Page 15 of 16
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 ...
Patient wellness and preventative care are increasingly becoming a concern for many patients, employers, and healthcare professionals. The federal government has increased spending for wellness alongside new l...
We collect big data use cases for a representative sample of telecom companies worldwide and observe a wide and skewed distribution of big data returns, with a few companies reporting large impact for a long t...
Road and traffic accidents are an important concern around the world. Road accidents not only affects the public health with different level of injury but also results in property damage. Data analysis has the...
This paper proposes an algorithm called optimized relativity search to reduce the number of nodes in a graph when attempting to decrease the running time for personalized page rank (PPR) estimation. Even thoug...
The big data phenomenon is becoming a fact. Continuous increase of digitization and connecting devices to Internet are making current solutions and services smarter, richer and more personalized. The emergence...
The use of Big Data in today’s world has become a necessity due to the massive number of technologies developed recently that keeps on providing us with data such as sensors, surveillance system and even smart...
Machine learning and data mining techniques have been used in numerous real-world applications. An assumption of traditional machine learning methodologies is the training data and testing data are taken from ...
Road accident data analysis plays an important role in identifying key factors associated with road accidents. These associated factors help in taking preventive measures to overcome the road accidents. Variou...
Finding topics from a collection of documents, such as research publications, patents, and technical reports, is helpful for summarizing large scale text collections and the world wide web. It can also help fo...
For getting up-to-date insight into online services, extracted data has to be processed in near real time. For example, major big data companies (Facebook, LinkedIn, Twitter) analyse streaming data for develop...
“Data streams” is defined as class of data generated over “text, audio and video” channel in continuous form. The streams are of infinite length and may comprise of structured or unstructured data. With these ...
This paper describes the vision behind and the mission of the Maxeler Application Gallery (AppGallery.Maxeler.com) project. First, it concentrates on the essence and performance advantages of the Maxeler dataf...
Analysis of online user-generated content is receiving attention for its wide applications from both academic researchers and industry stakeholders. In this pilot study, we address common Big Data problems of...
Using a random sample consisting of hundreds of companies worldwide, we are testing the impact on company performance of investing in big data projects targeted on three major business domains (namely, custome...
Recently, many researchers have focused on data stream processing as an efficient method for extracting knowledge from big data. Data stream clustering is an unsupervised approach that is employed for huge dat...
The Internet of things promises a continuous flow of data where traditional database and data-mining methods cannot be applied. This paper presents improvements on the Ubiquitous Self-Organized Map (UbiSOM), a...
One of the key objectives in accident data analysis to identify the main factors associated with a road and traffic accident. However, heterogeneous nature of road accident data makes the analysis task difficu...
The term smart manufacturing refers to a future-state of manufacturing, where the real-time transmission and analysis of data from across the factory creates manufacturing intelligence, which can be used to ha...
With an ever-increasing amount of options, the task of selecting machine learning tools for big data can be difficult. The available tools have advantages and drawbacks, and many have overlapping uses. The wor...
Online reviews are often the primary factor in a customer’s decision to purchase a product or service, and are a valuable source of information that can be used to determine public opinion on these products or...
This paper provides a multi-disciplinary overview of the research issues and achievements in the field of Big Data and its visualization techniques and tools. The main aim is to summarize challenges in visuali...
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 effic...
The manufacturing industry is currently in the midst of a data-driven revolution, which promises to transform traditional manufacturing facilities in to highly optimised smart manufacturing facilities. These s...
The original article was published in Journal of Big Data 2015 2:11
For over forty years, relational databases have been the leading model for data storage, retrieval and management. However, due to increasing needs for scalability and performance, alternative systems have eme...
Rapid growth of high dimensional datasets in recent years has created an emergent need to extract the knowledge underlying them. Clustering is the process of automatically finding groups of similar data points...
Big data application has many data resources and data. In the first stage of software engineering, a service overview or a system overview cannot be seen. In this paper, we propose that two processes of “Big d...
Treemaps are well-known for visualizing hierarchical data. Most related approaches have been focused on layout algorithms and paid little attention to other display properties and interactions. Furthermore, th...
W e h a v e e n t e r e d t h e b i g data age. Knowledge extraction from massive data is becoming more and more urgent. MapReduce provides a feasible framework for programming machine learning algorithms in M...
Intuitive formulation of informative and computationally-efficient queries on big and complex datasets present a number of challenges. As data collection is increasingly streamlined and ubiquitous, data explor...
Knowledge-as-a service is an emerging research trend that constitutes a promising path for organizations aiming to achieve better customer support and decision-making across a wide range of content providers. ...
Community structure is thought to be one of the main organizing principles in most complex networks. Big data and complex networks represent an area which researchers are analyzing worldwide. Of special intere...
The Erratum to this article has been published in Journal of Big Data 2015 2:19
Automated workflows are the key concept of big data pipelines in science, engineering and enterprise applications. The performance analysis of automated workflows is an important topic of the continuous improv...
The promise of Big Biomedical Data may be offset by the enormous challenges in handling, analyzing, and sharing it. In this paper, we provide a framework for developing practical and reasonable data sharing po...
Document summarization provides an instrument for faster understanding the collection of text documents and has a number of real life applications. Semantic similarity and clustering can be utilized efficientl...
Sentiment analysis or opinion mining is one of the major tasks of NLP (Natural Language Processing). Sentiment analysis has gain much attention in recent years. In this paper, we aim to tackle the problem of s...
The paper discusses the shift in the computing paradigm and the programming model for Big Data problems and applications. We compare DataFlow and ControlFlow programming models through their quantity and quali...
Intrusion Detection has been heavily studied in both industry and academia, but cybersecurity analysts still desire much more alert accuracy and overall threat analysis in order to secure their systems within ...
The ability to detect and process anomalies for Big Data in real-time is a difficult task. The volume and velocity of the data within many systems makes it difficult for typical algorithms to scale and retain ...
Big Data Analytics and Deep Learning are two high-focus of data science. Big Data has become important as many organizations both public and private have been collecting massive amounts of domain-specific info...
Citation Impact
10.835 - 2 Year Impact Factor (2021)
4.661 - Source Normalized Impact per Paper (SNIP)
2.592 - SCImago Journal Rank (SJR)
14.4 - CiteScore
Speed
94 days to first decision for all manuscripts (Median)
106 days to first decision for reviewed manuscripts only (Median)
Usage
2,250,289 downloads (2022)
982 Altmetric mentions (2021)
