Big data

Digital twin can be defined as a virtual representation of a physical asset enabled through data and simulators for real-time prediction, optimization, monitoring, controlling, and improved decision making. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, big data cybernetics, data processing and management tools bring the promise of digital twins and their impact on society closer to reality. Digital twinning is now an important and emerging trend in many applications. Also referred to as a computational megamodel, device shadow, mirrored system, avatar or a synchronized virtual prototype, there can be no doubt that a digital twin plays a transformative role not only in how we design and operate cyber-physical intelligent systems, but also in how we advance the modularity of multi-disciplinary systems to tackle fundamental barriers not addressed by the current, evolutionary modeling practices. In this work, we review the recent status of methodologies and techniques related to the construction of digital twins mostly from a modeling perspective. Our aim is to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.

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Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

The age of big data is now coming. But the traditional data 
analytics may not be able to handle such large quantities of data. The question that arises now is, how to develop a high performance platform to efficiently analyze big data and 
how to design an appropriate mining algorithm to find the useful things from big data. To deeply discuss this issue, this paper begins with a brief introduction to data analytics, followed by the discussions of big data analytics. 
Some important open issues and further research directions will also be presented for the next step of big data analytics.

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Big data analytics: a survey

Big Data has gained much attention from the academia and the IT industry. In the digital and computing world, information is generated and collected at a rate that rapidly exceeds the boundary range. Currently, over 2 billion people worldwide are connected to the Internet, and over 5 billion individuals own mobile phones. By 2020, 50 billion devices are expected to be connected to the Internet. At this point, predicted data production will be 44 times greater than that in 2009. As information is transferred and shared at light speed on optic fiber and wireless networks, the volume of data and the speed of market growth increase. However, the fast growth rate of such large data generates numerous challenges, such as the rapid growth of data, transfer speed, diverse data, and security. Nonetheless, Big Data is still in its infancy stage, and the domain has not been reviewed in general. Hence, this study comprehensively surveys and classifies the various attributes of Big Data, including its nature, definitions, rapid growth rate, volume, management, analysis, and security. This study also proposes a data life cycle that uses the technologies and terminologies of Big Data. Future research directions in this field are determined based on opportunities and several open issues in Big Data domination. These research directions facilitate the exploration of the domain and the development of optimal techniques to address Big Data.

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Big Data: Survey, Technologies, Opportunities, and Challenges

Presently, educational institutions compile and store huge volumes of data, such as student enrolment and attendance records, as well as their examination results. Mining such data yields stimulating information that serves its handlers well. Rapid growth in educational data points to the fact that distilling massive amounts of data requires a more sophisticated set of algorithms. This issue led to the emergence of the field of educational data mining (EDM). Traditional data mining algorithms cannot be directly applied to educational problems, as they may have a specific objective and function. This implies that a preprocessing algorithm has to be enforced first and only then some specific data mining methods can be applied to the problems. One such preprocessing algorithm in EDM is clustering. Many studies on EDM have focused on the application of various data mining algorithms to educational attributes. Therefore, this paper provides over three decades long (1983–2016) systematic literature review on clustering algorithm and its applicability and usability in the context of EDM. Future insights are outlined based on the literature reviewed, and avenues for further research are identified.

A Systematic Review on Educational Data Mining

Big data is a term for massive data sets having large, more varied and complex structure with the difficulties of storing, analyzing and visualizing for further processes or results. The process of research into massive amounts of data to reveal hidden patterns and secret correlations named as big data analytics. These useful informations for companies or organizations with the help of gaining richer and deeper insights and getting an advantage over the competition. For this reason, big data implementations need to be analyzed and executed as accurately as possible. This paper presents an overview of big data's content, scope, samples, methods, advantages and challenges and discusses privacy concern on it.

Big data: A review

This article presents a brief overview on cloud computing security in terms of security considerations, models, threats and precautions. The articles published in recent years were classified and they were then analyzed in terms of problems, solutions and challenges. It should be concluded that standards, measurements and metrics for evaluations of threats need to be initially discussed carefully and then applied to the real applications.

A review on cloud security

The increase in the volume and velocity of credit card transactions causes class imbalance and concept deviation problems in data sets where credit card fraud is detected. These problems make it very difficult for traditional approaches to produce robust detection models. In this study, a different perspective has been developed for this problem and a novel approach named Fraud Detection with Image Conversion (FDIC) is proposed. FDIC handles credit card transactions as time series and transforms them into images. These images, which comprise temporal correlations and bilateral relationships of features, are classified by a convolutional neural network architecture as fraudulent or legitimate. When the obtained results are compared with the related studies, FDIC has the best F1-score and recall values, which are 85.49% and 80.35%, respectively. Since the images created during the FDIC process are difficult to interpret, a new explainable artificial intelligence approach is also presented. In this way, feature relationships that have a dominant effect on fraud detection are revealed.

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Explainable Credit Card Fraud Detection with Image Conversion

In this study, malicious users who cause to resource exhausting are tried to detect in a telecommunication company network. Non-Legitimate users could cause lack of information availability and need countermeasures to prevent threat or limit permissions on the system. For this purpose, ANN based intelligent system is proposed and compared to SVM which is well known classification technique. According to results, proposed technique has achieved approximately 70% general success rate, 33% false positive rate and 27% false negative rate in controlled environment. Also ANN has high ability to work compare to SVM for our dataset. As a result proposed technique and developed application shows sufficient and acceptable defense mechanism in huge company networks. We discussed about this is initial study and ongoing research which is compared to the current literature. By the way, this study also shows that non security information such as users mobile experiences could be potential usage to prevent resource exhausting also known as DoS related attacks.

An Intelligent Technique for Detecting Malicious Users on Mobile Stores

The rapid growth of the web has caused to availability of data effective if its content is well organized. Despite the fact that Wikipedia is the biggest encyclopedia on the web, its quality is suspect due to its Open Editing Schemas (OES). In this study, zoology and botany pages are selected in English Wikipedia and their html contents are converted to text then Artificial Neural Network (ANN) is used for classification to prevent disinformation or misinformation. After the train phase, some irrelevant words added in the content about politics or terrorism in proportion to the size of the text. By the time unsuitable content is added in a page until the moderators' intervention, the proposed system realized the error via wrong categorization. The results have shown that, when words number 2% of the content is added anomaly rate begins to cross the 50% border.

Duygu Sinanc

Papers

Big data: A review

A review on cloud security

Explainable Credit Card Fraud Detection with Image Conversion

An Intelligent Technique for Detecting Malicious Users on Mobile Stores

A New Approach to Detecting Content Anomalies in Wikipedia