Visual analytics: designing flexible filtering in parallel coordinate graph

Idrus, Zainura; Zainuddin, Hedzlin; Jaafar, Ahmed

doi:10.4314/jfas.v9i5s.3

Cited by 5 publications

(7 citation statements)

References 16 publications

(21 reference statements)

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“…Similarly, Idrus et al. (2017), with zero citations on the WoS, earned four on Google Scholar. The difference in the citation count among different bibliometrics databases is typical, with Google Scholar more likely to record higher citations, compared to WoS (webofknowledge.com).…”

Section: Analysis Of Results and Discussionmentioning

confidence: 98%

Multiple criteria analysis of the popularity and growth of research and practice of visual analytics, and a forecast of the future trajectory

Akpan

2021

Int Trans Operational Res

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This study employs Google trends and data from the web of science to evaluate the popularity, growth, and impacts of visual analytics (VA) as a research field and data science technique. The paper undertakes quantitative analyses and visualization of the temporal trends, from VA's emergence in 2000 to the end of 2019. The trend analysis helps to forecast future growth in the research and practice of VA. The study highlights four outcomes. First, there is a robust direct relationship among the variables, including VA's growth on the Google trends, the scientific literature production (SLP), and usage of the published documents. Second, the SLP's growth pattern highlights VA's popularity as an emerging field with an overall annual increase of 17.4%. The high citation counts of the published scholarship indicate a significant impact and a continuous growth of the VA field. Third, VA contributes to diverse disciplines other than computer science and information systems, from business and economics to engineering, healthcare, biomedical and chemical sciences, and arts and humanities. VA helps researchers and practitioners in multidisciplinary fields analyze multidimensional data, enhance data visualization, knowledge discovery, generating insights, and make informed decisions. On the reverse, other disciplines contribute to propelling VA's popularity through research productivity, usage, and citation impacts. Finally, a trend analysis predicts sustained future growth of VA technology in research and practice to dissect and sensemaking of the increasingly massive and complex data structures, which is now the norm in many fields.

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Section: Analysis Of Results and Discussionmentioning

confidence: 98%

Multiple criteria analysis of the popularity and growth of research and practice of visual analytics, and a forecast of the future trajectory

Akpan

2021

Int Trans Operational Res

View full text Add to dashboard Cite

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“…Each alphabet has its own size and distance between each stroke. For the word centroids distance training, random sampling is applied to verify the accuracy of the output and the variety of the selected documents [30,48].…”

Section: Rq3: What Is the Outcome Of These Techniques In Various Data...mentioning

confidence: 99%

“…Another application of systematic sampling is to measure the correlation of two objects. The correlation is defined as the max-min distance between two points set and could identify the parallel between two points [34,48]. A Measure two-point set is known as Hausdorff distance or HD.…”

Section: Rq3: What Is the Outcome Of These Techniques In Various Data...mentioning

confidence: 99%

“…Other challenges when dealing with big data are the effectiveness and efficiency in understanding, storing, managing, and developing data visualization [7]. Plotting these big data to form visualizations, require high end and expensive hardware and software facilities.…”

Section: Introductionmentioning

confidence: 99%

“…During analysis, it is common to visualize and analyze smaller datasets at a time as it is easier to identify data patterns [7,20]. On the other hand, a larger dataset is explored at an early stage of data analysis to get a bird eye view and to identify interesting patterns for further analysis [13,21].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Data Clutter Reduction in Sampling Technique

Jamalludin¹,

Idrus²,

Idrus³

et al. 2022

IJACSA

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Visualization is a process of converting data into its visual form as such data patterns can be extracted from the data. Data patterns are knowledge hidden behind the data. However, when data is big, it tends to overlap and clutter on visualization which distorts the data patterns. Data is overly crowded on visualization thus, it has become a challenge to extract knowledge patterns. Besides, big data is costly to visualize because it requires expensive hardware facilities due to its size. Moreover, it is timely to plot the data since it takes time for data to render on visualizations. Due to those reasons, there is a need to reduce the size of big datasets and at the same time maintain the data patterns. There are many methods of data reduction, which are preprocessing operations, dimension reduction, compression, network theory, redundancy elimination, data mining, machine learning, data filtering and sampling techniques. However, the commonly used data reduction technique is sampling technique that derives samples from data populations. Thus, sampling technique is chosen as a study for data reduction in this paper. However, the studies are scattered and are not discussed in a single paper. Consequently, the objective of this paper is to collect them in a single paper for further analysis in order to understand them in great detail. To achieve the objective, three interdisciplinary databases which are ACM Digital Library, IEEE Explore and Science Direct have been selected. From the database, a total of 48 studies have been extracted and they are from the years 2017 to 2021. Other than sampling techniques, this paper also seeks information on big data, data visualization, data clutter, and data reduction.

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