Big Data, Chatbots, and Data Mining: Transforming Academic Libraries – A Bibliometric Analysis

Big data analysis and mining aims to discover implicit, previously unknown, and potentially useful information and knowledge from big databases that contain high volumes of valuable veracious data collected or generated at a high velocity from a wide variety of data sources. Among different big data mining tasks, this chapter focuses on big data analysis and mining for frequent patterns. By relying on the MapReduce programming model, researchers only need to specify the “map” and “reduce” functions to discover frequent patterns from (1) big databases of precise data in a breadth-first manner or in a depth-first manner and/or from (2) big databases of uncertain data. Such a big data analysis and mining process can be sped up. The resulting (constrained or unconstrained) frequent patterns mined from big databases provide users with new insights and a sound understanding of users' patterns. Such knowledge is useful is many real-life information science and technology applications.

This bibliometric study on data mining in education synonymous with big educational data utilizes VOSviewer and Harzing’s Publish and Perish to analyze the metadata of 1,439 journal articles found in Scopus from 2010 to 2022. As bibliometric analyses in this field are lacking, this study aims to provide a comprehensive outlook on the current developments and impact of research in this field. This study employs descriptive and trends analysis, co-authorship analysis, co-citation analysis, co-occurrences of keywords, terms map analysis, and analysis of the impact and performance of publications. It also partially replicates a similar study conducted by Wang et al. (2022), who used the Web of Science (WoS) database. The study is reported in an article entitled ‘Big data and data mining in education: A bibliometrics study from 2010 to 2022’. Results show that data mining in education is a growing research field. There is also a significant difference between the publications in Scopus and WoS. The study found several research areas and topics, such as student academic performance prediction, e-learning, machine learning, and innovative data mining techniques, to be the core basis for collaborating and continuing current research in this field. These results highlight the importance of continuing research on data mining in education, guiding future research in tackling educational challenges.

Big data analysis is essential for modern applications in areas such as healthcare, assistive technology, intelligent transportation, environment and climate monitoring. Traditional algorithms in data mining and machine learning do not scale well with data size. Mining and learning from big data need time and memory efficient techniques, albeit the cost of possible loss in accuracy. We have developed a data aggregation structure to summarize data with large number of instances and data generated from multiple data sources. Data are aggregated at multiple resolutions and resolution provides a trade-off between efficiency and accuracy. The structure is built once, updated incrementally, and serves as a common data input for multiple mining and learning algorithms. Data mining algorithms are modified to accept the aggregated data as input. Hierarchical data aggregation serves as a paradigm under which novel data representations and algorithms work together for analysis and mining of big data. To evaluate its performance, we have implemented a multi-resolution Naive Bayes Classifier on the data aggregation structure. Experimental results show that the proposed structure helps the classifier to reduce computation time to 25% on average and reduce the memory usage while preserving the accuracy of results.

Data is becoming the world's new natural resource and big data use grows quickly. The trend of computing technology is that everything is merged into the Internet and 'big data' are integrated to comprise complete information for collective intelligence. With the increasing size of big data, refining big data themselves to reduce data size while keeping critical data (or useful information) is a new approach direction. In this paper, we provide a novel data consumption model, which separates the consumption of data from the raw data, and thus enable cloud computing for big data applications. We define a new Data-as-a-Product (DaaP) concept, a data product is a small sized summary of the original data and can directly answer users' queries. Thus, we separate the mining of big data into two classes of processing modules: the refine modules to change raw big data into small sized data products, and application-oriented mining modules to discover desired knowledge further for applications from well-defined data products. Our practices of mining big stream data, including medical sensor stream data, streams of text data and trajectory data, demonstrated the efficiency and precision of our DaaP model for answering users' queries.

This is an age of big data. Big data is usually defined as a huge collection of unstructured information stored in multiple data sets that due to their size and complexity cannot be processed by using traditional database management systems or data mining/analysis applications. Despite it bring tremendous benefits to society, the rise of 'Big Data mining' in the private sector poses new challenges for privacy advocates. First, this article reviews a definition of big data mining. Second, this article will set out several essential technologies related to the big data mining, with particular reference to the cloud computing, and hydoop etc. Third, this article will address risks arising from big data mining, especially privacy harm.

Big data deals with data of very large data size, heterogeneous data types and from different sources. The data is very complex in nature and having growing data. Dealing with big data is one of the emerging areas of research which is expanding at a rapid rate in all domains of engineering and medical sciences. A major challenge imposes on the analysis of big data is originated from big data generation source, which generate data with very fast speed with varying data distribution due to which the classical methods are unable to process big data. This paper discusses the characteristics, challenges, and issues with big data mining. It also illustrates the examples taken from various fields like medical, finance, social networking sites, stock exchange, etc. to realize the application and importance of big data mining. This paper explains about the use of parallel computing in data mining security issues and how to deal with them. Furthermore, this paper also discusses challenges associated with big streaming data with concept drifts.

Nowadays, the daily amount of generated data is measured in exabytes. Such huge data is now referred to as Big Data. Big data mining leads to the discovery of the useful information from huge data repositories. However, this huge amount of data hinders existing data mining tools and thus creates new research challenges that open the door for new research opportunities. In this paper, we provide an overview of the research challenges and opportunities of big data mining. We present the technologies and platforms that are required for mining big data. A number of applications that can benefit from mining big data are also discussed. We discuss the status of big data mining, current efforts and future research directions in the UAE.

Big data analytics and mining aims to discover implicit, previously unknown, and potentially useful information and knowledge from big data sets that contain huge volumes of valuable veracious data collected or generated at a high velocity from a wide variety of rich data sources. Among different big data analytic and mining tasks, this chapter focuses on frequent pattern mining. By relying on the MapReduce programming model, researchers only need to specify the “map” and “reduce” functions to discover (organizational) knowledge from (i) big data sets of precise data in a breadth-first manner or depth-first manner and/or from (ii) big data sets of uncertain data. Such a big data analytics process can be sped up by focusing the mining according to the user-specified constraints that express the user interests. The resulting (constrained or unconstrained) frequent patterns mined from big data sets provide users with new insights and a sound understanding of users' patterns. Such (organizational) knowledge is useful is many real-life information science and technology applications.

Big data analytics and mining aims to discover implicit, previously unknown, and potentially useful information and knowledge from big data sets that contain huge volumes of valuable veracious data collected or generated at a high velocity from a wide variety of rich data sources. Among different big data analytic and mining tasks, this chapter focuses on frequent pattern mining. By relying on the MapReduce programming model, researchers only need to specify the “map” and “reduce” functions to discover (organizational) knowledge from (i) big data sets of precise data in a breadth-first manner or depth-first manner and/or from (ii) big data sets of uncertain data. Such a big data analytics process can be sped up by focusing the mining according to the user-specified constraints that express the user interests. The resulting (constrained or unconstrained) frequent patterns mined from big data sets provide users with new insights and a sound understanding of users' patterns. Such (organizational) knowledge is useful is many real-life information science and technology applications.

BackgroundLow Back Pain (LBP) is a pervasive and complex musculoskeletal condition affecting over 80% of the global population. Lumbar Disc Degeneration (LDD) significantly contributes to LBP, and MRI is crucial for its diagnosis and understanding. This study aimes to provide a comprehensive bibliometric analysis of MRI research on LDD with LBP, shedding light on research patterns, collaborations, and potential knowledge gaps.MethodsA comprehensive online search was conducted in the Scopus database to retrieve published literature on LDD with LBP. Bibliometric analysis was conducted to assess publication patterns, co-authorship networks, keyword co-occurrence, and co-citation analysis within the MRI applications for LDD research domain. Bibliometric analysis tools such as VOSviewer and the R package “bibliometrix” were utilized for quantitative assessments.ResultsA total of 1,619 publications related to MRI and LDD were analyzed. The analysis indicated a consistent annual growth rate of 4.62% in publications related to MRI and lumbar disc degeneration, reflecting a steady increase in research output over the past two decades. The USA, China, and Japan emerged as leading contributors. “SPINE“, “European Spine Journal“, and “Spine Journal” were the most productive journals in this domain. Key research themes identified included lumbar spine, low back pain, and magnetic resonance imaging. Network visualization shows that low back pain and magnetic resonance imaging were the most widely used keywords.ConclusionThe comprehensive bibliometric analysis of MRI applications for Lumbar Disc Degeneration offers insights into prevailing research patterns, highlights key contributors and journals, and identifies significant research themes. This study provides a foundation for future research efforts and clinical practices in the field, ultimately contributing to the advancement of patient care for individuals suffering from LDD and associated Low Back Pain.

Solve the problem of agricultural product big data mining based on e-commerce platform, meet the needs of e-commerce development to agricultural products, meet the diversified needs of e-commerce platforms, and improve people’s living standards and convenience. According to 1000 online questionnaires, 866 people believe that e-commerce can bring them convenience, and 134 people believe that the convenience is insufficient. Even agricultural products, as a traditional primary industry, have begun to be “involved” in the sales mode of e-commerce platforms. In the face of the increasingly huge online consumer demand market, the agricultural product economy has redisplayed a strong market vitality. Of course, the huge market base also makes the e-commerce model of agricultural products pay attention to big data mining and analysis. This paper focuses on how to carry out big data mining and analysis of agricultural products more efficiently from the technical level. Therefore, the agricultural product user data mining technology of e-commerce platform based on Hadoop is proposed. Through the intervention of association rule analysis and algorithm, the improvement of relevant algorithms and agricultural products user behavior analysis system under e-commerce platform based on Hadoop is proposed. The results show that the system can realize the analysis of commodity association degree under various agricultural products user behavior modes and can better help the e-commerce platform of agricultural products realize precision marketing.

With the increasing degree of information technology in the electric-power industry, the amount of big data in thermal power has increased geometrically. To address the problem of the computational bottlenecks in traditional data mining deal with big data of thermal power, big data mining of thermal power method based on Spark is presented in this paper. According to the characteristics of the actual operation of the unit, the proposed method determines the steady-state conditions of big data of thermal power and divides the working conditions based on external constraints. In addition, data mining method based on distributed computing is used to mine big data of thermal power to get the strong association rules, thus the best value of the parameters under each working condition can be got. Lastly, the historical knowledge base is established, which can guide the operation of the unit by the proposed method. This method is applied to a 300 MW unit in a power plant in Anhui Province, and mines the operation data of the unit for 10 days in a month. The results of simulation show that the proposed method can effectively mine big data of thermal power and has the advantage of computational efficiency compared with traditional data mining for big data.

The importance of agricultural big data is becoming more and more important to people, in the industry market space will have a huge application development space, become an important strategic resources and important technical research direction, the deep integration of big data and agriculture is expected to become the gas pedal of the development of agricultural modernization. The collection and mining of big data is the first priority for the development of "big data + agriculture". Agricultural big data involves agricultural water, soil, light, heat, climate resources, as well as crop breeding, planting, fertilization, plant protection, process management, harvesting, processing, storage, mechanization and other aspects of the collection of multiple types of complex data, processing analysis, mining and application issues. Agricultural big data in the integration and sharing of a variety of agricultural data on the basis of in-depth mining and analysis, to find the hidden value of the data, can greatly improve the government's decision-making and management level, but also can really play out the role of big data on the development of modern agriculture to boost.

Association rules provide an important clue on how hidden knowledge and patterns can be extracted and the attributes extracted are associated when big data is mined by means of a particular data mining algorithm. The context of HEIs, where big data mining is uncommon, there is a need to know how association rules could be utilized to discover hidden knowledge and forecast student performance based on time-to-degree and cumulative grade point average (CGPA) and also make decisions. This research has demonstrated this by taking the example of the dataset of 337 graduated students of a university in Bahrain belonging to the Bachelor's degree in Accounting and Finance. Two types of data mining concepts were applied, regression and a priori algorithm. Regression results showed that there is a statistically substantial link between time-to-degree and course-difficulty and not CGPA. Similarly, association rules showed the link between time-to-degree, course-taking pattern, no. of courses and course-difficulty pattern but not CGPA. The association rules generated provided the basis to predict optimum time-to-degree by controlling the no. of courses, course-taking pattern, and course-taking difficulty. The findings of this research provide an important window of opportunity for HEIs to enhance student performance using data mining techniques. This investigation has contributed theoretically showing the application of data mining theory using apriori algorithm and regression, for HEI big data mining where student factors and organizational factors are said to be fairly different from that of the HEIs where research on big data is seen to be a well-known activity.

BackgroundPremature infants, defined as those born before 37 weeks of gestation, face numerous health challenges due to their underdeveloped systems. One critical aspect of their health is the gut microbiota, which plays a vital role in their immune function and overall development. This study provides a comprehensive bibliometric analysis of research trends, influential contributors, and evolving themes in the study of gut microbiota in premature infants over the past two decades.MethodsWe conducted a bibliometric analysis using the Web of Science Core Collection database, covering publications from January 1, 2004, to June 17, 2024. We employed VOSviewer, the R package “bibliometrix”, and Citespace for data visualization and analysis, focusing on co-authorship, co-citation, and keyword co-occurrence networks.ResultsThe temporal analysis revealed a significant increase in research output on gut microbiota in premature infants, particularly in the last decade. Early research primarily focused on characterizing the gut microbiota of premature infants, identifying less diversity and a higher prevalence of pathogenic bacteria compared to full-term infants. Key research themes identified include probiotics, necrotizing enterocolitis (NEC), and breastfeeding. Probiotic studies highlighted the potential of strains like Bifidobacterium and Lactobacillus in reducing NEC and sepsis incidences. Breastfeeding research consistently showed the benefits of human milk in fostering a healthier gut microbiota profile. Co-authorship and co-citation analyses identified key contributors and influential studies, emphasizing strong international collaborations, particularly among researchers from the United States, China, and European countries.ConclusionThis bibliometric analysis underscores the growing recognition of the gut microbiota’s crucial role in the health of premature infants. The field has seen significant advancements, particularly in understanding how interventions like probiotics and breastfeeding can modulate gut microbiota to improve health outcomes. Continued research and international collaboration are essential to further unravel the complexities of gut microbiota in premature infants and develop effective therapeutic strategies.