Knowledge-infused and Consistent Complex Event Processing over Real-time and Persistent Streams

Emerging applications in Internet of Things (IoT) and Cyber-Physical Systems (CPS) present novel challenges to Big Data platforms for performing online analytics. Ubiquitous sensors from IoT deployments are able to generate data streams at high velocity, that include information from a variety of domains, and accumulate to large volumes on disk. Complex Event Processing (CEP) is recognized as an important real-time computing paradigm for analyzing continuous data streams. However, existing work on CEP is largely limited to relational query processing, exposing two distinctive gaps for query specification and execution: (1) infusing the relational query model with higher level knowledge semantics, and (2) seamless query evaluation across temporal spaces that span past, present and future events. These allow accessible analytics over data streams having properties from different disciplines, and help span the velocity (real-time) and volume (persistent) dimensions. In this article, we introduce a Knowledge-infused CEP (X-CEP) framework that provides domain-aware knowledge query constructs along with temporal operators that allow end-to-end queries to span across real-time and persistent streams. We translate this query model to efficient query execution over online and offline data streams, proposing several optimizations to mitigate the overheads introduced by evaluating semantic predicates and in accessing high-volume historic data streams. The proposed X-CEP query model and execution approaches are implemented in our prototype semantic CEP engine, SCEPter. We validate our query model using domain-aware CEP queries from a real-world Smart Power Grid application, and experimentally analyze the benefits of our optimizations for executing these queries, using event streams from a campus-microgrid IoT deployment.Comment: 34 pages, 16 figures, accepted in Future Generation Computer Systems, October 27, 201

Plataforma baseada na arquitetura lambda aplicada a cenário IoT

Mestrado em Sistemas de InformaçãoDesde o início da primeira década do presente milénio, tem-se testemunhado um aumento exponencial da quantidade de dados produzidos de dia para dia. Numa primeira instância, o aumento foi atribuído aos dados gerados pelos dispositivos GPS; numa segunda fase, à rápida expansão das redes sociais, agora não devido a um fator específico, mas devido ao surgimento de um novo conceito denominado de Internet das Coisas. Este novo conceito, com resultados já mensuráveis, nasceu da premissa de facilitar o dia-a-dia das pessoas fazendo com que os dispositivos eletrónicos comunicassem entre si com o objetivo de sugerir e assistir a pequenas decisões dado os comportamentos observados no passado. Com o objetivo de manter o conceito possível e o estender para além das já existentes aplicações, os dados gerados pelos dispositivos necessitam não apenas de serem armazenados, mas igualmente processados. Adicionando ao volume de dados a sua variedade e velocidade de produção, estes são igualmente fatores que quando não ultrapassados da maneira correta podem apresentar diversas dificuldades, ao ponto de inviabilizarem a criação de novas aplicações baseadas neste novo conceito. Os mecanismos e tecnologias existentes não acompanharam a evolução das novas necessidades, e para que o conceito possa evoluir, novas soluções são obrigatórias. A liderar a lista das novas tecnologias preparadas para este novo tipo de desafios, composto por um sistema de ficheiros distribuído e uma plataforma de processamento distribuída, está o Hadoop. O Hadoop é uma referência para a resolução desta nova gama de problemas, e já comprovou ser capaz de processar enormes quantidades de dados de maneira económica. No entanto, dadas as suas características, tem alguma dificuldade em processar menores quantidades de dados e tem como desvantagem a grande latência necessária para a iniciação do processamento de dados. Num mercado volátil, ser capaz de processar grandes quantidades de dados baseadas em dados passados não é o suficiente. Tecnologias capazes de processar dados em tempo real são igualmente necessárias para complementar as necessidades de processamento de dados anteriores. No panorama atual, as tecnologias existentes não se demonstram à prova de tão distintas necessidades e, quando postas à prova, diferentes produtos tecnológicos necessitam ser combinados. Resultado de um ambiente com as características descritas é o ambiente que servirá de contexto para a execução do trabalho que se segue. Tendo com base as necessidades impostas por um caso de uso pertencente a IoT, através da arquitetura Lambda, diferentes tecnologias serão combinadas com o objetivo de que no final todos os requisitos impostos possam ser ultrapassados. No final, a solução apresentada será avaliada sobre um ambiente real como forma de prova de conceito.Since the beginning of the first decade of current millennium, it has been witnessed an exponential grow of data being produced every day. First, the increase was given to the amount of data generated by GPS devices, then, the quickly arise of social networks, and now because a new trend as emerged named Internet of Things. This new concept, which is already a reality, was born from the premise of facilitating people's lives by having small electronic devices communicating with each other with the goal to suggest small daily decisions based on the behaviours experienced in the past. With the goal to keep this concept alive and extended further to other applications, the data produced by the target electronic devices is however need to be process and storage. The data volume, velocity and variety are the main variables which when not over planned on the correct way, a wall is created at the point of enviabilize the leverage of the true potential of this new group of applications. Traditional mechanisms and technologies did not follow the actual needs and with the goal to keep the concept alive the address of new technologies are now mandatory. On top of the line, leading the resolution of this new set of challenges, composed by a distributed file system and a parallel processing Framework is Hadoop. Hadoop have proven to fit under the new imposed challenges being capable of process and storage high volumes of data on a cost-effective batch-oriented way. However, given its characteristics on other hand it presents some drawbacks when faced with small amounts of data. In order to gain leverage on market, the companies need not only to be capable of process the data, but process it in a profitable way. Real time processing technologies are needed to complement batch oriented technologies. There is no one size fits all system and with the goal to address the multiples requirements, different technologies are required to be combined. Result of the demanding requirements imposed by the IoT concepts, is the environment which on will be relied the address of the business use case under analyses. Based on the needs imposed by a use case belonging to IoT, through the Lambda architecture, different technologies will be combined with the goal that in the end all the imposed requirements can be accomplished and exceeded. In the end, the solution presented will be evaluated on a real environment as proof of concept

Proceedings of the 2012 Workshop on Ambient Intelligence Infrastructures (WAmIi)

This is a technical report including the papers presented at the Workshop on Ambient Intelligence Infrastructures (WAmIi) that took place in conjunction with the International Joint Conference on Ambient Intelligence (AmI) in Pisa, Italy on November 13, 2012. The motivation for organizing the workshop was the wish to learn from past experience on Ambient Intelligence systems, and in particular, on the lessons learned on the system architecture of such systems. A significant number of European projects and other research have been performed, often with the goal of developing AmI technology to showcase AmI scenarios. We believe that for AmI to become further successfully accepted the system architecture is essential

Proceedings of the 2012 Workshop on Ambient Intelligence Infrastructures (WAmIi)

This is a technical report including the papers presented at the Workshop on Ambient Intelligence Infrastructures (WAmIi) that took place in conjunction with the International Joint Conference on Ambient Intelligence (AmI) in Pisa, Italy on November 13, 2012. The motivation for organizing the workshop was the wish to learn from past experience on Ambient Intelligence systems, and in particular, on the lessons learned on the system architecture of such systems. A significant number of European projects and other research have been performed, often with the goal of developing AmI technology to showcase AmI scenarios. We believe that for AmI to become further successfully accepted the system architecture is essential

Knowledge based system development as an engineering process

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Knowledge Based System (KBS) development is a difficult and challenging task, in particular in knowledge intensive domains. The traditional view of knowledge engineering is one of mining experts' knowledge and somehow transforming it into a machine usable form. This process, in general, suffers from insufficient or misconstrued representation of experts' problem solving behaviour. It is also unstructured and unduly biased at an early stage by design and implementation issues - normally in the form of incremental prototyping. We believe that both knowledge acquisition and KBS development for real life applications will require a 'structured' approach. This approach should harness a KBS developer's ability in extracting knowledge and developing systems. The structure should also be sufficiently flexible to allow the knowledge engineer to use his sense of creativity in developing a KBS. This thesis puts forward such a structured approach, in which KBS development is carried out in an engineering fashion. A process in which the worker is provided with an environment for developing knowledge based systems as an engineering process, as opposed to that of an artform or crafting. The main emphasis of this work is that part of the process which deals with the analysis and design phases in developing KBS. The analysis is performed at an 'epistemological' level, not coloured by design or implementation issues. The output of this phase captures both an expert's problem solving capability, and the business constraints placed upon the intended system. This is then used by the design process in order to create an optimal, workable, and elegant design architecture for the ultimate system.Commission for the European Communities' ESPRIT programme (Project Number 1098

A Big Data perspective on Cyber-Physical Systems for Industry 4.0: modernizing and scaling complex event processing

Doctoral program in Advanced Engineering Systems for IndustryNowadays, the whole industry makes efforts to find the most productive ways of working and it already understood that using the data that is being produced inside and outside the factories is a way to improve the business performance. A set of modern technologies combined with sensor-based communication create the possibility to act according to our needs, precisely at the moment when the data is being produced and processed. Considering the diversity of processes existing in a factory, all of them producing data, Complex Event Processing (CEP) with the capabilities to process that amount of data is needed in the daily work of a factory, to process different types of events and find patterns between them. Although the integration of the Big Data and Complex Event Processing topics is already present in the literature, open challenges in this area were identified, hence the reason for the contribution presented in this thesis. Thereby, this doctoral thesis proposes a system architecture that integrates the CEP concept with a rulebased approach in the Big Data context: the Intelligent Event Broker (IEB). This architecture proposes the use of adequate Big Data technologies in its several components. At the same time, some of the gaps identified in this area were fulfilled, complementing Event Processing with the possibility to use Machine Learning Models that can be integrated in the rules' verification, and also proposing an innovative monitoring system with an immersive visualization component to monitor the IEB and prevent its uncontrolled growth, since there are always several processes inside a factory that can be integrated in the system. The proposed architecture was validated with a demonstration case using, as an example, the Active Lot Release Bosch's system. This demonstration case revealed that it is feasible to implement the proposed architecture and proved the adequate functioning of the IEB system to process Bosch's business processes data and also to monitor its components and the events flowing through those components.Hoje em dia as indústrias esforçam-se para encontrar formas de serem mais produtivas. A utilização dos dados que são produzidos dentro e fora das fábricas já foi identificada como uma forma de melhorar o desempenho do negócio. Um conjunto de tecnologias atuais combinado com a comunicação baseada em sensores cria a possibilidade de se atuar precisamente no momento em que os dados estão a ser produzidos e processados, assegurando resposta às necessidades do negócio. Considerando a diversidade de processos que existem e produzem dados numa fábrica, as capacidades do Processamento de Eventos Complexos (CEP) revelam-se necessárias no quotidiano de uma fábrica, processando diferentes tipos de eventos e encontrando padrões entre os mesmos. Apesar da integração do conceito CEP na era de Big Data ser um tópico já presente na literatura, existem ainda desafios nesta área que foram identificados e que dão origem às contribuições presentes nesta tese. Assim, esta tese de doutoramento propõe uma arquitetura para um sistema que integre o conceito de CEP na era do Big Data, seguindo uma abordagem baseada em regras: o Intelligent Event Broker (IEB). Esta arquitetura propõe a utilização de tecnologias de Big Data que sejam adequadas aos seus diversos componentes. As lacunas identificadas na literatura foram consideradas, complementando o processamento de eventos com a possibilidade de utilizar modelos de Machine Learning com vista a serem integrados na verificação das regras, propondo também um sistema de monitorização inovador composto por um componente de visualização imersiva que permite monitorizar o IEB e prevenir o seu crescimento descontrolado, o que pode acontecer devido à integração do conjunto significativo de processos existentes numa fábrica. A arquitetura proposta foi validada através de um caso de demonstração que usou os dados do Active Lot Release, um sistema da Bosch. Os resultados revelaram a viabilidade da implementação da arquitetura e comprovaram o adequado funcionamento do sistema no que diz respeito ao processamento dos dados dos processos de negócio da Bosch e à monitorização dos componentes do IEB e eventos que fluem através desses.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020, the Doctoral scholarship PD/BDE/135101/2017 and by European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project nº 039479; Funding Reference: POCI-01- 0247-FEDER-039479]

Inferring Complex Activities for Context-aware Systems within Smart Environments

The rising ageing population worldwide and the prevalence of age-related conditions such as physical fragility, mental impairments and chronic diseases have significantly impacted the quality of life and caused a shortage of health and care services. Over-stretched healthcare providers are leading to a paradigm shift in public healthcare provisioning. Thus, Ambient Assisted Living (AAL) using Smart Homes (SH) technologies has been rigorously investigated to help address the aforementioned problems. Human Activity Recognition (HAR) is a critical component in AAL systems which enables applications such as just-in-time assistance, behaviour analysis, anomalies detection and emergency notifications. This thesis is aimed at investigating challenges faced in accurately recognising Activities of Daily Living (ADLs) performed by single or multiple inhabitants within smart environments. Specifically, this thesis explores five complementary research challenges in HAR. The first study contributes to knowledge by developing a semantic-enabled data segmentation approach with user-preferences. The second study takes the segmented set of sensor data to investigate and recognise human ADLs at multi-granular action level; coarse- and fine-grained action level. At the coarse-grained actions level, semantic relationships between the sensor, object and ADLs are deduced, whereas, at fine-grained action level, object usage at the satisfactory threshold with the evidence fused from multimodal sensor data is leveraged to verify the intended actions. Moreover, due to imprecise/vague interpretations of multimodal sensors and data fusion challenges, fuzzy set theory and fuzzy web ontology language (fuzzy-OWL) are leveraged. The third study focuses on incorporating uncertainties caused in HAR due to factors such as technological failure, object malfunction, and human errors. Hence, existing studies uncertainty theories and approaches are analysed and based on the findings, probabilistic ontology (PR-OWL) based HAR approach is proposed. The fourth study extends the first three studies to distinguish activities conducted by more than one inhabitant in a shared smart environment with the use of discriminative sensor-based techniques and time-series pattern analysis. The final study investigates in a suitable system architecture with a real-time smart environment tailored to AAL system and proposes microservices architecture with sensor-based off-the-shelf and bespoke sensing methods. The initial semantic-enabled data segmentation study was evaluated with 100% and 97.8% accuracy to segment sensor events under single and mixed activities scenarios. However, the average classification time taken to segment each sensor events have suffered from 3971ms and 62183ms for single and mixed activities scenarios, respectively. The second study to detect fine-grained-level user actions was evaluated with 30 and 153 fuzzy rules to detect two fine-grained movements with a pre-collected dataset from the real-time smart environment. The result of the second study indicate good average accuracy of 83.33% and 100% but with the high average duration of 24648ms and 105318ms, and posing further challenges for the scalability of fusion rule creations. The third study was evaluated by incorporating PR-OWL ontology with ADL ontologies and Semantic-Sensor-Network (SSN) ontology to define four types of uncertainties presented in the kitchen-based activity. The fourth study illustrated a case study to extended single-user AR to multi-user AR by combining RFID tags and fingerprint sensors discriminative sensors to identify and associate user actions with the aid of time-series analysis. The last study responds to the computations and performance requirements for the four studies by analysing and proposing microservices-based system architecture for AAL system. A future research investigation towards adopting fog/edge computing paradigms from cloud computing is discussed for higher availability, reduced network traffic/energy, cost, and creating a decentralised system. As a result of the five studies, this thesis develops a knowledge-driven framework to estimate and recognise multi-user activities at fine-grained level user actions. This framework integrates three complementary ontologies to conceptualise factual, fuzzy and uncertainties in the environment/ADLs, time-series analysis and discriminative sensing environment. Moreover, a distributed software architecture, multimodal sensor-based hardware prototypes, and other supportive utility tools such as simulator and synthetic ADL data generator for the experimentation were developed to support the evaluation of the proposed approaches. The distributed system is platform-independent and currently supported by an Android mobile application and web-browser based client interfaces for retrieving information such as live sensor events and HAR results

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version