5Ws Model for Big Data Analysis and Visualization

Zhang, Jinson; Huang, Mao Lin

doi:10.1109/cse.2013.149

Cited by 32 publications

(10 citation statements)

References 9 publications

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“…Some open issues, such as data source heterogeneity and uncorrelated data filtering, and possible research directions are also given in the same study. In [101], Zhang and Huang used the 5Ws model to explain what kind of framework and method we need for different big data approaches. Zhang and Huang further explained that the 5Ws model represents what kind of data, why we have these data, where the data come from, when the data occur, who receive the data, and how the data are transferred.…”

Section: Comparison Between the Framework/platforms Of Big Datamentioning

confidence: 99%

Big data analytics: a survey

Tsai

Lai

Chao

et al. 2015

Journal of Big Data

656

310

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Section: Comparison Between the Framework/platforms Of Big Datamentioning

confidence: 99%

Big data analytics: a survey

Tsai

Lai

Chao

et al. 2015

Journal of Big Data

656

310

View full text Add to dashboard Cite

“…Current research in empirical measurements and evaluation of software development focuses on monitoring developers on the level of interactions, which brings us into dealing with Big Data in software engineering. More precisely, interaction data fulfils the 4 Vs rule of Big Data [24] and also occurs in real-time, thus the sensible approach is to represent this as a data stream that may be computationally analysed to reveal patterns [12], trends, and associations, especially related to human behaviour and interactions [21]. The motivation for gathering, processing and evaluating such streams of interaction data in software engineering is to get a detailed overview of the developers' work [14], to understand how they behave individually or in groups, and to avoid problems in development.…”

Section: Big Data In Software Engineeringmentioning

confidence: 91%

“…Interaction events occur very fast during a developer's work, from the finest level of granularity with recording of every keystroke, up to recording changes in source code contents after every widget interaction, e.g., navigation or scrolling in a document. Using the 4 Vs characteristics of Big Data, we may look upon interaction data as Big Data as well [24]:…”

Section: Software Developer Interaction Data Can Become Bigmentioning

confidence: 99%

Visualising Software Developers' Activity Logs to Facilitate Explorative Analysis

2016

APH

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In this paper, we discuss whether data collected from monitoring software developers' logs can be considered big. We hypothesize that it falls within the category of Big Data. The main topic of our paper however, is how to facilitate analysis of such data. Due to the specificity of the monitored activity, the analysis is at least partially explorative in its nature. We hypothesize that visualisation can be a productive approach in such a case. We present several visualisation schemes (diagram types) and show those applied to explorative analysis of data gathered within one four year project that we have been participating in.

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“…For example, posting pictures and writing comments on Facebook, uploading and downloading videos on YouTube, sending and receiving messages through smart phones and sending attacks through Internet all count as BigData. To analyze BigData, new analytical methods have to be developed to meet business, government and organization needs .…”

Section: Introductionmentioning

confidence: 99%

Density approach: a new model for BigData analysis and visualization

Zhang

Huang

2014

Concurrency and Computation

Self Cite

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Summary In this paper, we extended our density model to BigData analysis and visualization. BigData, which contains images, videos, texts, audio files and other forms of data collected from multiple datasets, is difficult to process and visualize using traditional database management and visualization tools. The challenges are in representing multiple datasets and illustrating and visualizing data patterns to meet business, government and organization needs. We have established the 5Ws density model which uses the 5Ws dimensions for BigData analysis and visualization. The 5Ws dimensions are what the data contain, why the data were transferred, where the data came from, when the data occurred, who received the data and how the data were transferred. According to the network log dataset, an example of BigData, each data incident can be classified into these 5Ws dimensions. The network log dataset ISCX2012 is tested throughout our model. This new model not only classifies network attributes and patterns but also establishes density patterns that provide more analytical features for BigData analysis and visualization. The experimental result shows that this new model with clustered visualization can be efficiently used for BigData analysis and network intrusion detection. Concurrency and Computation: Practice and Experience, 2014.© 2014 Wiley Periodicals, Inc.

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5Ws Model for Big Data Analysis and Visualization

Cited by 32 publications

References 9 publications

Big data analytics: a survey

Big data analytics: a survey

Visualising Software Developers' Activity Logs to Facilitate Explorative Analysis

Density approach: a new model for BigData analysis and visualization

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