Design and implementation of a holistic framework for data integration in industrial machine and sensor networks

Digitalization and connectivity trends in industrial plants and production equipment create vast and heterogeneous networks of data sources, data sinks and various communication protocols. Data fusion and evaluation of these resources result in high costs for data integration and maintenance. Therefore, we propose a new framework, called MyGateway, enabling effortless integration of heterogeneous data sources, their fusion within the framework and publication to data sinks as needed. For easy integration, deployment, and expansion of the framework we provide an implementation in JAVA using open-source adapters for common industrial protocols and a simple API for usage in user specified setups

On Design and Applications of Practical Concurrent Data Structures

The proliferation of multicore processors is having an enormous impact on software design and development. In order to exploit parallelism available in multicores, there is a need to design and implement abstractions that programmers can use for general purpose applications development. A common abstraction for coordinated access to memory is a concurrent data structure. Concurrent data structures are challenging to design and implement as they are required to be correct, scalable, and practical under various application constraints. In this thesis, we contribute to the design of efficient concurrent data structures, propose new design techniques and improvements to existing implementations. Additionally, we explore the utilization of concurrent data structures in demanding application contexts such as data stream processing.In the first part of the thesis, we focus on data structures that are difficult to parallelize due to inherent sequential bottlenecks. We present a lock-free vector design that efficiently addresses synchronization bottlenecks by utilizing the combining technique. Typical combining techniques are blocking. Our design introduces combining without sacrificing non-blocking progress guarantees. We extend the vector to present a concurrent lock-free unbounded binary heap that implements a priority queue with mutable priorities.In the second part of the thesis, we shift our focus to concurrent search data structures. In order to offer strong progress guarantee, typical implementations of non-blocking search data structures employ a "helping" mechanism. However, helping may result in performance degradation. We propose help-optimality, which expresses optimization in amortized step complexity of concurrent operations. To describe the concept, we revisit the lock-free designs of a linked-list and a binary search tree and present improved algorithms. We design the algorithms without using any language/platform specific constructs; we do not use bit-stealing or runtime type introspection of objects. Thus, our algorithms are portable. We further delve into multi-dimensional data and similarity search. We present the first lock-free multi-dimensional data structure and linearizable nearest neighbor search algorithm. Our algorithm for nearest neighbor search is generic and can be adapted to other data structures.In the last part of the thesis, we explore the utilization of concurrent data structures for deterministic stream processing. We propose solutions to two challenges prevalent in data stream processing: (1) efficient processing on cloud as well as edge devices and (2) deterministic data-parallel processing at high-throughput and low-latency. As a first step, we present a methodology for customization of streaming aggregation on low-power multicore embedded platforms. Then we introduce Viper, a communication module that can be integrated into stream processing engines for the coordination of threads analyzing data in parallel

The Usage of Personal Data as Content in Integrated Marketing Communications

Personal user data has proven extremely valuable for firms in the digital age. The wealth of data available to firms has provided unprecedented access into the world of the consumer. Companies hoping to capitalize on their user's data have turned to several interesting outlets. This research addresses the repurposing of user data as content in marketing. By analyzing four cases of data presented as marketing communications across two companies, this research provides new insights into the public release of private user data for marketing purposes. Four cases of personal data used in marketing communications were chosen specifically for their time proximity, characteristics of the sending firms, and their disparate outcomes. These instances of marketing communications, two by Spotify and two by Netflix, were released during November and December of 2017 and each resulted in a diverse range of public opinion. An analysis of these cases was conducted using the comprehensive framework of integrated marketing communications (Tafesse & Kitchen, 2017). There is a significant difference in the perceptual outcomes of integrated marketing communication campaigns which display user data as content. This analysis provides insights into the characteristics of marketing communications and how their outcomes fit into broader marketing strategies. These case studies provide opportunities for marketers to improve their campaigns in line with their desired audience outcome. Patterns of scope, strategy, mode, and outcome do not suggest success or failure in the context of marketing communications, but rather a set of insights marketers should keep in mind when pursuing communication strategies which harness personal user data.No embargoAcademic Major: Marketin

Advanced analytics through FPGA based query processing and deep reinforcement learning

Today, vast streams of structured and unstructured data have been incorporated in databases, and analytical processes are applied to discover patterns, correlations, trends and other useful relationships that help to take part in a broad range of decision-making processes. The amount of generated data has grown very large over the years, and conventional database processing methods from previous generations have not been sufficient to provide satisfactory results regarding analytics performance and prediction accuracy metrics. Thus, new methods are needed in a wide array of fields from computer architectures, storage systems, network design to statistics and physics. This thesis proposes two methods to address the current challenges and meet the future demands of advanced analytics. First, we present AxleDB, a Field Programmable Gate Array based query processing system which constitutes the frontend of an advanced analytics system. AxleDB melds highly-efficient accelerators with memory, storage and provides a unified programmable environment. AxleDB is capable of offloading complex Structured Query Language queries from host CPU. The experiments have shown that running a set of TPC-H queries, AxleDB can perform full queries between 1.8x and 34.2x faster and 2.8x to 62.1x more energy efficient compared to MonetDB, and PostgreSQL on a single workstation node. Second, we introduce TauRieL, a novel deep reinforcement learning (DRL) based method for combinatorial problems. The design idea behind combining DRL and combinatorial problems is to apply the prediction capabilities of deep reinforcement learning and to use the universality of combinatorial optimization problems to explore general purpose predictive methods. TauRieL utilizes an actor-critic inspired DRL architecture that adopts ordinary feedforward nets. Furthermore, TauRieL performs online training which unifies training and state space exploration. The experiments show that TauRieL can generate solutions two orders of magnitude faster and performs within 3% of accuracy compared to the state-of-the-art DRL on the Traveling Salesman Problem while searching for the shortest tour. Also, we present that TauRieL can be adapted to the Knapsack combinatorial problem. With a very minimal problem specific modification, TauRieL can outperform a Knapsack specific greedy heuristics.Hoy en día, se han incorporado grandes cantidades de datos estructurados y no estructurados en las bases de datos, y se les aplican procesos analíticos para descubrir patrones, correlaciones, tendencias y otras relaciones útiles que se utilizan mayormente para la toma de decisiones. La cantidad de datos generados ha crecido enormemente a lo largo de los años, y los métodos de procesamiento de bases de datos convencionales utilizados en las generaciones anteriores no son suficientes para proporcionar resultados satisfactorios respecto al rendimiento del análisis y respecto de la precisión de las predicciones. Por lo tanto, se necesitan nuevos métodos en una amplia gama de campos, desde arquitecturas de computadoras, sistemas de almacenamiento, diseño de redes hasta estadísticas y física. Esta tesis propone dos métodos para abordar los desafíos actuales y satisfacer las demandas futuras de análisis avanzado. Primero, presentamos AxleDB, un sistema de procesamiento de consultas basado en FPGAs (Field Programmable Gate Array) que constituye la interfaz de un sistema de análisis avanzado. AxleDB combina aceleradores altamente eficientes con memoria, almacenamiento y proporciona un entorno programable unificado. AxleDB es capaz de descargar consultas complejas de lenguaje de consulta estructurado desde la CPU del host. Los experimentos han demostrado que al ejecutar un conjunto de consultas TPC-H, AxleDB puede realizar consultas completas entre 1.8x y 34.2x más rápido y 2.8x a 62.1x más eficiente energéticamente que MonetDB, y PostgreSQL en un solo nodo de una estación de trabajo. En segundo lugar, presentamos TauRieL, un nuevo método basado en Deep Reinforcement Learning (DRL) para problemas combinatorios. La idea central que está detrás de la combinación de DRL y problemas combinatorios, es aplicar las capacidades de predicción del aprendizaje de refuerzo profundo y el uso de la universalidad de los problemas de optimización combinatoria para explorar métodos predictivos de propósito general. TauRieL utiliza una arquitectura DRL inspirada en el actor-crítico que se adapta a redes feedforward. Además, TauRieL realiza el entrenamieton en línea que unifica el entrenamiento y la exploración espacial de los estados. Los experimentos muestran que TauRieL puede generar soluciones dos órdenes de magnitud más rápido y funciona con un 3% de precisión en comparación con el estado del arte en DRL aplicado al problema del viajante mientras busca el recorrido más corto. Además, presentamos que TauRieL puede adaptarse al problema de la Mochila. Con una modificación específica muy mínima del problema, TauRieL puede superar a una heurística codiciosa de Knapsack Problem.Postprint (published version

A proposed synthesis method for Application-Specific Instruction Set Processors

Due to the rapid technology advancement in integrated circuit era, the need for the high computation performance together with increasing complexity and manufacturing costs has raised the demand for high-performance con fi gurable designs; therefore, the Application-Speci fi c Instruction Set Processors (ASIPs) are widely used in SoC design. The automated generation of software tools for ASIPs is a commonly used technique, but the automated hardware model generation is less frequently applied in terms of fi nal RTL implementations. Contrary to this, the fi nal register-transfer level models are usually created, at least partly, manually. This paper presents a novel approach for automated hardware model generation for ASIPs. The new solution is based on a novel abstract ASIP model and a modeling language (Algorithmic Microarchitecture Description Language, AMDL) optimized for this architecture model. The proposed AMDL-based pre-synthesis method is based on a set of pre-de fi ned VHDL implementation schemes, which ensure the qualities of the automatically generated register-transfer level models in terms of resource requirement and operation frequency. The design framework implementing the algorithms required by the synthesis method is also presented

e-Business challenges and directions: important themes from the first ICE-B workshop

A three-day asynchronous, interactive workshop was held at ICE-B’10 in Piraeus, Greece in July of 2010. This event captured conference themes for e-Business challenges and directions across four subject areas: a) e-Business applications and models, b) enterprise engineering, c) mobility, d) business collaboration and e-Services, and e) technology platforms. Quality Function Deployment (QFD) methods were used to gather, organize and evaluate themes and their ratings. This paper summarizes the most important themes rated by participants: a) Since technology is becoming more economic and social in nature, more agile and context-based application develop methods are needed. b) Enterprise engineering approaches are needed to support the design of systems that can evolve with changing stakeholder needs. c) The digital native groundswell requires changes to business models, operations, and systems to support Prosumers. d) Intelligence and interoperability are needed to address Prosumer activity and their highly customized product purchases. e) Technology platforms must rapidly and correctly adapt, provide widespread offerings and scale appropriately, in the context of changing situational contexts

Development of an adaptable model for distributed collaborative computer aided design

Master'sMASTER OF ENGINEERIN