Big Data and Data Science in Critical Care

Addressing minority health and health disparities has been a missing piece of the puzzle in Big Data science. This article focuses on three priority opportunities that Big Data science may offer to the reduction of health and health care disparities. One opportunity is to incorporate standardized information on demographic and social determinants in electronic health records in order to target ways to improve quality of care for the most disadvantaged populations over time. A second opportunity is to enhance public health surveillance by linking geographical variables and social determinants of health for geographically defined populations to clinical data and health outcomes. Third and most importantly, Big Data science may lead to a better understanding of the etiology of health disparities and understanding of minority health in order to guide intervention development. However, the promise of Big Data needs to be considered in light of significant challenges that threaten to widen health disparities. Care must be taken to incorporate diverse populations to realize the potential benefits. Specific recommendations include investing in data collection on small sample populations, building a diverse workforce pipeline for data science, actively seeking to reduce digital divides, developing novel ways to assure digital data privacy for small populations, and promoting widespread data sharing to benefit under-resourced minority-serving institutions and minority researchers. With deliberate efforts, Big Data presents a dramatic opportunity for reducing health disparities but without active engagement, it risks further widening them.

Big data are one of the representative phenomena of the information era of human societies. A basic study on the cognitive foundations of big data science is presented with a coherent set of general principles and analytic methodologies for big data manipulations. It leads to a set of mathematical theories that rigorously describe the general patterns of big data across pervasive domains in sciences, engineering, and societies. A significant finding towards big data science is that big data systems in nature are a recursive n-dimensional typed hyperstructure (RNTHS). The fundamental topological property of big data system enables the inherited complexities and unprecedented challenges of big data to be formally dealt with as a set of denotational mathematical operations in big data engineering. The cognitive relationship and transformability between data, information, knowledge, and intelligence are formally revealed towards big data science.

In this study the evolution of Big Data (BD) and Data Science (DS) literatures and the relationship between the two are analyzed by bibliometric indicators that help establish the course taken by publications on these research areas before and after forming concepts. We observe a surge in BD publications along a gradual increase in DS publications. Interestingly, a new publications course emerges combining the BD and DS concepts. We evaluate the three literature streams using various bibliometric indicators including research areas and their origin, central journals, the countries producing and funding research and startup organizations, citation dynamics, dispersion and author commitment. We find that BD and DS have differing academic origin and different leading publications. Of the two terms, BD is more salient, possibly catalyzed by the strong acceptance of the pre-coordinated term by the research community, intensive citation activity, and also, we observe, by generous funding from Chinese sources. Overall, DS literature serves as a theory-base for BD publications.

This chapter introduces data science with its history and importance in this modern era briefly. This chapter also elaborates the discussion by relating data science to various modern fields like big data analytics, artificial intelligence, deep learning, and machine learning. This chapter also discuss the necessary of data analytics in this big data era. This chapter also briefly introduces another emerging field, Internet of Things (IoT) and explores the contribution IoT towards big data analytics and data science in research perspective. It also briefly introduces the programming and non-programming tools used in the data science field.

Recently, Big Data science has been a hot topic in the scientific, industrial and the business worlds. The healthcare and biomedical sciences have rapidly become data-intensive as investigators are generating and using large, complex, high dimensional, and diverse domain specific datasets. This paper provides a general survey of recent progress and advances in Big Data science, healthcare, and biomedical research. Big Data science impacts, important features, infrastructures, and basic and advanced analytical tools are presented in detail. Additionally, various challenges, debates, and opportunities inside this quickly emerging scientific field are explored. The human genome research, one of the most promising medical and health areas as an example and application of Big Data science, is discussed to demonstrate how the adaptive advanced computational analytical tools could be utilized for transforming millions of data points into predictions and diagnostics for precision medicine and personalized healthcare with better patient outcomes.

Data Scientist - The New ProfessionData scientist is probably the most trendy job in Information Technology (IT) nowadays. This new profession emerged with the Big Data wave. Even though there is no such thing like an exact job profile, we expect that the data scientist can handle all the Big Data challenges that are novel to us. Without being a magician she or he shall help to deliver us all the magic Big Data promises. The data scientist capitalizes on unstructured data without taking the roles of a programmer, database expert, statistician, or content manager. All these professions are around for decades. So, why invent a new one?More than anything, what data scientists do is make discoveries while swimming in data [...] they are able to bring structure to large quantities of formless data and make analysis possible. [3] They sketch, orchestrate, and control the discovery process. The leading paradigm in this process it to find information that meets a certain need or provides an answer for a certain problem. need to avoid the temptation of following a data-driven approach instead of a problem-driven one. [5] The data scientist has to develop an idea about the required information that meets our information need. We can expect that data scientists have a deep understanding of the foundations of both, the nature of information and the domain. They follow a mental model on the information demand that abstractly reflects the facts they expect to encounter in data and the way to detect and present them to us. We expect the data scientist to discover novel information that may provide us with new insights. And we want these insights to be true. It is thus part of the data scientist's responsibility to make sure that the discovered information is not only novel but also trustworthy. The data scientists cannot prove data analysis models. That exceeds their capabilities. We cannot hold them liable for information that eventually turns out to be wrong. Nevertheless their skills should include a sound sensation of plausibility that helps them to raise doubts and to prompt a closer look when the results of data analysis seem too questionable to them. However, separating questionable results from plausible ones is a task that is far from being trivial.Separating Data Science from Data FictionWhen driving a car we often encounter these nice roadside signs that sometimes make us realize that we're driving too fast. Furthermore, on some occasions there is official personnel not far from these signs measuring our speed, explaining to us our traffic infraction face-to-face and documenting it on a speeding ticket. Doesn't this sound a bit outdated? There are enough sensors in a car that measure location, speed, time, and more. Even if the car's sensors don't measure those, the driver's cellphone can do it. Combining this sensor information with the cartographic data about roads and speed limits we can easily imagine that, by the end of day, the car or the phone exactly knows every infraction we committed and can automatically trigger the issuing of speeding tickets. Aren't there just legal aspects that hamper this technically feasible scenario making its way into reality?One trait of Big Data is the availability of sensor data and their combination with already available data generating new information, like the detection of traffic infraction. We can extrapolate this scenario in many directions, including more types of infraction, driving for an irresponsibly long time without a substantial break, or detecting patterns of aggressive driving. We can extend it also towards future possibilities if we think about sensor information from smart watches indicating a possibly problematic pulse rate or from the car's air control sensor that the driver may drive under the influence of alcohol.Much like in Data Mining, the strength of Big Data originates from the combination of facts producing new information. …

In the recent decade, several technologies have boomed up due to recent development in many technologies. These technologies have changed the life of human being and are increasing the profit for individual, organisations like NetFlix, Alibaba, Flipkart, etc. Today in this world, maximum people are surrounded by smart objects or using smart objects to make their life easier to live and convenient. But, on another side life is being made easier to live by analytics companies/ industries. For example, which user like which type of plays, movies, songs, etc., things are extracted by many companies. Such recommendations are being made and being provided improved services to the respective users. Such jobs are being done by Data Scientist. Whereas, Data science (a future of Artificial Intelligence) is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured (labelled) and unstructured (unlabelled) data. Moreover this, Big Data Analytics are the analysis mechanism used in Data Science by Data Scientist. Several tools like Hadoop, etc., are used to analysis the large amount of data and used in predicting the valuable information/ making decisions. But, in analysing we faces several concerns like complexity, scalability, privacy leaking and trust. So, this article discusses about such concerns, challenges (rising) in this emerging field in detail (with a comparative analysis/ taxonomy).

The way Big Data Science was enabled through the marriage of technology in the form of computer science and the discipline of statistics was told by Gil Press in his (2013) Forbes piece titled ‘A Very Short History of Data Science.’ Data Science is now the discipline charged with utilizing Big Data. But making sense of data has a much longer history. Karl Weick (1993) introduced the concept of Sensemaking in organizational decision making to account for failures in data-driven decision making. This paper explores Sensemaking and Big Data Science being used in marketing to solve social and behavioral problems.

Big Data refers to large and disparate volumes of data generated by people, applications and machines. It is gaining increasing attention from a variety of domains, including education. What are the challenges of engaging with Big Data research in education? This paper identifies a wide range of critical issues that researchers need to consider when working with Big Data in education. The issues identified include diversity in the conception and meaning of Big Data in education, ontological, epistemological disparity, technical challenges, ethics and privacy, digital divide and digital dividend, lack of expertise and academic development opportunities to prepare educational researchers to leverage opportunities afforded by Big Data. The goal of this paper is to raise awareness on these issues and initiate a dialogue. The paper was inspired partly by insights drawn from the literature but mostly informed by experience researching into Big Data in education.

The Internet of Things (IoT) devices generate a large amount of data over networks; therefore, the efficiency, complexity, interfaces, dynamics, robustness, and interaction need to be re-examined on a large scale. This phenomenon will lead to seamless network connectivity and the capability to provide support for the IoT. The traditional IoT is not enough to provide support. Therefore, we designed this study to provide a systematic analysis of next-generation advancements in the IoT. We propose a systematic catalog that covers the most recent advances in the traditional IoT. An overview of the IoT from the perspectives of big data, data science, and network science disciplines and also connecting technologies is given. We highlight the conceptual view of the IoT, key concepts, growth, and most recent trends. We discuss and highlight the importance and the integration of big data, data science, and network science along with key applications such as artificial intelligence, machine learning, blockchain, federated learning, etc. Finally, we discuss various challenges and issues of IoT such as architecture, integration, data provenance, and important applications such as cloud and edge computing, etc. This article will provide aid to the readers and other researchers in an understanding of the IoT’s next-generation developments and tell how they apply to the real world.

There are different paradigms in educational technology. Under the background of big data era, data science, learning analysis and education have made great achievements. In the field of education under big data, all kinds of new paradigms are constantly emerging and have achieved very good results in actual education. In the era of education big data, how to fully tap the value of big data for online education practice, decision-making, evaluation and research, and how to avoid the risk of big data are important issues in the current education reform and development. This paper analyzes the application of the current scientific paradigm in education, constructs the construction paradigm of online education evaluation model, and puts forward a new education concept in order to promote the development of the new paradigm of big data online education technology research. Applying this paradigm, a series of educational evaluation models are constructed from the macro, miso and micro levels, which play a positive role in the research, decision-making, practice and evaluation of related fields.

In a world increasingly driven by data, most developed economies are leveraging big data to achieve greater feats in various sectors of their economies. From advertisement, commerce, healthcare, and energy to defense, big data has given new insights into the huge volume of data accumulated over the past few decades that is helping reshape our knowledge and understanding of these sectors. Unfortunately, the same cannot be said about the state of big data in the developing world, where investments in IT infrastructure are dangerously low, keeping huge proportions of the population offline. This chapter discussed the challenges that exist in developing countries, which affect the smooth take-off of big data and data science as well as recommendations as to how countries and companies in the developing world can overcome these challenges to harness the benefits and opportunities presented by this technology.

BackgroundInfectious diseases represent a major challenge for health systems worldwide. With the recent global pandemic of COVID-19, the need to research strategies to treat these health problems has become even more pressing. Although the literature on big data and data science in health has grown rapidly, few studies have synthesized these individual studies, and none has identified the utility of big data in infectious disease surveillance and modeling.ObjectiveThe aim of this study was to synthesize research and identify hotspots of big data in infectious disease epidemiology.MethodsBibliometric data from 3054 documents that satisfied the inclusion criteria retrieved from the Web of Science database over 22 years (2000-2022) were analyzed and reviewed. The search retrieval occurred on October 17, 2022. Bibliometric analysis was performed to illustrate the relationships between research constituents, topics, and key terms in the retrieved documents.ResultsThe bibliometric analysis revealed internet searches and social media as the most utilized big data sources for infectious disease surveillance or modeling. The analysis also placed US and Chinese institutions as leaders in this research area. Disease monitoring and surveillance, utility of electronic health (or medical) records, methodology framework for infodemiology tools, and machine/deep learning were identified as the core research themes.ConclusionsProposals for future studies are made based on these findings. This study will provide health care informatics scholars with a comprehensive understanding of big data research in infectious disease epidemiology.

It is our great pleasure to welcome you to the 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). The annual ACM SIGKDD conference is the premier international forum for data mining, knowledge discovery and big data. It brings together researchers and practitioners from academia, industry, and government to share their ideas, research results and experiences. KDD-2013 features plenary presentations, paper presentations, poster sessions, workshops, tutorials, exhibits, demonstrations, and the KDD Cup competition. Today, you hear a lot about big data, data science and data intensive computing. The core of this work is extracting knowledge and useful information from data, which for science leads to beautiful insights, and for applications leads to actions, alerts and decisions. The KDD community has always been at the center of this activity and it is clear from this conference that it will continue to drive this broader field of big data. This year there were 726 submissions to the KDD Research Track, and 125 papers were accepted. There were 136 submissions to the KDD Industry and Government Track, and 34 papers were accepted. KDD also has a history of inviting talks that are of broad interest to the KDD community. This year we chose to have 4 plenary talks. A program committee also selected 8 talks to present at the Industry Practice Exposition. A strength of the KDD conference is the number of workshops and tutorials that are co-located with it. This year there were 10 full-day workshops, 5 half-day workshops, and 6 tutorials. We thank all sponsors, who are a very important part of the conference, and the members of the Organizing Committee and our other colleagues who volunteered their time during the past year to make this conference a success. Special thanks goes to the Research Track Co-Chairs and the Industry and Government Track Co-Chairs. Also special thanks are due to the Local Arrangements Chair, the Treasurer, the Proceedings Co-Chairs, and the KDD Cup Committee. We are grateful to the several program committees that provided the advice necessary to put together a quality program - the Research Track Program Committee, the Research Track Senior Program Committee, the Industry and Government Track Program Committee, the Industry Practice Expo Program Committee, the Workshop Program Committee, the Tutorial Program Committee, and the Demo Program Committee. We know that you will find this year's exhibits and demonstrations exciting and remind you that some of the most interesting discussions can be found there. Please join us for KDD-2013 to gain new knowledge and to exchange exciting new research results, leading practices, and high impact applications in big data, knowledge discovery and data mining. We hope that you will find this program interesting and thought-provoking and that the conference will provide you with a valuable opportunity to share ideas with other researchers and practitioners from institutions around the world.

The article summarizes modern approaches of big data analytics implementation in healthcare, including OMIC’s data. The existing challenges for big data in medicine, barriers for implementation, reasons for slow development of practical usage of big data analytics and artificial intelligence in healthcare are discusses as well as perspective for the nearest future.