Design and Development of a Big Data Platform for Disease Burden Based on the Spark Engine.

Community safety has become an important part of social public safety. The construction of a safe community focuses on the accumulation of community safety capabilities. This paper discusses the application of big data technology in community safety construction and the improvement of community safety promotion capabilities. We analyzed the sources and collection methods of community data, classified multisource heterogeneous community data, and constructed seven types of community data. We designed the conceptual structure and storage structure of the community database. On the basis of the construction of the community database, the architecture design of the big data platform for community security was launched. From the perspective of different user types, the functional requirements of the big data platform were analyzed. Combined with demand analysis, the overall architecture design of the community big data platform was carried out. On the basis of the overall architecture, the application architecture and technical architecture were designed in more detail, and the key technologies of the community big data platform were analyzed. Finally, it analyzes how to use the community big data platform to predict public security risks by constructing a CART regression tree model.

Objective: Through the practice discussion and exchange of the integration of big data and pharmaceutical standards, to provide experience reference for the practice of information technology in the management of rational drug use. Methods: The application of data information in the practice of hospital pharmacy management is demonstrated through the introduction of practical cases and data achievements at different levels. Results: The integration of medical big data and pharmaceutical standards effectively improved the hospital's quantitative quality management indicators and the informatization of many pharmaceutical management methods in the hospital is realized. Conclusions: The integration of medical big data and pharmaceutical standards can effectively help improve the management level of rational drug use in hospitals, and there are still many possibilities for future exploration.

As the exponential growth of data in internet era, the era of big data is coming. Big data integration creates huge values that makes it a research hotspot. However, in big data era, data shows characters of large volume, velocity, veracity and especially variety which is also called heterogeneity. Multiple different sources of data lead to data heterogeneity. Multi-source heterogeneous data brings challenges to big data integration. In this paper, we proposes a big data integration platform based on hybrid hierarchy architecture, which classifies the underlying multi-source heterogeneous data sources according to themes, and uses the data warehouse as the middle-tier data source. Then user uses the mediator to directly access the middle-tier data source through pattern mapping. Experiments show that compared with the traditional method of directly connecting the underlying data source, this data integration platform improves query performance.

Modelling and analysis of big data platform group adoption behaviour based on social network analysis

The word Big Data is commonly used and it is not new today. Large, medium and small companies are starting to use Big Data to obtain their customers insight in order to serve them in a better way. The use of Big Data has become quite a crucial way for businesses to compete with their competitors. Also not only companies gain from the value of Big Data, it is also the customer’s hugely benefit from its usage. In association with Big Data’s real time information, which is one of the most heavily used application of personal and location data. As there is a significant growth in the use of smart phones and the use of GPS services from the phones and other devices, the use of smart traffic routing will definitely grow and in turn it will hugely benefit the customers. Big Data is not a single packaged technology, it is in general a platform consists of usage of various components to achieve a common goal. There are plenty of components available in the market for the businesses to customise their Big Data platform. The utilization of Big Data is becoming more and more essential to businesses and it is even more important for them to adopt the right Big Data platform to accomplish their goals. The main aim of this study is to propose a framework for building a Big Data platform for publishing industry. The proposed framework was validated in an UK based news publishing organisation to find out the suitability and adoptability of the framework for their Big Data platform.

Abstract. With the wide application of Big Data, Artificial Intelligence and Internet of Things in geographic information technology and industry, geospatial big data arises at the historic moment. In addition to the traditional "5V" characteristics of big data, which are Volume, Velocity, Variety, Veracity and Valuable, geospatial big data also has the characteristics of "Location Attribute". At present, the study of geospatial big data are mainly concentrated in: knowledge mining and discovery of geospatial data, Spatiotemporal big data mining, the impact of geospatial big data on visualization, social perception and smart city, geospatial big data services for government decision-making support four aspects. Based on the connotation and extension of geospatial big data, this paper comprehensively defines geospatial big data comprehensively. The application of geospatial big data in location visualization, industrial thematic geographic information comprehensive service and geographic data science and knowledge service is introduced in detail. Furthermore, the key technologies and design indicators of the National Geospatial Big Data Platform are elaborated from the perspectives of infrastructure, functional requirements and non-functional requirements, and the design and application of the National Geospatial Public Service Big Data Platform are illustrated. The challenges and opportunities of geospatial big data are discussed from the perspectives of open resource sharing, management decision support and data security. Finally, the development trend and direction of geospatial big data are summarized and prospected, so as to build a high-quality geospatial big data platform and play a greater role in social public application services and administrative management decision-making.

In order to improve the efficiency of locomotive organization and the quality of locomotive operation, this paper analyzes and discusses the big data platform and some key technologies suitable for the big data application of the railway locomotive system. Firstly, the definition of big data of the railway locomotive system is proposed, and the current data characteristics of the railway locomotive system are summarized, then the status quo and demands of big data application of the railway locomotive system are analyzed. Secondly, the overall architecture of the big data platform for the railway locomotive system is proposed. Furthermore, seven application scenarios available for the big data platform are analyzed, including locomotive running organization, high-speed railway, repair, maintenance and other fields. Finally, some key technologies, which consist of data collection system of front-line operations, locomotive equipment portrait analysis, staff portrait analysis, transmission and analysis of locomotive video, intelligent auxiliary driving system, are provided to increase efficiency of the locomotive organization and capability of safety management. The obtained results can play a positive role in the construction and application of big data of the railway locomotive system.

China Unicom, the largest WCDMA 3G operator in China, meets the requirements of the historical Mobile Internet Explosion, or the surging of Mobile Internet Traffic from mobile terminals. According to the internal statistics of China Unicom, mobile user traffic has increased rapidly with a Compound Annual Growth Rate (CAGR) of 135%. Currently China Unicom monthly stores more than 2 trillion records, data volume is over 525 TB, and the highest data volume has reached a peak of 5 PB. Since October 2009, China Unicom has been developing a home-brewed big data storage and analysis platform based on the open source Hadoop Distributed File System (HDFS) as it has a long-term strategy to make full use of this Big Data. All Mobile Internet Traffic is well served using this big data platform. Currently, the writing speed has reached 1 390 000 records per second, and the record retrieval time in the table that contains trillions of records is less than 100 ms. To take advantage of this opportunity to be a Big Data Operator, China Unicom has developed new functions and has multiple innovations to solve space and time constraint challenges presented in data processing. In this paper, we will introduce our big data platform in detail. Based on this big data platform, China Unicom is building an industry ecosystem based on Mobile Internet Big Data, and considers that a telecom operator centric ecosystem can be formed that is critical to reach prosperity in the modern communications business.

Advances in the field of technology enabled individuals and businesses to collect large amounts of data (structured and unstructured) from various sources like never before. Data from social media, user-generated, internet, health care, manufacturing, supply chain, financial institution, and sensors have grown exponentially. This paper’s objective is to review how big data drive and impact innovation, competitive advantage, productivity, and decision support. Methodology: A comprehensive literature review on big data and identifying the impact of big data analytics on innovation, competitive advantage, productivity, and decision support are studied. The reviewed literature created the foundation for studying, a model that was developed based on an extensive review of literature as well as case studies and future forecast by market leaders. Big data is the latest buzzword among businesses. A new model is suggested identifying big data and the correlation between innovation, competitive advantage, productivity, and decision support. Findings: A review of scholarly literature and existing case studies finds that there is a gap between existing frameworks and the integration of big data into various business and management functions and objectives. The findings are interesting that literature is rich with concepts and frameworks for achieving the end goal for business or management function along with framework but very little is available in the literature on the question of how to integrate big data analytics into those frameworks. This paper finds that a key question is missing i.e., what are the essential steps that businesses should perform to implement and integrate big data analytics into existing frameworks to fully exploit the big data potential. Research Limitations/Implications: The research was limited to a review of selective literature focused on in-depth understanding of big data. Additionally, it focuses on how big data leads to innovation, competitive advantage, productivity, and decision support. Although there are many other related fields of studies where big data impact can be studied but is not part of this study effort. Future studies can lead to more in-depth studies of other related areas of studies. Practical Implications: The review of the literature suggested that “Big Data” is playing an important role in innovations, creating competitive advantage, enhancing productivity, and assisting in data-driven decisions. Businesses are taking advantage of the customer insights that are innovating products and services which are very customer-centric, keeping the competition on the run, improving productivity at all levels, and making educated decisions every day. The future will be driven by smarter big data solutions and insights. Originality/Value: The study provides evidence that big data is the catalyst for innovation, creates competitive advantage, enhances productivity, and assists in decision making. The methodology is to review scholarly literature and case studies. It supports the need for developing new models, implementation frameworks for better insights, and patterns. The big data implementation methodologies, framework, and governance have been ignored in empirical research.

Biological data are generated at unprecedentedly exponential rates, posing considerable challenges in big data deposition, integration and translation. The BIG Data Center, established at Beijing Institute of Genomics (BIG), Chinese Academy of Sciences, provides a suite of database resources, including (i) Genome Sequence Archive, a data repository specialized for archiving raw sequence reads, (ii) Gene Expression Nebulas, a data portal of gene expression profiles based entirely on RNA-Seq data, (iii) Genome Variation Map, a comprehensive collection of genome variations for featured species, (iv) Genome Warehouse, a centralized resource housing genome-scale data with particular focus on economically important animals and plants, (v) Methylation Bank, an integrated database of whole-genome single-base resolution methylomes and (vi) Science Wikis, a central access point for biological wikis developed for community annotations. The BIG Data Center is dedicated to constructing and maintaining biological databases through big data integration and value-added curation, conducting basic research to translate big data into big knowledge and providing freely open access to a variety of data resources in support of worldwide research activities in both academia and industry. All of these resources are publicly available and can be found at http://bigd.big.ac.cn.

It is of great significance to establish a scientific and perfect application model of medical and health big data analysis for promoting the application of medical and health big data. This paper mainly studies the computer-aided medical and health data analysis of Inner Mongolia Autonomous Region based on big data. This paper adopts literature research method, field research method and data science method. Literature research is used to understand big data platforms and their technologies, and to analyze the limitations of data analysis methods in traditional data warehouses. The application process of data analysis based on big data platform is studied by using data science method. The established medical and health data analysis application process based on the big data platform and the described medical and health big data application theme were consulted by using the expert consultation method, and they were modified and improved according to the expert opinions.KeywordsBig data platformComputer aidedHealthcare dataData analysis

The emergence of big data platforms has alleviated the restrictions of space and time on people's enjoyment of music information services. The need to improve user experience has become very urgent. One of the effective solutions to information overload is the recommendation system, which can help people discover interesting content from complex information. Therefore, the purpose of this article is to study a personalized film and television recommendation system based on a big data platform. This article first introduces the core technology of big data through an overview of the basic theory of big data. And combined with the traditional film and television recommendation method, analyze the existing problems and shortcomings, and analyze and discuss it with the core technology of big data. This article systematically expounds the overall process, system composition design and algorithm analysis of the personalized film and television recommendation system based on the big data platform. Experimental research shows that the algorithm proposed in this paper is better than the traditional User-basedCF strategy and Item-basedCF strategy under the same number of movies recommended by Hit-rate.

Dimension reduction or feature selection is thought to be the backbone of big data applications in order to improve performance. Many scholars have shifted their attention in recent years to data science and analysis for real-time applications using big data integration. It takes a long time for humans to interact with big data. As a result, while handling high workload in a distributed system, it is necessary to make feature selection elastic and scalable. In this study, a survey of alternative optimizing techniques for feature selection are presented, as well as an analytical result analysis of their limits. This study contributes to the development of a method for improving the efficiency of feature selection in big complicated data sets.

Big data is a term that is used to describe data that is high volume, high velocity, and/or high variety; requires new technologies and techniques to capture, store, and analyze it; and is used to enhance decision making, provide insight and discovery, and support and optimize processes. With regard to the definition of big data, IBM Company uses volume, velocity, variety, value and veracity as 5Vs to summarize the concept of big data. There are different types of big data, for example, structured, semi-structured and un-structured data. The contents of big data can be text data, audio data, video data and still image and it indicates that the big data may have diverse data types as well as data qualities. Big data has variety of sources such as healthcare center, commercial system, industries, social media, telecommunication, transportation, sensor machines and others. In this paper, I reviewed three the most security challenging perspectives and I studied lack of concentrations in these areas by most research works. To confirm security in the big data platforms, it is critical to ascertain the data rendering points and their security techniques to safeguard the data in this pacing digital world. Then I envisage directions for the future research. In this paper, I have reviewed the big data sources and its security issues in the three directions such as data at rest, data at communication and data in process/use. Keywords: Big Data, Big Data Security, Big Data source, Attribute based encryption, storage path, Transport layer security, secure shell, Cloud service Provider DOI: 10.7176/CEIS/12-3-01 Publication date: November 30 th 2021

The rapid growth of new computing paradigms such as Cloud Computing and Big Data has unleashed great opportunities for companies to shift their business model towards a fully digital strategy. A major obstacle in this matter is the requirement of highly specialized ICT infrastructures that are expensive and difficult to manage. It is at this point that the IaaS (infrastructure as a service) model offers an efficient and cost‐affordable solution to supply companies with their required computing resources. In the Big Data context, it is often a hard task to design an optimal IaaS solution that meets user requirements. In this context, we propose a methodology to optimize the definition of IaaS cloud models for hosting Big Data platforms, following a threefold criterion: cost, reliability, and computing capacity. Specifically, the proposed methodology hinges on evolutionary heuristics in order to find IaaS configurations in the cloud that optimally balance such objectives. We also define measures to quantify the aforementioned metrics over a Big Data platform hosted within an IaaS cloud model. The proposed method is validated by using real information from three IaaS providers and three Big Data platforms. The obtained results provide an insightful input for system managers when initially designing cloud infrastructures for Big Data applications.