Data-intensive Processes Research Articles

Three Horizons of Technical Skills in Artificial Intelligence for the Sustainability of Insurance Companies

Insurance companies are experiencing unprecedented growth due to several emerging technology functionalities that have transformed the industry’s operations. Through the Three Horizons framework, this study explores the technical skills required to use artificial intelligence (AI) for the sustainability of insurance companies. Methodologically, it was carried out in two stages: First, defining the state-of-the-art, which included analysis of the current situation and studying technological surveillance. Second, technical skills and their strategic prevalence were identified for the design of each horizon. As a result, the adoption of AI in insurance companies allows them to transform their personal and data-intensive processes into engines of efficiency and knowledge, redefining the way companies in the sector offer their services. This study identifies the immediate benefits of AI in insurance companies. It provides a strategic framework for future innovation, emphasizing the importance of developing AI competencies to ensure long-term sustainability.

Quantum 3.0: Quantum Learning, Quantum Heuristics and Beyond

Quantum learning paradigms address the question of how best to harness conceptual elements of quantum mechanics and information processing to improve operability and functionality of a computing system for specific tasks through experience. It is one of the fastest evolving framework, which lies at the intersection of physics, statistics and information processing, and is the next frontier for data sciences, machine learning and artificial intelligence. Progress in quantum learning paradigms is driven by multiple factors: need for more efficient data storage and computational speed, development of novel algorithms as well as structural resonances between specific physical systems and learning architectures. Given the demand for better computation methods for data-intensive processes in areas such as advanced scientific analysis and commerce as well as for facilitating more data-driven decision-making in education, energy, marketing, pharmaceuticals and healthcare, finance and industry.

Quantum 3.0: Quantum Learning, Quantum Heuristics and Beyond

Quantum learning paradigms address the question of how best to harness conceptual elements of quantum mechanics and information processing to improve operability and functionality of a computing system for specific tasks through experience. It is one of the fastest evolving framework, which lies at the intersection of physics, statistics and information processing, and is the next frontier for data sciences, machine learning and artificial intelligence. Progress in quantum learning paradigms is driven by multiple factors: need for more efficient data storage and computational speed, development of novel algorithms as well as structural resonances between specific physical systems and learning architectures. Given the demand for better computation methods for data-intensive processes in areas such as advanced scientific analysis and commerce as well as for facilitating more data-driven decision-making in education, energy, marketing, pharmaceuticals and healthcare, finance and industry.

Optimizing Quality-Aware Big Data Applications in the Cloud

The last years witnessed a steep rise in data generation worldwide and, consequently, the widespread adoption of software solutions able to support data-intensive application. Competitiveness and innovation have strongly benefited from these new platforms and methodologies, and there is a great deal of interest around the new possibilities that Big Data analytics promise to make reality. Many companies currently engage in data-intensive processes as part of their core businesses; however, fully embracing the data-driven paradigm is still cumbersome, and establishing a production-ready, fine-tuned deployment is time-consuming, expensive, and resource-intensive. This situation calls for innovative models and techniques to streamline the process of deployment configuration for Big Data applications. In particular, the focus in this paper is on the rightsizing of Cloud deployed clusters, which represent a cost-effective alternative to installation on premises. This paper proposes a novel tool, integrated in a wider DevOps-inspired approach, implementing a parallel and distributed simulation-optimization technique that efficiently and effectively explores the space of alternative Cloud configurations, seeking the minimum cost deployment that satisfies quality of service constraints. The soundness of the proposed solution has been thoroughly validated in a vast experimental campaign encompassing different applications and Big Data platforms.

Environmental impacts and costs of residential building retrofits – What matters?

Building resilient communities plays an important role in the face of climate change and other societal stressors. Life-cycle assessment (LCA) approaches can assist stakeholders and policy makers to promote good environmental practices and support climate-change mitigation and adaptation. However, LCA approaches face several challenges when applied to buildings. Buildings are complex systems that result in data-intensive processes. For this matter, it is important to identify the most influential attributes to efficiently provide environmental and cost feedback and streamline the building decision-making process. The main objectives of this article are: apply a streamline approach to assess the environmental and cost performance of building retrofits for different types of houses and occupancy patterns, in alternative climate locations; and perform a comprehensive sensitivity analysis to identify key drivers of environmental and cost impacts. The most influential attributes to climate change impacts are the same irrespective to location, type of house and wall-system; however, they differ depending on occupancy. The key drivers to eco-efficiency are discount rate, and exterior-wall insulation. The most influential attributes can change depending on family size or set-point meaning that an early definition of these attributes can change which decisions are the most important in an early design stage.

Cognitive Effects of Visualization Techniques for Inconsistency Metrics on Monitoring Data-Intensive Processes

ABSTRACT Due to the increasing amounts of process data, monitoring company processes is currently evolving into a data-intensive analytics problem. In this report, we present the results of an experiment analyzing the cognitive effects of different visualization techniques for inconsistency metrics in the scope of monitoring data-intensive processes. Our results indicate that a ranked overview of metrics is associated with better understanding efficiency and less mental effort compared to other visualization techniques.

CROP- CLIMATE MANAGEMENT: A REVIEW

Climate-crop connection is extremely important in agriculture to build new data-intensive research opportunities in multidisciplinary agricultural technology. Current machine learning, deep learning algorithms, and specific crop models are used to generate new opportunities to unravel, measure, and understand data-intensive processes in agricultural operational environments. In this paper,a detailed study of the various agricultural technologies and the work analyzed are categorized into (a) Crop models (b) Weather Prediction (c) Machine learning in agriculture (d) deep learning in agriculture. Through using machine training for sensor data, agricultural management systems evolve into artificial intelligence applications allowing rich guidance and insights for promoting and acting farmer decisions.

Survey of big data programming frameworks

Advances in ICT today has made data more voluminous and multifarious and its being transferred at high speed. Applications in cloud like Yahoo weather, Facebook photo gallery, and Google search index are changing the IT landscape in a profound way. Reasons for these trend includes scientific organizations solving big problems related to high performance computing workloads, diverse public services being digitized; mobile devices, global positioning systems, sensors, social media, medical imaging, financial transaction logs and lots of them are all sources of massive data generating large sets of complex data. These applications are evolving to be data-intensive, processing very large volumes of data hence, require dynamically scalable, virtualized resources to handle them. This paper compared several of these big data programming frameworks used in data-intensive processes. Area of strengths and weaknesses of these frameworks were highlighted with the intention that future framework will harness all positives from this study for a better system.Keywords: Big Data, Framework, Hadoop, Analytics, Workload, Resource Allocation

Data-Driven Microstructure and Microhardness Design in Additive Manufacturing Using a Self-Organizing Map

Abstract To design microstructure and microhardness in the additive manufacturing (AM) of nickel (Ni)-based superalloys, the present work develops a novel data-driven approach that combines physics-based models, experimental measurements, and a data-mining method. The simulation is based on a computational thermal-fluid dynamics (CtFD) model, which can obtain thermal behavior, solidification parameters such as cooling rate, and the dilution of solidified clad. Based on the computed thermal information, dendrite arm spacing and microhardness are estimated using well-tested mechanistic models. Experimental microstructure and microhardness are determined and compared with the simulated values for validation. To visualize process–structure–properties (PSPs) linkages, the simulation and experimental datasets are input to a data-mining model—a self-organizing map (SOM). The design windows of the process parameters under multiple objectives can be obtained from the visualized maps. The proposed approaches can be utilized in AM and other data-intensive processes. Data-driven linkages between process, structure, and properties have the potential to benefit online process monitoring control in order to derive an ideal microstructure and mechanical properties.

Analytical composite performance models for Big Data applications

Recent years witnessed a steep rise in data generation and, consequently, the widespread adoption of software solutions able to support data-intensive applications. Many companies currently engage in data-intensive processes, however, fully embracing a data-driven paradigm is still cumbersome, and establishing a production-ready and fine-tuned deployment is time-consuming. This situation calls for innovative models and techniques to streamline the process of deployment configuration for Big Data applications. Moreover, many companies are using Cloud deployed clusters, which represent a cost-effective alternative to installation on premises. Accurate and fast prediction of the execution time of a Big Data application helps improving design time decisions, reduces Cloud over allocation charges, and assists budget management. In this paper, analytical models based on Stochastic Activity Networks (SANs) are proposed to accurately model the execution of Hadoop, Tez and Spark applications, i.e., the most referred frameworks to support Big Data analyses. The proposed SANs model these applications together with the underlying cluster in order to accurately estimate the execution time. We evaluate the accuracy of the proposed models over the TPC-DS industry benchmark across different configurations. Results obtained by numerically solving the SAN models show an average error of 4.5%, 5.8%, and 2.7% in estimating the execution time of MR, Tez, and Spark applications, respectively, against the data gathered from the experiments demonstrating higher accuracy compared with the state-of-the-art. Moreover, the time required to solve the proposed models is lower than the simulation time of the previously presented approaches in this area.

Models of Research and the Dissemination of Research Results: the Influences of E-Science, Open Access and Social Networking

In contrast with practice in recent times past, computational and data intensive processes are increasingly driving collaborative research in science and technology. Large amounts of data are being generated in experiments or simulations and these require real-time, or near real-time, analysis and visualisation. The results of these evaluations need to be validated and then published quickly and openly in order to facilitate the overall progress of research on a national and international basis. Research is increasingly undertaken in large teams and is also increasingly interdisciplinary as many of the major research challenges lie at the boundaries between existing disciplines. The move to open access for peer reviewed publications is rapidly becoming a required option in the sector. At the same time, communication and dissemination procedures are also utilising non-traditional forms facilitated by burgeoning developments in social networking. It is proposed that these elements, when combined, constitute a paradigm shift in the model of research and the dissemination of research results.

S/4HANA: EARLY ADOPTERS, SUCCESSES AND FAILURES

In this research project, we will be discussing SAP’s next big ERP Suite which could very well define the future of the company. SAP is betting big on S/4HANA to help establish themselves in an increasingly competitive global market. It is the next-generation business suite designed to facilitate the smooth operations of an organization in a digital, dynamic, interconnected and ever-changing economy. SAP S/4HANA had been designed to leverage in-memory computing to expedite data-intensive processes while ensuring a quality product is delivered to the customer. SAP has developed an ERP solution which integrates and assimilates real-time analytics, real-time applications on a real-time platform. Real-time Analytics enables massive parallel processing with in-memory column storage architecture. The S/4HANA platform offers libraries for text processing, spatial & business analytics, predictive planning which makes it ideal for implementing next generation, real-time applications & analytics. Some of the benefits that could be had from the S/4HANA suite include useable/actionable information from a myriad of data, business with rapid reporting and analysis, reduced Total Cost of Ownership due to diminished requirements for hardware and maintenance.

DALEC: a framework for the systematic evaluation of data-centric approaches to process management software

The increasing importance of data in business processes has led to the emergence of data-centric business process management, which deviates from the widely used activity-centric paradigm. Data-centric approaches set their focus on data, aiming at supporting data-intensive business processes and increased process flexibility. The objective of this article is to gain profound insights into the maturity of different data-centric approaches as well as their capabilities. In particular, this article will provide a framework for systematically evaluating and comparing data-centric approaches, with regard to the phases of the business process lifecycle. To this end, a systematic literature review (SLR) was conducted with the goal of evaluating the capabilities of data-centric process management approaches. The SLR comprises 38 primary studies which were thoroughly analyzed. The studies were categorized into different approaches, whose capabilities were thoroughly assessed. Special focus was put on the tooling and software of the approaches. The article provides the empirically grounded DALEC framework to evaluate and compare data-centric approaches. Furthermore, the results of the SLR offer insights into existing data-centric approaches and their capabilities. Data-centric approaches promise better support of loosely structured and data-intensive business processes, which may not be adequately represented by activity-centric paradigms.

Real-Time Dense Stereo Matching With ELAS on FPGA-Accelerated Embedded Devices

For many applications in low-power real-time robotics, stereo cameras are the sensors of choice for depth perception as they are typically cheaper and more versatile than their active counterparts. Their biggest drawback, however, is that they do not directly sense depth maps; instead, these must be estimated through data-intensive processes. Therefore, appropriate algorithm selection plays an important role in achieving the desired performance characteristics. Motivated by applications in space and mobile robotics, we implement and evaluate a field programmable gate arrays (FPGA) accelerated adaptation of the efficient large-scale stereo (ELAS) algorithm. Despite offering one of the best tradeoffs between efficiency and accuracy, ELAS has only been shown to run at 1.5-3 fps on a high-end CPU. Our system preserves all intriguing properties of the original algorithm, such as the slanted plane priors, but can achieve a frame rate of 47 fps whilst consuming under 4 W of power. Unlike previous FPGA-based designs, we take advantage of both components on the CPU/FPGA system-on-chip to showcase the strategy necessary to accelerate more complex and computationally diverse algorithms for such low power, real-time systems.

Big data and predictive analytics for supply chain sustainability: A theory-driven research agenda

Big data and predictive analytics (BDPA) tools and methodologies are leveraged by businesses in many ways to improve operational and strategic capabilities, and ultimately, to positively impact corporate financial performance. BDPA has become crucial for managing supply chain functions, where data intensive processes can be vastly improved through its effective use. BDPA has also become a competitive necessity for the management of supply chains, with practitioners and scholars focused almost entirely on how BDPA is used to increase economic measures of performance. There is limited understanding, however, as to how BDPA can impact other aspects of the triple bottom-line, namely environmental and social sustainability outcomes. Indeed, this area is in immediate need of attention from scholars in many fields including industrial engineering, supply chain management, information systems, business analytics, as well as other business and engineering disciplines. The purpose of this article is to motivate such research by proposing an agenda based in well-established theory. This article reviews eight theories that can be used by researchers to examine and clarify the nature of BDPA’s impact on supply chain sustainability, and presents research questions based upon this review. Scholars can leverage this article as the basis for future research activity, and practitioners can use this article as a means to understand how company-wide BDPA initiatives might impact measures of supply chain sustainability.