[pt] A popularização da Internet das Coisas (IoT, Internet of Things) provocou uma crescente oportunidade para a criação de aplicações em diversas áreas, através da combinação do uso de sensores e/ou atuadores. Em ambientes de IoT, o papel de elementos chamados de gateways consiste em fornecer uma camada de comunicação intermediária entre os dispositivos de IoT e serviços de nuvem. Um fator crucial para a construção de aplicações em larga escala é que os dispositivos de IoT possam ser utilizados de maneira transparente, num paradigma orientado a serviços, onde detalhes de comunicação e configuração destes objetos são tratados pelos gateways. No modelo de serviços, as aplicações devem descobrir as interfaces de alto-nível dos dispositivos e não precisam lidar com detalhes subjacentes, que são tratados pelos gateways. Em cenários de grande dinamismo e mobilidade (com conexões e desconexões de dispositivos acontecendo a todo momento), a descoberta e configuração de objetos deve ocorrer de forma contínua. Os protocolos de descoberta de serviços tradicional, como o Universal Plug and Play (UPnP) ou o Service Location Protocol (SLP), não foram desenvolvidos levando em consideração o alto dinamismo de ambientes IoT. Nesse sentido, introduzimos o processamento de eventos complexos (CEP), que é uma tecnologia para processamento em tempo real de fluxos de eventos heterogêneos, que permite a utilização de consultas em linguagem CQL (Continuous Query Language) para a busca de eventos de interesse. Em um modelo onde os eventos relacionados à descoberta de sensores são enviados para um fluxo CEP, consultas expressivas são escritas para que uma aplicação descubra continuamente serviços de interesse. Este trabalho apresenta a extensão do MHub/CDDL para o suporte à descoberta contínua de serviços em IoT, utilizando CEP. O MHub/CDDL (Mobile Hub / Context Data Distribution Layer) é um middleware para descoberta de serviços e gerenciamento de qualidade de contexto em IoT, desenvolvido numa parceria entre o Laboratory for Advanced Collaboration (LAC) da PUC-Rio e o Laboratório de Sistemas Distribuídos Inteligentes (LSDi) da Universidade Federal do Maranhão (UFMA). A implementação deste trabalho é feita para a plataforma Android (Java) e um estudo de caso no domínio de estacionamentos inteligentes é conduzido e implementado, elucidando o uso do mecanismo de descoberta contínuo. / [en] The popularization of the Internet of Things sparked a growing opportunity for the creation of applications in various areas, by combining the use of sensors and/or actuators. In IoT environments, the role of elements called gateways is to provide an intermediate communication layer between IoT devices and cloud services. A crucial factor for the construction of large-scale applications is to allow the use of IoT devices in a transparent manner, in a service-oriented paradigm, where details of communication and configuration are handled by the gateways. In service model, applications must discover the high-level interfaces of the devices and do not have to deal with underlying details that are handled by gateways. In scenarios of high dynamism and mobility (with connections and disconnections of devices occuring all the time), this discovery and configuration must occur continuously. Traditional service discovery protocols, such as Universal Plug and Play (UPnP) or Service Location Protocol (SLP), have not been developed taking into consideration the high dinamicity of IoT environments. In this sense, we introduce complex event processing (CEP), which is a technology for real-time processing of heterogeneous event flows, which allows the use of CQL (Continuous Query Language for the search of events of interest. In a model where events related to sensor discovery are sent to a CEP flow, expressive queries are written for an application to continuously discover services of interest. This work presents the extension of MHub / CDDL to support continuous service discovery in IoT, using CEP. The MHub / CDDL (Mobile Hub / Context Data Distribution Layer) is a middleware for service discovery and quality context management in IoT, developed in a partnership between the Laboratory for Advanced Collaboration (LAC) from PUC-Rio and the Laboratório de Sistemas Distribuídos Inteligentes (LSDi) from Universidade Federal do Maranhão (UFMA). The implementation of this work is done in Android (Java) platform and a case study in the domain of smart parking is conducted and implemented, elucidating the use of the continuous discovery mechanism.

[pt] A disseminação atual da IoT aumenta a implantação de soluções de processamento de fluxo de dados para monitorar e controlar elementos do mundo real. Uma dessas soluções é o Processamento de Eventos Complexos (CEP). Inicialmente, um único computador ou cluster concentraria toda a execução do CEP. No entanto, a execução centralizada do CEP não é ideal para lidar com o alto volume, velocidade e volatilidade dos fluxos de dados dos sensores IoT. Em vez disso, as aplicações CEP devem criar e decentralizar o processamento de eventos CEP, de preferência tendo agentes CEP na nuvem e em dispositivos na borda. Além disso, tão importante quanto a descentralização, é decidir como o processamento será dividido entre esses dispositivos. Dito isso, estar ciente do contexto atual de cada dispositivo, por exemplo, sua localização e sensores disponíveis, pode ajudar a coletar e (parcialmente) processar os dados em dispositivos próximos ao local onde os dados foram produzidos. Este trabalho apresenta uma plataforma de CEP distribuído com ciência de contexto chamada Global CEP Manager (GCM). GCM é um serviço do middleware ContextNet que oferece suporte à implantação e ao rearranjo dinâmico de consultas CEP baseados em contexto para motores CEP em execução na nuvem, em dispositivos na borda estacionários e M-Hubs, que são dispositivos na borda móveis do ContextNet. O GCM usa o ContextMatcher, que também faz parte deste trabalho. ContextMatcher é um módulo para aplicações ContextNet que permite a entrega de mensagens para nós cujo contexto esteja de compatível com um determinado conjunto de características contextuais. / [en] The current dissemination of IoT increases the deployment of stream processing solutions for monitoring and controlling elements of the real world. One of those solutions is Complex Event Processing (CEP). Initially, a single computer/cluster would concentrate all the CEP execution. However, a centralized execution of CEP is not suitable for coping with the high volume, velocity, and volatility of IoT sensors’ data streams. Instead, applications using CEP should deploy a distributed CEP Event Processing Network, preferably having CEP agents both in the cloud and at edge devices. Also, deciding the arrangement used to split the processing among these tiers and their devices can be just as important. That said, being aware of each of the devices current context, for instance, their location and available sensors, can help to collect and (partially) process the data on devices close to the data s production site. This work presents a contextaware distributed CEP platform called Global CEP Manager (GCM). GCM is a service of the ContextNet middleware that supports the context-based deployment, and dynamic rearrangement of CEP queries to CEP engines executing in the cloud, stationary edge devices, and M-Hubs, which are ContextNet s mobile edge devices. GCM uses the ContextMatcher, which is also part of this work. ContextMatcher is a module for ContextNet applications that enables the delivery of messages for nodes that match a specified set of contextual requirements.

Résumé en français non fourni par l'auteur. / There is a paradigm shift in the nature and processing means of today’s data: data are used to being mostly static and stored in large databases to be queried. Today, with the advent of new applications and means of collecting data, most applications on the Web and in enterprises produce data in a continuous manner under the form of streams. Thus, the users of these applications expect to process a large volume of data with fresh low latency results. This has resulted in the introduction of Data Stream Processing Systems (DSMSs) and a Complex Event Processing (CEP) paradigm – both with distinctive aims: DSMSs are mostly employed to process traditional query operators (mostly stateless), while CEP systems focus on temporal pattern matching (stateful operators) to detect changes in the data that can be thought of as events. In the past decade or so, a number of scalable and performance intensive DSMSs and CEP systems have been proposed. Most of them, however, are based on the relational data models – which begs the question for the support of heterogeneous data sources, i.e., variety of the data. Work in RDF stream processing (RSP) systems partly addresses the challenge of variety by promoting the RDF data model. Nonetheless, challenges like volume and velocity are overlooked by existing approaches. These challenges require customised optimisations which consider RDF as a first class citizen and scale the processof continuous graph pattern matching. To gain insights into these problems, this thesis focuses on developing scalable RDF graph stream processing, and semantically-enabled CEP systems (i.e., Semantic Complex Event Processing, SCEP). In addition to our optimised algorithmic and data structure methodologies, we also contribute to the design of a new query language for SCEP. Our contributions in these two fields are as follows: • RDF Graph Stream Processing. We first propose an RDF graph stream model, where each data item/event within streams is comprised of an RDF graph (a set of RDF triples). Second, we implement customised indexing techniques and data structures to continuously process RDF graph streams in an incremental manner. • Semantic Complex Event Processing. We extend the idea of RDF graph stream processing to enable SCEP over such RDF graph streams, i.e., temporalpattern matching. Our first contribution in this context is to provide a new querylanguage that encompasses the RDF graph stream model and employs a set of expressive temporal operators such as sequencing, kleene-+, negation, optional,conjunction, disjunction and event selection strategies. Based on this, we implement a scalable system that employs a non-deterministic finite automata model to evaluate these operators in an optimised manner. We leverage techniques from diverse fields, such as relational query optimisations, incremental query processing, sensor and social networks in order to solve real-world problems. We have applied our proposed techniques to a wide range of real-world and synthetic datasets to extract the knowledge from RDF structured data in motion. Our experimental evaluations confirm our theoretical insights, and demonstrate the viability of our proposed methods

Modern hardware has the potential to play a central role in scalable data management systems. A realization of this potential arises in the context of indexing queries, a recurring theme in real-time data analytics, targeted advertising, algorithmic trading, and data-centric workflows, and of indexing data, a challenge in multi-version analytical query processing. To enhance query and data indexing, in this thesis, we present an efficient, extensible, and hardware-aware indexing kernel. This indexing kernel rests upon novel data structures and (parallel) algorithms that utilize the capabilities offered by modern hardware, especially abundance of main memory, multi-core architectures, hardware accelerators, and solid state drives. This thesis focuses on presenting our query indexing techniques to cope with processing queries in data-intensive applications that are susceptible to ever increasing data volume and velocity. At the core of our query indexing kernel lies the BE-Tree family of memory-resident indexing structures that scales by overcoming the curse of dimensionality through a novel two-phase space-cutting technique, an effective Top-k processing, and adaptive parallel algorithms to operate directly on compressed data (that exploits the multi-core architecture). Furthermore, we achieve line-rate processing by harnessing the unprecedented degrees of parallelism and pipelining only available through low-level logic design using FPGAs. Finally, we present a comprehensive evaluation that establishes the superiority of BE-Tree in comparison with state-of-the-art algorithms. In this thesis, we further expand the scope of our indexing kernel and describe how to accelerate analytical queries on (multi-version) databases by enabling indexes on the most recent data. Our goal is to reduce the overhead of index maintenance, so that indexes can be used effectively for analytical queries without being a heavy burden on transaction throughput. To achieve this end, we re-design the data structures in the storage hierarchy to employ an extra level of indirection over solid state drives. This indirection layer dramatically reduces the amount of magnetic disk I/Os that is needed for updating indexes and localizes the index maintenance. As a result, by rethinking how data is indexed, we eliminate the dilemma between update vs. query performance and reduce index maintenance and query processing cost substantially.

Modern hardware has the potential to play a central role in scalable data management systems. A realization of this potential arises in the context of indexing queries, a recurring theme in real-time data analytics, targeted advertising, algorithmic trading, and data-centric workflows, and of indexing data, a challenge in multi-version analytical query processing. To enhance query and data indexing, in this thesis, we present an efficient, extensible, and hardware-aware indexing kernel. This indexing kernel rests upon novel data structures and (parallel) algorithms that utilize the capabilities offered by modern hardware, especially abundance of main memory, multi-core architectures, hardware accelerators, and solid state drives. This thesis focuses on presenting our query indexing techniques to cope with processing queries in data-intensive applications that are susceptible to ever increasing data volume and velocity. At the core of our query indexing kernel lies the BE-Tree family of memory-resident indexing structures that scales by overcoming the curse of dimensionality through a novel two-phase space-cutting technique, an effective Top-k processing, and adaptive parallel algorithms to operate directly on compressed data (that exploits the multi-core architecture). Furthermore, we achieve line-rate processing by harnessing the unprecedented degrees of parallelism and pipelining only available through low-level logic design using FPGAs. Finally, we present a comprehensive evaluation that establishes the superiority of BE-Tree in comparison with state-of-the-art algorithms. In this thesis, we further expand the scope of our indexing kernel and describe how to accelerate analytical queries on (multi-version) databases by enabling indexes on the most recent data. Our goal is to reduce the overhead of index maintenance, so that indexes can be used effectively for analytical queries without being a heavy burden on transaction throughput. To achieve this end, we re-design the data structures in the storage hierarchy to employ an extra level of indirection over solid state drives. This indirection layer dramatically reduces the amount of magnetic disk I/Os that is needed for updating indexes and localizes the index maintenance. As a result, by rethinking how data is indexed, we eliminate the dilemma between update vs. query performance and reduce index maintenance and query processing cost substantially.

Submitted by Daniele Amaral (daniee_ni@hotmail.com) on 2016-10-20T18:13:56Z No. of bitstreams: 1 DissDCC.pdf: 2421455 bytes, checksum: 5fd16625959b31340d5f845754f109ce (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-11-08T18:42:36Z (GMT) No. of bitstreams: 1 DissDCC.pdf: 2421455 bytes, checksum: 5fd16625959b31340d5f845754f109ce (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-11-08T18:42:42Z (GMT) No. of bitstreams: 1 DissDCC.pdf: 2421455 bytes, checksum: 5fd16625959b31340d5f845754f109ce (MD5) / Made available in DSpace on 2016-11-08T18:42:49Z (GMT). No. of bitstreams: 1 DissDCC.pdf: 2421455 bytes, checksum: 5fd16625959b31340d5f845754f109ce (MD5) Previous issue date: 2016-06-06 / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / The growing amount of data produced daily, by both businesses and individuals in the web, increased the demand for analysis and extraction of knowledge of this data. While the last two decades the solution was to store and perform data mining algorithms, currently it has become unviable even to supercomputers. In addition, the requirements of the Big Data age go far beyond the large amount of data to analyze. Response time requirements and complexity of the data acquire more weight in many areas in the real world. New models have been researched and developed, often proposing distributed computing or different ways to handle the data stream mining. Current researches shows that an alternative in the data stream mining is to join a real-time event handling mechanism with a classic mining association rules or sequential patterns algorithms. In this work is shown a data stream mining approach to meet the Big Data response time requirement, linking the event handling mechanism in real time Esper and Incremental Miner of Stretchy Time Sequences (IncMSTS) algorithm. The results show that is possible to take a static data mining algorithm for data stream environment and keep tendency in the patterns, although not possible to continuously read all data coming into the data stream. / O crescimento da quantidade de dados produzidos diariamente, tanto por empresas como por indivíduos na web, aumentou a exigência para a análise e extração de conhecimento sobre esses dados. Enquanto nas duas últimas décadas a solução era armazenar e executar algoritmos de mineração de dados, atualmente isso se tornou inviável mesmo em super computadores. Além disso, os requisitos da chamada era do Big Data vão muito além da grande quantidade de dados a se analisar. Requisitos de tempo de resposta e complexidade dos dados adquirem maior peso em muitos domínios no mundo real. Novos modelos têm sido pesquisados e desenvolvidos, muitas vezes propondo computação distribuída ou diferentes formas de se tratar a mineração de fluxo de dados. Pesquisas atuais mostram que uma alternativa na mineração de fluxo de dados é unir um mecanismo de tratamento de eventos em tempo real com algoritmos clássicos de mineração de regras de associação ou padrões sequenciais. Neste trabalho é mostrada uma abordagem de mineração de fluxo de dados (data stream) para atender ao requisito de tempo de resposta do Big Data, que une o mecanismo de manipulação de eventos em tempo real Esper e o algoritmo Incremental Miner of Stretchy Time Sequences (IncMSTS). Os resultados mostram ser possível levar um algoritmo de mineração de dados estático para o ambiente de fluxo de dados e manter as tendências de padrões encontrados, mesmo não sendo possível ler todos os dados vindos continuamente no fluxo de dados.

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2016-10-31T12:04:21Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) DissertacaoHerbertt_CIN_UFPE.pdf: 3162767 bytes, checksum: 3208dfce28e7404730479384c2ba99a0 (MD5) / Made available in DSpace on 2016-10-31T12:04:21Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) DissertacaoHerbertt_CIN_UFPE.pdf: 3162767 bytes, checksum: 3208dfce28e7404730479384c2ba99a0 (MD5) Previous issue date: 2016-03-04 / Cada vez mais se evidencia uma maior escassez de recursos e uma disputa por espaços físicos, em decorrência da crescente e demasiada concentração populacional nas grandes cidades. Nesse âmbito, surge a necessidade de soluções que vão de encontro à iniciativa de “Cidades Inteligentes" (Smart Cities). Essas soluções buscam centralizar o monitoramento e controle, para auxiliar no apoio à tomada de decisão. No entanto, essas fontes de TICs formam estruturas complexas e geram um grande volume de dados, que apresentam enormes desafios e oportunidades. Uma das principais ferramentas tecnológicas utilizadas nesse contexto é o Complex Event Processing (CEP), o qual pode ser considerado uma boa solução, para lidar com o aumento da disponibilidade de grandes volumes de dados, em tempo real. CEPs realizam captação de eventos de maneira simplificada, utilizando linguagem de expressão, para definir e executar regras de processamento. No entanto, apesar da eficiência comprovada dessas ferramentas, o fato das regras serem expressas em baixo nível, torna o seu uso exclusivo para usuários especialistas, dificultando a criação de soluções. Com intuito de diminuir a complexidade das ferramentas de CEP, em algumas soluções, tem-se utilizado uma abordagem de modelos Model-Driven Development (MDD), a fim de se produzir uma camada de abstração, que possibilite criar regras, sem que necessariamente seja um usuário especialista em linguagem de CEP. No entanto, muitas dessas soluções acabam tornando-se mais complexas no seu manuseio do que o uso convencional da linguagem de baixo nível. Este trabalho tem por objetivo a construção de uma Graphic User Interface (GUI) para criação de regras de CEP, utilizando MDD, a fim de tornar o desenvolvimento mais intuitivo, através de um modelo adaptado as necessidades do usuário não especialista. / Nowadays is Increasingly evident a greater resources scarcity and competition for physical space, in result of growing up and large population concentration into large cities. In this context, comes up the necessity of solutions that are in compliance with initiative of smart cities. Those solutions seek concentrate monitoring and control, for help to make decisions. Although, this sources of information technology and communications (ITCs) forming complex structures and generates a huge quantity of data that represents biggest challenges and opportunities. One of the main technological tools used in this context is the Complex Event Processing (CEP), which may be considered a good solution to deal with increase of the availability and large volume of data, in real time. The CEPs realizes captation of events in a simple way, using expressive languages, to define and execute processing rules. Although the efficient use of this tools, the fact of the rules being expressed in low level, becomes your use exclusive for specialists, difficulting the creation of solutions. With the aim of reduce the complexity of the CEPs tools, solutions has used an approach of models Model-Driven Development (MDD), in order to produce an abstraction layer, that allows to create rules, without necessarily being a specialist in CEP languages. however, many this tools become more complex than the conventional low level language approach. This work aims to build a Graphic User Interface (GUI) for the creation of CEP rules, using MDD, in order to a more intuitive development, across of the adapted model how necessities of the non specialist users.

[pt] Dirigir é uma tarefa diária que permite uma locomoção mais rápida e mais confortável, no entanto, mais da metade dos acidentes fatais estão relacionados à imprudência. Manobras imprudentes podem ser detectadas com boa precisão, analisando dados relativos à interação motorista-veículo, por exemplo, curvas, aceleração e desaceleração abruptas. Embora existam algoritmos para detecção online de anomalias, estes normalmente são projetados para serem executados em computadores com grande poder computacional. Além disso, geralmente visam escala através da computação paralela, computação em grid ou computação em nuvem. Esta tese apresenta uma abordagem baseada em complex event processing para a detecção online de anomalias e classificação do comportamento de condução. Além disso, objetivamos identificar se dispositivos móveis com poder computacional limitado, como os smartphones, podem ser usados para uma detecção online do comportamento de condução. Para isso, modelamos e avaliamos três algoritmos de detecção online de anomalia no paradigma de processamento de fluxos de dados, que recebem os dados dos sensores do smartphone e dos sensores à bordo do veículo como entrada. As vantagens que o processamento de fluxos de dados proporciona reside no fato de que este reduz a quantidade de dados transmitidos do dispositivo móvel para servidores/nuvem, bem como se reduz o consumo de energia/bateria devido à transmissão de dados dos sensores e possibilidade de operação mesmo se o dispositivo móvel estiver desconectado. Para classificar os motoristas, um mecanismo estatístico utilizado na mineração de documentos que avalia a importância de uma palavra em uma coleção de documentos, denominada frequência de documento inversa, foi adaptado para identificar a importância de uma anomalia em um fluxo de dados, e avaliar quantitativamente o grau de prudência ou imprudência das manobras dos motoristas. Finalmente, uma avaliação da abordagem (usando o algoritmo que obteve melhor resultado na primeira etapa) foi realizada através de um estudo de caso do comportamento de condução de 25 motoristas em cenário real. Os resultados mostram uma acurácia de classificação de 84 por cento e um tempo médio de processamento de 100 milissegundos. / [en] Driving is a daily task that allows individuals to travel faster and more comfortably, however, more than half of fatal crashes are related to recklessness driving behaviors. Reckless maneuvers can be detected with accuracy by analyzing data related to driver-vehicle interactions, abrupt turns, acceleration, and deceleration, for instance. Although there are algorithms for online anomaly detection, they are usually designed to run on computers with high computational power. In addition, they typically target scale through parallel computing, grid computing, or cloud computing. This thesis presents an online anomaly detection approach based on complex event processing to enable driving behavior classification. In addition, we investigate if mobile devices with limited computational power, such as smartphones, can be used for online detection of driving behavior. To do so, we first model and evaluate three online anomaly detection algorithms in the data stream processing paradigm, which receive data from the smartphone and the in-vehicle embedded sensors as input. The advantages that stream processing provides lies in the fact that reduce the amount of data transmitted from the mobile device to servers/the cloud, as well as reduce the energy/battery usage due to transmission of sensor data and possibility to operate even if the mobile device is disconnected. To classify the drivers, a statistical mechanism used in document mining that evaluates the importance of a word in a collection of documents, called inverse document frequency, has been adapted to identify the importance of an anomaly in a data stream, and then quantitatively evaluate how cautious or reckless drivers maneuvers are. Finally, an evaluation of the approach (using the algorithm that achieved better result in the first step) was carried out through a case study of the 25 drivers driving behavior. The results show an accuracy of 84 percent and an average processing time of 100 milliseconds.

L’internet des objets est au coeur desprocessus industriels intelligents grâce à lacapacité de détection d’évènements à partir dedonnées de capteurs. Cependant, beaucoup resteà faire pour tirer le meilleur parti de cettetechnologie récente et la faire passer à l’échelle.Cette thèse vise à combler le gap entre les fluxmassifs de données collectées par les capteurs etleur exploitation effective dans la gestion desprocessus métier. Elle propose une approcheglobale qui combine le traitement de flux dedonnées, l’apprentissage supervisé et/oul’utilisation de règles sur des évènementscomplexes permettant de prédire (et doncéviter) des évènements indésirables, et enfin lagestion des processus métier étendue par cesrègles complexes.Les contributions scientifiques de cette thèse sesituent dans différents domaines : les processusmétiers plus intelligents et dynamiques; letraitement d’évènements complexes automatisépar l’apprentissage de règles; et enfin et surtout,dans le domaine de la fouille de données deséries temporelles multivariéespar la prédiction précoce de risques.L’application cible de cette thèse est le transportinstrumenté d’oeuvres d’art / Internet of things is at the core ofsmart industrial processes thanks to its capacityof event detection from data conveyed bysensors. However, much remains to be done tomake the most out of this recent technologyand make it scale. This thesis aims at filling thegap between the massive data flow collected bysensors and their effective exploitation inbusiness process management. It proposes aglobal approach, which combines stream dataprocessing, supervised learning and/or use ofcomplex event processing rules allowing topredict (and thereby avoid) undesirable events,and finally business process managementextended to these complex rules. The scientificcontributions of this thesis lie in several topics:making the business process more intelligentand more dynamic; automation of complexevent processing by learning the rules; and lastand not least, in datamining for multivariatetime series by early prediction of risks. Thetarget application of this thesis is theinstrumented transportation of artworks.

[pt] Muitas aplicações necessitam do processamento de eventos de streeams de fontes diferentes em combinação com grandes quantidades de dados de bases de conhecimento. CEP Semântico é um paradigma especificamente designado para isso, ele extende o processamento complexo de eventos (CEP) para adicionar o suporte para a linguagem RDF e utiliza uma rede de operadores para processar streams RDF em combinação com bases de conhecimento em RDF. Outra classe popular de sistemas projetados para um proposito similar são os processadores de stream RDF (RSPs). Estes são sistemas que extendem a linguagem SPARQL (a linguaguem de query padrão para RDF) para adicionar a capacidade de fazer queries em stream. CEP Semântico e RSPs possuem propositos similares porém focam em objetivos diferentes. O CEP Semântico, foca na scalabilidade e processamento distribuido enquanto os RSPs focam nos desafios do processamento de streams RDF. Nesta tese, propomos o uso de RSPs como unidades para processamento de streams RDF dentro do contexto de CEP Semântico. Apresentamos uma infraestrutura, chamada DSCEP, que permite o encapsulamento de RSPs existentes em operadores do estilo CEP, de maneira que estes RSPs possam ser interconectados formando uma rede de operadores distribuída e descentralizada. DSCEP lida com os desafios e obstáculos desta interconexão, como comunicação confiável, divisão e agregação de streams, identificação de eventos e time-stamping, etc., permitindo que os usuários se concentrem nas consultas. Também discutimos nesta tese como o DSCEP pode ser usado para diminuir o tempo de processamento de consultas SPARQL monolíticas, seja dividindo-as em subconsultas e operando-as em paralelo através do uso de operadores ou seja dividingo a stream de entrada em multiplos operadores que possuem a mesma query e são executados em paralelo. Além disso também é avaliado o impacto que a base de conhecimento possui no tempo de processamento de queires contínuas. / [en] Many applications require the processing of event streams from different sources in combination with large amounts of background knowledge. Semantic CEP is a paradigm explicitly designed for that. It extends complex event processing (CEP) with RDF support and uses a network of operators to process RDF streams combined with RDF knowledge bases. Another popular class of systems designed for a similar purpose is the RDF stream processors (RSPs). These are systems that extend SPARQL (the RDF query language) with stream processing capabilities. Semantic CEP and RSPs have similar purposes but focus on different things. The former focuses on scalability and distributed processing, while the latter tends to focus on the intricacies of RDF stream processing per se. In this thesis, we propose the use of RSP engines as building blocks for Semantic CEP. We present an infrastructure, called DSCEP, that allows the encapsulation of existing RSP engines into CEP-like operators so that these can be seamlessly interconnected in a distributed, decentralized operator network. DSCEP handles the hurdles of such interconnection, such as reliable communication, stream aggregation and slicing, event identification and time-stamping, etc., allowing users to concentrate on the queries. We also discuss how DSCEP can be used to speed up monolithic SPARQL queries; by splitting them into parallel subqueries that can be executed by the operator network or even by splitting the input stream into multiple operators with the same query running in parallel. Additionally, we evaluate the impact of the knowledge base on the processing time of SPARQL continuous queries.