Big Data Issues Research Articles

THE VIRTUALIZATION OF CH FOR HISTORICAL RECONSTRUCTION: THE AR FRUITION OF THE FOUNTAIN OF ST. GEORGE SQUARE IN VALLETTA (MALTA)

Abstract. Improving accessibility to Cultural Heritage (CH) is an increasingly urgent challenge today. It is not only a matter of physical inaccessibility but also temporal, considering that part of CH now lost. Fortunately, the most modern technological tools are helping to break down both space and time barriers. In facts, recent advances in representation, 3D modelling and survey methodologies opened new scenarios for valorization and conservation of CH. In particular, the improvement of quality in resolution and sensor sensitivity of cameras allowed to achieve the right level of 3D reconstruction through digital photogrammetry procedures. In the same field, terrestrial laser scanners (TLS) allowed acquiring dense point clouds of complex environments with a millimetric level of accuracy. At the same time, the application of Augmented Reality (AR) and Virtual Reality (VR) technologies is an excellent solution for improving the accessibility to monuments, museums and archaeological sites. It is possible to share new levels of information about CH, in space and time, for touristic, managerial and scientific aims. This work is focused on the virtualization of CH, considering the study case of the fountain of Wignacourt, today present in St. Philip Garden in Floriana and initially located in Valletta (Malta). The application presented allows the virtual fruition of the monument placed in its original location, St. George Square. A simplified plant of the square will enable tourists to make a temporal journey in the past with their mobile device. The work is part of the Interreg Italia-Malta European project named I-Access, dedicated to the improvement of CH accessibility. It focuses the attention to the experimentation of new specific procedures in Geomatics necessary to solve big data issues of complex environment visualization.

Privacy and Security issues in Big Data : A Case Study of Characteristics, Challenges, and Solution

Privacy and Security issues in Big Data : A Case Study of Characteristics, Challenges, and Solution

Development of international mirroring system for real-time web of meteorological satellite data

The third-generation meteorological satellites equipped with highly-improved imagers provide a huge amount of Earth observation data. Himawari-8 is the first unit of the Japan Meteorological Agency’s third generation of geostationary satellites. After its starting operation in 2015, there are several websites that provide remotely sensed images in real time. However, it is hard to develop a real-time and full-resolution website, due to the large amount of data to be handled. Himawari-8 real-time web is only one website that provides full-resolution remotely sensed images in real time. To reduce network traffic and increase the access speed of it from other countries out of Japan, mirror websites of each country are needed. In this paper, we propose a cost-effective mirroring system for the Himawari-8 real-time web. A mirroring model is introduced to avoid the problem of big data processing in the mirror websites. We adopt a file copy tool based on high-performance and flexible protocol (HpFP) to transfer meteorological satellite data from the Himawari-8 real-time web to the mirror websites. Our first target is Thailand, one of the most disaster-prone countries in South-East Asia. The mirror website is set up at an institution in Thailand connected via collaborative international networks, e.g., Japan Gigabit Network (JGN) and Asia Pacific Advanced Network (APAN). The results show that the proposed mirroring system is able to overcome the big data issue by reducing the central processing unit (CPU) usage in the mirror website and transferring remotely sensed image files at high speed over international networks even under packet loss conditions. This suggests that our mirroring system has a potential for deployment in other Asian and Oceanian countries.

Feature engineering strategies based on a One-point Crossover for fraud detection on Big Data Analytics

A wide range of new opportunities for fraudulent online activities has arisen with the growing popularity of online shopping and big data issue. E-payment fraud schemes are collecting billions of dollars from customers, distributors and service providers every year. A lot of machine learning methods for fraud detection problems have been proposed which can be categorized into supervised, unsupervised and semi-supervised methods. In this paper, we proposed biologically inspired technic in the feature engineering phase for handling imbalanced data to increase the total data of a small number of classes by oversampling. One-point crossover used to generate the new data of minority classes. The best algorithm performance obtained to predict the fraud transaction from various machine learning models is Classification and Regression Tree with the corresponding accuracy, precision, recall, and F-1 Score are 96%.

Fog computing in health: A systematic literature review

Currently, technology greatly benefits the area of healthcare. Modern computers can quickly process a large volume of patient health records. Due to recent advances in the area of Internet of Things and healthcare, patient data can be dispersed in multiple locations. As a result, scientists have been proposing solutions based on Cloud Computing to manage healthcare data. However, suchs solutions present challenges regarding access latency, context-awareness, and large volumes of data. There is an increased probability of processing and transmission errors are more likely to occur as health data sets become larger and more complex. In this context, Fog Computing presents itself as an alternative to reduce health data management complexity, consequently increasing its reliability. To that end, it is important to comprehend the associated challenges before defining a Fog Computing-based architecture to manage healthcare data. This article presents a systematic literature review of fog computing being applied to healthcare area. We propose a taxonomy to explore the open issues and most important challenges on these fields of study. We selected 1070 scientific articles published in the last 10 years, filtering the 44 most significant works for an in-depth analysis. We found that there is several challenges to be addressed such as interoperability, privacy, security, data processing, management of resources and Big Data issues. Also, our contribution include developing a taxonomy for the Fog Computing and healthcare fields of study and finding out challenges and open questions of this area.

Fuzzy Object Oriented Database for Big Data

Due to the huge amount of uncertain data collection, mankind facing a colossal amount and fast data having such a composite configurations. The data can be transact on the web and manually exchanges, interpersonal organizations and through our everyday life exercises. A very helpful result of designing an appropriate Big Data in different zones, for example, medical services, human services, executives and administrators. Managing the Big Data efficiently, new investigation perspective has been secured at this point the viewpoints about huge information (Big Data) requires extra-long stretch innovative interests. The Fuzzy Logic s have been implemented here for Big Data because of their capacities to handle the vagueness and uncertainties in the information. A couple of imaginative approach for Big Data processing is presented previously. To abbreviate the current duties and extant a point of view of further enhancements. We survey the different examinations and concentrated on that there are constrained Fuzzy systems have been adopted in large information (Big Data preparing. We also examine the various advantages of Fuzzy Logic s in large information (Big Data) issues. Therefore in this paper a Fuzzy object-oriented database is designed for Big Data and perform some Fuzzy queries to check the performance of the designed Fuzzy object-oriented database. We focused on the continuously propelled augmentations of Fuzzy sets and their blends in with various contraptions could offer a novel promising planning condition.

Mining conditional functional dependency rules on big data

Current Conditional Functional Dependency (CFD) discovery algorithms always need a well-prepared training dataset. This condition makes them difficult to apply on large and low-quality datasets. To handle the volume issue of big data, we develop the sampling algorithms to obtain a small representative training set. We design the fault-tolerant rule discovery and conflict-resolution algorithms to address the low-quality issue of big data. We also propose parameter selection strategy to ensure the effectiveness of CFD discovery algorithms. Experimental results demonstrate that our method can discover effective CFD rules on billion-tuple data within a reasonable period.

Hybrid coherent encryption scheme for multimedia big data management using cryptographic encryption methods

In today's world of technology, data has been playing an imperative role in many different technical areas. Data confidentiality, integrity and data security over internet from different media and applications have greater challenging tasks. Data generation from multimedia and IoT data is another biggest big data in internet now. When sensitive and confidential data are counterfeit by attacks this requires serious countermeasures for security and privacy. Many cryptographic techniques are used for multimedia and IoT but when massive data are developed there are more computational challenges. This paper proposes a new coherent encryption algorithm solving issue of IoT and multimedia big data. Handling huge data with the help of GPU is included in order to enhance the data processing more efficiently. The proposed algorithm is compared with other symmetric cryptographic algorithms based on architecture, flexibility, scalability, security level and also based on computational running time, throughput for both encryption and decryption process and avalanche effect.

Factors Affecting The Usability Of Unstructured Big Data

Big data is a valuable asset for organisation as it analyses and help to understand the customers, changes within their business environment, market analysis and future trends. The big data is multifaceted (different data types and versatile), and mostly exists in unstructured formats. The extraction of value from this data is challenging. The usability and productivity of this multifaceted unstructured data is greatly compromised. A number of factors and associated reasons affect the usability of unstructured big data. The present research work investigates these factors and associated reasons behind the usability issues of multifaceted unstructured big data. The identification of these factors contribute to develop solutions to reduce the lack of usability of highly unstructured big data. A detailed study of existing literature followed by survey questionnaire has been conducted to identify the factors and their reasons. Descriptive statistics has been used to analyse and interpret the data and results.

Big Data Ethics in Social Media-- A Case Study of Evaluating Websites

This article combines qualitative and quantitative analysis to study the ethical issues of Big Data in social media, especially in evaluating websites. First, this article discusses the Big Data ethics of evaluation websites, and finds that there are some problems in the evaluation websites, such as false information, hidden information, and lack of user information protection. Second, this article uses questionnaires to investigate the awareness of users of different genders and ages on the evaluation website and their personal information protection consciousness.

An Iterative Methodology for Defining Big Data Analytics Architectures

Thanks to the advances achieved in the last decade, the lack of adequate technologies to deal with Big Data characteristics such as Data Volume is no longer an issue. Instead, recent studies highlight that one of the main Big Data issues is the lack of expertise to select adequate technologies and build the correct Big Data architecture for the problem at hand. In order to tackle this problem, we present our methodology for the generation of Big Data pipelines based on several requirements derived from Big Data features that are critical for the selection of the most appropriate tools and techniques. Thus, thanks to our approach we reduce the required know-how to select and build Big Data architectures by providing a step-by-step methodology that leads Big Data architects into creating their Big Data Pipelines for the case at hand. Our methodology has been tested in two use cases.

Unfair Competition Issues of Big Data in China

The sound development of the market in the data-driven economy depends on the free and fair competition of big data in the industries. Since 2015, more and more unfair competition cases concerning big data have occurred in China, such as masking advertisement, click fraud, malicious incompatibility, and gathering user’s personal data from competitors by unfair means, which can be categorized to unfair competition about illegal collection/use of competitors’ big data and about network traffic. Whether China’s current legal system of anti-unfair competition can resolve the above-mentioned disputes is concerned in this article. As the Paris Convention only regulates the basic principles of “fairness” and “honest practice” for anti-unfair competition, member states have room to develop their own legal systems according to their special economic, social and cultural conditions. In order to usher in the era of digital economy and big data and to regulate more and more unfair competition events, China amended the Anti-Unfair Competitive Law in 2017 in which a new provision for regulating the operation of e-commerce was added. This article finds that the 2017 Amendment, which is far more specific and clearer than the Paris Convention, has significantly improved China’s ability to deal with unfair competition behaviors regarding big data. However, since the patterns of unfair competition in big data are changing and “innovating” quickly and constantly, law amendments will hardly or even never catch up with the changes, so judgement of unfair competition is inherently difficult. The court cannot determine that a company constitutes unfair competition simply because its business operations have substantially reduced the performance or operating effectiveness of its competitors. When judging whether an enterprise’s competitive behavior constitutes unfair competition, no matter the court is applying one of the specific provisions or the general provision, it is essential to consider whether the enterprise has malicious and dishonest practices.

Big Data Issues in Smart Grids: A Survey

The smart power systems are based upon information and communication technologies, which lead to a deluge of data originating from various sources. To address these challenges concerning accumulated voluminous data, big data analysis in smart power systems is inevitable. This article comprehensively surveys the literature related to the big data issues in smart power systems. The background and motivation of the big data paradigm in smart power systems are first provided, and then the major issues related to the architectures, the key technologies, and standardizations of big data analytics in smart power systems are analyzed. Also, the potential applications of big data in smart power systems based upon the state-of-the-art research are highlighted. Finally, the future issues and challenges of the big data issues in modern power systems are discussed.

Integrated Data and Knowledge Management as Key Factor for Industry 4.0

The Industry 4.0 paradigm has seen increased interest by scientific research. The emergence of new information and communication technologies (ICT) in manufacturing shop floors generates large quantities of data and information flows, leading to large databases and big data issues. To resolve these issues, there is a need of efficient data and knowledge management approaches to support the exploitation, sharing, and interpretation of useful data. This article discusses the topic of ICT implementation within an Industry 4.0 paradigm. This article identifies problems of digital chain disruption and the need for interoperability. A conceptual framework for the management and structuring of data and knowledge is proposed as a solution to address these concerns.

Big Data Analytics for Deriving Business Intelligence Rules

Big data is a large volume of data pool and processing and analyzing these data is tedious jobs. The aim of fulfilling huge information storage needs is that the structural transformation of repository system using traditional approaches to NoSQL technology. However, the existing technologies for storage are inefficient since, they do not generated data that are scalable, consistent and solutions for rapidly evolving diversified data. The primary method for storing huge amounts of data is used for analytics in real time applications like healthcare, scientific experiments, e-business and networks. In this paper, it is in sighted the characteristics, application, tools of big data, Technologies, Big data analytics, challenges and issues in Big data.

Big Data in Capturing Business Value

ABSTRACT The study investigates an issue of Big Data (BD) and elements shaping BD-based business value creation. The outcome of this research is to build and verify a framework to provide value based on BD. The framework contains three components: dynamic capabilities of organizations, integrated process of BD resource exploration and exploitation, and identification and measurement of business value creation. The framework has been subjected to initial verification by conducting the survey among 25 organizations.

Veracity handling and instance reduction in big data using interval type-2 fuzzy sets

Within the aspect of big data, veracity refers to the existing uncertainty in the dataset. The continuous flow of unstructured data with unwanted noise may bring abnormality in the dataset making them unusable. In this paper, we propose a novel method to handle the veracity characteristic of the big data using the concept of footprint of uncertainty (FOU) in interval type-2 fuzzy sets (IT2 FSs). The proposed method helps in handling the veracity issue in big data and reduces the instances to a manageable extent. We have compared the results with the existing clustering based methods and examined the relationship between the clusters and the FOUs by comparing their centroids and defuzzified values. To scrutinize the validity of our results, we have also performed a number of additional experiments by appending extra instances to the datasets. To check its consistency and efficacy, the proposed methodology is assessed from three different aspects. Experimental result validates that the proposed method can suitably handle the veracity issue in big datasets and is efficient in reducing the instances.

Addressing big data issues using RNN based techniques

Most of the real world prediction problems are naturally associated with time component which requires time series data as input. Presently, machine learning approaches are used for forecasting purpose from time series data. Though the time parameter provides more information but it is also accompanied by many problems like temporal dependence and temporal structures. Neural network based approaches has the ability to address this problem as it has the capability of automatic learning and feature extraction from raw Big data. In this paper, Recurrent Neural Networks and its variants, namely, LSTM and GRU were tried for the purpose of forecasting from time series data. The experimentation was done on Yahoo Finance data in different conditions for accessing the prediction accuracy based on hourly historical data. The resulting performance with respect to prediction accuracy was analysed. The results confirm that among RNN, LSTM and GRU, GRU has the best predictive ability in case of temporal problems.

A survey of current challenges in partitioning and processing of graph-structured data in parallel and distributed systems

One of the concepts that attracts attention since entering of big data era is the graph-structured data. Suitable frameworks to handle such data would face several constraints, especially scalability, partitioning challenges, processing complexity and hardware configurations. Unfortunately, although several works deal with big data issues, there is a lack of literature review concerning the challenges related to query answering on large-scale graph data. In this survey paper, we review current problems related to the partitioning and processing of graph-structured data. We discuss existing graph processing systems and provide some insights to know how to choose the right system for parallel and distributed processing of large-scale graph data. Finally, we survey current open challenges in this field.

API deployment for big data management towards sustainable energy prosumption in smart cities-a layered architecture perspective

ABSTRACTThe smart city has emerged as a universal term for the pervasive utilisation of information and communication technologies deployed to provide value-added services to citizens based on data generated from sectors such as energy, mobility, etc. However, current approaches are faced with interoperability as a challenging issue in processing big data. Therefore, this study explores the role of application programming interfaces (APIs) for managing real-time, online, and historical energy data in the context of residential buildings and electric vehicles. Moreover, a layered architecture that employs APIs in big data is developed for district energy management towards providing energy information intelligence and support decision-making on energy sustainability in facilitating prosumption operations. Practically, the layered architecture collects energy data and provides data to prosumers who are citizens that produce, consume, share, and sell energy generated from renewable sources such as solar and wind to better improve energy prosumption in smart grid.