Usage Of Big Data Research Articles

Fine-grained data-locality aware MapReduce job scheduler in a virtualized environment

Big data overwhelmed industries and research sectors. Reliable decision making is always a challenging task, which requires cost-effective big data processing tools. Hadoop MapReduce is being used to store and process huge volume of data in a distributed environment. However, due to huge capital investment and lack of expertise to set up an on-premise Hadoop cluster, big data users seek cloud-based MapReduce service over the Internet. Mostly, MapReduce on a cluster of virtual machines is offered as a service for a pay-per-use basis. Virtual machines in MapReduce virtual cluster reside in different physical machines and co-locate with other non-MapReduce VMs. This causes to share IO resources such as disk and network bandwidth, leading to congestion as most of the MapReduce jobs are disk and network intensive. Especially, the shuffle phase in MapReduce execution sequence consumes huge network bandwidth in a multi-tenant environment. This results in increased job latency and bandwidth consumption cost. Therefore, it is essential to minimize the amount of intermediate data in the shuffle phase rather than supplying more network bandwidth that results in increased service cost. Considering this objective, we extended multi-level per node combiner for a batch of MapReduce jobs to improve makespan. We observed that makespan is improved up to 32.4 $$\%$$ by minimizing the number of intermediate data in shuffle phase when compared to classical schedulers with default combiners.

Health Professionals' Perception about Big Data Technology in Greece.

Big data is massive amounts of information that can work wonders. In the healthcare industry, various sources for big data include hospital records, medical records of patients, results of medical examinations, and devices that are a part of internet of things. The research aim of this study is to investigate the perceptions of the Health Professionals about the Big Data Technology in Healthcare. An empirical study was conducted among 151 health professionals (doctors and nurses) to assess their knowledge about the Big Data Technology and their perceptions about using this technology in healthcare. A questionnaire was developed in order to measure the aforementioned dimensions. The survey's population was formed by 151 doctors and nurses who are working at private and public hospitals in Greece. The majority of the population have never heard about Big Data. As a result, most of them were not aware of the format of Big Data. Based on the study findings, it can be assumed that the majority of the responders did not have knowledge about the Big Data Technology. It is also important that most of them had never been informed about Big Data. It can be assumed that the Healthcare Sector in Greece is not familiar with Big Data Technology yet. Finally,the current study reveals a rather positive attitude toward the usage of Big Data in the Helathcare domain, although there are some doubts about the implementation of the aforementioned technology in the Greek national healthcare system.

A methodology for strategic diagnosis of business corruption behaviours using network analysis

Business corruption behaviours directly impact the business environment and the way in which strategic diagnosis is conducted at organizational level. Thus, the diagnosis of these business corruption cases becomes highly important in the process of strategic decisions. The methodology we use is based on networks analysis. We propose a Strategic Diagnosis Based on Networks Analysis (SDBN) which incorporates modularity and clustering coefficient measurements. These measurements allow for a cross-diagnosis of business corruption behaviours. A large quantity of event data from mass-media was extracted from the Global Database of Events, Language, and Tone regarding European Union business corruption, over the year of 2019. The data encompasses countries and organisations & institutions as the nodes, and the number of times that > and > appear together in our search, as the edges of the networks. These networks are then located in our proposed SDBN model, and are separated in concentration or diversification strategies, weakly connected or highly connected. The distinctiveness of our research consists of involving the theory of network analysis in the process of strategic diagnosis. The results reveal the specific business corruption behaviours conducted by actors involved in business corruption cases, with the limits being given by the usage of big-data and the magnitude of corruption behaviours. Future research will intend to apply the proposed SDBN to a larger sample of data and to different perspectives.

How Healthcare Can Change Based On Big Data and Biomedical Sciences

The current prediction is that within the next 30 to 40 years, the global average lifespan will be around 80 years and that personalized medicine will be standard [1]. It is very promising and it seems that it can be reached within this relative short time due to the fact of increased internationally cooperation research as well a better usage of big data [1.2]. And although the technologies are leading to new insights and possibilities, it is not guaranteed that the outcome will be better healthcare for everyone. At the moment, the potential risks, benefit evaluation and outcomes are unknown [3]. In addition, policies require changes in regards to data usage and sharing, access and rights [4]. And consensus needs to be reached about analytic methods and adjustment of research design [4]. Otherwise, the huge amount of data and different approaches without taking into consideration the outcomes might overwhelm us and lead to biased and factually flawed conclusions. And as the techniques and tools are developing so quickly and promising so much, it is time to take a break to think about which direction we want to do, with what kind of risks and uncertainties.

Big Data Management in Maritime Transport

As maritime transport produces a large amount of data from various sources and in different formats, authors have analysed current applications of Big Data by researching global applications and experiences and by studying journal and conference articles. Big Data innovations in maritime transport (both cargo and passenger) are demonstrated, mainly in the fields of seaport operations, weather routing, monitoring/tracking and security. After the analysis, the authors have concluded that Big Data analyses can provide deep understanding of causalities and correlations in maritime transport, thus improving decision making. However, there exist major challenges of an efficient data collection and processing in maritime transport, such as technology challenges, challenges due to competitive conditions etc. Finally, the authors provide a future perspective of Big Data usage in maritime transport.

Picturing diphtheria outbreak in Indonesia using national annual report data: what are the lessons learned?

Diphtheria is one of the vaccine preventable diseases. Due to the advances in medical technology and vaccine availability, this disease should no longer be a health problem. Its outbreak which reached the highest point in 2017 has opened our eyes that diphtheria remains a major threat, is highly contagious, and leads to morbidity and mortality. The large number of diphtheria cases also indicates that the protection of immunization in the community has decreased. This paper aims to discuss the usage of the reliable official big data namely the Indonesia Health Profile from year 2004 to 2017 released by the Ministry of Health to describe the magnitude of diphtheria in Indonesia. Providing big open data related to infectious disease is very challenging. The released data should be managed properly so it can be used by the government and related institutions to generate better policies and recommendations in order to end the outbreak and provide adequate protection against diphtheria amongst Indonesian community.

Big Data Use and Challenges: Insights from Two Internet-Mediated Surveys

Big data and analytics have received great attention from practitioners and academics, nowadays representing a key resource for the renewed interest in artificial intelligence, especially for machine learning techniques. In this article we explore the use of big data and analytics by different types of organizations, from various countries and industries, including the ones with a limited size and capabilities compared to corporations or new ventures. In particular, we are interested in organizations where the exploitation of big data and analytics may have social value in terms of, e.g., public and personal safety. Hence, this article discusses the results of two multi-industry and multi-country surveys carried out on a sample of public and private organizations. The results show a low rate of utilization of the data collected due to, among other issues, privacy and security, as well as the lack of staff trained in data analysis. Also, the two surveys show a challenge to reach an appropriate level of effectiveness in the use of big data and analytics, due to the shortage of the right tools and, again, capabilities, often related to a low rate of digital transformation.

Задачі та методи аналізу великих даних (огляд)

We review tasks and methods most relevant to Big Data analysis. Emphasis is made on the conceptual and pragmatic issues of the tasks and methods (avoiding unnecessary mathematical details). We suggest that all scope of jobs with Big Data fall into four conceptual modes (types): four modes of large-scale usage of Big Data: 1) intelligent information retrieval; 2) massive (large-scale) conveyed data processing (mining); 3) model inference from data; 4) knowledge extraction from data (regularities detection and structures discovery). The essence of various tasks (clustering, regression, generative model inference, structures discovery etc.) are elucidated. We compare key methods of clustering, regression, classification, deep learning, generative model inference and causal discovery. Cluster analysis may be divided into methods based on mean distance, methods based on local distance and methods based on a model. The targeted (predictive) methods fall into two categories: methods which infer a model; "tied to data" methods which compute prediction directly from data. Common tasks of temporal data analysis are briefly overviewed. Among diverse methods of generative model inference we make focus on causal network learning because models of this class are very expressive, flexible and are able to predict effects of interventions under varying conditions. Independence-based approach to causal network inference from data is characterized. We give a few comments on specificity of task of dynamical causal network inference from timeseries. Challenges of Big Data analysis raised by data multidimensionality, heterogeneity and huge volume are presented. Some statistical issues related to the challenges are summarized. Problems in programming 2019; 3: 58-85

Impact of technology in financial reporting: The case of Amazon Go

AbstractSince the beginning of the 21st century, there is no doubt that humanity has made a huge step forward in the field of robotics. In the next 20 years, financial reporting will see a stronger change thanks to machine learning, artificial intelligence, block chain, and big data usage. With the convergence of artificial intelligence and block chain, it is now clear that in the near future the work of the accounting profession will be supported by automation. Thus, the aim of the study to introduce the effects of technological changes and transformations on the future of financial reporting. The study first introduces technological tools and provides an overview of the advantages and threats of technological changes to financial reporting. Then, the study provides a case of Amazon Go to display the transformation of financial reporting with the technological changes.

Spatio Temporal with Scalable Automatic Bisecting-Kmeans for Network Security Analysis in Matagaruda Project

Internet attacks are a frequent occurrence and the incidence is always increasing every year, therefore Matagaruda project is built to monitor and analyze internet attacks using IDS (Intrusion Detection System). Unfortunately, the Matagaruda project has lacked in the absence of trend analysis and spatiotemporal analysis. It causes difficulties to get information about the usual seasonal attacks, then which sector is the most attacked and also the country or territory where the internet attack originated. Due to the number of unknown clusters, this paper proposes a new method of automatic bisecting K-means with the average of SSE is 93 percents better than K-means and bisecting K-means. The usage of big spark data is highly scalable for processing massive data attack.

New(s) data for entrepreneurship research? An innovative approach to use Big Data on media coverage

Although conventional register and survey data on entrepreneurship have enabled remarkable insights into the phenomenon, the added value has slowed down noticeably over the last decade. There is a need for fresh approaches utilising modern data sources such as Big Data. Until now, it has been quite unknown whether Big Data actually embodies valuable contributions for entrepreneurship research and where it can perform better or worse than conventional approaches. To contribute towards the exploration of Big Data in entrepreneurship research, we use a newly developed dataset based on publications of the German Press Agency (dpa) to explore the relationship between news coverage of entrepreneurship and regional entrepreneurial activity. Furthermore, we apply sentiment analysis to investigate the impact on sentiment of entrepreneurial press releases. Our results show mixed outcomes regarding the relationship between reporting of entrepreneurial events, i.e. media coverage, and entrepreneurial activity in German planning regions. At this stage, our empirical results reject the idea of a strong relationship between actual entrepreneurial activities in regions and the intensity of it being reported. However, the results also imply much potential of Big Data approaches for further research with more sophisticated methodology approaches. Our paper provides an entry point into Big Data usage in entrepreneurship research and we suggest a number of relevant research opportunities based on our results.

Big Data Analytics Implications for Smart Tourism Destinations Towards the Enrichment of Content Tourism

Smart tourism has huge amount of Social Big data available from tourists can cherish the value conception process for a Smart Tourism Destination. Applying a multiple-case study analysis, a set of regional tourist experiences related and destination, to derive patterns and opportunities of value creation generated by Big Data in tourism. Near conclusions and data in terms of improving decision making, creating marketing strategies with more personalized offerings, transparency and trust in dialogue with customers and stakeholders, and emergence of new business models, exploitation of Big Data in the context of information-intensive industries and mainly in Tourism. Smart Tourism Destination today is the front line of study in the tourism field and is a promising area from various research perspectives in terms of models, tools and strategies to keep up the process of intelligent configuration of destinations.

Accelerating the Internet in the presence of Big Data: Reducing user delays by leveraging historical user request patterns for web caching

Approximately 4 billion people have access to the Internet, additionally 23 billion devices are connected as of 2018. This has allowed for a substantial growth in data collection which has allowed for Big Data to flourish. The continued increase in user, devices, and Big Data usage has created a significant intensification in Internet traffic. This in turn has the potential to increase user delays when accessing data on the Internet. There are a number of ways to help reduce user latency, web caching is able to reduce web user delays in addition to reducing network traffic and the load on web servers. In this study we propose a proxy level web caching mechanism leveraging historical web patterns to help reduce user latency and accelerate the Internet. In addition we survey the state of the art of other caching approaches. Our investigation shows that using historical patterns as part of a proxy caching mechanisms in large scale networks can significantly shorten the latency for users in this era of Big Data

Big data for product innovation in manufacturing

The article analyses big data usage in the Croatian manufacturing sector. Big data usage is still low but present. We analysed the influence of six sources of big data and their influence on share of returns generated by new products using two step OLS regression analysis. The results are robust but they show that some sources have positive and some have negative effects on share of returns generated by new products. Based on the most recent research of scholarly papers we define big data and show a clear research gap by linking big data and innovation. That is, only six papers deal with big data and innovation. In five papers big data comes from social media data, and in the remaining one paper they use data from sensors but predominantly to reduce cost or support the product. Therefore, we contribute by closing this research gap of linking big data and innovation.

The Market for Data Privacy

We scrape a comprehensive set of US firms' privacy policies to facilitate research on the supply of data privacy. We analyze these data with the help of expert legal evaluations, and also acquire data on firms' web tracking activities. We find considerable and systematic variation in privacy policies along multiple dimensions including ease of access, length, readability, and quality, both within and between industries. Motivated by a simple theory of big data acquisition and usage, we analyze the relationship between firm size, knowledge capital intensity, and privacy supply. We find that large firms with intermediate data intensity have longer, legally watertight policies, but are more likely to share user data with third parties.

Usage of the Term Big Data in Biomedical Publications: A Text Mining Approach

In this study, we attempt to assess the value of the term Big Data when used by researchers in their publications. For this purpose, we systematically collected a corpus of biomedical publications that use and do not use the term Big Data. These documents were used as input to a machine learning classifier to determine how well they can be separated into two groups and to determine the most distinguishing classification features. We generated 100 classifiers that could correctly distinguish between Big Data and non-Big Data documents with an area under the Receiver Operating Characteristic (ROC) curve of 0.96. The differences between the two groups were characterized by terms specific to Big Data themes—such as `computational’, `mining’, and `challenges’—and also by terms that indicate the research field, such as `genomics’. The ROC curves when plotted for various time intervals showed no difference over time. We conclude that there is a detectable and stable difference between publications that use the term Big Data and those that do not. Furthermore, the use of the term Big Data within a publication seems to indicate a distinct type of research in the biomedical field. Therefore, we conclude that value can be attributed to the term Big Data when used in a publication and this value has not changed over time.

Big Data Driven Healthcare Supply Chain: Understanding Potentials and Capabilities

Increasing costs and operational inefficiency in healthcare supply chain has drawn the attention of professionals and researchers worldwide. A competent supply chain can provide a tighter control and visibility of greater focus on quality patient care. Today, Big Data Analytics (BDA) is progressively being adopted with the determination of building treasured information from big data. The analytic process, including positioning and use of BDA tools improves operational efficiency and help in reducing human errors that can often be deadly. Recent studies show that better healthcare planning can be done by the usage of Big Data. Suitable methods and practices can be used for healthcare data analytics which can help in better decision making for an increase in the business value and customer interest. However, the usage and applicability of BDA tools in the healthcare supply chain sector is a relatively new phenomenon as compared to other service sectors like e-commerce, finance. This paper is an effort to explore the implementation of BDA tools in the healthcare supply chain settings of hospitals for quality improvements and better patient care. The paper then summaries a number of challenges and opportunities connected with the employment of Big Data in the environment of health care supply chain services.

A Game-Based Economic Model for Price Decision Making in Cyber-Physical-Social Systems

Cyber-physical-social (CPS) systems integrate Big Data Collectors (BDCs), Service Organizers (SOs) and users to build a unified data-centric computing framework. In CPS systems, BDCs leverage a vast variety of sensing devices to collect cyber-physical-social data, and report these data to SOs to orchestrate various services provided to users, thus offering a great potential for solving complex network tasks that are far beyond the capabilities of existing networks. However, due to the lack of an economic model to describe such complex data interactions, their applications are limited. So, a game-based economic model is proposed in this paper to make smart price decisions in CPS systems. Specifically, it has the following innovations: (a) The economic model gives a dynamic game income matrix which can accurately describe the revenue changes of BDCs in the game, so as to help BDCs select appropriate game parameters and strategies, and make BDCs competitive in the game. (b) The economic model can help SOs to make optimized data purchase price and service selling price based on data collection cost and competitor price analysis, so that SOs can have a better Quality of Service (QoS) and users attraction, and maximize the profit. Experimental results demonstrate that the proposed model can help BDCs and SOs find the most suitable game strategy and price adjustment principle, which has great significance in applications.

Аналітика великих даних: принципи, напрямки і задачі (огляд)

We review directions (avenues) of Big Data analysis and their practical meaning as well as problems and tasks in this field. Big Data Analytics appears a dominant trend in development of modern information technologies for management and planning in business. A few examples of real applications of Big Data are briefly outlined. Analysis of Big Data is aimed to extract useful sense from raw data collection. Big Data and Big Analytics have evolved as computer society’s response to the challenges raised by rapid grows in data volumes, variety, heterogeneity, velocity and veracity. Big Data Analytics may be seen as today’s phase of researches and developments known under names ‘Data Mining’, ‘Knowledge Discovery in Data’, ‘intelligent data analysis’ etc. We suggest that there exist three modes of large-scale usage of Big Data: 1) ‘intelligent information retrieval; 2) massive “intermediate” data processing (concentration, mining), which may be performed during one or two scanning; 3) model inference from data; 4) knowledge discovery in data. Stages in data analysis cycle are outlined. Because of Big Data are raw, distributed, unstructured, heterogeneous and disaggregated (vertically splitted), this data should be prepared for deep analysis. Data preparation may comprise such jobs as data retrieval, access, filtering, cleaning, aggregation, integration, dimensionality reduction, reformatting etc. There are several classes of typical data analysis problems (tasks), including: cases grouping (clustering), predictive model inference (regression, classification, recognition etc.), generative model inference, extracting structures and regularities from data. Distinction between model inference and knowledge discovery is elucidated. We give some suggestion why ‘deep learning’ (one of the most attractive topic by now) is so successive and popular. One of drawbacks of traditional models is they disability to make prediction under incomplete list of predictors (when some predictors are missed) or under augmented list of predictors. One may overcome this drawback using causal model. Causal networks are illuminated in the survey as attractive in that they appear to be expressive generative models and (simultaneously) predictive models in strict sense. This means they pretend to explain how the object at hand is acting (provided they are adequate). Being adequate, causal network facilitates predicting causal effect of local intervention on the object. Methods used in Big Data Analytics will be reviewed in the next paper.

Big Data in Political Communication: Implications for Group Privacy

A growing body of academic literature explores privacy implications stemming from the adoption of big data analytics technologies in the area of political marketing and communication. While academic and public discourse on privacy focuses on the individual level, this paper explores a scarcely studied issue: group privacy. We elaborate on the importance and role of group privacy showing that even when the members of a group have their data protected, there are still privacy risks concerning the group and we identify and analyse threats to group privacy stemming from the exploitation of big data for political purposes. This paper argues that the use of big data analysis technologies in a political context can have severe implications for group privacy, including (political) targeting of particular groups and biased decision making based on group behaviour. We also show that threats to group privacy may have long term implications for society, especially with regard to the impact of populist movements.