Fuzzy Ontology for Requirements Determination and Documentation During Software Development

Every business has an underlying information system. Quality and creditability of a system depend mainly on provided requirements. Good quality requirements of a system increase the degree of quality of the system. Hence, requirements determinations is of prime importance. Inadequate and misunderstood requirements are major problems in requirements determination. Major stakeholders of the requirements are non-computer professional users, who may provide imprecise, vague, and ambiguous requirements. Further, the system development process may be partly automated and based on platform such as web or Semantic Web. In this case, a proper ontology to represent requirements is needed. The chapter proposes a fuzzy RDF/XML-based ontology to document various requirements. A generic architecture of requirements management system is also provided. To demonstrate the presented approach, a case of student monitoring and learning is presented with sample software requirements specifications and interfaces to collect requirements. The chapter concludes with advantages, applications, and future enhancements.

Elderly Healthcare System for Chronic Ailments using Machine Learning Techniques – a Review

World statistics declare that aging has direct correlations with more and more health problems with comorbid conditions. As healthcare communities evolve with a massive amount of data at a faster pace, it is essential to predict, assist, and prevent diseases at the right time, especially for elders. Similarly, many researchers have discussed that elders suffer extensively due to chronic health conditions. This work was performed to review literature studies on prediction systems for various chronic illnesses of elderly people. Most of the reviewed papers proposed machine learning prediction models combined with, or without, other related intelligence techniques for chronic disease detection of elderly patients at an early stage to avoid emergency situations. This method provides a promising approach in the analysis of either structured or unstructured datasets to produce very substantial pattern discoveries. By defining the generic architecture for the prediction model, we reviewed various papers involved in similar fields, based on suggested methodologies and their associated outcomes. The study discussed the pros and cons of different prediction models using traditional and modern machine learning techniques.

Generating High-Performance FPGA Accelerator Designs for Big Data Analytics with Fletcher and Apache Arrow

As big data analytics systems are squeezing out the last bits of performance of CPUs and GPUs, the next near-term and widely available alternative industry is considering for higher performance in the data center and cloud is the FPGA accelerator. We discuss several challenges a developer has to face when designing and integrating FPGA accelerators for big data analytics pipelines. On the software side, we observe complex run-time systems, hardware-unfriendly in-memory layouts of data sets, and (de)serialization overhead. On the hardware side, we observe a relative lack of platform-agnostic open-source tooling, a high design effort for data structure-specific interfaces, and a high design effort for infrastructure. The open source Fletcher framework addresses these challenges. It is built on top of Apache Arrow, which provides a common, hardware-friendly in-memory format to allow zero-copy communication of large tabular data, preventing (de)serialization overhead. Fletcher adds FPGA accelerators to the list of over eleven supported software languages. To deal with the hardware challenges, we present Arrow-specific components, providing easy-to-use, high-performance interfaces to accelerated kernels. The components are combined based on a generic architecture that is specialized according to the application through an extensive infrastructure generation framework that is presented in this article. All generated hardware is vendor-agnostic, and software drivers add a platform-agnostic layer, allowing users to create portable implementations.

An optimization model for the provision of flexibility and dispatching resources by multi-vector smart energy districts

The Clean Energy Package expects a fundamental contribute for the decarbonisation of European energy system from Distributed Energy Resources (DERs), pushing Member States to favour the diffusion of energy production plants for individual and collective self-consumption. At the same time, DERs are required to contribute to system security mainly providing dispatching resources. The model developed includes the possibility to provide real-time balancing flexibility in a generic architecture where different energy vectors can be integrated through energy production, consumption and storage facilities. The optimization problem is built over a weekly time horizon with a stepwise approach where internal and external energy exchanges are defined updating meteorological forecasts, energy demands and markets results while approaching real-time operations. According to the Italian Authority consultation document 322/2019, both energy-only and capacity remunerated services are included in the model. The aim of the model is both to estimate the economic opportunities coming from energy markets participation for smart energy districts in the future energy framework, and to assess the actual capability and reliability of diverse DERs aggregates to provide flexibility to the external electric grid. These evaluations are carried out applying the presented model to a university campus case study where different energy conversion and storage plants are integrated at a Distribution Network level.

Sustainability of Cloud-Based Smart Society

Today, everything is progressing to ‘Smart' to enhance the environment via technological progress including IoT, big data, AI, ICT, and so on. But, in this whole process, the sustainability of being ‘smart' is implemented by the cloud-based technology, which also acts as an engine. Smart society is another live example of this era that makes potential use of digital technology and sensor devices to improve people's lives through the internet. It also incorporates cloud computing, which significantly benefits them by offering a sustainable environment to access the computing power at large scale, which they could not previously access due to lack of resources. This chapter provide a broad overview of smart society that covers the scope and services, technological pillars, features necessities to be titled as ‘smart', and a sustainable development. Also, it broadly covers the role of cloud computing, related technologies, and generic architecture for the sustainability of smart societies followed by applications with a case study and challenges.

CRYPTO-SPATIAL: AN OPEN STANDARDS SMART CONTRACTS LIBRARY FOR BUILDING GEOSPATIALLY ENABLED DECENTRALIZED APPLICATIONS ON THE ETHEREUM BLOCKCHAIN

Blockchain is an emerging immature technology that disrupt many well established industries nowadays, like finance, supply chain, transportation, energy, official registries (identity, vehicles, …). In this contribution we present a smart contracts library, named Crypto-Spatial, written for the Ethereum Blockchain and designed to serve as a framework for geospatially enabled decentralized applications (dApps) development. The main goal of this work is to investigate the suitability of Blockchain technology for the storage, retrieval and processing of vector geospatial data. The design and the proof-of-concept implementation presented are both based on the Open Geospatial Consortium standards: Simple Feature Access, Discrete Global Grid Systems (DGGS) and Well Known Binary (WKB). Also, the FOAM protocol concept of Crypto-Spatial Coordinate (CSC) was used to uniquely identify spatial features on the Blockchain immutable ledger. The design of the Crypto-Spatial framework was implemented as a set of smart contracts using the Solidity object oriented programming language. The implemented library was assessed toward Etheruem’s best practices design patterns and known security issues (common attacks). Also, a generic architecture for geospatially enabled decentralized applications, combining blockchain and IPFS technologies, was proposed. Finally, a proof-of-concept was developed using the proposed approach which main purpose is to port the UN/FAO-SOLA to Blockchain techspace allowing more transparency and simplifying access to users communities. The smart contracts of this prototype are live on the Rinkeby testnet and the frontend is hosted on Github pages. The source code of the work presented here is available on Github under Apache 2.0 license.