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Mass appraisals in the rental housing market are far less common than those in the sales market. However, there is evidence for substantial growth in the rental market and this lack of insight hampers commerci...
In a majority–minority classification problem, class imbalance in the dataset(s) can dramatically skew the performance of classifiers, introducing a prediction bias for the majority class. Assuming the positiv...
In the era of Big Data, with the increasing use of large-scale data-driven applications, visualization of very large high-resolution images and extracting useful information (searching for specific targets or ...
Currently, immense quantities of data cannot be managed by traditional database management systems. Instead, they must be managed by big data solutions using shared nothing architectures. Data warehouse system...
This paper presents a framework for discovering similar users on Twitter that can be used in profiling users for social, recruitment and security reasons. The framework contains a novel formula that calculates...
One of the biggest concerns of big data and analytics is privacy. We believe the forthcoming frameworks and theories will establish several solutions for the privacy protection. One of the known solutions is t...
To predict the next-year status in patients with rheumatoid arthritis using big data.
MapReduce (MR) is a criterion of Big Data processing model with parallel and distributed large datasets. This model knows difficult problems related to low-level and batch nature of MR that gives rise to an ab...
One of the challenges in diagnosing stroke disease is the lack of useful analysis tool to identify critical stroke data that contains hidden relationships and trends from a vast amount of data. In order to add...
Recently, the huge amounts of data and its incremental increase have changed the importance of information security and data analysis systems for Big Data. Intrusion detection system (IDS) is a system that mon...
Incredible amounts of data is being generated by various organizations like hospitals, banks, e-commerce, retail and supply chain, etc. by virtue of digital technology. Not only humans but machines also contri...
Stepwise regression is a popular data-mining tool that uses statistical significance to select the explanatory variables to be used in a multiple-regression model.
This paper presents an ontology based deep learning approach for extracting disease names from Twitter messages. The approach relies on simple features obtained via conceptual representations of messages to ob...
Big Data Analytics have become an integral part of Health Informatics over the past years, with the analysis of Internet data being all the more popular in health assessment in various topics. In this study, w...
In the United States, advances in technology and medical sciences continue to improve the general well-being of the population. With this continued progress, programs such as Medicare are needed to help manage...
Influence maximization in the social network becomes increasingly important due to its various benefit and application in diverse areas. In this paper, we propose DERND D-hops that adapt the radius-neighborhoo...
In this paper, we present a statistical model performed on the basis of a patient dataset. This model predicts efficiently the brain disease risk. Multiple regression was used to build the statistical model. T...
With the increasing of using workflow management systems workflow improvement becomes a new emerging problem. Many issues must be considered to handle all aspects of the workflow improvement. Workflows might b...
The immense amount of data generated on a daily basis by various devices and systems necessitates a change in data analysis methods. As an important part of analytics, data mining methods require a paradigm sh...
Drones are increasingly being used to perform risky and labor intensive aerial tasks cheaply and safely. To ensure operating costs are low and flights autonomous, their flight plans must be pre-built. In exist...
In this paper, we propose a framework for processing and analysing large-scale spatio-temporal data that uses a battery of machine learning methods based on a meta-data representation of point patterns. Existi...
Currently, the number of surveillance cameras is rapidly increasing responding to security issues. But constructing an intelligent detection system is not easy because it needs high computing performance. This...
This paper proposes a theoretical foundation for Big Data. More precisely, it explains how “functors”, a concept coming from Category Theory, can serve to model the various data structures commonly used to rep...
Privacy preserving data publication is the main concern in present days, because the data being published through internet has been increasing day by day. This huge amount of data was named as Big Data by its ...
This paper identifies a criterion for choosing an optimum set of selected features, or rejected null hypotheses, in high-dimensional data analysis. The method is designed for dimension reduction with multiple ...
The aim of this article is to analyze search and retrieval of workflows. It represents workflows relatedness based on transfer learning. Workflows from different domains (e.g. scientific or business) have simi...
Over the past decade and with the increasing use of the Internet, the assessment of health issues using online search traffic data has become an integral part of Health Informatics. Internet data in general an...
This paper deals with an efficient parallel and distributed framework for intensive computation with A* algorithm based on MapReduce concept. The A* algorithm is one of the most popular graph traversal algorit...
Data is being produced in large amounts and in rapid pace which is diverse in quality, hence, the term big data used. Now, big data has started to influence modern day life in almost every sphere, be it busine...
A data integration approach combines data from different sources and builds a unified view for the users. Big data integration inherently is a complex task, and the existing approaches are either potentially l...
Random swap algorithm aims at solving clustering by a sequence of prototype swaps, and by fine-tuning their exact location by k-means. This randomized search strategy is simple to implement and efficient. It r...
Gathering public opinion by analyzing big social data has attracted wide attention due to its interactive and real time nature. For this, recent studies have relied on both social media and sentiment analysis ...
The Correction to this article has been published in Journal of Big Data 2019 6:76
The present article describes a concept for the creation and application of energy forecasting models in a distributed environment. Additionally, a benchmark comparing the time required for the training and ap...
Massive graphs are ubiquitous and at the heart of many real-world problems and applications ranging from the World Wide Web to social networks. As a result, techniques for compressing graphs have become increa...
In the last decade, significant advances have been made in sensing and communication technologies. Such progress led to a considerable growth in the development and use of intelligent transportation systems. C...
Next-Generation Sequencing technologies are generating a huge amount of genetic data that need to be mapped and analyzed. Single machine sequence alignment tools are becoming incapable or inefficient in keepin...
Complications of pregnancy and childbirth are a leading cause of maternal morbidities and mortalities in developing countries. World Health Organization (WHO) estimates that over 500,000 women and girls die ea...
Extraction of valuable data from extensive datasets is a standout amongst the most vital exploration issues. Association rule mining is one of the highly used methods for this purpose. Finding possible associa...
A prevalent type of big data is in the form of space–time measurements. Cyclostationary empirical orthogonal function (CSEOF) analysis is introduced as an efficient and valuable technique to interpret space–ti...
Efficient management and analysis of large volumes of data is a demanding task of increasing scientific and industrial importance, as the ubiquitous generation of information governs more and more aspects of h...
Deep Learning and Big Data analytics are two focal points of data science. Deep Learning models have achieved remarkable results in speech recognition and computer vision in recent years. Big Data is important...
Despite the urban heat islands phenomenon has long been recognized as a major urban environmental problem, it was not until recently that this urban phenomenon gained attention from the discipline of urban pla...
Big data has fundamentally changed the way organizations manage, analyze and leverage data in any industry. One of the most promising fields where big data can be applied to make a change is healthcare. Big he...
The huge variety of NoSQL Big Data has tossed a need for new pathways to store, process and analyze it. The quantum of data created is inconceivable along with a mixed breath of unknown veracity and creative v...
Common clustering algorithms require multiple scans of all the data to achieve convergence, and this is prohibitive when large databases, with data arriving in streams, must be processed. Some algorithms to ex...
Socio-economic status measurement is an ongoing problem where different suggested measurements are given by researchers. This work investigates a socio-economic status measurement derived from natural correlat...
This paper addresses the problem of designing an efficient platform for pairs-trading implementation in real time. Capturing the stylised features of a spread process, i.e., the evolution of the differential b...
Big data is predominantly associated with data retrieval, storage, and analytics. Data analytics is prone to privacy violations and data disclosures, which can be partly attributed to the multi-user characteri...
Supervised learning algorithms are nowadays successfully scaling up to datasets that are very large in volume, leveraging the potential of in-memory cluster-computing Big Data frameworks. Still, massive datase...
In the era of big data, researchers interested in developing statistical models are challenged with how to achieve parsimony. Usually, some sort of dimension reduction strategy is employed. Classic strategies are...
2022 Citation Impact
8.1 - 2-year Impact Factor
5.095 - SNIP (Source Normalized Impact per Paper)
2.714 - SJR (SCImago Journal Rank)
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56 days submission to first editorial decision for all manuscripts (Median)
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