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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...
Data mining techniques and extracting patterns from large datasets play a vital role in knowledge discovery. Most of the decision makers encounter a large number of decision rules resulted from association rul...
The world of DNA sequencing has not only been a difficult field since it was first worked upon, but it is also growing at an exponential rate. The amount of data involved in DNA searching is huge, thereby norm...
Convolutional neural networks are widely adopted for solving problems in image classification. In this work, we aim to gain a better understanding of deep learning through exploring the miss-classified cases i...
An important source of information presently is social media, which reports any major event including natural disasters. Social media also includes conversational data. As a result, the volume of data on socia...
Data sensing, information processing, and networking technologies are being fast embedded into the very fabric of the contemporary city to enable the use of innovative solutions to overcome the challenges of s...
Recent US legislation imposes financial penalties on hospitals with excessive patient readmissions. Predictive analytics for hospital readmissions have seen an increase in research due to the passage of this l...
Big datasets are often stored in flat files and can contain contradictory data. Contradictory data undermines the soundness of the information from a noisy dataset. Traditional tools such as pie chart and bar ...
This paper proposes a reinforcement learning based message transfer model for transferring news report messages through a selected path in a trusted provenance network with the objective of maximizing the rewa...
Classifying short texts to one category or clustering semantically related texts is challenging, and the importance of both is growing due to the rise of microblogging platforms, digital news feeds, and the li...
The restrictions that are related to using single distribution resampling for some specific computing devices’ memory gives developers several difficulties as a result of the increased effort and time needed ...
The recent technology development in the concern of microarray experiments has provided many new potentialities in terms of simultaneous measurement. But new challenges have arisen from these massive quantitie...
Predicting stock market price is considered as a challenging task of financial time series analysis, which is of great interest to stock investors, stock traders and applied researchers. Many machine learning ...
The explosive growing number of data from mobile devices, social media, Internet of Things and other applications has highlighted the emergence of big data. This paper aims to determine the worldwide research ...
Transfer learning has been demonstrated to be effective for many real-world applications as it exploits knowledge present in labeled training data from a source domain to enhance a model’s performance in a tar...
The traditional methods of clustering are unable to cope with the exploding volume of data that the world is currently facing. As a solution to this problem, the research is intensified in the direction of par...
Big data has become popular for processing, storing and managing massive volumes of data. The clustering of datasets has become a challenging issue in the field of big data analytics. The K-means algorithm is ...
Predictive analytics has gained a lot of reputation in the emerging technology Big data. Predictive analytics is an advanced form of analytics. Predictive analytics goes beyond data mining. A huge amount of me...
Text similarity measurement aims to find the commonality existing among text documents, which is fundamental to most information extraction, information retrieval, and text mining problems. Cosine similarity b...
It is true that data is never static; it keeps growing and changing over time. New data is added and old data can either be modified or deleted. This incremental nature of data motivates the development of new...
Route prediction is an essential requirement for many intelligent transport systems (ITS) services like VANETS, traffic congestion estimation, resource prediction in grid computing etc. This work focuses on bu...
With the increasing demand for examining and extracting patterns from massive amounts of data, it is critical to be able to train large models to fulfill the needs that recent advances in the machine learning ...
Ever since the emergence of big data concept, researchers have started applying the concept to various fields and tried to assess the level of acceptance of it with renown models like technology acceptance mod...
In agriculture sector where farmers and agribusinesses have to make innumerable decisions every day and intricate complexities involves the various factors influencing them. An essential issue for agricultural...
Most scientists are accustomed to make predictions based on consolidated and accepted theories pertaining to the domain of prediction. However, nowadays big data analytics (BDA) is able to deliver predictions ...
The performance gap between compute and storage is fairly considerable. This results in a mismatch between the application needs from storage and what storage can deliver. The full potential of storage devices...
Determining user’s perception of a brand in short periods of time has become crucial for business. Distilling brand perception directly from people’s comments in social media has promise. Current techniques fo...
Network theory concepts form the core of algorithms that are designed to uncover valuable insights from various datasets. Especially, network centrality measures such as Eigenvector centrality, Katz centrality...
Online consumer reviews have become a baseline for new consumers to try out a business or a new product. The reviews provide a quick look into the application and experience of the business/product and market ...
Detecting botnets in a network is crucial because bots impact numerous areas such as cyber security, finance, health care, law enforcement, and more. Botnets are becoming more sophisticated and dangerous day-b...
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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