficient resource management. Indeed, obtaining the needed resources is crucial for the applications using the edge, but the resource picture of this paradigm is complex. First, as opposed to the nearly infinite resources provided by the cloud, the edge devices have finite resources. Moreover, different resource types are required depending on the applications and the devices supplying those resources are very heterogeneous. This thesis studies several challenges towards enabling efficient resource management for edge computing. The thesis begins by a review of the state-of-the-art research focusing on resource management in the edge computing context. A taxonomy is proposed for providing an overview of the current research and identify areas in need of further work. One of the identified challenges is studying the resource supply organization in the case where a mix of mobile and stationary devices is used to provide the edge resources. The ORCH framework is proposed as a means to orchestrate this edge device mix. The evaluation performed in a simulator shows that this combination of devices enables higher quality of service for latency-critical tasks. Another area is understanding the resource demand side. The thesis presents a study of the workload of a killer application for edge computing: mixed reality. The MR-Leo prototype is designed and used as a vehicle to understand the end-to-end latency, the throughput, and the characteristics of the workload for this type of application. A method for modeling the workload of an application is devised and applied to MR-Leo in order to obtain a synthetic workload exhibiting the same characteristics, which can be used in further studies.
The Semantic Web: ESWC 2018 Satellite Events
| Author | : Aldo Gangemi |
| Publisher | : Springer |
| Total Pages | : 498 |
| Release | : 2018-08-01 |
| Genre | : Computers |
| ISBN | : 3319981927 |
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This book constitutes the thoroughly refereed post-conference proceedings of the Satellite Events of the 15th Extended Semantic Web Conference, ESWC 2018, held in Heraklion, Crete, Greece, in June 2018. The volume contains 41 poster and demonstration papers, 11 invited workshop papers, and 9 full papers, selected out of a total of 70 submissions. They deal with all areas of semantic web research, semantic technologies on the Web and Linked Data. Ontology ABox Comparison” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Extensions for Distributed Moving Base Driving Simulators
| Author | : Anders Andersson |
| Publisher | : Linköping University Electronic Press |
| Total Pages | : 39 |
| Release | : 2017-03-30 |
| Genre | : |
| ISBN | : 9176855244 |
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Modern vehicles are complex systems. Different design stages for such a complex system include evaluation using models and submodels, hardware-in-the-loop systems and complete vehicles. Once a vehicle is delivered to the market evaluation continues by the public. One kind of tool that can be used during many stages of a vehicle lifecycle is driving simulators. The use of driving simulators with a human driver is commonly focused on driver behavior. In a high fidelity moving base driving simulator it is possible to provide realistic and repetitive driving situations using distinctive features such as: physical modelling of driven vehicle, a moving base, a physical cabin interface and an audio and visual representation of the driving environment. A desired but difficult goal to achieve using a moving base driving simulator is to have behavioral validity. In other words, A driver in a moving base driving simulator should have the same driving behavior as he or she would have during the same driving task in a real vehicle.". In this thesis the focus is on high fidelity moving base driving simulators. The main target is to improve the behavior validity or to maintain behavior validity while adding complexity to the simulator. One main assumption in this thesis is that systems closer to the final product provide better accuracy and are perceived better if properly integrated. Thus, the approach in this thesis is to try to ease incorporation of such systems using combinations of the methods hardware-in-the-loop and distributed simulation. Hardware-in-the-loop is a method where hardware is interfaced into a software controlled environment/simulation. Distributed simulation is a method where parts of a simulation at physically different locations are connected together. For some simulator laboratories distributed simulation is the only feasible option since some hardware cannot be moved in an easy way. Results presented in this thesis show that a complete vehicle or hardware-in-the-loop test laboratory can successfully be connected to a moving base driving simulator. Further, it is demonstrated that using a framework for distributed simulation eases communication and integration due to standardized interfaces. One identified potential problem is complexity in interface wrappers when integrating hardware-in-the-loop in a distributed simulation framework. From this aspect, it is important to consider the model design and the intersections between software and hardware models. Another important issue discussed is the increased delay in overhead time when using a framework for distributed simulation.
Automatic Verification of Parameterized Systems by Over-Approximation
| Author | : Vladislavs Jahundovics |
| Publisher | : Linköping University Electronic Press |
| Total Pages | : 155 |
| Release | : 2015-11-17 |
| Genre | : |
| ISBN | : 9176859185 |
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This thesis presents a completely automatic verification framework to check safety properties of parameterized systems. A parameterized system is a family of finite state systems where every system consists of a finite number of processes running in parallel the same algorithm. All the systems in the family differ only in the number of the processes and, in general, the number of systems in a family may be unbounded. Examples of parameterized systems are communication protocols, mutual exclusion protocols, cache coherence protocols, distributed algorithms etc. Model-checking of finite state systems is a well-developed formal verification approach of proving properties of systems in an automatic way. However, it cannot be applied directly to parameterized systems because the unbounded number of systems in a family means an infinite state space. In this thesis we propose to abstract an original family of systems consisting of an unbounded number of processes into one consisting of a fixed number of processes. An abstracted system is considered to consist of k+1 components—k reference processes and their environment. The transition relation for the abstracted system is an over-approximation of the transition relation for the original system, therefore, a set of reachable states of the abstracted system is an over-approximation of the set of reachable states of the original one. A safety property is considered to be parameterized by a fixed number of processes whose relationship is in the center of attention in the property. Such processes serve as reference processes in the abstraction. We propose an encoding which allows to perform reachability analysis for an abstraction parameterized by the reference processes. We have successfully verified three classic parameterized systems with replicated processes by applying this method.
Fusion Methodologies in Crisis Management
| Author | : Galina Rogova |
| Publisher | : Springer |
| Total Pages | : 544 |
| Release | : 2016-01-21 |
| Genre | : Technology & Engineering |
| ISBN | : 3319225278 |
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The book emphasizes a contemporary view on the role of higher level fusion in designing crisis management systems, and provide the formal foundations, architecture and implementation strategies required for building dynamic current and future situational pictures, challenges of, and the state of the art computational approaches to designing such processes. This book integrates recent advances in decision theory with those in fusion methodology to define an end-to-end framework for decision support in crisis management. The text discusses modern fusion and decision support methods for dealing with heterogeneous and often unreliable, low fidelity, contradictory, and redundant data and information, as well as rare, unknown, unconventional or even unimaginable critical situations. Also the book examines the role of context in situation management, cognitive aspects of decision making and situation management, approaches to domain representation, visualization, as well as the role and exploitation of the social media. The editors include examples and case studies from the field of disaster management.
Spatio-Temporal Stream Reasoning with Adaptive State Stream Generation
| Author | : Daniel de Leng |
| Publisher | : Linköping University Electronic Press |
| Total Pages | : 153 |
| Release | : 2017-09-08 |
| Genre | : |
| ISBN | : 9176854760 |
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A lot of today's data is generated incrementally over time by a large variety of producers. This data ranges from quantitative sensor observations produced by robot systems to complex unstructured human-generated texts on social media. With data being so abundant, making sense of these streams of data through reasoning is challenging. Reasoning over streams is particularly relevant for autonomous robotic systems that operate in a physical environment. They commonly observe this environment through incremental observations, gradually refining information about their surroundings. This makes robust management of streaming data and its refinement an important problem. Many contemporary approaches to stream reasoning focus on the issue of querying data streams in order to generate higher-level information by relying on well-known database approaches. Other approaches apply logic-based reasoning techniques, which rarely consider the provenance of their symbolic interpretations. In this thesis, we integrate techniques for logic-based spatio-temporal stream reasoning with the adaptive generation of the state streams needed to do the reasoning over. This combination deals with both the challenge of reasoning over streaming data and the problem of robustly managing streaming data and its refinement. The main contributions of this thesis are (1) a logic-based spatio-temporal reasoning technique that combines temporal reasoning with qualitative spatial reasoning; (2) an adaptive reconfiguration procedure for generating and maintaining a data stream required to perform spatio-temporal stream reasoning over; and (3) integration of these two techniques into a stream reasoning framework. The proposed spatio-temporal stream reasoning technique is able to reason with intertemporal spatial relations by leveraging landmarks. Adaptive state stream generation allows the framework to adapt in situations in which the set of available streaming resources changes. Management of streaming resources is formalised in the DyKnow model, which introduces a configuration life-cycle to adaptively generate state streams. The DyKnow-ROS stream reasoning framework is a concrete realisation of this model that extends the Robot Operating System (ROS). DyKnow-ROS has been deployed on the SoftBank Robotics NAO platform to demonstrate the system's capabilities in the context of a case study on run-time adaptive reconfiguration. The results show that the proposed system – by combining reasoning over and reasoning about streams – can robustly perform spatio-temporal stream reasoning, even when the availability of streaming resources changes.
