Big Data Visualization Tools: A Survey - The New Paradigms, Methodologies and Tools for Large Data Sets Visualization

Caldarola, Enrico G.; Rinaldi, Antonio M.

doi:10.5220/0006484102960305

Cited by 29 publications

(10 citation statements)

References 3 publications

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“…On the other hand, when using the term micro-visualization we refer to a small graph with a limited number of nodes for recognizing specific or local aspects of a particular topological area. There are several tools for displaying graphs in Neo4j and some of most used are Cytoscape and Gephi [11]. For the macro view we decided to use Gephi, because it is a very flexible software for the customization of the retrieved data.…”

Section: Knowledge Base Implementationmentioning

confidence: 99%

Using a multimedia semantic graph for web document visualization and summarization

Rinaldi

Russo

2020

Multimed Tools Appl

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The synthesis process of document content and its visualization play a basic role in the context of knowledge representation and retrieval. Existing methods for tag-clouds generations are mostly based on text content of documents, others also consider statistical or semantic information to enrich the document summary, while precious information deriving from multimedia content is often neglected. In this paper we present a document summarization and visualization technique based on both statistical and semantic analysis of textual and visual contents. The result of our framework is a Visual Semantic Tag Cloud based on the highlighting of relevant terms in a document using some features (font size, color, etc.) showing the importance of a term compared to other ones. The semantic information is derived from a knowledge base where concepts are represented through several multimedia items. The Visual Semantic Tag Cloud can be used not only to synthesize a document but also to represent a set of documents grouped by categories using a topic detection technique based on textual and visual analysis of multimedia features. Our work aims at demonstrating that with the help of semantic analysis and the combination of textual and visual features it is possible to improve the user knowledge acquisition by means of a synthesized visualization. The whole strategy has been evaluated by means of a ground truth and compared with similar approaches. Experimental results show the effectiveness of our approach, which outperforms state-of-art algorithms in topic detection combining both visual and semantic information.

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Section: Knowledge Base Implementationmentioning

confidence: 99%

Using a multimedia semantic graph for web document visualization and summarization

Rinaldi

Russo

2020

Multimed Tools Appl

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“…For example, maps are used to visualize diseases [28], flooding [29], traffic accident [11], and other risks and social issues. There are various tools for visualizing data; as discussed in [30] these include technical and nontechnical tools. Author in [20] applied bar and row charts with colored maps using a library called Dimensional Charting JavaScript (dc.js).…”

Section: B Visualizing Riskmentioning

confidence: 99%

“…The map also has bar charts with color variations to visualize the statistics of accidents, with the degree of risk according to accident locations. To create online interactive maps with charts, plotly and folium are powerful and famous libraries for visualizing data by python mentioned in [30].…”

Section: B Visualizing Riskmentioning

confidence: 99%

A Deep-Learning Model for Predicting and Visualizing the Risk of Road Traffic Accidents in Saudi Arabia: A Tutorial Approach

Alrajhi¹,

Kamel²

2019

IJACSA

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Around the world, road traffic accidents (RTAs) cause significant concerns for decision makers and researchers on traffic safety. The diversity, rarity, and interconnectivity of historical data on factors causing car accidents point to the need for more focused studies for analyzing, predicting, and visualizing the risk of accidents over the short and long term for preventive purposes. There are many techniques and tools applied to analyze, forecast, and visualize risk. Most RTA studies have applied linear time-series methods to forecasting the risk with limited studies applying machine-learning and deeplearning techniques, especially in Saudi Arabia. Recently, many global studies have applied long short-term memory (LSTM) networks, which can be used to automatically learn the temporal dependence structures for challenging time-series forecasting problems. This paper displays a tutorial for designing a prototype of an interactive analytical tool based on a multivariate LSTM model for time-series data to predict future car accidents, fatalities, and injuries in the Kingdom of Saudi Arabia (KSA). This interactive tool visualizes the real data with the predicted values regionally in a web browser with Python. The tutorial represents the annual data of the period between 1417 (1996) and 1433 (2013), then uses the data with some contributing factors, such as population, gender, nationality, number of vehicles, and length of road, to generate the input data and predict the future values of accidents, fatalities, and injuries up to the year 1452 (2030). After that the real and predicted values are visualized regionally on an interactive map that represents the degree of risk. Finally, the paper discusses the evaluation and utilization of the proposed prototype in the future in the field of road safety.

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“…This issue has been highlighted by Verma, Agrawal, Patel & Patel [11] in their study that clarified that the huge size of data make it difficult for common used software tools to create, manage and process data within suitable time. The enthusiasm and concerned for the Big Data has encourages a lot researchers and entrepreneurs to find solutions [12] on emerging big data aspect in their practices. Understanding the big data concepts is a first step need to be taken by all big data practitioner to receive overall ideas, trend and implementation of big data in current business processes and technologies.…”

Section: Related Workmentioning

confidence: 99%

Untitled

2018

JSSH

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Industry revolution 4.0 (IR 4.0) is focus on transforming all individual processes in computing. Data digitalization in IR 4.0 allows production to monitor performance, workflow, management of all machines and access company record remotely using diversity of electronic and smart devices like smart phones, tablets, laptops, TVs and smart watches. Yet, preparing IR 4.0 platform in an industry is very challenging and requires management to prepare all the nine IR 4.0 components including Big Data. Big Data has been leads from tremendous growth of data directed from the increasing number of communication devices every day. This data perceives meaningful information that can be utilized by the industry to improve their product quality, sales and services. Mostly, Big Data was aligned in unstructured format and entails expertise and powerful facilities to decode them. Hence, a questionnaire has been distributed to 29 respondents which represent IT educators at PTSS to measure their readiness toward IR 4.0 in the aspect of Big Data sources, issue and challenges. This study has proven that there were variety of data sources can be accessed for data analytic but private data might cause limitation in data gathering process as it required access rights from data owner. Therefore, encouraging data sharing between organizations and staffs would create new ideas and opportunities for future research on Big Data.

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Big Data Visualization Tools: A Survey - The New Paradigms, Methodologies and Tools for Large Data Sets Visualization

Cited by 29 publications

References 3 publications

Using a multimedia semantic graph for web document visualization and summarization

Using a multimedia semantic graph for web document visualization and summarization

A Deep-Learning Model for Predicting and Visualizing the Risk of Road Traffic Accidents in Saudi Arabia: A Tutorial Approach

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