A big data management approach for computer aided breast cancer diagnostic system supporting precision medicine

The core idea of quality work is to create enduring quality by providing users with reliable, easy-to-use, and surprising products. In the Internet+ era, user demand for quality has changed with the times. Customers are no longer satisfied with the requirements of basic quality functions, and more in pursuit of ease of use, pleasure and even identity. Even if the product has "satisfied with the specifications" in quality, users are still dissatisfied and express their dissatisfaction through the Internet. If we can use the Internet, big data, artificial intelligence and other technologies to collect and analyze user expectations or dissatisfaction with product quality and find improvement directions, then quality management will see great progress. Based on this, we can create a new type of quality big data analysis and management system, which we can call the Product Satisfaction Index (PSI) management system. Through the analysis and transformation of PSI, the customer’s textual expectations are turned into numerical quantifiable indicators. Through the management of PSI indicators, quality big data mining and analysis are performed to improve the product to meet customer expectations. In advancing the product satisfaction index (PSI) indicator management system, we should fully implement the definition of PSI indicators, strategic position, data analysis methods, closed-loop management models, review and continuous improvement, and data platforms. Visualize customer needs, analyze the matching between product characteristics and customer needs, and promote continuous improvement of PSI indicators through closed-loop management. The new PSI index management method opens up new ideas and methods for quality performance management, and helps build high-quality development and create customer satisfaction.

The energy data scope is very broad including oil and gas, coal, minerals, new energy, renewable and conversion energy, electricity, and others. The different volume, variety, veracity, and velocity of data have challenge to address with the energy data model. The works focus on the development of data model for big data storage implementation schemes in the energy data. The data model has produces by analyzed the national energy big data architecture, develop the data mapping and correlation, big data master data management with energy industry standardization, big data management portal for upstream energy and downstream energy. The research has goal to establishing a foundation for building technology and big data management in the energy sector which includes petroleum, coal, geothermal and renewable energy, and in the future can be the basis for predictive analysis and national energy production.

PurposeThe purpose of this paper is to scrutinize and classify the literature linking Big Data analytics and management phenomena.Design/methodology/approachAn objective bibliometric analysis is conducted, supported by subjective assessments based on the studies focused on the intertwining of Big Data analytics and management fields. Specifically, deeper descriptive statistics and document co-citation analysis are provided.FindingsFrom the document co-citation analysis and its evaluation, four clusters depicting literature linking Big Data analytics and management phenomena are revealed: theoretical development of Big Data analytics; management transition to Big Data analytics; Big Data analytics and firm resources, capabilities and performance; and Big Data analytics for supply chain management.Originality/valueTo the best of the authors’ knowledge, this is one of the first attempts to comprehend the research streams which, over time, have paved the way to the intersection between Big Data analytics and management fields.

During the last years leading scientists, researchers and analysts determine big data as revolution in scientific studies and one of the most challenging tendencies. The volume of stored genomic data has increased significantly. Main challenge in data analysis and knowledge discovery is to suggest efficient processing tools, methods and technologies. Software architecture for adaptive knowledge discovery based on big genomic data analytics is presented in this paper. The software architecture is comprised of layers for data integration and preprocessing, database/data warehouse server, data discovery engine, pattern evaluation and graphical user interface. The big genomic data architecture consists of data sources, storage, integration and preprocessing, real data stream, stream processing, analytical data store, analysis and reporting. An algorithm for prediction of breast cancer based on machine learning for processing and analysis of big genomic data and knowledge discovery with respect to personalized treatment is presented. The proposed algorithm for breast cancer classification is implemented by using Stochastic Dual Coordinate Ascent (SDCA) method and Wisconsin breast cancer database. Experimental results are presented and discussed. The purpose of the study is to apply the software architecture on big genomic data analytics by practical experiments for specific case study identifying regulatory genetic elements in sequenced genomes, and prediction of the type and malignancy of breast cancer. This will enable fast processing of clinical observations data and comparison with the available data accumulated so far in support of precision medicine.

To break the in-memory bottleneck and facilitate online sampling in cluster computing frameworks, we propose a new sampling-based system for approximate big data analysis on computing clusters. We address both computational and statistical aspects of big data across the main layers of cluster computing frameworks: big data storage, big data management, big data online sampling, big data processing, and big data exploration and analysis. We use the new Random Sample Partition (RSP) distributed data model to store a big data set as a set of ready-to-use random sample data blocks in Hadoop Distributed File System (HDFS), called RSP blocks. With this system, only a few RSP blocks are selected and processed using a sequential algorithm in a distributed data-parallel manner to produce approximate results for the entire data set. In this paper, we present a prototype RSP-based system and demonstrate its advantages. Our experiments show that RSP blocks can be used to get approximate models and summary statistics as well as estimate the proportions of inconsistent values without computing the entire data or running expensive online sampling operations. This new system enables big data exploration and analysis where the entire data set cannot be computed.

Big data analysis and optimization and platform components

The management of big data is mainly affected by the size of the big graph data that represents the huge volumes of data. The size of this structure may increase with the size of data to be handled over the time. Facing this issue, the querying time may be affected and the introduced delay may not be tolerated by running applications. Moreover, the investigation of attacks through the collected massive data could not be ensured using traditional approaches, which do not support big data constraints. In this context, we propose in this paper, a novel temporal conceptual graph to represent the big data and to optimize the size of the derived graph. The proposed scheme built on this novel graph structure enables tracing back of attacks using big data. The efficiency of the proposed scheme for the reconstruction of attack scenarios is illustrated using a case study in addition to a conducted comparative analysis showing how smart big graph data is obtained through the optimization of the graph size.

The primary goal was to empirically investigate the relationships amongst Big Data Analytics (BDA), Management Information System (MIS), optimal hiring strategies, leadership support, and talent acquisition process. This research aimed to fill crucial gaps in the literature by examining how technology adoption and leadership support influence the formulation and effectiveness of optimal hiring strategies, finally impacting talent acquisition results in the Chinese business landscape. Quantitative research was employed, targeting experts and decision-makers in human resource departments across numerous industries in China. The research used a random sampling approach to acquire facts through an online questionnaire, examining responses from 168 participants. Regression and mediation analyses, facilitated by SPSS, were applied to discover the relationships and mechanisms among BDA, MIS, optimal hiring strategies, leadership support, and talent acquisition success. The results discovered a significant and positive relationship between BDA and MIS with the optimal hiring techniques. Moreover, optimal hiring techniques have proven an advantageous association with talent acquisition success. The research similarly showed the mediating role of optimal hiring strategies in translating the advantages of BDA and MIS into talent acquisition success. Leadership support emerged as a moderator, amplifying the relationship between optimum hiring techniques and talent acquisition success. This study contributes empirical proof to the underexplored intersection of leadership support, technology, and talent hiring strategies. The findings offer practical insights for companies aiming to navigate the complexities of recruitment, emphasizing the significance of strategic decision-making, technology integration, and management endorsement in achieving talent acquisition success.

Big data management and application specialty is an emerging major in our school of information and business management. Based on the Internet plus and big data era, we mainly study the application of big data analysis theory and methods in economic management and the management and management of big data. This paper aims to explore how to improve the innovation and practical ability of students in this major through practical teaching. This paper mainly elaborates from four aspects, namely, the basic principles of practical teaching, the path of students' ability improvement, the construction of the completed practical teaching system and the application of practical teaching in this major.

Software developers and data scientists use and deal with big data to easily discover useful knowledge and find better solutions to improve healthcare services and patient safety. Big data analytics (BDA) is getting attention due to its role in decision-making across the healthcare field. Therefore, this article examines the adoption mechanism of big data analytics and management in healthcare organizations in Jordan. Additionally, it discusses health big data’s characteristics and the challenges, and limitations for health big data analytics and management in Jordan. This article proposes a conceptual framework that allows utilizing health big data. The proposed conceptual framework suggests a way to merge the existing health information system with the National Health Information Exchange (HIE), which might play a role in extracting insights from our massive datasets, increases the data availability and reduces waste in resources. When applying the framework, the collected data are processed to develop knowledge and support decision-making, which helps improve the health care quality for both the community and individuals by improving diagnosis, treatment, and other services.

Performance measurement is the process of making an evidence-based improvement. It reveals the performance gains or gaps, depending on the entity to be measured, being an organization, people, equipment, processes, or systems. After development, big data analytics (BDA) systems massively fail in organizational settings. The reasons, however, are not fully understood. This paper investigates how organizations can quantify the performance of their BDA systems. To answer this question, we investigated performance measures and performance-contributing factors in the existing literature and surveyed users’ perceptions of our findings. The results show that metrics of efficiency and effectiveness can be used to measure the performance of the BDA System. The results also demonstrate that technology, competency, and working conditions are the key factors that contribute to the performance of the BDA System.

<p>ABSTRACT<br />Over the last few decades, most of the pharmaceutical companies and research sponsors are facing a lot of challenges in clinical research for their<br />new drug approval. The sponsor research needs a high-quality data report for getting new drug approval from Food and Drug Administration for their<br />medical products. Clinical trial data are important for the drug and medical device development processing pharmaceutical companies to examine<br />and evaluate the efficacy and safety of the new medical product in human volunteers. The results of the clinical trial studies generate the most<br />valuable data and in recent years; there has been massive development in the field of clinical trials. A good clinical data management system reduces<br />the duration of the study and cost of drug development. Further a well-designed case report form (CRF) assists data collection and make facilitates<br />data management and statistical analysis. Nowadays, the electronic data capture (EDC) is very beneficial in data collection. EDC helps to speed up the<br />clinical trial process and reduces the duration, errors and make the work easy in the data management system. This article highlights the importance<br />of data management processes involved in the clinical trial and provides an overview of the clinical trial data management tools. The study concluded<br />that data management tools play a key role in the clinical trial and well-designed CRFs reduces the errors and save the time of the clinical trials and<br />facilitates the drug discovery and development.<br />Keywords: Pharmaceutical, Clinical trial, Clinical data management, Data capture.</p>

Big Data is known as huge data set that is not processed in single system. To increase the processing speed and storing high volume data we prefer big data than traditional techniques. Big data helps in decision making for business prospective also. Big Data also refers a huge quantity of data sets with a large variety can be stored, managed and processed. It make secure correct information fast and provide benefit to the researchers and customers by taking the properties of volume, variety, veracity, velocity, value, visualization, volatility, vulnerability, validity and variability into consideration. With the help of traditional database system we cannot handle large data. To handle huge dataset, consider big data tools and techniques. In this review paper we first proposed the introduction and their characteristics and related issues faced by big data. It also provided the tools for big data which are help to managing data. This article is like a small seed of helping to know about the data storage and management of big data.

with the booming development of Energy Internet recently, more and more variable data generated from power grid operation and equipment monitoring processes are featured of large scale, redundant species, and time sensitivity, which gradually form the “big data” in the Energy Internet system, resulting in that the traditional solution of data platform cannot meet the requirements. Accordingly, sources and characteristics of big data in Energy Internet are specially explored, in order to establish the desired unified supporting platform. The common information model followed by IEC 61968 standard is analyzed, strategies of resource virtualization incorporated with cloud computing are put forward, and blockchain technology for data security protection is utilized. Through the big data management, calculation, mining, visualization analysis and security protection processes based on the proposed unified supporting platform, the information among heterogeneous data sources can be shared and exchanged; besides, more efficient and rapid data processing can be realized, technologically laying the foundation for further secure and self-healing operation of the Energy Internet.

Machine learning for Big Data analytics in plants.