Big data analytics (BDA) in healthcare has made a positive difference in the integration of Artificial Intelligence (AI) in advancements of analytical capabilities, while lowering the costs of medical care. The aim of this study is to improve the existing healthcare eSystem by implementing a Big Data Analytics (BDA) platform and to meet the requirements of the Czech Republic National Health Service (Tender-Id. VZ0036628, No. Z2017-035520). In addition to providing analytical capabilities on Linux platforms supporting current and near-future AI with machine-learning and data-mining algorithms, there is the need for ethical considerations mandating new ways to preserve privacy, all of which are preconditioned by the growing body of regulations and expectations. The presented BDA platform, has met all requirements (N > 100), including the healthcare industry-standard Transaction Processing Performance Council (TPC-H) decision support benchmark in compliance with the European Union (EU) and the Czech Republic legislations. Currently, the presented Proof of Concept (PoC) that has been upgraded to a production environment has unified isolated parts of Czech Republic healthcare over the past seven months. The reported PoC BDA platform, artefacts, and concepts are transferrable to healthcare systems in other countries interested in developing or upgrading their own national healthcare infrastructure in a cost-effective, secure, scalable and high-performance manner.

Big data in healthcare has made a positive difference in advancing analytical capabilities and lowering the costs of medical care. In addition to providing analytical capabilities on platforms supporting current and near-future AI with machine-learning and data-mining algorithms, there is also a need for ethical considerations mandating new ways to preserve privacy, all of which are preconditioned by the growing body of regulations and expectations. The purpose of this study is to improve existing clinical care by implementing a big data platform for the Czech Republic National Health Service. Based on the achieved performance and its compliance with mandatory guidelines, the reported big-data platform was selected as the winning solution from the Czech Republic national tender (Tender Id. VZ0036628, No. Z2017-035520). The platform, based on analytical Vertica NoSQL database for massive data processing, complies with the TPC-H1 for decision support benchmark, the European Union (EU) and the Czech Republic requirements, well-exceeding defined system performance thresholds. The reported artefacts and concepts are transferrable to healthcare systems in other countries and are intended to provide personalised autonomous assessment from big data in a cost-effective, scalable and high-performance manner. The implemented platform allows: (1) scalability; (2) further implementations of newly-developed machine learning algorithms for classification and predictive analytics; (3) security improvements related to Electronic Health Records (EHR) by using automated functions for data encryption and decryption; and (4) the use of big data to allow strategic planning in healthcare.

There is a growing trend for enterprise-level Internet of Things (IoT) applications requiring real-time horizontally scalable data processing platforms. Real-time processing platforms receiving data streams from sensor networks (e.g., autonomous and connected vehicles, smart security for businesses and homes, smartwatches, fitness trackers, and other wearables) require distributed MQTT brokers. This case study presents an IoT data streaming testbed platform prepared for the Czech Post. The presented platform has met the throughput requirement of 2 million messages per 24 hours (comprising SMS and emails). The tested MQTT broker runs on a single virtual node of a horizontally scalable testbed platform. Soon the Czech Post will modernise its eServices to increase package deliveries aligned with eCommerce and eGovernment demands. The presented testbed platform fulfils all requirements, and it is also capable of processing thousands of messages per second. The presented platform and concepts are transferable to healthcare systems, transport operations, the automotive industry, and other domains such as smart cities.

The arrival of the era of autonomous robots is indisputable. In this paper, innovations in the distributed control systems realized by autonomous guided vehicles in the automotive industry are provided as proof of concept. The main goal of the considered distributed control system design is to bring all-in-one dependent and independent VDA 5050 compliant robots that are easily configurable and manageable with the web-based high-quality user interface responsive business-critical application. Special attention is paid to applying a platform to manage all autonomous IoT based robots in one seamless system. In addition, a "single point of truth" as one of the main issues of modern distributed controlled systems has been considered.