Combination Of Big Data Research Articles

On combining Big Data and machine learning to support eco-driving behaviours

A conscious use of the battery is one of the key elements to consider while driving an electric vehicle. Hence, supporting the drivers, with information about it, can be strategic in letting them drive in a better way, with the purpose of optimizing the energy consumption. In the context of electric vehicles, equipped with regenerative brakes, the driver’s braking style can make a significant difference. In this paper, we propose an approach which is based on the combination of big data and machine learning techniques, with the aim of enhancing the driver’s braking style through visual elements (displayed in the vehicle dashboard, as a Human–Machine Interface), actuating eco-driving behaviours. We have designed and developed a system prototype, by exploiting big data coming from an electric vehicle and a machine learning algorithm. Then, we have conducted a set of tests, with simulated and real data, and here we discuss the results we have obtained that can open interesting discussions about the use of big data, together with machine learning, so as to improve drivers’ awareness of eco-behaviours.

LSTM Model for Prediction of Heart Failure in Big Data.

The combination of big data and deep learning is a world-shattering technology that can make a great impact on any industry if used in a proper way. With the availability of large volume of health care datasets and progressions in deep learning techniques, systems are now well equipped in diagnosing many health problems. Utilizing the intensity of substantial historical information in electronic health record (EHR), we built up, a conventional predictive temporal model utilizing recurrent neural systems (RNN) like LSTM and connected to longitudinal time stepped EHR. Experience records were contribution to RNN to anticipate the analysis and prescription classes for a resulting visit during heart disappointment (e.g. diagnosis codes, drug codes or method codes). In this paper, we also investigated whether use of deep learning to model temporal relations among events in electronic health records (EHRs) would enhance the model performance in predicting initial diagnosis of heart failure (HF) compared to some of the traditional methods that disregard temporality. By examining these time stamped EHRs, we could recognize the associations between various diagnosis occasions and finally predicate when a patient is being analyzed for a disease. In any case, it is hard to access the current EHR data straightforwardly, since almost all data are sparse and not standardized. Along these lines, we proposed a robust model for prediction of heart failure. The fundamental commitment of this paper is to predict the failure of heart by means of a neural network model based on patient's electronic medicinal information. In order to, demonstrate the diagnosis events and prediction of heart failure, we used the medical concept vectors and the essential standards of a long short-term memory (LSTM) deep network model. The proposed LSTM model uses SiLU and tanh as activation function in the hidden layers and Softmax in output layer in the network. Bridgeout is used as a regularization technique for weight optimization throughout the network. Assessments subject to the real-time data exhibit the favorable effectiveness and feasibility of recommended model in the risk of heart failure prediction. The results showed improved accuracy in heart failure detection and the model performance is compared using the existing deep learning models. Enhanced prior detection could expose novel chances for deferring or anticipating movement to analysis of heart failure and diminish cost.

Human-in-the-loop energy flexibility integration on a neighbourhood level: Small and Big Data management

Modern buildings provide an enormous amount of data available from various sources ranging from modular wireless sensors to smart meters. As well as enhancing energy management and building performance, the analysis of these datasets can enhance the management of decentralized energy systems (electrical storage, PV generation, heat storage, etc.). To optimize the interaction between the building and the grid, it is essential to determine the total energy flexibility of the user and the building. A building has different possibilities for demand side management, energy storage and energy exchange for which a functional-layered approach is proposed from the user up to building and its interaction with the energy infrastructure. Central is the principle of the human-in-the-loop, where a bottom-up approach places the human needs as a central starting point for the energy interaction optimisation. The combination of Big Data with deep learning techniques offers new possibilities in the prediction of energy use and decentralized renewable energy production (e.g. from local weather data taking into account local phenomena such as urban heat islands). This combined with a more bottom-up approach of multi-agent systems with a gossip-based cooperative approach using Small Data offers decentralized control and monitoring autonomy to reduce the complexity of the energy system integration and transition. This makes it possible to relate the outcomes of the urban energy system integration on a neighbourhood level. The approach is being applied to a typically medium-sized office building. A first application of the human-in-the-loop controlling the lighting systems in the open-plan workplace of the test-bed office building showed some estimated annual energy saving of around 24%. Practical application: Analysis of a large database containing so called Big Data of clusters of buildings seems promising. Therefor there is the need to study the potential impact of utilization of big building operational data in building services industry. Besides this there is also a need for a data mining-based method for analyzing massive building operational data of a specific building, Small Data. This work sets out a general framework and method for doing both and to combine the strength of both approaches. The presented combined approach and results will be of interest to engineers and facility managers wondering what the key constraints to optimal use data to optimize low energy/carbon control strategies might have within their work.

ПРОГНОЗУВАННЯ РОЗВИТКУ ТЕХНОЛОГІЇ BLOCKCHAIN В ЕКОНОМІЦІ: ТОЧКА ЗОРУ ОБЛІКОВЦЯ

In the article the importance of blockchain technology in the economy and predicting its development from the accounting point of view was investigated. The methods used in the study are based on the analysis of literature related to disclosure issues and a description of existing blockchain claims on the world stage. On the basis of this, a predictive assessment of the considered results for the further development of blockchain technology in the economy, its impact on accounting and the profession of accountant was made. The findings include the positive and negative effects of blockchain technology on the medical and banking sectors, information technology, the financial sector, and accounting. The blockchain in the hype cycle was considered as a phenomenon that all new technologies undergo before stable existing or disappearing. Based on the consideration of the most famous blockchain projects with the combination of Big Data, the estimation of the development technologies of Blockchain and Big Data in finance was discussed. This study suggests to consider blockchain technology as (1) a new way of sending and processing invoices, documents, contracts, and payments, reducing errors, costs and transaction time; (2) a path to financial equality through affordability; (3) investments in the local economy so that developing countries can grow significantly; (4) updating the currency market and the international monetary and financial transaction system; (5) a major breakthrough in the economy together with the Big Data technology.

Cybersecurity and Liability in a Big Data World

The interplay between big data and cloud computing is at the same time undoubtedly promising, challenging and puzzling. The current technological landscape is not without paradoxes and risks, which under certain circumstances may raise liability issues for market operators. In this article we illustrate the several challenges in terms of security and resilience that market operators face as their overcoming is of strategic importance for businesses wishing to be deemed privacy-respectful and reliable market actors. After a brief overview of the potentialities and drawbacks deriving from the combination of big data and cloud computing, this article illustrates the challenges and the obligations imposed by the European institutions on providers processing personal data – pursuant to the General Data Protection Regulation – and on providers of digital services and essential services – according to the NIS Directive. We also survey the European institutions’ push towards the development and adoption of codes of conduct, standards and certificates, as well as their last proposal for a new Cybersecurity Act. We conclude by showing that, despite this articulate framework, big data and cloud service providers still leverage on their strong market power to use “contractual shields” and escape liability.

Cybersecurity and Liability in a Big Data World

The interplay between big data and cloud computing is without doubt simultaneously promising, challenging and puzzling. The current technological landscape is not without paradoxes and risks, which under certain circumstances may raise liability issues for market operators. In this article we illustrate the several challenges in terms of security and resilience that market operators face since overcoming those challenges is of strategic importance for businesses wishing to be deemed privacy-respectful and reliable market actors. After a brief overview of the potentialities and drawbacks deriving from the combination of big data and cloud computing, this paper illustrates the challenges and the obligations imposed by the European institutions on providers processing personal data – pursuant to the General Data Protection Regulation – and on providers of digital services and essential services – according to the NIS Directive. We also survey the European institution’s push towards the development and adoption of codes of conduct, standards and certificates, as well as its last proposal for a new Cybersecurity Act. We conclude by showing that, despite this articulate framework, big data and cloud service providers still leverage their strong bargaining power to use “contractual shield” and escape liability.

Leveraging Big Data for Personalized Treatment of Anxiety and Depression: Review and Possible Future Directions

The combination of big data, machine learning and medical devices can be very effective in determining a personalized treatment plan, as exemplified through our literature review. We focus in particular on considering this combination within context of treatment of anxiety and depression. However, since the required amount and type of data (for determining such an individualized treatment plan) may not be available, we establish the need of developing enhanced biosensors to produce such data. We also argue for development of next generation of medical devices that can deliver such a personalized treatment plan for anxiety and depression. Moreover, evidence is presented to support the utility of bio-markers as potentially useful clinical tools to enhance treatment response for depression.

Big data-driven stochastic business planning and corporate valuation

The research question of this paper is concerned with the investigation of the links between Internet of Things and related big data as input parameters for stochastic estimates in business planning and corporate evaluation analytics. Financial forecasts and company appraisals represent a core corporate ownership and control issue, impacting on stakeholder remuneration, information asymmetries, and other aspects. Optimal business planning and related corporate evaluations derive from an equilibrated mix of top-down and bottom-up approaches. While the former follows a traditional dirigistic methodology where companies set up their strategic goals, the latter are grass-rooted with big data-driven timely evidence. Real options can be embedded in big data-driven forecasting to make expected cash flows more flexible and resilient, improving Value for Money of the investment and reducing its risk profile. More accurate and timely big data-driven predictions reduce uncertainties and information asymmetries, making risk management easier and decreasing the cost of capital. Whereas stochastic modeling is traditionally used for budgeting and business planning, this probabilistic process is seldom nurtured by big data that can refresh forecasts in real time, improving their predictive ability. Combination of big data and stochastic estimates for corporate appraisal and governance issues represents a methodological innovation that goes beyond the traditional literature and practice.

New data, old tensions: Big data, personalized learning, and the challenges of progressive education

Personalized learning has become the most notable application of big data in primary and secondary schools in the United States. The combination of big data and adaptive technological platforms is heralded as a revolution that could transform education, overcoming the outdated classroom model, and realizing the progressive vision of interest-driven and self-initiated learning. Yet, even supporters concede that, in practice, personalized learning is geared toward behaviorist models of learning. This article explores how this gap between expectation and reality concerning big data can be understood as a reflection of existing tensions within progressive education. To explain the tension, I examine the interplay between Rousseau and Dewey’s theories of education, and the novel opportunities offered by big data. I hold that personalized learning could be understood as an iteration of Rousseau’s vision of well-regulated freedom, in which students’ freedom is perceived as a means toward increasing the effectiveness of their learning. The relegation of decision making to algorithms renders this regulation more feasible and justifiable. Dewey’s critique of Rousseau’s individualized and teleological model of education offers the contours of an alternative role for big data in education, which prioritizes social interaction and the cultivation of democratic citizens. Moreover, due to the increased capacity to operationalize complex learning processes in naturalistic learning environments, big data could allow tackling some of the lingering challenges to implementing Dewey’s approach.

Predictive maintenance: strategic use of IT in manufacturing organizations

A combination of big data and predictive analytics orchestrated through the Internet of Things (IoT) offers many opportunities for researchers in Information Systems, Operations Management and Strategy to look at old problems in new ways, and to identify completely new research areas. While there is much hype, little research has been conducted that informs companies about how to profitably integrate the IoT with strategic or operational processes. This paper views the IoT through the lens of predictive maintenance -- the use of real-time data and predictive analytics algorithms to dynamically manage preventive maintenance policies. These are being used by numerous manufacturing organizations to transition from product-oriented to service-oriented business models. In particular, we analyze optimal preventive maintenance policies in an environment where equipment is subject to a deterioration, which shifts it from its initial, fully-productive state, having a specified, age-dependent failure rate to a less-productive or deteriorated state, having a different, presumably higher, age-dependent failure rate. The deterioration, itself, is a random process.

A Comprehensive Study of MapReduce Over Lustre for Intermediate Data Placement and Shuffle Strategies on HPC Clusters

With high performance interconnects and parallel file systems, running MapReduce over modern High Performance Computing (HPC) clusters has attracted much attention due to its uniqueness of solving data analytics problems with a combination of Big Data and HPC technologies. Since the MapReduce architecture relies heavily on the availability of local storage media, the Lustre-based global storage in HPC clusters poses many new opportunities and challenges. In this paper, we perform a comprehensive study on different MapReduce over Lustre deployments and propose a novel high-performance design of YARN MapReduce on HPC clusters by utilizing Lustre as the additional storage provider for intermediate data. With a deployment architecture where both local disks and Lustre are utilized for intermediate data storage, we propose a novel priority directory selection scheme through which RDMA-enhanced MapReduce can choose the best intermediate storage during runtime by on-line profiling. Our results indicate that, we can achieve 44 percent performance benefit for shuffle-intensive workloads in leadership-class HPC systems. Our priority directory selection scheme can improve the job execution time by 63 percent over default MapReduce while executing multiple concurrent jobs. To the best of our knowledge, this is the first such comprehensive study for YARN MapReduce with Lustre and RDMA.

Explaining vertical gender segregation: a research agenda

This research agenda outlines possible routes to pursue an explanation of vertical gender segregation. The analysis emphasizes the expanding opportunities brought about by a combination of Big Data and public policies, like gender quotas, and uncovers important challenges for which possible solutions are offered. Experimental work is likely to remain very useful in the pursuit of answers to this asymmetric gender presence.

Mining customer requirements from online reviews: A product improvement perspective

Big data commerce has become an e-commerce trend. Learning how to extract valuable and real time insights from big data to drive smarter and more profitable business decisions is a main task of big data commerce. Using online reviews as an example, manufacturers have come to value how to select helpful online reviews and what can be learned from online reviews for new product development. In this research, we first proposed an automatic filtering model to predict the helpfulness of online reviews from the perspective of the product designer. The KANO method, which is based on the classical conjoint analysis model, is then innovatively applied to analyze online reviews to develop appropriate product improvement strategies. Moreover, an empirical case study using the new method is conducted with the data we acquired from JD.com, one of the largest electronic marketplaces in China. The case study indicates the effectiveness and robustness of the proposed approach. Our research suggests that the combination of big data and classical management models can bring success for big data commerce.

Engineering the public: Big data, surveillance and computational politics

Digital technologies have given rise to a new combination of big data and computational practices which allow for massive, latent data collection and sophisticated computational modeling, increasing the capacity of those with resources and access to use these tools to carry out highly effective, opaque and unaccountable campaigns of persuasion and social engineering in political, civic and commercial spheres. I examine six intertwined dynamics that pertain to the rise of computational politics: the rise of big data, the shift away from demographics to individualized targeting, the opacity and power of computational modeling, the use of persuasive behavioral science, digital media enabling dynamic real-time experimentation, and the growth of new power brokers who own the data or social media environments. I then examine the consequences of these new mechanisms on the public sphere and political campaigns.

빅데이터 속성이 재난대응 의사결정에 미치는 영향에 관한 연구

This research is to assess the relationship Big Data attributes and disaster response process. The hypothesis are designed to form decision making between situation awareness and disaster response by defining major attribute of Big Data(Volume, Variety, Velocity, Complexity). It is proved whether there is a moderating effect in cause-and-effect relationship by visualizing Big Data. To test the hypotheses, it was conducted a questionnaire survey of civil servants in charge of disaster-related government employees, and collected 320 data(without 12 undependable responses). The research findings are suggested the attributes of accumulation, expandability, flexibility, real-time, analytical, combination of Big Data have a strong effect on disaster manager's situation awareness.