A Metrics Suite of Cloud Computing Adoption Readiness

First online: 06 February 2016</p

MINING BIG DATA FOR SUSTAINABLE WATER MANAGEMENT

The power of advanced analytics is substantial. Massive scales of big, structured and unstructured data relieve unthinkable patterns and help us redefine economic models, solve operational inefficiencies and optimize costs. The water utilities could substantially benefit from the data available from new digital assets and smart technologies. Many are facing damaged and failing infrastructure and lack of financial resources for makeovers. However, Industry 4.0 and Digitalization open new fronts and bring new assets such as real-time monitoring of critical systems via IoT and sensors, advanced metering and predictive analytics to improve customer billing, remote data collection systems at pumping stations and water storage facilities and many more. The power of “digital twin”, as a virtual replica of a physical asset, and ways of enriching the traditional data sources with open source data increase considerably the available intelligence for more sophisticated correlation, linkages and insights. This study reviews the core values of big data, advanced analytics, smart technologies and its application in water resources management and it gives concrete recommendation how to accelerate the adoption of use of Big Data by leveraging on technology and innovation

Abstracts to be Presented at the 2016 Supercomputing Conference

No abstract availabl

Case study: IBM Watson Analytics cloud platform as Analytics-as-a-Service system for heart failure early detection

In the recent years the progress in technology and the increasing availability of fast connections have produced a migration of functionalities in Information Technologies services, from static servers to distributed technologies. This article describes the main tools available on the market to perform Analytics as a Service (AaaS) using a cloud platform. It is also described a use case of IBM Watson Analytics, a cloud system for data analytics, applied to the following research scope: detecting the presence or absence of Heart Failure disease using nothing more than the electrocardiographic signal, in particular through the analysis of Heart Rate Variability. The obtained results are comparable with those coming from the literature, in terms of accuracy and predictive power. Advantages and drawbacks of cloud versus static approaches are discussed in the last sections

A Survey on Transactional Stream Processing

Transactional stream processing (TSP) strives to create a cohesive model that merges the advantages of both transactional and stream-oriented guarantees. Over the past decade, numerous endeavors have contributed to the evolution of TSP solutions, uncovering similarities and distinctions among them. Despite these advances, a universally accepted standard approach for integrating transactional functionality with stream processing remains to be established. Existing TSP solutions predominantly concentrate on specific application characteristics and involve complex design trade-offs. This survey intends to introduce TSP and present our perspective on its future progression. Our primary goals are twofold: to provide insights into the diverse TSP requirements and methodologies, and to inspire the design and development of groundbreaking TSP systems