Mining big building operational data for improving building energy efficiency: A case study

Chapter 19 - An extensive discussion on utilization of data security and big data models for resolving healthcare problems

In today’s digital age, big data refers to the large amount of data companies can access for their daily activities. It encompasses data collected from various sources, which can later be interpreted, analyzed, and utilized for the company's benefit. The demand for large volumes arises from the market's need to scrutinize and leverage current data. With the increasing demand and availability of vast data also comes the need to find trends, patterns, and correlations in enormous amounts of unprocessed data to support data-driven decision-making, this process is known as big data analytics. With the advent of recent tools, these processes apply well-known statistical techniques—like regression and clustering—on larger datasets, so that they can be utilized to their utmost extent. Big data analytics is a subset of advanced analytics, involving complex applications that include predictive models, statistical algorithms, and what-if analysis powered by analytical systems. Proper and effective utilization and analysis of big data have emerged as a significant challenge for companies worldwide. It involves the intricate process of examining and perusing this large dataset, alongside insightful analysis of hidden patterns, correlations, market trends, and customer preferences. These insights help organizations make informed and intelligent business decisions. Data analytics technologies and techniques help organizations with the correct and necessary means to analyze datasets and extract new information. Simultaneously, all queries related to Business Intelligence (BI) are also addressed by the various business operations and actions performed. A prime example of big data analytics is the education industry, where a vast amount of student records, admission progress, assessments taken, and the overall growth of a student, along with other data, needs to be collected, aggregated, processed, and analyzed. In the field of education, big data analytics refers to the utilization of data for accounting, decision-making, predictive analytics, and numerous other purposes. Although this data varies to a colossal extent in type, quality, and accessibility, presenting substantial challenges, it also offers immense benefits.

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.

Big Data, Big Expectations, and Big Judgements.

Transportation big data is not limited to the transportation sector, but is a useful resource that will bring innovation to all aspects of our lives in the future, and various R&D for its utilization is currently in progress. However, the current level of utilization of transportation big data is very limited under the existing legal system. In this study, we will investigate the meaning and problems of the use of big data in the transportation sector, and investigate the improvement tasks to expand the use of big data. Research Design, Data and Methodology: The paper used a qualitative research methodology through the literature review. In this study, first, the definition and creation process of big data were studied. Second, the significance and problems of applying big data in the transportation sector were studied. Finally, the current status of research in the transportation sector using big data was investigated, and the tasks to be improved in the process from collecting transportation big data to analysis were reviewed. Results: Big data means creating new value by fusing data collected from different purposes. In the case of using big data, the transportation sector can establish more accurate and detailed transportation policies in basic data investigation, identification of phenomena, and prediction. In order to expand the use of big data, it is important to consider who owns it, what it was collected for, what the format of the collected data is, and what should be done to use it. Conclusion: Big data is a derivative thing, but it is becoming important enough to determine the success or failure of a country depending on how it is used. However, problems such as data errors or invasion of privacy that may occur when using big data are expected. This is not just a problem in the transportation sector. When using big data, there are many problems to be solved, such as data ownership, Big Brother problems, and the implementation of smart mobility. If the advent of the big data era is taken for granted, the task from now on is how to solve these problems and share their values.

ObjectivesWe reviewed applications of big data analysis of healthcare and social services in developed countries, and subsequently devised a framework for such an analysis in Korea.MethodsWe reviewed the status of implementing big data analysis of health care and social services in developed countries, and strategies used by the Ministry of Health and Welfare of Korea (Government 3.0). We formulated a conceptual framework of big data in the healthcare and social service sectors at the national level. As a specific case, we designed a process and method of social big data analysis on suicide buzz.ResultsDeveloped countries (e.g., the United States, the UK, Singapore, Australia, and even OECD and EU) are emphasizing the potential of big data, and using it as a tool to solve their long-standing problems. Big data strategies for the healthcare and social service sectors were formulated based on an ICT-based policy of current government and the strategic goals of the Ministry of Health and Welfare. We suggest a framework of big data analysis in the healthcare and welfare service sectors separately and assigned them tentative names: 'health risk analysis center' and 'integrated social welfare service network'. A framework of social big data analysis is presented by applying it to the prevention and proactive detection of suicide in Korea.ConclusionsThere are some concerns with the utilization of big data in the healthcare and social welfare sectors. Thus, research on these issues must be conducted so that sophisticated and practical solutions can be reached.

Health care systems generate a huge volume of different types of data. Due to the complexity and challenges inherent in studying medical information, it is not yet possible to create a comprehensive model capable of considering all the aspects of health care systems. There are different points of view regarding what the most efficient approaches toward utilization of this data would be. In this paper, we describe the potential role of big data approaches in improving health care systems and review the most common challenges facing the utilization of health care big data.

Big Data usage and Artificial Intelligence (AI) technology combined offer a potential approach to solving challenging problems. AI-driven solutions provide insightful analysis and creative solutions by utilizing the power of big data analytics. With an emphasis on the mediating role of technological literacy and the moderating effect of resource availability, this study investigates the effects of low-cost techniques, the usage of Big Data, and the integration of Artificial Intelligence (AI) on sustainability in landscape design. The purpose of this study is to look at the intricate connections between these factors and how they affect sustainable landscape design methods and results as a whole. A standardized questionnaire was answered by a sample of 458 landscape experts as part of a quantitative approach. Smart PLS (Partial Least Squares), which incorporates evaluations of measurement models, structural models, and mediation and moderation studies, was utilized for data analysis. The study found that using Big Data, implementing low-cost techniques, and incorporating AI all had very favourable effects on sustainability in landscape design. The efficient use of Big Data and AI was found to be mediated by technological literacy, highlighting the importance of this concept in this context. Additionally, resource availability emerged as a critical moderating factor, influencing the strength of these relationships. This research contributes to the field by offering a holistic understanding of the dynamics within sustainable landscape design, emphasizing the importance of integration of AI and utilization of Big Data. It provides practical insights for landscape professionals, informs policy development, and advances educational curricula about AI and Big Data in landscape architecture. The study's limitations include potential response bias due to self-reported data and the cross-sectional design, which restricts the establishment of causal relationships. Additionally, the study focused on professionals, limiting the generalizability of findings to broader community perspectives.

With the progress of Internet and computer technology, the scope of use of modern information technology has been continuously expanded, and a large amount of data has been obtained as an adjunct of development. Mankind has entered this era of information explosion. Now all industries in the society need the support of data, data has penetrated into all industries, people’s development and utilization of big data (BD), play a more and more important role, it has become an important factor for the development and promotion of the industry. In the era of BD, the education industry is facing unprecedented opportunities and challenges. Applying BD technology to teaching activities and giving full play to its role can enrich teaching methods and learning methods, promote the reform of traditional model of instruction and improve the quality of education and teaching. This paper takes BD as the background for analysis to build a new sports model of instruction, and use the formula for calculation, the efficiency and quality of new sports teaching pattern on the efficiency and quality of data with 13% and 31% respectively than traditional sports model of instruction, prove that BD context (BDC) using CMT to build a sports model of instruction is of great significance.KeywordsBig dataComputerMultimedia technologyPhysical educationModel of instruction

The COVID-19 pandemic has prompted the adoption of distance education worldwide, including in 3T regions facing challenges of limited infrastructure and accessibility. The utilization of Big Data holds great potential in enhancing the quality and accessibility of education in these 3T regions. This study aims to analyze the utilization of Big Data in distance education during the pandemic in 3T regions, identify its benefits in personalized learning, monitoring the learning process, and predicting student needs, and provide recommendations for further research. The research approach employed data analysis from a survey involving 500 schools in 3T regions. Data was collected through questionnaires that encompassed the aspects of Big Data usage in distance education and its impact on educational quality in 3T regions. Data analysis reveals that the utilization of Big Data in distance education in 3T regions has significantly contributed to student data collection and analysis, monitoring the learning process, and predicting student needs. These findings are consistent with previous research that highlights the benefits of Big Data in personalized learning and educational performance management. The utilization of Big Data in distance education in 3T regions during the pandemic holds immense potential in improving the quality and accessibility of education. Adequate training and education should be provided to teachers and school staff regarding the utilization of Big Data.

The impact of big data although being very insubstantial in its inception however has gradually originated to make significant contributions to variety of spheres of life. Development of smart cities, big data in e-business, big data in health care sector, big data in environmental sciences and implementation of Big Data in other areas has indisputably revealed to critics what can be achieved by Big Data. Big Data undeniably is a paradigm shift in the perception of ways of understanding and studying the world. Big data can be utilized to analyze fine grained data for public and private use. Big data utilization in education sector is relatively less but it has started growing gradually. As a matter of fact as Big Data can be effectively utilized to improve education delivery, it can equally be utilized to provide decision support to higher management to attain excellence in the field of education. This research paper is a mere effort to explore various possibilities of Big Data utilization in educational establishments and their implications.

Due to a sudden increase of data when internet of things, which uses sensor network such as RFID and NFC, was applied, interest in collection, processing, analysis and utilisation of big data is increasing. These big data, which are mostly unstructured data, are showing a great degree of difference in the aspects of data movement speed, variety, speed, and quantity when compared to the existing typical relational data. Especially, as an open source platform, the Hadoop system, which is used for processing of big data that occurs in logistics which is a major utilisation field of internet of things in the technical perspective, is providing opportunities for anyone to build a platform with comparatively lower cost. This thesis attempts to provide data that can be referred to in introducing platform that is adequate to our company by designing various Hadoop platforms that can be utilised in processing big data which occurs in logistics based on IoT and conducting comparative evaluation of its performance. Also, we present the integrated verification results for the data analysis performance of the analysis software system running on top of realised prototypes by employing American Standard Association (ASA) dataset.

The COVID-19 pandemic has accelerated the adoption of digital technologies across sectors, including retail, where the use of big data has become increasingly important to understand consumer behavior and facilitate targeted marketing. In this context, this study aims to analyze the opportunities and challenges retailers face in leveraging big data to improve their marketing effectiveness in a post-pandemic world. The method used in this study is a qualitative approach through in-depth interviews with industry leaders and case study analysis of companies that have successfully implemented big data strategies. The results show that the utilization of big data provides retailers with valuable insights into consumer preferences, allowing them to create more personalized and relevant marketing campaigns. However, the study also identified significant challenges, including data privacy concerns, complexity in system integration, and the need to develop analytical skills among the workforce. These findings provide important insights for retailers in formulating more responsive and adaptive marketing strategies, as well as addressing the challenges that arise from the use of big data. This research is expected to contribute to the literature on digital marketing and provide practical guidance for practitioners in the retail industry.

Analytical investigation of autoencoder-based methods for unsupervised anomaly detection in building energy data

PurposeBig pharma, which comprise the most important companies in the pharmaceutical sector, are ambidextrous organizations by nature. Big data can heavily influence this characteristic by simultaneously requiring adequate business process management. In fact, the impact of big data on business process management can assist big pharma in increasing process efficiency (which is related to the research and development pipeline) and process efficacy (related to product portfolio management). The purpose of this paper is to investigate this possibility and opportunity.Design/methodology/approachIn the absence of specific scientific studies, as indicated by a review of the existing literature, the authors have adopted a grounded theory approach. This research has observed multiple cases (the 15 most important big pharma companies worldwide) through an electronic survey conducted on secondary data. The study has allowed the generation of a theoretical framework based on the (direct) relationship between knowledge process standardization (as the dependent variable) and big data (as the independent variable) in organizations oriented toward ambidexterity, such as big pharma in the specific scope of this research.FindingsAs big data utilization becomes widespread along the pipeline (or even along the value chain/supply chain), business process management increasingly uses (or tends to use) standardization, adopting process standardization as the main coordination mechanism to manage big knowledge. This theory is even more true when considering the moderating role of ambidexterity. An enterprise oriented toward structural ambidexterity (such as big pharma) that uses big data will require increased process standardization to manage big knowledge. Alternatively, an enterprise oriented toward contextual ambidexterity that uses big data will require increased output standardization.Originality/valueBased on an analytical literature review, no research to date has focused strict attention on the influence that big data can have on business process management to improve the natural ambidexterity of big pharma. The main unique feature of this research relies on this point. The main value of the research originates from the theoretical framework reconstructed by grounded theory, which constitutes a powerful strategic tool to support executives and managers of big pharma in organizing business process management for their ambidextrous organizations using big data.