BIG DATA IN SOCIAL AND ECONOMIC ANALYSES

Online social networking is one of the most prominent features and main reasons of the big data era. In this paper, we first present the immense opportunities brought by big data in: advancing the technical level of the Chinese IT industry, leading development of the new Internet economy, and accelerating interdisciplinary research between sociology and information science. Next we analyze key issues in online social networks and point out new challenges of big social data research on semantic understanding and analysis, multi-modal association and fusion, group behavior analysis and mining, multidimensional analysis and visualization, and system development and integration. We then focus on introducing key international and domestic achievements in big data and online social networks. We conclude with a look at future trends in big social data analytics and mining.

Social media data analytics for business decision making system to competitive analysis

Big Data - Towards a New Techno-Determinism?

Reservoir engineering is a critical discipline in the oil and gas industry, aimed at optimizing the extraction and management of subsurface resources. Traditionally, reservoir models have relied on static data and conventional simulation methods, which often fall short in capturing the dynamic complexities of reservoirs. With the advent of big data and predictive analytics, there is a significant opportunity to revolutionize reservoir modeling by integrating real-time data for continuous updates and more accurate predictions. This review introduces a theoretical exploration of how big data and predictive analytics can transform dynamic reservoir models, highlighting their potential to improve reservoir management and hydrocarbon recovery. Big data technologies enable the collection, storage, and processing of vast amounts of structured and unstructured data from multiple sources, including well logs, seismic surveys, drilling operations, and production data. Predictive analytics, through machine learning and statistical techniques, can extract actionable insights from these datasets to predict reservoir behavior, enhance decision-making, and optimize production strategies. This integration facilitates real-time monitoring and adaptive reservoir management, allowing engineers to respond to changing reservoir conditions and uncertainties more effectively. The review further explores the implications of big data-driven predictive models for enhanced hydrocarbon recovery techniques, such as water flooding and gas injection. By automating data processing and integrating real-time field data, predictive analytics can improve the accuracy of reservoir simulations, reduce downtime, and increase operational efficiency. Ultimately, the future of dynamic reservoir modeling lies in the seamless integration of big data and predictive analytics, providing a pathway to smarter, more sustainable resource management and maximized recovery factors in increasingly complex reservoir environments. Keywords: Big Data, Predictive Analytics, Reservoir Engineering, Dynamic Reservoir, Models.

The role of technology in risk management and innovation is crucial in the modern business world, where uncertainty and rapid change are key challenges. This article discusses how technology plays a role in identifying, analyzing, and mitigating risks, as well as accelerating and improving the effectiveness of the innovation process. In risk management, technologies such as big data, predictive analytics, the Internet of Things (IoT), artificial intelligence (AI), and integrated risk management systems enable organizations to monitor and address potential risks more efficiently. Meanwhile, in terms of innovation, technology supports the creation of new products and services, accelerates the development process, and opens up opportunities for global collaboration. Innovation can also introduce new business models that are more adaptive to market changes. The synergy between risk management and innovation through the use of technology helps organizations not only survive but also thrive amidst increasingly fierce challenges and competition. This article highlights the importance of technology adoption as a key to managing uncertainty and creating new value in an ever-changing business world.

The ever-increase in the quality and quantity of data generated from day-to-day businesses operations in conjunction with the continuously imported related social data have made the traditional statistical approaches inadequate to tackle such data floods. This has dictated researchers to design and develop advance and sophisticated analytics that can be incorporated to gain valuable insights that benefit the business domain. This chapter sheds the light on core aspects that lay the foundations for social big data analytics. In particular, the significance of predictive analytics in the context of SBD is discussed fortified with presenting a framework for SBD predictive analytics. Then, various predictive analytical algorithms are introduced with their usage in several important application and top-tier tools and APIs. A case study on using predictive analytics to social data is provided supported with experiments to substantiate the significance and utility of predictive analytics.

The ever-increasing quality and quantity of data generated from day-to-day business operations, in conjunction with the continuously imported related social data, has rendered the traditional statistical approaches inadequate to deal with such data floods. This inadequacy can be attributed to the fact that traditional statistical methods were developed before the advent of the internet. Because of this, academics have been compelled to design and develop advanced and complex analytics that may be incorporated to acquire useful insights that are beneficial to the commercial area. This chapter shines a light on fundamental characteristics that are the building blocks for social big data analytics and lays out those building blocks. In particular, the importance of predictive analytics within the scope of SBD is examined, and this analysis is bolstered by the presentation of a framework for SBD predictive analytics. After that, a number of different predictive analytical algorithms are discussed, along with their implementation in a number of essential applications, top-tier tools, and APIs. Experiments are presented alongside a case study that demonstrates how predictive analytics may be used to social data. This is done to demonstrate the significance and practicality of predictive analytics.

Global trade systems are becoming increasingly complex due to interconnected markets, fluctuating demand, geopolitical uncertainties, and sustainability concerns. Predictive analytics offers transformative potential in optimizing supply chain operations by leveraging data-driven insights for proactive decision-making. This explores the advancements in predictive analytics models tailored for global trade systems, emphasizing their role in enhancing supply chain efficiency, resilience, and agility. By integrating machine learning algorithms, big data analytics, and real-time data feeds, these models enable accurate forecasting of demand, inventory levels, and transportation routes. Key innovations include the incorporation of artificial intelligence (AI) for pattern recognition, predictive maintenance of assets, and dynamic route optimization. The use of ensemble modeling and deep learning enhances the predictive accuracy, while adaptive algorithms accommodate evolving market trends and disruptions. The review also highlights the challenges of data silos, scalability, and model interpretability, which hinder the full potential of predictive analytics in global supply chains. Ethical considerations such as data privacy and fairness in AI-driven decision-making are addressed to ensure responsible model implementation. Case studies from various industries demonstrate how advanced predictive models mitigate risks, reduce costs, and improve sustainability by optimizing resource utilization and reducing carbon footprints. Emerging trends, such as the integration of blockchain for transparent data sharing and IoT for real-time monitoring, further enrich the predictive capabilities. This underscores the critical need for collaborative frameworks between stakeholders, continuous innovation, and investment in analytical capabilities to future-proof supply chains. By advancing predictive analytics, global trade systems can achieve a new paradigm of efficiency and resilience in a volatile and competitive landscape. Keywords: Predictive Analytics, Models, Supply Chain Optimization, Global Trade Systems.

A massive bulk of data is being created due to digitalisation in various industries, including medical, manufacturing, sales, internet of things (IoT) devices, the web, and businesses. To find data patterns for data attributes machine learning (ML) algorithms are used. In this fast-growing world, we can see that data is generated in abundance by people, machines, and corporations. With the increase in computer science market, researchers are integrating heterogeneous and diverse data into accurate patterns by applying machine learning algorithms and complex strategies on data sets. The overabundance of high-dimensional big data has made it more difficult for scientists to extract important information from these data efficiently. Conventional data mining approaches are ineffective when dealing with large amounts of data. As big data increase exponentially, predictive analytics has become widely known. To evaluate a large number of data patterns, data driven technology predictive big data analytics (PBA) can be used and ML algorithms to investigate the present and future data based on the records of data patterns. In this research paper, predictive analysis on big data has been proposed using the splitting random forest (SRF) methodology with help of hyperparameter optimization and dimension reduction technique.

Visualization of big data is crucial for meaningful interpretations and especially for healthcare. Brief discussions are made for big data, background for healthcare, and recent work in big data analytics for healthcare. This research pertains to different levels of big data: 5,110 vs. 101,766 vs. 320,200 vs. 1 million data values. Data visualizations and predictive analytics are presented of big data for selected diseases of stroke with 5,110 data values, diabetes with 101,766 data values, and two COVID-19 studies: one with 320,200 data values and another with 1 million data values. Data visualizations are generated for these diseases with big data using Tableau. For stroke patients, an investigation was performed to determine how different living environments affect relationship between strokes. The data visualizations for diabetes showed impact of insulin use yielded reduced hospital stays. Data visualizations for COVID-19 provided temporal trends in confirmed cases, mortality, and recovery rates for 2020-2023. Conclusions and future directions of research are presented.

Big data has evolved as a most challenging area in scientific study and research. It has drawn much attention during the last few years. It influences our modern society, business, government, healthcare, research and almost every discipline. In this data-driven era, where data is continuously acquired from a verity of sources for different purposes, the ability to make timely decisions based on available data is a very critical task. The massive data size, variety, velocity, accuracy and high dimensionality presents new challenges to big data. This paper attempts to present some challenges of big data. In addition, a study on the conceptual design of big data architecture presented on specific big data applications. In this paper, we presented research work on big data analytics techniques in the areas of text, sentiment, video, social media and predictive analytics. A comparative analysis on selected big data applications has been presented in detail.

The use of ‘big data’ and predictive analytics has transformed a sizeable number of industries, from insurance companies to print and e-book online retailers, and both the mass media and the scholarly literature have covered these developments. Online retailers have used big data systems to capture tremendous amounts of data about consumers and their purchases, which has enabled them to use predictive analytics and collaborative filtering systems to make purchase suggestions to consumers. Unfortunately, many university presses—even the largest presses with substantial endowments—have not been able to capitalize on the formidable marketing assets offered by big data and predictive analytics. In this article, the authors review the published literature and significant data sets, and present suggestions for the Canadian and US university press community to launch a non-profit direct-to-consumer Web site generating continuous-time sales and marketing data.

Predictive Analytics is of great interest when it comes to enhancing Business Intelligence. Businesses have already started to use Big Data Analytics, particularly predictive and prescriptive analytics, to strengthen and increase their business yields. Not only has analytics resulted in business growth, but has also provided a significant competitive edge over others. The voluminous data generated from various resources is highly unstructured in nature and adding a structure to it would leverage the actual potential of the data. New techniques and frameworks should serve as human aids in automatically and intelligently analyzing large datasets in order to acquire useful information. In this paper, we attempt to perform Big Data Analytics on data from one of the most important and growing sources, namely, Telecommunication. To keep pace with the growing telecommunication market and ever increasing demands of the consumers for quality service, the telecom service providers are required to observe and estimate various trends in customer's usage to plan future upgrades and deployments driven by real data. We have attempted to use several data mining techniques to find hidden and interesting patterns from the telecom data generated by Telecoms Italia cellular network for the city of Milano, Italy. K-means clustering is used to categorize the usage statistics while several machine learning algorithms like Decision Tree, Random Forest, Logistic Regression and SVM are used for predicting the usage of telecom services. In the end, a performance comparison matrix is generated to rate the performance of these algorithms for the given dataset. All these experiments are performed on the big data environment set up at the supercomputing infrastructure of C-DAC. Given such a matrix, the result can be applied to similar dataset pertaining to other domains as well.

Historically, domains such as business intelligence would require a single analyst to engage with data, develop a model, answer operational questions, and predict future behaviors. However, as the problems and domains become more complex, organizations are employing teams of analysts to explore and model data to generate knowledge. Furthermore, given the rapid increase in data collection, organizations are struggling to develop practices for intelligence analysis in the era of big data. Currently, a variety of machine learning and data mining techniques are available to model data and to generate insights and predictions, and developments in the field of visual analytics have focused on how to effectively link data mining algorithms with interactive visuals to enable analysts to explore, understand, and interact with data and data models. Although studies have explored the role of single analysts in the visual analytics pipeline, little work has explored the role of teamwork and visual analytics in the analysis of big data. In this article, we present an experiment integrating statistical models, visual analytics techniques, and user experiments to study the role of teamwork in predictive analytics. We frame our experiment around the analysis of social media data for box office prediction problems and compare the prediction performance of teams, groups, and individuals. Our results indicate that a team's performance is mediated by the team's characteristics such as openness of individual members to others' positions and the type of planning that goes into the team's analysis. These findings have important implications for how organizations should create teams in order to make effective use of information from their analytic models.

The development of mobile Internet, Internet of Things, and cloud computing has contributed to the unprecedented growth of information data. Big data plays a very important role in education. Currently, the literature review and in-depth research on big educational data are not very extensive, mainly involved in two fields: education mining and learning analysis. For a perfect research about education big data, this paper comprehensively reviewed three major aspects (Predictive Analytics, Learning Analytics, and Recommendation Systems) of educational data analytics for an intensive investigation and analysis: (1) Predictive Analytics: It predicts students’ learning performance by tracking students’ learning information and then analyzes students’ learning competence to build an academic early warning system; teachers can be allowed to intervene in students in time and adopts different teaching ways for different students. Therefore, both students’ learning and ability can be individualized and improved; (2) Learning Analytics: This part can identify the learners’ behavior patterns and obtain more implicit learner characteristics by studying the hidden meaning behind learning behaviors and strategies; (3) Recommendation Systems: It can match the needs of learners and recommend appropriate learning resources through different methods. All the above proved that the application of big data technology in education provides powerful data support for the development of education.