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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...
A smart grid is an intelligent electricity grid that optimizes the generation, distribution and consumption of electricity through the introduction of Information and Communication Technologies on the electric...
The ability to process large volumes of data on the fly, as soon as they become available, is a fundamental requirement in today’s information systems. Modern distributed stream processing engines (SPEs) addre...
Outliers are samples that are generated by different mechanisms from other normal data samples. Graphs, in particular social network graphs, may contain nodes and edges that are made by scammers, malicious pro...
Cloud data stores that can handle very large amounts of data, such as Apache HBase, have accelerated the use of non-relational databases (coined as NoSQL databases) as a way of addressing RDB database limitati...
Chronic Obstructive Pulmonary Disease (COPD) is a chronic lung disease that affects airflow to the lungs. Discovering the co-occurrence of COPD with other diseases, symptoms, and medications is invaluable to m...
Proliferation of structural, semi-structural and no-structural data, has challenged the scalability, flexibility and processability of the traditional relational database management systems (RDBMS). The next g...
Using traditional machine learning approaches, there is no single feature engineering solution for all text mining and learning tasks. Thus, researchers must determine and implement the best feature engineerin...
Social media data has provided various insights into the behaviour of consumers and businesses. However, extracted data may be erroneous, or could have originated from a malicious source. Thus, quality of soci...
A fully self-managed DBMS which does not require administrator intervention is the ultimate goal of database developers. This system should automate deploying, configuration, administration, monitoring, and tu...
Expansion of Internet and its use for on-line activities such as E-Commerce and social networking are producing large volumes of transactional data. This huge data volume resulted from these activities facilit...
The popularity of social media and computer-mediated communication has resulted in high-volume and highly semantic data about digital social interactions. This constantly accumulating data has been termed as B...
Network infections that are already in progress cause challenges to those officers trying to preserve those nodes not yet infected. Static solutions can take advantage of global knowledge of the network to pro...
In order to forecast prices of arbitrary agricultural commodity in different wholesale markets in one city, this paper proposes a mixed model, which combines ARIMA model and PLS regression method based on time...
Graph clustering is an important technique to understand the relationships between the vertices in a big graph. In this paper, we propose a novel random-walk-based graph clustering method. The proposed method ...
Big data is a term used for very large data sets that have more varied and complex structure. These characteristics usually correlate with additional difficulties in storing, analyzing and applying further pro...
Human behavior is essentially social and humans start their daily routines by interacting with others. There are many forms of social interactions and we have used mobile phone based social interaction feature...
Nowadays, big data is a key component in (bio)medical research. However, the meaning of the term is subject to a wide array of opinions, without a formal definition. This hampers communication and leads to mis...
Community structures and relation patterns, and ranking them for social networks provide us with great knowledge about network. Such knowledge can be utilized for target marketing or grouping similar, yet dist...
In recent years, there has been an increasing amount of data being produced and stored, which is known as Big Data. The social networks, internet of things, scientific experiments and commercial services play ...
Life Sciences have been established and widely accepted as a foremost Big Data discipline; as such they are a constant source of the most computationally challenging problems. In order to provide efficient sol...
Big Data is the term used for larger data sets that are very complex and not easily processed by the traditional devices. Today is the need of the new technology for processing these large data sets. Apache Ha...
A very important area of financial risk management is systemic risk modelling, which concerns the estimation of the interrelationships between financial institutions, with the aim of establishing which of them...
We introduce d2o, a Python module for cluster-distributed multi-dimensional numerical arrays. It acts as a layer of abstraction between the algorithm code and the data-distribution logic. The main goal is to achi...
Because communities are the fundamental component of big data/large data network graphs, community detection in large-scale graphs is an important area to study. Communities are a collection of a set of nodes ...
Citation Impact 2023
Journal Impact Factor: 8.6
5-year Journal Impact Factor: 12.4
Source Normalized Impact per Paper (SNIP): 3.853
SCImago Journal Rank (SJR): 2.068
Speed 2023
Submission to first editorial decision (median days): 56
Submission to acceptance (median days): 205
Usage 2023
Downloads: 2,559,548
Altmetric mentions: 280
