Effective Utilization Of Big Data Research Articles

Navigating the landscape of enzyme design: from molecular simulations to machine learning.

Global environmental issues and sustainable development call for new technologies for fine chemical synthesis and waste valorization. Biocatalysis has attracted great attention as the alternative to the traditional organic synthesis. However, it is challenging to navigate the vast sequence space to identify those proteins with admirable biocatalytic functions. The recent development of deep-learning based structure prediction methods such as AlphaFold2 reinforced by different computational simulations or multiscale calculations has largely expanded the 3D structure databases and enabled structure-based design. While structure-based approaches shed light on site-specific enzyme engineering, they are not suitable for large-scale screening of potential biocatalysts. Effective utilization of big data using machine learning techniques opens up a new era for accelerated predictions. Here, we review the approaches and applications of structure-based and machine-learning guided enzyme design. We also provide our view on the challenges and perspectives on effectively employing enzyme design approaches integrating traditional molecular simulations and machine learning, and the importance of database construction and algorithm development in attaining predictive ML models to explore the sequence fitness landscape for the design of admirable biocatalysts.

Unpacking associations between positive-negative valence and ambidexterity of big data. Implications for firm performance

Motivated by the importance of big data utilization and its impact on firm performance, the current study examines the influence of the perceived valence factors of the top management team (TMT) on the ambidexterity of big data utilization and firm performance. Despite the growing recognition of the significance of ambidexterity and the role of TMTs in leveraging big data, there remains a lack of empirical research that comprehensively examines the associations between TMT valence factors, the ambidexterity of big data utilization, and firm performance. By integrating valence theory and ambidexterity theory, this study fills this research gap and provides valuable insights into the relationship between TMT valence factors, big data utilization, and firm performance outcomes. Data were collected from 357 respondents, and the findings indicate that positive TMT valence factors – such as data proficiency, industry expertise, and knowledge diversity – as well as negative valence factors – such as data compatibility, complexity, and benefit disconfirmation – are negatively associated with ambidexterity. Furthermore, the findings of our study have important implications for organizations seeking to enhance their operational and financial performance through the effective utilization of big data. Notably, our results highlight that promoting ambidexterity in handling big data within firms results in improved operational and financial outcomes. These findings provide valuable insights into the relatively unexplored area of TMT valence factors and their impact on driving ambidexterity in big data utilization, ultimately leading to enhanced organizational performance.

A Standardized GIS Web Service Oriented Shared-nothing Architecture - Taking Species Distribution Modeling as an Example

The rapid development of big data analytics technologies and tools, particularly the related distributed computing technologies, which divide large problems into smaller ones, provides important technical support for developing big earth data research. To realize the effective utilization of big data computing resources, it is important to overcome the potential incompatibility and inconsistency among the distributed computing nodes. Using standardized GIS web service interfaces is a promising approach to link existing heterogeneous GIS platforms through standardized protocols and establish a universal, platform-independent computing layer to improve the efficiency of computing resources for big earth data analysis. This research analyzed the requirements of distributed computing, and further proposed a standardized GIS web service-oriented shared-nothing architecture by comparing various mainstream distributed computing architectures and related web service specifications in the industry. For example, the prediction of Lophelia pertusa coral distribution by random forest is the application of the method discussed in this paper.

Covid-19 impact upon the digitalization of the transport sector in Bulgaria

Digitalization is gradually spreading through the transport and logistics industry, and the new technologies are profoundly reshaping the market, producing radical changes in every single stage of the supply chain to help reduce expenses and improve efficiency. Transport, in many respects, is an industry that is highly open to automation and implementation of IoT (Internet of Things) solutions. The successful integration of IoT technologies is likely to bring considerable benefits for transportation companies, such as diminished transportation delays, sporadic operator errors, preventive measures for poor cargo tracking and monitoring, IT failures and identity theft, in addition to the effective utilization of big data for the provision of automated real-time business analytics solutions. The present paper attempts to critically examine the impact of COVID-19 on the digitalization of the transport sector in Bulgaria, and offers, thereupon, a systematization of the major problems the enterprises face.

A Novel Information Processing in IoT Based Real Time Health Care Monitoring System

The recent technology developments and innovations improves the life style of people through smart applications, sensors, wireless communication networks, etc., for all those technologies internet is the backbone and the information processing like accessing, distributing the necessary information is achieved through Internet of Things (IoT). IoT supports multi-disciplinary applications as an active entity in engineering, science and business discipline. Based on the user preference these applications and its services could be framed in IoT. On contrary to the development, IoT has flaws in information processing as huge volume of data is need to be handled in a single environment. Considering these facts, the proposed research work is aimed to develop a novel information processing system in IoT platform through a reliable health care monitoring system. The effective utilization of big data in IoT environment is analysed through the proposed architecture to attain minimum delay in a real time environment. Conventional models are used to compare the performance of proposed design and the experimentation is performed to verify the superior performance of proposed approach using accuracy, cost functions in terms of transmission and storage, f-measure, sensitivity and specificity.

30th Annual Drug Information Association (DIA) Europe 2018 (April 17-19, 2018 - Basel, Switzerland).

The Drug Information Association (DIA) Europe held its annual meeting from April 17-19, 2018, in Basel, Switzerland. The key topics discussed in the 3-day meeting were related to pharmacovigilance, clinical development, patient engagement, data and data standards, preclinical development and early-phase clinical research, regulatory science, translational medicine and science, and value and access. The program was principally focused on the current opportunities and future landscape of the healthcare system as a result of the increasingly innovative technologies and effective utilization of big data. In addition, the critical need for collaboration and partnership between all the stakeholders of the healthcare system was highlighted. This report covers some of the regulatory sessions presented at the meeting in which regulators, payers, industry and patients presented their perspectives for discussion.

Sustainable Development Plan for Korea through Expansion of Green IT: Policy Issues for the Effective Utilization of Big Data

The South Korean government is providing full support for green IT as one of the growth engines of Korea. The purpose of this study is to derive policy issues needed for the sustainable development of Korea through utilizing Big Data by applying green IT. The analysis is done using a Delphi technique. Results show that the establishment of computing platforms that can easily share data and generate value is prioritized for the effective use of Big Data from the environment. In addition, the government-led publication of genetic information and electronic medical records for research purposes has been derived as an important policy issue for the use of bio-Big Data. Besides, a guideline concerning the standardization of machine to machine and Internet of Things communication and data security is needed to effectively use Big Data from machines/things. Moreover, a review of legislation related to the utilization of Big Data from digital media has been derived as an important policy issue. The results of this study propose the direction in which the Korean government should move for green growth through effective utilization of Big Data. The results can be also useful resources for establishing relevant policies for various countries that are accelerating sustainable development.