Distributed Graph Queries for Runtime Monitoring of Cyber-Physical Systems

In safety-critical cyber-physical systems (CPS), a service failure may result in severe financial loss or damage in human life. Smart CPSs have complex interaction with their environment which is rarely known in advance, and they heavily depend on intelligent data processing carried out over a heterogeneous computation platform and provide autonomous behavior. This complexity makes design time verification infeasible in practice, and many CPSs need advanced runtime monitoring techniques to ensure safe operation. While graph queries are a powerful technique used in many industrial design tools of CPSs, in this paper, we propose to use them to specify safety properties for runtime monitors on a high-level of abstraction. Distributed runtime monitoring is carried out by evaluating graph queries over a distributed runtime model of the system which incorporates domain concepts and platform information. We provide a semantic treatment of distributed graph queries using 3-valued logic. Our approach is illustrated and an initial evaluation is carried out using an educational demonstrator of CPSs

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

Distributed Runtime Verification of Cyber-Physical Systems Based on Graph Pattern Matching

Cyber-physical systems process a huge amount of data coming from sensors and other information sources and they often have to provide real-time feedback and reaction. Cyber-physical systems are often critical, which means that their failure can lead to serious injuries or even loss of human lives. Ensuring correctness is an important issue, however traditional design-time verification approaches can not be applied due to the complex interaction with the changing environment, the distributed behavior and the intelligent/autonomous solutions. In this paper we present a framework for distributed runtime verification of cyber-physical systems including the solution for executing queries on a distributed model stored on multiple nodes

A Selectivity based approach to Continuous Pattern Detection in Streaming Graphs

Cyber security is one of the most significant technical challenges in current times. Detecting adversarial activities, prevention of theft of intellectual properties and customer data is a high priority for corporations and government agencies around the world. Cyber defenders need to analyze massive-scale, high-resolution network flows to identify, categorize, and mitigate attacks involving networks spanning institutional and national boundaries. Many of the cyber attacks can be described as subgraph patterns, with prominent examples being insider infiltrations (path queries), denial of service (parallel paths) and malicious spreads (tree queries). This motivates us to explore subgraph matching on streaming graphs in a continuous setting. The novelty of our work lies in using the subgraph distributional statistics collected from the streaming graph to determine the query processing strategy. We introduce a "Lazy Search" algorithm where the search strategy is decided on a vertex-to-vertex basis depending on the likelihood of a match in the vertex neighborhood. We also propose a metric named "Relative Selectivity" that is used to select between different query processing strategies. Our experiments performed on real online news, network traffic stream and a synthetic social network benchmark demonstrate 10-100x speedups over selectivity agnostic approaches.Comment: in 18th International Conference on Extending Database Technology (EDBT) (2015

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]