Usage Of Big Data Research Articles

Public health awareness of autoimmune diseases after the death of a celebrity.

Autoimmune disorders impose a high burden, in terms of morbidity and mortality worldwide. Vasculitis is an autoimmune disorder that causes inflammation and destruction of blood vessels. Harold Allen Ramis, a famous American actor, director, writer, and comedian, died on the February 24, 2014, of complications of an autoimmune inflammatory vasculitis. To investigate the relation between interests and awareness of an autoimmune disease after a relevant event such as the death of a celebrity, we systematically mined Google Trends, Wikitrends, Google News, YouTube, and Twitter, in any language, from their inception until October 31, 2016. Twenty-eight thousand eight hundred fifty-two tweets; 4,133,615 accesses to Wikipedia; 6780 news; and 11,400 YouTube videos were retrieved, processed, and analyzed. The Harold Ramis death of vasculitis resulted into an increase in vasculitis-related Google searches, Wikipedia page accesses, and tweet production, documenting a peak in February 2014. No trend could be detected concerning uploading YouTube videos. The usage of Big Data is promising in the fields of immunology and rheumatology. Clinical practitioners should be aware of this emerging phenomenon.

Big Data for Remote Sensing: Challenges and Opportunities

Every day a large number of Earth observation (EO) spaceborne and airborne sensors from many different countries provide a massive amount of remotely sensed data. Those data are used for different applications, such as natural hazard monitoring, global climate change, urban planning, etc. The applications are data driven and mostly interdisciplinary. Based on this it can truly be stated that we are now living in the age of big remote sensing data. Furthermore, these data are becoming an economic asset and a new important resource in many applications. In this paper, we specifically analyze the challenges and opportunities that big data bring in the context of remote sensing applications. Our focus is to analyze what exactly does big data mean in remote sensing applications and how can big data provide added value in this context. Furthermore, this paper describes the most challenging issues in managing, processing, and efficient exploitation of big data for remote sensing problems. In order to illustrate the aforementioned aspects, two case studies discussing the use of big data in remote sensing are demonstrated. In the first test case, big data are used to automatically detect marine oil spills using a large archive of remote sensing data. In the second test case, content-based information retrieval is performed using high-performance computing (HPC) to extract information from a large database of remote sensing images, collected after the terrorist attack to the World Trade Center in New York City. Both cases are used to illustrate the significant challenges and opportunities brought by the use of big data in remote sensing applications.

Downstream Consent: A Better Legal Framework for Big Data

Reconciling big data techniques with a legal approach relying on prior consent has proved difficult. By definition, when organisations collection personal information for data-led investigations they do not know what the results and impact of their processing will be. This paper suggests how other parts of the current European data protection framework can provide more effective protection for individuals, better guidance for big data users, and more effective tools for regulators. Splitting big data into collection, analysis and intervention stages, and applying appropriate legal provisions to each, could guide the development of privacy-respecting big data techniques that support the needs of individuals, businesses and society.

Design and implementation of an office standby–power management system through physical and virtual management by user–device habitual pattern analysis in energy–Internet of Things environments

In the changing environment of the Internet of Things, optimal energy management in smart spaces requires intelligent and reliable energy-aware-based context sensing and technologies that are capable of recognizing and analyzing the big-data user pattern. In this article, we propose an intelligent and reliable standby power management system. The system uses physical and virtual user behavioral pattern analysis based on energy-aware management to cut-off the standby power of office appliances in the office environment. We propose a two-step priority power-aware method. The first step entails physical perception and management that controls devices through user recognition and device relationship scenarios. The second step is virtual perception and management that controls the standby power by collecting user behavioral patterns and performs an analysis based on a rule mechanism. The proposed system was applied to three locations (offices A, B, and C) in the university test-bed. Power consumption was reduced to 23% of the original consumption through the elimination of unnecessary standby power consumption.

Making value a priority: how this paradigm shift is changing the landscape in health care.

The world of health care is changing dramatically, as reflected in the number, magnitude, and scope of innovative new approaches-to how illness is treated and how better health is promoted-that are being implemented around the globe. The changes triggered by these initiatives affect both how care is organized, managed, and paid for and the kinds of approaches that are being developed to keep people healthy. Underlying these changes is a more fundamental paradigm shift, a shift in the priority given to "value" in the formulation of policy and management practice. This brief essay highlights five trends that are central in this shift: increasing emphasis on health promotion, movement toward value-based payment, advances in digital/mobile technology, exploitation of big data, and changes in support for biomedical research. Each of these has its own value controversies, and the individual impact of each is impossible to predict. Collectively, however, their impact is likely to be significant.

Analysis of Big Data technologies for use in agro-environmental science

Recent developments like the movements of open access and open data and the unprecedented growth of data, which has come forward as Big Data, have shifted focus to methods to effectively handle such data for use in agro-environmental research. Big Data technologies, together with the increased use of cloud based and high performance computing, create new opportunities for data intensive science in the multi-disciplinary agro-environmental domain. A theoretical framework is presented to structure and analyse data-intensive cases and is applied to three case studies, together covering a broad range of technologies and aspects related to Big Data usage. The case studies indicate that most persistent issues in the area of data-intensive research evolve around capturing the huge heterogeneity of interdisciplinary data and around creating trust between data providers and data users. It is therefore recommended that efforts from the agro-environmental domain concentrate on the issues of variety and veracity.

Jose Maria Cavanillas, Edward Curry, and Wolfgang Wahlster (editors): new horizons for a data-driven economy: a roadmap for usage and exploitation of big data in Europe

Jose Maria Cavanillas, Edward Curry, and Wolfgang Wahlster (editors): new horizons for a data-driven economy: a roadmap for usage and exploitation of big data in Europe

AHP 기반의 빅데이터 활용을 위한 산업 탐색

빅데이터가 다양한 산업 분야에서 모든 관심을 끌고 있다. 사물과 사물 간 연결과 모바일 장치들의 용도 확 대는 데이터의 폭발적인 증가를 불러오고 있다. 이러한 데이터를 분석하여 민간과 공공 분야에서는 비용 절감과 생 산성 분야에 있어서 혜택을 누리고 있다. 한국 정부는 이러한 활용을 촉진하기 위해서, 빅데이터 산업발전전략을 활 발하게 추진하고 있다. 본 연구는 빅데이터의 적극적인 육성이 필요한 산업 분야를 전문가의 검증을 통해 선정하였 다. 전문가의 50여명의 체계적인 의견 도출을 위해 계층분석법(AHP)을 적용하였다. 분석 결과 의료,복지, 운송/창고 보관업, 정보통신/정보보안, 에너지, 금융 분야가 빅데이터 적용이 유망한 것으로 확인되었다. 도출 결과는 앞으로 빅 데이터 시범사업으로 인한 모범사례의 발굴 등에 활용되어 빅데이터 산업 발전에 기여할 것이다.

STRATEGY OF SUSTAINABLE COMPETITIVENESS: METHODOLOGY OF REAL-TIME CUS¬TOMERS’ SEGMENTATION FOR RETAIL SHOPS

The objective of this article is to develop a methodology in order to implement real-time customers segmentation analysis in the decision making process of the enterprise. A review of big data usage in retail stores was conducted along with a document-based descriptive analysis of secondary data and further critical literature analysis. Decision making strategies and flow charts were used for the development of competitiveness methodology by referring to a case of a supermarket chain. Customer segmentation researchers analyse mainly the algorithms or behaviour pattern behind the clustering process; however, neither of them offers a proper strategy for implementing a realtime customer segmentation process inside the enterprise. Sustainable competitiveness advantage may be achieved by implementing the segmentation theory with concepts of data mining and internet of things (Iot). The process of developed data mining shows many ways for the enterprise to maximize competitiveness. However, time and large investments may be required to develop proper methods for unique solutions. A concrete case study of the selected retail store should be analysed before implementing the real-time customer segmentation methodology inside the enterprise. There is a multicultural population in every market that has different culture, beliefs, preferences and shopping patterns; therefore, constant analysis is essential for efficient usage of customer segmentation. Practically none of the prior research results carried out by other authors offered a concrete methodology how to implement real-time customer’s segmentation inside the enterprise. The authors created such a methodology that can provide sustainable long-term competitiveness advantage.

Identifying and prioritizing critical factors for promoting the implementation and usage of big data in healthcare

Big data is expected to provide many benefits to healthcare organizations through cost reduction, disease prediction, and patient care improvement. Big data in healthcare at a nascent stage faces many challenges in satisfying expectations. This study investigates factors for promoting the implementation and usage of big data in healthcare in Korea, using an analytical hierarchy process (AHP). We established a research framework that considers four domains and 15 factors. We collected data by a survey of experts engaged in big data and healthcare IT. The results show that data and organization have greater impacts than technology and support domains. Organizational analytics capability, data quality, data privacy and security, organizational application capability, data standardization, and data experts are the most important factors for promoting the implementation and usage of big data in healthcare. The study presents the implications in terms of the policies and ecosystem for countries, including Korea, which intend to activate big data in healthcare.

Urban function connectivity: Characterisation of functional urban streets with social media check-in data

Social media check-in data, one type of crowdsourcing open data about individual activity-related choices, provides a new perspective to sense people's spatial and temporal preference in urban places. In this paper, through the analysis of the interaction between these scored places on streets, we aim to advance our knowledge of network accessibility with social media check-ins to portray urban structure and related socioeconomic performance more explicitly. By conceptualising an interface graph to reflect the interplay between land-use points and the co-visual paths, we propose a novel framework to characterise the urban streets with land-use connectivity indices that are measured with a new type of place-function signature. A “3-Ds” model is introduced to package three principal dimensions of urban function network, including accessible density, accessible diversity and delivery efficiency, as one integrated index that works towards a comprehensive understanding of function connectivity from each street's midpoints to all reachable land-use points. Streets are further partitioned to the annotated function regions based on function connectivity in different types of active land-use. The results of preliminary studies in the city of Tianjin, China show that the proposed metrics can explicitly describe the inherent function structure and the regions' typology across scales. Compared with space syntax measurements at the same radius for describing the variation of empirically observed house price, the integrated metric can improve the predictability of statistic models sufficiently, and each specified index is confirmed to be statistically significant by controlling other factors. Overall, this research shows that the usage of ubiquitous big social media data can enrich the current description of the urban network system and enhance the predictability of network accessibility on socioeconomic performance.

The role of big data and cognitive computing in the learning process

In this paper, we investigate how the raise of big data and cognitive computing systems is going to redesign the labor market, also impacting on the learning processes. In this respect, we make reference to higher education and we depict a model of a smart university, which relies on the concepts that are at the basis of the novel smart-cities’ development trends. Thus, we regard education as a process so that we can find specific issues to solve to overcome existing criticisms, and provide some suggestions on how to enhance universities’ performances. We highlight inputs, outputs, and dependencies in a block diagram, and we propose a solution built on a new paradigm called smarter-university, in which knowledge grows rapidly, is easy to share, and is regarded as a common heritage of both teachers and students. Among the others, a paramount consequence is that there is a growing demand for competences and skills that recall the so called T-shape model and we observe that this is pushing the education system to include a blend of disciplines in the curriculums of their courses. In this overview, among the wide variety of recent innovations, we focus our attention on cognitive computing systems and on the exploitation of big data, that we expect to further accelerate the refurbishment process of the key components of the knowledge society and universities as well.

Big data - a review in health sciences

Technologies are changing very fast and data has an impact on the change of technology and development of world. Data are obtained by social media, the Internet and mobile technologies. For years, academics, researchers and companies utilize some sources and information to analyze them for their studies and jobs. Increasing usage of mobile devices, social networks, electronic records of customers in public and private sectors have led to increase in data. Obtained massive amount of data is called big data. There are a lot of description of big data in the literature, but simply it can be said that; big data is the data which have a massive size and can be obtained from every environment. One of these environment is health environment and it has grown fastly through that huge amount of data exist in this sector like patients’ electronic health record. Health sector has a high cost and decision will be taken as soon as possible and correctly in this sector in which timing is critically important. In this manner, the usage of big data in health is important to increase the quality of service, innovative health operations and decrease the cost. In this study, a brief review of literature has done for the use of big data in health sciences for last five years. Big data’s content, methods, advantages and difficulties are discussed in this review study. Keywords: Health science, Big data, Medicine, data mining

Towards user-centric operation in 5G networks

There are three pillars that characterize the new 5G revolution, namely, the use of heterogeneous wireless access technologies conforming an ultra-dense network, the software-driven flexibility of this network, and the simplified and user-centric operation and management of the system. This next-generation network operation and management shall be based on the usage of Big Data Analytics techniques to monitor the end-user quality of experience through direct measures of the network. This paper describes the Astellia approach towards this network revolution and presents some results on the performance of quality estimation techniques in current cellular networks. Thanks to the use of this approach, operators may fill the gap of knowledge between network key performance indicators and user experience. This way, they can operate in a proactive manner and have actual measurements of the users’ experience, which leads to a fairer judgement of the users’ complaints.

Big Data, Data Analyst, and Improving the Competence of Librarian

Issue of Big Data was already raised by Fremont Rider, an American Librarian from Westleyan University, in 1944. He predicted that the volume of American universities collection would reach 200 million copies in 2040. As a result, it brings to fore multiple issues such as big data users, storage capacity, and the need to have data analysts. In Indonesia, data analysts is still a rare profession, and therefore urgently needed. One of its distinctive tasks is to conduct visual analyses from various data resources and also to present the result visually as interesting knowledge. It becomes science enliven by interactive visualization. In response to the issue, librarians have already been equipped with basic information management. Yet, they can see the opportunity and improve themselves as data analysts. In developed countries, it is common that librarian are also regarded as data analysts. They enhance themselves with various skills required, such as cloud computing and smart computing. In the end librarian with data analysts competency are eloquent to extract and present complex data resources as interesting and discernible knowledge.

Application of Big Data Technology in Knowledge Transfer Process between Business and Academia

Abstract New data and information are being constantly produced as a result of technological development. The data and information are used by many companies as a source for creating knowledge in their sphere of business. The important part of the knowledge creating process is both adequate amount of relevant data, and an ability to transform this data to information. Various technologies are helpful, such as Big Data technology. Mostly big companies with sufficiency of money dispose of Big Data by reason of its complexity and significant expenses in terms of maintenance, specialized staff, technology infrastructure etc. Constantly changing demands of customers together with a strong competitive environment is forcing companies to innovate, for instance by designing new products, improving internal processes, or implementing new technologies. By obtaining such innovation is very helpful cooperation with research and development (R&D) institutions from academia. Universities are in a large extent dealing with R&D activities, on which companies don’t have capacities, but the results from these R&D activities are for companies interesting and mostly usable in practice. The purpose of this paper is following findings obtained from analysis of the knowledge transfer and Big Data theory finding and describing the possibility of connecting these two areas in practice. The paper is focusing on knowledge transfer creation within cooperation of companies and universities through access for university to company's database during contract research with usage of Big Data. Knowledge of cooperating subjects is filling up, deepening and verifying through united access to collecting, storing, processing and interpreting available data, whereby emerging knowledge transfer between cooperating subjects.

Learning Analytics towards the Usage of Educational Big Data

Learning Analytics towards the Usage of Educational Big Data

Building the search pattern of web users using conceptual semantic space model

As the web enters Big Data age, users and search engines may find it more and more difficult to effectively use and manage such big data. This paper proposes a method based on user tasks to perceive user intent, the meaningful user intent can be separated, and the elusiveness of user intent can be solved. One more thing, the method based on user task can predict the user behaviour to some extent. The main work of this paper is to propose such a concept space to fit the above features and the method to build the concept space on a large text database. We propose a new search pattern termed as ExNa, which can be incorporated into search engines to support more efficient search with better results. We conduct experimental studies upon a prototype search engine KNOWLE, so as to evaluate the validity and performance of our proposed ExNa mechanism.

Importance of Big Data in financial fraud detection

Big Data is playing a very significant role to take any industry forward. In the context of the financial sector and fraud detection, automated fraud detection tries to collect useful information to reduce financial frauds by doing analysis and data mining of Big Data, especially structured data. Even a significant attention of Big Data usage has shifted towards supply chain management (SCM). Although, many dimensions of Big Data have been studied and researched in SCM, there lies a missing gap in the understanding of unstructured data for financial fraud detection. With the help of this paper, we would like to propose a theoretical framework to study this dimension and analyse how individual enablers of unstructured data impacts SCM. As such, the paper intends to evaluate how much useful this unstructured data can be in reducing financial frauds. Finally, we outline the limitations and challenges of our study and further research directions.

Big data, organizational learning, and sensemaking: Theorizing interpretive challenges under conditions of dynamic complexity

In this conceptual article, the relations between sensemaking, learning, and big data in organizations are explored. The availability and usage of big data by organizations is an issue of emerging importance, raising new and old themes for diverse commentators and researchers to investigate. Drawing on sensemaking, learning, and complexity perspectives, this article highlights four key challenges to be addressed if organizations are to engage the phenomenon of big data effectively and reflexively: responding to the dynamic complexity of big data in terms of “simplexity,” analyzing big data using interdisciplinary processes, responsible reflection on ideologies of learning and knowledge production when handling big data, and mutually aligning sensemaking with big data topics to map domains of application. This article concludes with additional implications arising from considering sensemaking in conjunction with big data analytics as a critical way of understanding unique aspects of learning and technology in the 21st century.