Формальная спецификация свойств баз нечетких знаний Мамдани на основе метаграфа

У роботі розглянуто подання бази нечітких знань Мамдані у вигляді метаграфа. Проведено аналіз властивостей бази нечітких знань для підвищення достовірності нечіткого логічного виведення. Сформульовано визначення властивостей бази нечітких знань Мамдані: ненадлишковості, лінгвістичної несуперечливості, відсутності зациклювання та лінгвістичної повноти бази нечітких знань Мамдані. Визначено вимоги до структури метаграфа, який подає базу нечітких знань Мамдані, для випадків, коли база нечітких знань є ненадлишковою, лінгвістично несуперечливою, не містить зациклювання та є лінгвістично повною. Запропоновано проводити статичну верифікацію баз нечітких знань на основі структури метаграфа, а також його графічного подання.The paper describes Mamdani fuzzy knowledge base representation in the form of a metagraph. The fuzzy knowledge base properties which have an influence on confidence of fuzzy inference are analyzed. The definitions of non-redundant, linguistic non-contradicted, without circularity and linguistic complete Mamdani fuzzy knowledge base are given. The metagraph structure requirements corresponding to non-redundant, linguistic non-contradicted and linguistic complete Mamdani fuzzy knowledge base are defined. Mamdani fuzzy knowledge base properties static verification based on metagraph structure analysis is proposed.В работе рассмотрено представление базы нечетких знаний Мамдани в виде метаграфа. Проведен анализ свойств базы нечетких знаний для повышения достоверности нечеткого логического вывода. Сформулированы определения свойств базы нечетких знаний Мамдани: неизбыточности, лингвистической непротиворечивости, отсутствия зацикливания и лингвистической полноты, базы нечетких знаний Мамдани. Определены требования к структуре метаграфа, соответствующего базе нечетких знаний Мамдани, для случаев, когда база нечетких знаний является неизбыточной, лингвистически непротиворечивой, не содержит зацикливания и является лингвистически полной. Предложено проводить статическую верификацию баз нечетких знаний на основе структуры метаграфа, а также его графического представления

Integration of tools for the Design and Assessment of High-Performance, Highly Reliable Computing Systems (DAHPHRS), phase 1

Systems for Space Defense Initiative (SDI) space applications typically require both high performance and very high reliability. These requirements present the systems engineer evaluating such systems with the extremely difficult problem of conducting performance and reliability trade-offs over large design spaces. A controlled development process supported by appropriate automated tools must be used to assure that the system will meet design objectives. This report describes an investigation of methods, tools, and techniques necessary to support performance and reliability modeling for SDI systems development. Models of the JPL Hypercubes, the Encore Multimax, and the C.S. Draper Lab Fault-Tolerant Parallel Processor (FTPP) parallel-computing architectures using candidate SDI weapons-to-target assignment algorithms as workloads were built and analyzed as a means of identifying the necessary system models, how the models interact, and what experiments and analyses should be performed. As a result of this effort, weaknesses in the existing methods and tools were revealed and capabilities that will be required for both individual tools and an integrated toolset were identified

Facilitating dynamic network control with software-defined networking

This dissertation starts by realizing that network management is a very complex and error-prone task. The major causes are identified through interviews and systematic analysis of network config- uration data on two large campus networks. This dissertation finds that network events and dynamic reactions to them should be programmatically encoded in the network control program by opera- tors, and some events should be automatically handled for them if the desired reaction is general. This dissertation presents two new solutions for managing and configuring networks using Software- Defined Networking (SDN) paradigm: Kinetic and Coronet. Kinetic is a programming language and central control platform that allows operators to implement traffic control application that reacts to various kinds of network events in a concise, intuitive way. The event-reaction logic is checked for correction before deployment to prevent misconfigurations. Coronet is a data-plane failure recovery service for arbitrary SDN control applications. Coronet pre-plans primary and backup routing paths for any given topology. Such pre-planning guarantees that Coronet can perform fast recovery when there is failure. Multiple techniques are used to ensure that the solution scales to large networks with more than 100 switches. Performance and usability evaluations show that both solutions are feasible and are great alternative solutions to current mechanisms to reduce misconfigurations.Ph.D

Mining and Managing Large-Scale Temporal Graphs

Large-scale temporal graphs are everywhere in our daily life. From online social networks, mobile networks, brain networks to computer systems, entities in these large complex systems communicate with each other, and their interactions evolve over time. Unlike traditional graphs, temporal graphs are dynamic: both topologies and attributes on nodes/edges may change over time. On the one hand, the dynamics have inspired new applications that rely on mining and managing temporal graphs. On the other hand, the dynamics also raise new technical challenges. First, it is difficult to discover or retrieve knowledge from complex temporal graph data. Second, because of the extra time dimension, we also face new scalability problems. To address these new challenges, we need to develop new methods that model temporal information in graphs so that we can deliver useful knowledge, new queries with temporal and structural constraints where users can obtain the desired knowledge, and new algorithms that are cost-effective for both mining and management tasks.In this dissertation, we discuss our recent works on mining and managing large-scale temporal graphs.First, we investigate two mining problems, including node ranking and link prediction problems. In these works, temporal graphs are applied to model the data generated from computer systems and online social networks. We formulate data mining tasks that extract knowledge from temporal graphs. The discovered knowledge can help domain experts identify critical alerts in system monitoring applications and recover the complete traces for information propagation in online social networks. To address computation efficiency problems, we leverage the unique properties in temporal graphs to simplify mining processes. The resulting mining algorithms scale well with large-scale temporal graphs with millions of nodes and billions of edges. By experimental studies over real-life and synthetic data, we confirm the effectiveness and efficiency of our algorithms.Second, we focus on temporal graph management problems. In these study, temporal graphs are used to model datacenter networks, mobile networks, and subscription relationships between stream queries and data sources. We formulate graph queries to retrieve knowledge that supports applications in cloud service placement, information routing in mobile networks, and query assignment in stream processing system. We investigate three types of queries, including subgraph matching, temporal reachability, and graph partitioning. By utilizing the relatively stable components in these temporal graphs, we develop flexible data management techniques to enable fast query processing and handle graph dynamics. We evaluate the soundness of the proposed techniques by both real and synthetic data. Through these study, we have learned valuable lessons. For temporal graph mining, temporal dimension may not necessarily increase computation complexity; instead, it may reduce computation complexity if temporal information can be wisely utilized. For temporal graph management, temporal graphs may include relatively stable components in real applications, which can help us develop flexible data management techniques that enable fast query processing and handle dynamic changes in temporal graphs

Book of Abstracts of the Sixth SIAM Workshop on Combinatorial Scientific Computing

Book of Abstracts of CSC14 edited by Bora UçarInternational audienceThe Sixth SIAM Workshop on Combinatorial Scientific Computing, CSC14, was organized at the Ecole Normale Supérieure de Lyon, France on 21st to 23rd July, 2014. This two and a half day event marked the sixth in a series that started ten years ago in San Francisco, USA. The CSC14 Workshop's focus was on combinatorial mathematics and algorithms in high performance computing, broadly interpreted. The workshop featured three invited talks, 27 contributed talks and eight poster presentations. All three invited talks were focused on two interesting fields of research specifically: randomized algorithms for numerical linear algebra and network analysis. The contributed talks and the posters targeted modeling, analysis, bisection, clustering, and partitioning of graphs, applied in the context of networks, sparse matrix factorizations, iterative solvers, fast multi-pole methods, automatic differentiation, high-performance computing, and linear programming. The workshop was held at the premises of the LIP laboratory of ENS Lyon and was generously supported by the LABEX MILYON (ANR-10-LABX-0070, Université de Lyon, within the program ''Investissements d'Avenir'' ANR-11-IDEX-0007 operated by the French National Research Agency), and by SIAM