44 research outputs found

    Social and Semantic Contexts in Tourist Mobile Applications

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    The ongoing growth of the World Wide Web along with the increase possibility of access information through a variety of devices in mobility, has defi nitely changed the way users acquire, create, and personalize information, pushing innovative strategies for annotating and organizing it. In this scenario, Social Annotation Systems have quickly gained a huge popularity, introducing millions of metadata on di fferent Web resources following a bottom-up approach, generating free and democratic mechanisms of classi cation, namely folksonomies. Moving away from hierarchical classi cation schemas, folksonomies represent also a meaningful mean for identifying similarities among users, resources and tags. At any rate, they suff er from several limitations, such as the lack of specialized tools devoted to manage, modify, customize and visualize them as well as the lack of an explicit semantic, making di fficult for users to bene fit from them eff ectively. Despite appealing promises of Semantic Web technologies, which were intended to explicitly formalize the knowledge within a particular domain in a top-down manner, in order to perform intelligent integration and reasoning on it, they are still far from reach their objectives, due to di fficulties in knowledge acquisition and annotation bottleneck. The main contribution of this dissertation consists in modeling a novel conceptual framework that exploits both social and semantic contextual dimensions, focusing on the domain of tourism and cultural heritage. The primary aim of our assessment is to evaluate the overall user satisfaction and the perceived quality in use thanks to two concrete case studies. Firstly, we concentrate our attention on contextual information and navigation, and on authoring tool; secondly, we provide a semantic mapping of tags of the system folksonomy, contrasted and compared to the expert users' classi cation, allowing a bridge between social and semantic knowledge according to its constantly mutual growth. The performed user evaluations analyses results are promising, reporting a high level of agreement on the perceived quality in use of both the applications and of the speci c analyzed features, demonstrating that a social-semantic contextual model improves the general users' satisfactio

    Graph Pattern Matching on Symmetric Multiprocessor Systems

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    Graph-structured data can be found in nearly every aspect of today's world, be it road networks, social networks or the internet itself. From a processing perspective, finding comprehensive patterns in graph-structured data is a core processing primitive in a variety of applications, such as fraud detection, biological engineering or social graph analytics. On the hardware side, multiprocessor systems, that consist of multiple processors in a single scale-up server, are the next important wave on top of multi-core systems. In particular, symmetric multiprocessor systems (SMP) are characterized by the fact, that each processor has the same architecture, e.g. every processor is a multi-core and all multiprocessors share a common and huge main memory space. Moreover, large SMPs will feature a non-uniform memory access (NUMA), whose impact on the design of efficient data processing concepts should not be neglected. The efficient usage of SMP systems, that still increase in size, is an interesting and ongoing research topic. Current state-of-the-art architectural design principles provide different and in parts disjunct suggestions on which data should be partitioned and or how intra-process communication should be realized. In this thesis, we propose a new synthesis of four of the most well-known principles Shared Everything, Partition Serial Execution, Data Oriented Architecture and Delegation, to create the NORAD architecture, which stands for NUMA-aware DORA with Delegation. We built our research prototype called NeMeSys on top of the NORAD architecture to fully exploit the provided hardware capacities of SMPs for graph pattern matching. Being an in-memory engine, NeMeSys allows for online data ingestion as well as online query generation and processing through a terminal based user interface. Storing a graph on a NUMA system inherently requires data partitioning to cope with the mentioned NUMA effect. Hence, we need to dissect the graph into a disjunct set of partitions, which can then be stored on the individual memory domains. This thesis analyzes the capabilites of the NORAD architecture, to perform scalable graph pattern matching on SMP systems. To increase the systems performance, we further develop, integrate and evaluate suitable optimization techniques. That is, we investigate the influence of the inherent data partitioning, the interplay of messaging with and without sufficient locality information and the actual partition placement on any NUMA socket in the system. To underline the applicability of our approach, we evaluate NeMeSys against synthetic datasets and perform an end-to-end evaluation of the whole system stack on the real world knowledge graph of Wikidata

    Querying and managing complex networks

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    Orientador: AndrĂ© SantanchĂšTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Compreender e quantificar as propriedades emergentes de redes naturais e de redes construĂ­das pelo homem, tais como cadeias alimentares, interaçÔes sociais e infra-estruturas de transporte Ă© uma tarefa desafiadora. O campo de redes complexas foi desenvolvido para agregar mediçÔes, algoritmos e tĂ©cnicas para lidar com tais tĂłpicos. Embora as pesquisas em redes complexas tenham sido aplicadas com sucesso em vĂĄrias ĂĄreas de atividade humana, ainda hĂĄ uma falta de infra-estruturas comuns para tarefas rotineiras, especialmente aquelas relacionadas Ă  gestĂŁo de dados. Por outro lado, o campo de bancos de dados tem se concentrado em questĂ”es de gestĂŁo de dados desde o seu inĂ­cio, hĂĄ vĂĄrias dĂ©cadas. Sistemas de banco de dados, no entanto, oferecem suporte reduzido Ă  anĂĄlise de redes. Para prover um melhor suporte para tarefas de anĂĄlise de redes complexas, um sistema de banco de dados deve oferecer recursos de consulta e gerenciamento de dados adequados. Esta tese defende uma maior integração entre as ĂĄreas e apresenta nossos esforços para atingir este objetivo. Aqui nĂłs descrevemos o Sistema de Gerenciamento de Dados Complexos (CDMS), que permite consultas exploratĂłrias sobre redes complexas atravĂ©s de uma linguagem de consulta declarativa. Os resultados da consulta sĂŁo classificados com base em mediçÔes de rede avaliadas no momento da consulta. Para suportar o processamento de consultas, nĂłs introduzimos a Beta-ĂĄlgebra, que oferece um operador capaz de representar diversas mediçÔes tĂ­picas de anĂĄlise de redes complexas. A ĂĄlgebra oferece oportunidades para otimizaçÔes transparentes de consulta baseadas em reescritas, propostas e discutidas aqui. TambĂ©m introduzimos o mecanismo mapper de gestĂŁo de relacionamentos, que estĂĄ integrado Ă  linguagem de consulta. Os mecanismos de consulta e gerenciamento de dados flexĂ­veis propostos sĂŁo tambĂ©m Ășteis em cenĂĄrios alĂ©m da anĂĄlise de redes complexas. NĂłs demonstramos o uso do CDMS em aplicaçÔes tais como integração de dados institucionais, recuperação de informação, classificação e recomendação. Todos os aspectos da proposta foram implementadas e testados com dados reais e sintĂ©ticosAbstract: Understanding and quantifying the emergent properties of natural and man-made networks such as food webs, social interactions, and transportation infrastructures is a challenging task. The complex networks field was developed to encompass measurements, algorithms, and techniques to tackle such topics. Although complex networks research has been successfully applied to several areas of human activity, there is still a lack of common infrastructures for routine tasks, especially those related to data management. On the other hand, the databases field has focused on mastering data management issues since its beginnings, several decades ago. Database systems, however, offer limited network analysis capabilities. To enable a better support for complex network analysis tasks, a database system must offer adequate querying and data management capabilities. This thesis advocates for a tighter integration between the areas and presents our efforts towards this goal. Here we describe the Complex Data Management System (CDMS), which enables explorative querying of complex networks through a declarative query language. Query results are ranked based on network measurements assessed at query time. To support query processing, we introduce the Beta-algebra, which offers an operator capable of representing diverse measurements typical of complex network analysis. The algebra offers opportunities for transparent query optimization through query rewritings, proposed and discussed here. We also introduce the mapper mechanism for relationship management, which is integrated in the query language. The flexible query language and data management mechanisms are useful in scenarios other than complex network analysis. We demonstrate the use of the CDMS in applications such as institutional data integration, information retrieval, classification and recommendation. All aspects of the proposal are implemented and have been tested with real and synthetic dataDoutoradoCiĂȘncia da ComputaçãoDoutor em CiĂȘncia da Computação2012/15988-9FAPESPCAPE

    Enabling Complex Semantic Queries to Bioinformatics Databases through Intuitive Search Over Data

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    Data integration promises to be one of the main catalysts in enabling new insights to be drawn from the wealth of biological data already available publicly. However, the heterogene- ity of the existing data sources still poses significant challenges for achieving interoperability among biological databases. Furthermore, merely solving the technical challenges of data in- tegration, for example through the use of common data representation formats, leaves open the larger problem. Namely, the steep learning curve required for understanding the data models of each public source, as well as the technical language through which the sources can be queried and joined. As a consequence, most of the available biological data remain practically unexplored today. In this thesis, we address these problems jointly, by first introducing an ontology-based data integration solution in order to mitigate the data source heterogeneity problem. We illustrate through the concrete example of Bgee, a gene expression data source, how relational databases can be exposed as virtual Resource Description Framework (RDF) graphs, through relational-to-RDF mappings. This has the important advantage that the original data source can remain unmodified, while still becoming interoperable with external RDF sources. We complement our methods with applied case studies designed to guide domain experts in formulating expressive federated queries targeting the integrated data across the domains of evolutionary relationships and gene expression. More precisely, we introduce two com- parative analyses, first within the same domain (using orthology data from multiple, inter- operable, data sources) and second across domains, in order to study the relation between expression change and evolution rate following a duplication event. Finally, in order to bridge the semantic gap between users and data, we design and im- plement Bio-SODA, a question answering system over domain knowledge graphs, that does not require training data for translating user questions to SPARQL. Bio-SODA uses a novel ranking approach that combines syntactic and semantic similarity, while also incorporating node centrality metrics to rank candidate matches for a given user question. Our results in testing Bio-SODA across several real-world databases that span multiple domains (both within and outside bioinformatics) show that it can answer complex, multi-fact queries, be- yond the current state-of-the-art in the more well-studied open-domain question answering. -- L’intĂ©gration des donnĂ©es promet d’ĂȘtre l’un des principaux catalyseurs permettant d’extraire des nouveaux aperçus de la richesse des donnĂ©es biologiques dĂ©jĂ  disponibles publiquement. Cependant, l’hĂ©tĂ©rogĂ©nĂ©itĂ© des sources de donnĂ©es existantes pose encore des dĂ©fis importants pour parvenir Ă  l’interopĂ©rabilitĂ© des bases de donnĂ©es biologiques. De plus, en surmontant seulement les dĂ©fis techniques de l’intĂ©gration des donnĂ©es, par exemple grĂące Ă  l’utilisation de formats standard de reprĂ©sentation de donnĂ©es, on laisse ouvert un problĂšme encore plus grand. À savoir, la courbe d’apprentissage abrupte nĂ©cessaire pour comprendre la modĂ©li- sation des donnĂ©es choisie par chaque source publique, ainsi que le langage technique par lequel les sources peuvent ĂȘtre interrogĂ©s et jointes. Par consĂ©quent, la plupart des donnĂ©es biologiques publiquement disponibles restent pratiquement inexplorĂ©s aujourd’hui. Dans cette thĂšse, nous abordons l’ensemble des deux problĂšmes, en introduisant d’abord une solution d’intĂ©gration de donnĂ©es basĂ©e sur ontologies, afin d’attĂ©nuer le problĂšme d’hĂ©tĂ©- rogĂ©nĂ©itĂ© des sources de donnĂ©es. Nous montrons, Ă  travers l’exemple de Bgee, une base de donnĂ©es d’expression de gĂšnes, une approche permettant les bases de donnĂ©es relationnelles d’ĂȘtre publiĂ©s sous forme de graphes RDF (Resource Description Framework) virtuels, via des correspondances relationnel-vers-RDF (« relational-to-RDF mappings »). Cela prĂ©sente l’important avantage que la source de donnĂ©es d’origine peut rester inchangĂ©, tout en de- venant interopĂ©rable avec les sources RDF externes. Nous complĂ©tons nos mĂ©thodes avec des Ă©tudes de cas appliquĂ©es, conçues pour guider les experts du domaine dans la formulation de requĂȘtes fĂ©dĂ©rĂ©es expressives, ciblant les don- nĂ©es intĂ©grĂ©es dans les domaines des relations Ă©volutionnaires et de l’expression des gĂšnes. Plus prĂ©cisĂ©ment, nous introduisons deux analyses comparatives, d’abord dans le mĂȘme do- maine (en utilisant des donnĂ©es d’orthologie provenant de plusieurs sources de donnĂ©es in- teropĂ©rables) et ensuite Ă  travers des domaines interconnectĂ©s, afin d’étudier la relation entre le changement d’expression et le taux d’évolution suite Ă  une duplication de gĂšne. Enfin, afin de mitiger le dĂ©calage sĂ©mantique entre les utilisateurs et les donnĂ©es, nous concevons et implĂ©mentons Bio-SODA, un systĂšme de rĂ©ponse aux questions sur des graphes de connaissances domaine-spĂ©cifique, qui ne nĂ©cessite pas de donnĂ©es de formation pour traduire les questions des utilisateurs vers SPARQL. Bio-SODA utilise une nouvelle ap- proche de classement qui combine la similaritĂ© syntactique et sĂ©mantique, tout en incorporant des mĂ©triques de centralitĂ© des nƓuds, pour classer les possibles candidats en rĂ©ponse Ă  une question utilisateur donnĂ©e. Nos rĂ©sultats suite aux tests effectuĂ©s en utilisant Bio-SODA sur plusieurs bases de donnĂ©es Ă  travers plusieurs domaines (tantĂŽt liĂ©s Ă  la bioinformatique qu’extĂ©rieurs) montrent que Bio-SODA rĂ©ussit Ă  rĂ©pondre Ă  des questions complexes, en- gendrant multiples entitĂ©s, au-delĂ  de l’état actuel de la technique en matiĂšre de systĂšmes de rĂ©ponses aux questions sur les donnĂ©es structures, en particulier graphes de connaissances

    Graph database management systems: storage, management and query processing

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    The proliferation of graph data, generated from diverse sources, have given rise to many research efforts concerning graph analysis. Interactions in social networks, publication networks, protein networks, software code dependencies and transportation systems are all examples of graph-structured data originating from a variety of application domains and demonstrating different characteristics. In recent years, graph database management systems (GDBMS) have been introduced for the management and analysis of graph data. Motivated by the growing number of real-life applications making use of graph database systems, this thesis focuses on the effectiveness and efficiency aspects of such systems. Specifically, we study the following topics relevant to graph database systems: (i) modeling large-scale applications in GDBMS; (ii) storage and indexing issues in GDBMS, and (iii) efficient query processing in GDBMS. In this thesis, we adopt two different application scenarios to examine how graph database systems can model complex features and perform relevant queries on each of them. Motivated by the popular application of social network analytics, we selected Twitter, a microblogging platform, to conduct our detailed analysis. Addressing limitations of existing models, we pro- pose a data model for the Twittersphere that proactively captures Twitter-specific interactions. We examine the feasibility of running analytical queries on GDBMS and offer empirical analysis of the performance of the proposed approach. Next, we consider a use case of modeling software code dependencies in a graph database system, and investigate how these systems can support capturing the evolution of a codebase overtime. We study a code comprehension tool that extracts software dependencies and stores them in a graph database. On a versioned graph built using a very large codebase, we demonstrate how existing code comprehension queries can be efficiently processed and also show the benefit of running queries across multiple versions. Another important aspect of this thesis is the study of storage aspects of graph systems. Throughput of many graph queries can be significantly affected by disk I/O performance; therefore graph database systems need to focus on effective graph storage for optimising disk operations. We observe that the locality of edges plays an important role and we address the edge-labeling problem which aims to label both incoming and outgoing edges of a graph maximizing the ‘edge-consecutiveness’ metric. By achieving a better layout and locality of edges on disk, we show that our proposed algorithms result in significantly improved disk I/O performance leading to faster execution of neighbourhood queries. Some applications require the integrated processing of queries from graph and the textual domains within a graph database system. Aggregation of these dimensions facilitates gaining key insights in several application scenarios. For example, in a social network setting, one may want to find the closest k users in the network (graph traversal) who talk about a particular topic A (textual search). Motivated by such practical use cases, in this thesis we study the top-k social-textual ranking query that essentially requires efficient combination of a keyword search query with a graph traversal. We propose algorithms that leverage graph partitioning techniques, based on the premise that socially close users will be placed within the same partition, allowing more localised computations. We show that our proposed approaches are able to achieve significantly better results compared to standard baselines and demonstrating robust behaviour under changing parameters

    A history and theory of textual event detection and recognition

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