Railway Communication Network Digital Reporting System: A Case Study

The digital transformation of the railway communication network operations is contributing to an increase in data volumes across various functional areas, collecting and processing information from equipment, customers, and the company\u27s various business process management systems. The implementation of a specialized solution for analysis and operational monitoring was a logical extension of the development of data handling, opening up opportunities for building both operational reporting and predictive analytics, distribution and unification of reporting forms, which the reporting in production systems and manual analytics generation tools used before did not allow to achieve. In this paper, we propose to use a digital framework based on open-source big data technologies with different distributed services to process and store KPIs data from Railway Communication Network. We also review literature about challenges and solutions of Data Lake in KPI\u27s Railway Communications Network domain. To assess the applicability of the framework proposed, incidents and event data relating to the production business processes have been tested on the actual railway communication network. The suggested framework effectively processed the data and the implementation setup settings were presented, as well as the results of experiments

Extract, Transform, and Load data from Legacy Systems to Azure Cloud

Internship report presented as partial requirement for obtaining the Master’s degree in Information Management, with a specialization in Knowledge Management and Business IntelligenceIn a world with continuously evolving technologies and hardened competitive markets, organisations need to continually be on guard to grasp cutting edge technology and tools that will help them to surpass any competition that arises. Modern data platforms that incorporate cloud technologies, support organisations to strive and get ahead of their competitors by providing solutions that help them capture and optimally use untapped data, and scalable storages to adapt to ever-growing data quantities. Also, adopt data processing and visualisation tools that help to improve the decision-making process. With many cloud providers available in the market, from small players to major technology corporations, this offers much flexibility to organisations to choose the best cloud technology that will align with their use cases and overall products and services strategy. This internship came up at the time when one of Accenture’s significant client in the financial industry decided to migrate from legacy systems to a cloud-based data infrastructure that is Microsoft Azure cloud. During this internship, development of the data lake, which is a core part of the MDP, was done to understand better the type of challenges that can be faced when migrating data from on-premise legacy systems to a cloud-based infrastructure. Also, provided in this work, are the main recommendations and guidelines when it comes to performing a large scale data migration

A Business Intelligence Solution, based on a Big Data Architecture, for processing and analyzing the World Bank data

The rapid growth in data volume and complexity has needed the adoption of advanced technologies to extract valuable insights for decision-making. This project aims to address this need by developing a comprehensive framework that combines Big Data processing, analytics, and visualization techniques to enable effective analysis of World Bank data. The problem addressed in this study is the need for a scalable and efficient Business Intelligence solution that can handle the vast amounts of data generated by the World Bank. Therefore, a Big Data architecture is implemented on a real use case for the International Bank of Reconstruction and Development. The findings of this project demonstrate the effectiveness of the proposed solution. Through the integration of Apache Spark and Apache Hive, data is processed using Extract, Transform and Load techniques, allowing for efficient data preparation. The use of Apache Kylin enables the construction of a multidimensional model, facilitating fast and interactive queries on the data. Moreover, data visualization techniques are employed to create intuitive and informative visual representations of the analysed data. The key conclusions drawn from this project highlight the advantages of a Big Data-driven Business Intelligence solution in processing and analysing World Bank data. The implemented framework showcases improved scalability, performance, and flexibility compared to traditional approaches. In conclusion, this bachelor thesis presents a Business Intelligence solution based on a Big Data architecture for processing and analysing the World Bank data. The project findings emphasize the importance of scalable and efficient data processing techniques, multidimensional modelling, and data visualization for deriving valuable insights. The application of these techniques contributes to the field by demonstrating the potential of Big Data Business Intelligence solutions in addressing the challenges associated with large-scale data analysis

How can SMEs benefit from big data? Challenges and a path forward

Big data is big news, and large companies in all sectors are making significant advances in their customer relations, product selection and development and consequent profitability through using this valuable commodity. Small and medium enterprises (SMEs) have proved themselves to be slow adopters of the new technology of big data analytics and are in danger of being left behind. In Europe, SMEs are a vital part of the economy, and the challenges they encounter need to be addressed as a matter of urgency. This paper identifies barriers to SME uptake of big data analytics and recognises their complex challenge to all stakeholders, including national and international policy makers, IT, business management and data science communities. The paper proposes a big data maturity model for SMEs as a first step towards an SME roadmap to data analytics. It considers the ‘state-of-the-art’ of IT with respect to usability and usefulness for SMEs and discusses how SMEs can overcome the barriers preventing them from adopting existing solutions. The paper then considers management perspectives and the role of maturity models in enhancing and structuring the adoption of data analytics in an organisation. The history of total quality management is reviewed to inform the core aspects of implanting a new paradigm. The paper concludes with recommendations to help SMEs develop their big data capability and enable them to continue as the engines of European industrial and business success. Copyright © 2016 John Wiley & Sons, Ltd.Peer ReviewedPostprint (author's final draft

Designing an open-source cloud-native MLOps pipeline

Deploying machine learning models is found to be a massive issue in the field. DevOps and Continuous Integration and Continuous Delivery (CI/CD) has proven to streamline and accelerate deployments in the field of software development. Creating CI/CD pipelines in software that includes elements of Machine Learning (MLOps) has unique problems, and trail-blazers in the field solve them with the use of proprietary tooling, often offered by cloud providers. In this thesis, we describe the elements of MLOps. We study what the requirements to automate the CI/CD of Machine Learning systems in the MLOps methodology. We study if it is feasible to create a state-of-the-art MLOps pipeline with existing open-source and cloud-native tooling in a cloud provider agnostic way. We designed an extendable and cloud-native pipeline covering most of the CI/CD needs of Machine Learning system. We motivated why Machine Learning systems should be included in the DevOps methodology. We studied what unique challenges machine learning brings to CI/CD pipelines, production environments and monitoring. We analyzed the pipeline’s design, architecture, and implementation details and its applicability and value to Machine Learning projects. We evaluate our solution as a promising MLOps pipeline, that manages to solve many issues of automating a reproducible Machine Learning project and its delivery to production. We designed it as a fully open-source solution that is relatively cloud provider agnostic. Configuring the pipeline to fit the client needs uses easy-to-use declarative configuration languages (YAML, JSON) that require minimal learning overhead

LEAN DATA ENGINEERING. COMBINING STATE OF THE ART PRINCIPLES TO PROCESS DATA EFFICIENTLYS

The present work was developed during an internship, under Erasmus+ Traineeship program, in Fieldwork Robotics, a Cambridge based company that develops robots to operate in agricultural fields. They collect data from commercial greenhouses with sensors and real sense cameras, as well as with gripper cameras placed in the robotic arms. This data is recorded mainly in bag files, consisting of unstructured data, such as images and semi-structured data, such as metadata associated with both the conditions where the images were taken and information about the robot itself. Data was uploaded, extracted, cleaned and labelled manually before being used to train Artificial Intelligence (AI) algorithms to identify raspberries during the harvesting process. The amount of available data quickly escalates with every trip to the fields, which creates an ever-growing need for an automated process. This problem was addressed via the creation of a data engineering platform encom- passing a data lake, data warehouse and its needed processing capabilities. This platform was created following a series of principles entitled Lean Data Engineering Principles (LDEP), and the systems that follows them are called Lean Data Engineering Systems (LDES). These principles urge to start with the end in mind: process incoming batch or real-time data with no resource wasting, limiting the costs to the absolutely necessary for the job completion, in other words to be as lean as possible. The LDEP principles are a combination of state-of-the-art ideas stemming from several fields, such as data engineering, software engineering and DevOps, leveraging cloud technologies at its core. The proposed custom-made solution enabled the company to scale its data operations, being able to label images almost ten times faster while reducing over 99.9% of its associated costs in comparison to the previous process. In addition, the data lifecycle time has been reduced from weeks to hours while maintaining coherent data quality results, being able, for instance, to correctly identify 94% of the labels in comparison to a human counterpart.Este trabalho foi desenvolvido durante um estágio no âmbito do programa Erasmus+ Traineeship, na Fieldwork Robotics, uma empresa sediada em Cambridge que desenvolve robôs agrícolas. Estes robôs recolhem dados no terreno com sensores e câmeras real- sense, localizados na estrutura de alumínio e nos pulsos dos braços robóticos. Os dados recolhidos são ficheiros contendo dados não estruturados, tais como imagens, e dados semi- -estruturados, associados às condições em que as imagens foram recolhidas. Originalmente, o processo de tratamento dos dados recolhidos (upload, extração, limpeza e etiquetagem) era feito de forma manual, sendo depois utilizados para treinar algoritmos de Inteligência Artificial (IA) para identificar framboesas durante o processo de colheita. Como a quantidade de dados aumentava substancialmente com cada ida ao terreno, verificou-se uma necessidade crescente de um processo automatizado. Este problema foi endereçado com a criação de uma plataforma de engenharia de dados, composta por um data lake, uma data warehouse e o respetivo processamento, para movimentar os dados nas diferentes etapas do processo. Esta plataforma foi criada seguindo uma série de princípios intitulados Lean Data Engineering Principles (LDEP), sendo os sistemas que os seguem intitulados de Lean Data Engineering Systems (LDES). Estes princípios incitam a começar com o fim em mente: processar dados em batch ou em tempo real, sem desperdício de recursos, limitando os custos ao absolutamente necessário para a concluir o trabalho, ou seja, tornando-os o mais lean possível. Os LDEP combinam vertentes do estado da arte em diversas áreas, tais como engenharia de dados, engenharia de software, DevOps, tendo no seu cerne as tecnologias na cloud. O novo processo permitiu à empresa escalar as suas operações de dados, tornando-se capaz de etiquetar imagens quase 10× mais rápido e reduzindo em mais de 99,9% os custos associados, quando comparado com o processo anterior. Adicionalmente, o ciclo de vida dos dados foi reduzido de semanas para horas, mantendo uma qualidade equiparável, ao ser capaz de identificar corretamente 94% das etiquetas em comparação com um homólogo humano

Microservices suite for smart city applications

Smart Cities are approaching the Internet of Things (IoT) World. Most of the first-generation Smart City solutions are based on Extract Transform Load (ETL); processes and languages that mainly support pull protocols for data gathering. IoT solutions are moving forward to event-driven processes using push protocols. Thus, the concept of IoT applications has turned out to be widespread; but it was initially “implemented” with ETL; rule-based solutions; and finally; with true data flows. In this paper, these aspects are reviewed, highlighting the requirements for smart city IoT applications and in particular, the ones that implement a set of specific MicroServices for IoT Applications in Smart City contexts. Moreover; our experience has allowed us to implement a suite of MicroServices for Node-RED; which has allowed for the creation of a wide range of new IoT applications for smart cities that includes dashboards, IoT Devices, data analytics, discovery, etc., as well as a corresponding Life Cycle. The proposed solution has been validated against a large number of IoT applications, as it can be verified by accessing the https://www.Snap4City.org portal; while only three of them have been described in the paper. In addition, the reported solution assessment has been carried out by a number of smart city experts. The work has been developed in the framework of the Select4Cities PCP (PreCommercial Procurement), funded by the European Commission as Snap4City platform

Big Data Now, 2015 Edition

Now in its fifth year, O’Reilly’s annual Big Data Now report recaps the trends, tools, applications, and forecasts we’ve talked about over the past year. For 2015, we’ve included a collection of blog posts, authored by leading thinkers and experts in the field, that reflect a unique set of themes we’ve identified as gaining significant attention and traction. Our list of 2015 topics include: Data-driven cultures Data science Data pipelines Big data architecture and infrastructure The Internet of Things and real time Applications of big data Security, ethics, and governance Is your organization on the right track? Get a hold of this free report now and stay in tune with the latest significant developments in big data