A Novel Real-Time Edge-Cloud Big Data Management and Analytics Framework for Smart Cities

Exposing city information to dynamic, distributed, powerful, scalable, and user-friendly big data systems is expected to enable the implementation of a wide range of new opportunities; however, the size, heterogeneity and geographical dispersion of data often makes it difficult to combine, analyze and consume them in a single system. In the context of the H2020 CLASS project, we describe an innovative framework aiming to facilitate the design of advanced big-data analytics workflows. The proposal covers the whole compute continuum, from edge to cloud, and relies on a well-organized distributed infrastructure exploiting: a) edge solutions with advanced computer vision technologies enabling the real-time generation of “rich” data from a vast array of sensor types; b) cloud data management techniques offering efficient storage, real-time querying and updating of the high-frequency incoming data at different granularity levels. We specifically focus on obstacle detection and tracking for edge processing, and consider a traffic density monitoring application, with hierarchical data aggregation features for cloud processing; the discussed techniques will constitute the groundwork enabling many further services. The tests are performed on the real use-case of the Modena Automotive Smart Area (MASA)

Accelerating Event Stream Processing in On- and Offline Systems

Due to a growing number of data producers and their ever-increasing data volume, the ability to ingest, analyze, and store potentially never-ending streams of data is a mission-critical task in today's data processing landscape. A widespread form of data streams are event streams, which consist of continuously arriving notifications about some real-world phenomena. For example, a temperature sensor naturally generates an event stream by periodically measuring the temperature and reporting it with measurement time in case of a substantial change to the previous measurement. In this thesis, we consider two kinds of event stream processing: online and offline. Online refers to processing events solely in main memory as soon as they arrive, while offline means processing event data previously persisted to non-volatile storage. Both modes are supported by widely used scale-out general-purpose stream processing engines (SPEs) like Apache Flink or Spark Streaming. However, such engines suffer from two significant deficiencies that severely limit their processing performance. First, for offline processing, they load the entire stream from non-volatile secondary storage and replay all data items into the associated online engine in order of their original arrival. While this naturally ensures unified query semantics for on- and offline processing, the costs for reading the entire stream from non-volatile storage quickly dominate the overall processing costs. Second, modern SPEs focus on scaling out computations across the nodes of a cluster, but use only a fraction of the available resources of individual nodes. This thesis tackles those problems with three different approaches. First, we present novel techniques for the offline processing of two important query types (windowed aggregation and sequential pattern matching). Our methods utilize well-understood indexing techniques to reduce the total amount of data to read from non-volatile storage. We show that this improves the overall query runtime significantly. In particular, this thesis develops the first index-based algorithms for pattern queries expressed with the Match_Recognize clause, a new and powerful language feature of SQL that has received little attention so far. Second, we show how to maximize resource utilization of single nodes by exploiting the capabilities of modern hardware. Therefore, we develop a prototypical shared-memory CPU-GPU-enabled event processing system. The system provides implementations of all major event processing operators (filtering, windowed aggregation, windowed join, and sequential pattern matching). Our experiments reveal that regarding resource utilization and processing throughput, such a hardware-enabled system is superior to hardware-agnostic general-purpose engines. Finally, we present TPStream, a new operator for pattern matching over temporal intervals. TPStream achieves low processing latency and, in contrast to sequential pattern matching, is easily parallelizable even for unpartitioned input streams. This results in maximized resource utilization, especially for modern CPUs with multiple cores

Towards Image-Guided Pediatric Atrial Septal Defect Repair

Congenital heart disease occurs in 107.6 out of 10,000 live births, with Atrial Septal Defects (ASD) accounting for 10\% of these conditions. Historically, ASDs were treated with open heart surgery using cardiopulmonary bypass, allowing a patch to be sewn over the defect. In 1976, King et al. demonstrated use of a transcatheter occlusion procedure, thus reducing the invasiveness of ASD repair. Localization during these catheter based procedures traditionally has relied on bi-plane fluoroscopy; more recently trans-esophageal echocardiography (TEE) and intra-cardiac echocardiography (ICE) have been used to navigate these procedures. Although there is a high success rate using the transcatheter occlusion procedure, fluoroscopy poses radiation dose risk to both patient and clinician. The impact of this dose to the patients is important as many of those undergoing this procedure are children, who have an increased risk associated with radiation exposure. Their longer life expectancy than adults provides a larger window of opportunity for expressing the damaging effects of ionizing radiation. In addition, epidemiologic studies of exposed populations have demonstrated that children are considerably more sensitive to the carcinogenic effects radiation. Image-guided surgery (IGS) uses pre-operative and intra-operative images to guide surgery or an interventional procedure. Central to every IGS system is a software application capable of processing and displaying patient images, registration between multiple coordinate systems, and interfacing with a tool tracking system. We have developed a novel image-guided surgery framework called Kit for Navigation by Image Focused Exploration (KNIFE). This software system serves as the core technology by which a system for reduction of radiation exposure to pediatric patients was developed. The bulk of the initial work in this research endevaour was the development of KNIFE which itself went through countless iterations before arriving at its current state as per the feature requirements established. Secondly, since this work involved the use of captured medical images and their use in an IGS software suite, a brief analysis of the physics behind the images was conducted. Through this aspect of the work, intrinsic parameters (principal point and focal point) of the fluoroscope were quantified using a 3D grid calibration phantom. A second grid phantom was traversed through the fluoroscopic imaging volume of II and flat panel based systems at 2 cm intervals building a scatter field of the volume to demonstrate pincushion and \u27S\u27 distortion in the images. Effects of projection distortion on the images was assessed by measuring the fiducial registration error (FRE) of each point used in two different registration techniques, where both methods utilized ordinary procrustes analysis but the second used a projection matrix built from the fluoroscopes calculated intrinsic parameters. A case study was performed to test whether the projection registration outperforms the rigid transform only. Using the knowledge generated were able to successfully design and complete mock clinical procedures using cardiac phantom models. These mock trials at the beginning of this work used a single point to represent catheter location but this was eventually replaced with a full shape model that offered numerous advantages. At the conclusion of this work a novel protocol for conducting IG ASD procedures was developed. Future work would involve the construction of novel EM tracked tools, phantom models for other vascular diseases and finally clinical integration and use

Data semantic enrichment for complex event processing over IoT Data Streams

This thesis generalizes techniques for processing IoT data streams, semantically enrich data with contextual information, as well as complex event processing in IoT applications. A case study for ECG anomaly detection and signal classification was conducted to validate the knowledge foundation

SOCIAL MEDIA ANALYTICS − A UNIFYING DEFINITION, COMPREHENSIVE FRAMEWORK, AND ASSESSMENT OF ALGORITHMS FOR IDENTIFYING INFLUENCERS IN SOCIAL MEDIA

Given its relative infancy, there is a dearth of research on a comprehensive view of business social media analytics (SMA). This dissertation first examines current literature related to SMA and develops an integrated, unifying definition of business SMA, providing a nuanced starting point for future business SMA research. This dissertation identifies several benefits of business SMA, and elaborates on some of them, while presenting recent empirical evidence in support of foregoing observations. The dissertation also describes several challenges facing business SMA today, along with supporting evidence from the literature, some of which also offer mitigating solutions in particular contexts. The second part of this dissertation studies one SMA implication focusing on identifying social influencer. Growing social media usage, accompanied by explosive growth in SMA, has resulted in increasing interest in finding automated ways of discovering influencers in online social interactions. Beginning 2008, many variants of multiple basic approaches have been proposed. Yet, there is no comprehensive study investigating the relative efficacy of these methods in specific settings. This dissertation investigates and reports on the relative performance of multiple methods on Twitter datasets containing between them tens of thousands to hundreds of thousands of tweets. Accordingly, the second part of the dissertation helps further an understanding of business SMA and its many aspects, grounded in recent empirical work, and is a basis for further research and development. This dissertation provides a relatively comprehensive understanding of SMA and the implementation SMA in influencer identification

Applied (Meta)-Heuristic in Intelligent Systems

Engineering and business problems are becoming increasingly difficult to solve due to the new economics triggered by big data, artificial intelligence, and the internet of things. Exact algorithms and heuristics are insufficient for solving such large and unstructured problems; instead, metaheuristic algorithms have emerged as the prevailing methods. A generic metaheuristic framework guides the course of search trajectories beyond local optimality, thus overcoming the limitations of traditional computation methods. The application of modern metaheuristics ranges from unmanned aerial and ground surface vehicles, unmanned factories, resource-constrained production, and humanoids to green logistics, renewable energy, circular economy, agricultural technology, environmental protection, finance technology, and the entertainment industry. This Special Issue presents high-quality papers proposing modern metaheuristics in intelligent systems

More than communities: organizing in online interaction spaces

This dissertation examined four online forums for evidence of organizing in order to evaluate the accuracy of the term "online community" for describing all online interaction spaces. The Four Flows Model (McPhee & Zaug, 2000) was used as a guiding theoretical framework during a content analysis of the messages within each forum in order to identify the type and amount of organizational processes enacted through forum members' interactions. Mintzberg's (1979) conceptualization of the organization and the Four Flows Model were used to interpret the results of the content analysis and a network analysis of the forums' communication networks in order to determine whether any of the forums functioned and were constituted as organizations. Evidence of all four types of organizing processes were found within each of the forums, and two forums were determined to function as organizations. The definition of online community was revised in light of the results, and a definition was offered for the new concept, "online organization" that describes how larger communities of shared interest can organize within online interaction spaces to accomplish members' shared goals. A theoretical model was also developed to situate all online interaction spaces relative to one another according to the prevalence of organizational and social messages within them

A software to manage rehabilitation sessions with a robotic walker

Dissertação de mestrado integrado em Informatics EngineeringCerebellar ataxia arises from damage or dysfunction that affects the cerebellum and its pathways. As a result, the motor abilities of individuals with this condition become weakened. Robotics-assisted therapy is still an emerging area, but it has several advantages that could boost the rehabilitation of these individuals. Considering this problematic, WALKit Smart Walker is being developed. Its main purpose is to improve the treatment of ataxic patients through intelligent and multidisciplinary rehabilitation sessions. Thus, it is equipped with several sensors that provide monitoring capabilities through a continuous evaluation of the end-user gait and posture. A vast amount of data is acquired during each session by the walker sensors. For health professionals to analyse this data and have feedback on the patient’s status throughout therapy, tools are needed to control, manage, and monitor sessions in a clear, practical and intuitive way. Therefore, the main goal of this dissertation is centred on implementing an effective way to store the acquired data, along with the development of software that satisfies these requirements. To address these goals, a polyglot persistence database system, composed of a relational and a non-relational database, was implemented to store the required data while maintaining efficiency. Furthermore, a web application was developed to provide, not only to health professionals, but also to patients themselves, the management of the rehabilitation sessions with the walker. The application provides an individual and temporal analysis of the sessions through interactive graphics adapted to each patient. Additionally, it allows the management of the several patients who are/were in treatment and the addition of clinical ratting scales, which are useful to assess their motor condition and adapt therapies as needed. In this way, professionals can have a better perception of the patient’s condition, and can show patients their evolution, possibly contributing to increase their motivation in therapy. Moreover, in the context of this dissertation, the embedded software of WALKit SmartW, which allows the therapy configuration, was optimized. This software had no security mechanisms, thus the main goal was on the implementation of techniques capable of making the software secure. Additionally, other functionalities such as feedback alerts, were added to the existing application. Throughout the development of this project, it was possible to have continuous feedback from health professionals of the Hospital of Braga. Usability tests and questionnaires were also applied, and the results were very promising, enhancing the need for a system with these characteristics. Professionals claimed the system may help in analysing the patient clinical status in an intuitive form while keeping them motivated during treatments.A ataxia cerebelar surge a partir de danos ou disfunções que afetam o cerebelo e as suas vias. Como resultado, as capacidades motoras dos indivíduos que possuem esta condição ficam fragilizadas. A terapia assistida por robôs é ainda uma área em desenvolvimento, no entanto apresenta diversas vantagens que poderão agilizar os tratamentos destes indivíduos. Atendendo a esta problemática, o WALKit SmartW encontra-se a ser desenvolvido. O seu principal propósito é auxiliar os tratamentos de pacientes ataxicos através de sessões de reabilitação inteligentes e multidisciplinares. Para tal, é composto por um conjunto de sensores que fornecem uma monitorização e avaliação contínua da marcha e da postura do utilizador. Uma grande quantidade de dados é adquirida ao longo de cada sessão através dos sensores. De forma a que os profissionais de saúde analisem estes dados e tenham feedback do estado do paciente ao longo da terapia, são necessárias ferramentas que permitam controlar, gerir e monitorizar as sessões, de forma clara, prática e intuitiva. O principal objetivo desta dissertação centra-se na implementação de uma estratégia eficiente para armazenar os dados, juntamente com o desenvolvimento de um software que satisfaça estes requisitos. Para cumprir estes objetivos, um sistema de base de dados com persistência poliglota, composto por uma base de dados relacional e uma não relacional, foi implementado para armazenar os dados mantendo a eficiência. Além disso, uma aplicação web foi desenvolvida para proporcionar, não só aos profissionais de saúde, como também aos próprios pacientes, a gestão das sessões de reabilitação com o andarilho. A aplicação disponibiliza uma análise individual e temporal das sessões através de gráficos interativos adaptados a cada paciente. Adicionalmente, possibilita também a gestão dos diversos pacientes que estão/estiveram em tratamento, e a adição de escalas de classificação clínica, que são úteis para avaliar a condição motora e adaptar as terapias conforme necessário. Desta forma, os profissionais conseguem ter uma melhor perceção acerca do estado do paciente, e os pacientes podem ver a sua evolução, contribuindo para aumentar a motivação na terapia. Ainda no contexto desta dissertação, otimizou-se a aplicação embebida no software do andarilho WALKit, que permite as configurações da terapia. O software era isento de qualquer mecanismo de segurança, pelo que o maior foco centrou-se na aplicação de técnicas capazes de o tornar seguro. Adicionalmente, outras funcionalidades, como alertas e configurações de algoritmos, foram adicionadas à aplicação existente. Ao longo do desenvolvimento deste projeto, foi possível obter o feedback contínuo de profissionais de saúde do Hospital de Braga. Testes e questionários de usabilidade foram também aplicados, e os resusltados foram bastante promissores, reforçando a necessidade de um sistema com estas características. Os profissionais afirmaram que o sistema irá ajudar a analisar o estado do paciente de forma intuitiva, mantendo-o motivado durante os tratamentos

Hierarchical categorisation of tags for delicious

In the scenario of social bookmarking, a user browsing the Web bookmarks web pages and assigns free-text labels (i.e., tags) to them according to their personal preferences. In this technical report, we approach one of the practical aspects when it comes to represent users' interests from their tagging activity, namely the categorization of tags into high-level categories of interest. The reason is that the representation of user profiles on the basis of the myriad of tags available on the Web is certainly unfeasible from various practical perspectives; mainly concerning the unavailability of data to reliably, accurately measure interests across such fine-grained categorisation, and, should the data be available, its overwhelming computational intractability. Motivated by this, our study presents the results of a categorization process whereby a collection of tags posted at Delicious #http://delicious.com# are classified into 200 subcategories of interest.Preprin