Spanning trees of 3-uniform hypergraphs

Masbaum and Vaintrob's "Pfaffian matrix tree theorem" implies that counting spanning trees of a 3-uniform hypergraph (abbreviated to 3-graph) can be done in polynomial time for a class of "3-Pfaffian" 3-graphs, comparable to and related to the class of Pfaffian graphs. We prove a complexity result for recognizing a 3-Pfaffian 3-graph and describe two large classes of 3-Pfaffian 3-graphs -- one of these is given by a forbidden subgraph characterization analogous to Little's for bipartite Pfaffian graphs, and the other consists of a class of partial Steiner triple systems for which the property of being 3-Pfaffian can be reduced to the property of an associated graph being Pfaffian. We exhibit an infinite set of partial Steiner triple systems that are not 3-Pfaffian, none of which can be reduced to any other by deletion or contraction of triples. We also find some necessary or sufficient conditions for the existence of a spanning tree of a 3-graph (much more succinct than can be obtained by the currently fastest polynomial-time algorithm of Gabow and Stallmann for finding a spanning tree) and a superexponential lower bound on the number of spanning trees of a Steiner triple system.Comment: 34 pages, 9 figure

Large-Scale Storage and Reasoning for Semantic Data Using Swarms

Scalable, adaptive and robust approaches to store and analyze the massive amounts of data expected from Semantic Web applications are needed to bring the Web of Data to its full potential. The solution at hand is to distribute both data and requests onto multiple computers. Apart from storage, the annotation of data with machine-processable semantics is essential for realizing the vision of the Semantic Web. Reasoning on webscale data faces the same requirements as storage. Swarm-based approaches have been shown to produce near-optimal solutions for hard problems in a completely decentralized way. We propose a novel concept for reasoning within a fully distributed and self-organized storage system that is based on the collective behavior of swarm individuals and does not require any schema replication. We show the general feasibility and efficiency of our approach with a proof-of-concept experiment of storage and reasoning performance. Thereby, we positively answer the research question of whether swarm-based approaches are useful in creating a large-scale distributed storage and reasoning system. © 2012 IEEE

LinkedScales : bases de dados em multiescala

Orientador: André SantanchèTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: As ciências biológicas e médicas precisam cada vez mais de abordagens unificadas para a análise de dados, permitindo a exploração da rede de relacionamentos e interações entre elementos. No entanto, dados essenciais estão frequentemente espalhados por um conjunto cada vez maior de fontes com múltiplos níveis de heterogeneidade entre si, tornando a integração cada vez mais complexa. Abordagens de integração existentes geralmente adotam estratégias especializadas e custosas, exigindo a produção de soluções monolíticas para lidar com formatos e esquemas específicos. Para resolver questões de complexidade, essas abordagens adotam soluções pontuais que combinam ferramentas e algoritmos, exigindo adaptações manuais. Abordagens não sistemáticas dificultam a reutilização de tarefas comuns e resultados intermediários, mesmo que esses possam ser úteis em análises futuras. Além disso, é difícil o rastreamento de transformações e demais informações de proveniência, que costumam ser negligenciadas. Este trabalho propõe LinkedScales, um dataspace baseado em múltiplos níveis, projetado para suportar a construção progressiva de visões unificadas de fontes heterogêneas. LinkedScales sistematiza as múltiplas etapas de integração em escalas, partindo de representações brutas (escalas mais baixas), indo gradualmente para estruturas semelhantes a ontologias (escalas mais altas). LinkedScales define um modelo de dados e um processo de integração sistemático e sob demanda, através de transformações em um banco de dados de grafos. Resultados intermediários são encapsulados em escalas reutilizáveis e transformações entre escalas são rastreadas em um grafo de proveniência ortogonal, que conecta objetos entre escalas. Posteriormente, consultas ao dataspace podem considerar objetos nas escalas e o grafo de proveniência ortogonal. Aplicações práticas de LinkedScales são tratadas através de dois estudos de caso, um no domínio da biologia -- abordando um cenário de análise centrada em organismos -- e outro no domínio médico -- com foco em dados de medicina baseada em evidênciasAbstract: Biological and medical sciences increasingly need a unified, network-driven approach for exploring relationships and interactions among data elements. Nevertheless, essential data is frequently scattered across sources with multiple levels of heterogeneity. Existing data integration approaches usually adopt specialized, heavyweight strategies, requiring a costly upfront effort to produce monolithic solutions for handling specific formats and schemas. Furthermore, such ad-hoc strategies hamper the reuse of intermediary integration tasks and outcomes. This work proposes LinkedScales, a multiscale-based dataspace designed to support the progressive construction of a unified view of heterogeneous sources. It departs from raw representations (lower scales) and goes towards ontology-like structures (higher scales). LinkedScales defines a data model and a systematic, gradual integration process via operations over a graph database. Intermediary outcomes are encapsulated as reusable scales, tracking the provenance of inter-scale operations. Later, queries can combine both scale data and orthogonal provenance information. Practical applications of LinkedScales are discussed through two case studies on the biology domain -- addressing an organism-centric analysis scenario -- and the medical domain -- focusing on evidence-based medicine dataDoutoradoCiência da ComputaçãoDoutor em Ciência da Computação141353/2015-5CAPESCNP

Social influence analysis in microblogging platforms - a topic-sensitive based approach

The use of Social Media, particularly microblogging platforms such as Twitter, has proven to be an effective channel for promoting ideas to online audiences. In a world where information can bias public opinion it is essential to analyse the propagation and influence of information in large-scale networks. Recent research studying social media data to rank users by topical relevance have largely focused on the “retweet", “following" and “mention" relations. In this paper we propose the use of semantic profiles for deriving influential users based on the retweet subgraph of the Twitter graph. We introduce a variation of the PageRank algorithm for analysing users’ topical and entity influence based on the topical/entity relevance of a retweet relation. Experimental results show that our approach outperforms related algorithms including HITS, InDegree and Topic-Sensitive PageRank. We also introduce VisInfluence, a visualisation platform for presenting top influential users based on a topical query need

Decomposing the blocks of a Steiner triple system of order 4v-3 into partial parallel classes of size v-1

In this report we present a summary and our new results on finding partial parallel classes of uniform size of Steiner triple systems, STS(v). We show several results for STS(4v - 3), where v = 3 mod 12 and v = 9 mod 12. In Chapter 1 we provide background knowledge and introduce the problem. In Chapter 2 we discuss some important known results to the problem, introduce the needed ingredients, and explain the methodology of the construction. Finally, in Chapter 3, we conclude with a summary and discuss possibilities for future work

Distributed storage and queryng techniques for a semantic web of scientific workflow provenance

In scientific workflow environments, scientists depend on provenance, which records the history of an experiment. Resource Description Framework is frequently used to represent provenance based on vocabularies such as the Open Provenance Model. For complex scientific workflows that generate large amounts of RDF triples, single-machine provenance management becomes inadequate over time. In this thesis, we research how HBase capabilities can be leveraged for distributed storage and querying of provenance data represented in RDF. We architect the ProvBase system that incorporates an HBase/Hadoop backend, propose a storage schema to hold provenance triples, and design querying algorithms to evaluate SPARQL queries in the system. We conduct an experimental study to show the feasibility of our approach

Improved Classical Cryptanalysis of SIKE in Practice

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Multipartite graph decomposition: cycles and closed trails

This paper surveys results on cycle decompositions of complete multipartite graphs (where the parts are not all of size 1, so the graph is not <em>K</em>_<em>n</em> ), in the case that the cycle lengths are “small”. Cycles up to length <em>n</em> are considered, when the complete multipartite graph has <em>n</em> parts, but not hamilton cycles. Properties which the decompositions may have, such as being gregarious, are also mentioned.<br /