introduction
Data visualization is the presentation of data in different forms in different systems, including unit information of attributes and variables. A visualization-based approach to discovering data allows users to create custom analyses using different data sources. Advanced analytics integrates many methods to create graphical desktops on desktop computers, laptops or mobile devices such as tablets, smartphones in order to support interactive animations. According to the survey, Table 1 shows the benefits of data visualization.
Here are some suggestions for visualization
Big data is a dataset of large volume, high velocity, and widely varying data, so new processing methods are needed to optimize the flow of decision-making. The challenge of big data lies in data acquisition, storage, analysis, sharing, search and visualization
1. "All data must be visualized ": Don't rely too much on visualization, some data don't need a visualization method to express its message.
2. "Only good data should be visualized" : Easy visualization can help find errors just as data can help find interesting trends.
3. "Visualization always makes the right decision" : Visualization is not a substitute for critical thinking.
4. "Visualization will mean accuracy": Data visualization does not focus on showing an accurate image, but it can express different effects.
General Data Visualization Methods
Many traditional data visualization methods are often used, such as tables, histograms, scatter charts, line charts, column charts, pie charts, area charts, flowcharts, bubble charts, etc. as well as multiple data series or combinations of charts like timelines , Venn diagrams, data flow diagrams, entity relationship diagrams, etc. In addition, some data visualization methods are often used, but not as widely used as the previous ones, they are parallel coordinates, dendrogram, cone dendrogram and semantic network.
Parallel coordinates are used to plot multidimensional individual data. Parallel coordinates are useful when displaying multidimensional data. Figure 1 is a parallel coordinate; a dendrogram is an effective way to visualize the hierarchy. The area of each sub-rectangle represents one measurement, and its color is often used to represent data from another measurement. A dendrogram showing a selection of streaming music and videos is data obtained in a social networking community; a cone dendrogram is another way to display hierarchical data, such as an organization in three-dimensional space, its branches is a form of cone growth; a semantic network is a graph that represents the logical relationship between different concepts. It generates directed graphs, combining nodes or vertices, edges or arcs, and marking each edge.
Visualization is not just a static form, but should be interactive. Interactive visualizations allow for detailed overviews by zooming, etc. It has the following steps:
1. Selection: Interactively select data entities or complete datasets, as well as subsets thereof, based on user interests.
2. Links: Find useful information in multiple views, as shown in Figure 3.
3. Filtering: Help users adjust the amount of information displayed, reduce the amount of information and focus on the information that users are interested in.
Rearrangement or Remapping: Spatial layout is the most important visual mapping, and rearranging the spatial layout of information is very effective in generating different insights.
New database technologies and cutting-edge network visualization methods may be important factors in reducing costs and helping to improve the process of scientific research. With the advent of the Internet age, data is being updated all the time, which greatly reduces the timeliness of visualization. These "low-end" visualizations are often used for business analysis and open government data, but they are not very helpful for scientific research. The visualization tools used by many scientists do not allow these network tools to be connected.
Challenges of Big Data Visualization
Scalability and dynamic analysis are the two main challenges of visualization. According to the size of the data, Table 2 shows the research status of static data and dynamic data. For large dynamic data, the original answer to question A and the answer to question B may not be applicable when dealing with both questions AB at the same time.
A visualization-based approach takes the challenges of the four "Vs" and translates them into the following opportunities.
• Volume : Develop with large datasets and derive meaning from big data.
• Variety : The development process requires as many data sources as possible.
• Velocity : Enterprises no longer need to batch data, but can process all data in real time.
• Quality (Value) : not only create attractive infographics and heat maps for users, but also obtain opinions through big data and create business value.
The diversity and heterogeneity (structured, semi-structured and unstructured) of big data visualization is a big problem. High speed is an element of big data analytics. In big data, designing a new visualization tool with efficient indexing is not an easy task. Cloud computing and advanced graphical user interface are more conducive to the development of the scalability of big data.
Visualization systems must contend with unstructured data forms (such as charts, tables, text, treemaps, and other metadata, etc.), and big data usually comes in unstructured forms. Due to bandwidth constraints and energy demands, visualization should be closer to the data and efficiently extract meaningful information. Visualization software should run in situ. Due to the capacity issue of big data, massive parallelization becomes a challenge for visualization process. The difficulty of parallel visualization algorithms is how to decompose a problem into multiple independent tasks that can be run simultaneously.
Efficient data visualization is a key part of the development process in the era of big data. The complexity and high dimensionality of big data has given rise to several different dimensionality reduction methods. However, they may not always be so applicable. The more effective a high-dimensional visualization is, the higher the probability of identifying potential patterns, correlations, or outliers.
Big data visualization also has the following problems:
• Visual noise : In the dataset, most objects have strong correlations between them. The user cannot display them separately as separate objects.
• Loss of information : It is possible to reduce the visual data set, but this will result in a loss of information.
• Large-scale image perception: Data visualization is not only limited by the aspect ratio and resolution of the device, but also by the perception of the real world.
• High-speed image transformation : Users can observe data, but cannot react to changes in data intensity.
• High performance requirements : There is almost no such requirement in static visualization, because the visualization speed is low and the performance requirements are not high.
The scalability of perceptible interactions is also a challenge for big data visualization. Visualizing each data point can lead to overplotting that reduces the user's ability to identify, and outliers can be removed by sampling or filtering the data. Querying data from large-scale databases can result in high latency and slow interaction rates.
In big data applications, large-scale data and high-dimensional data can make data visualization difficult. Most current big data visualization tools are terrible at scalability, functionality, and response time. In the process of visual analysis, uncertainty is a great challenge to effectively consider the uncertainty of the visualization process.
Visualization and big data face many challenges, here are some possible solutions:
1. To meet the needs of high speed: First , to improve the hardware , you can try to increase the memory and improve the ability of parallel processing. The second is that many machines will use, store data and use grid computing methods.
2. Understand the data: Ask the appropriate professional to interpret the data.
3. Access data quality : Ensuring clean data through data governance or information management is essential.
4. Display meaningful results : Aggregate data into a higher-level view where small data sets and data can be effectively visualized.
5. Handle outliers : Eliminate outliers in the data or create a separate chart for outliers.
Some Advances in Big Data Visualization
In the era of big data, how does visualization work? The visualization first provides the user with a general overview, and then zooms and filters to give people the more in-depth details they need. The visualization process plays a key role in helping people use big data to obtain more complete customer information. Intricate relationships are an important part of many big data scenarios, and social networks may be the most prominent example. It is very difficult to understand the big data information in the form of text or tables; on the contrary, visualization can The trends and inherent patterns of the network are more clearly displayed. Cloud computing-based visualization methods are usually used to visualize the relationships among social network users. The correlation model is used to describe the hierarchical relationship of user nodes in the social network, which can intuitively display the social relationship of users. In addition, it can parallelize the visualization process with the help of the Hadoop software platform that utilizes cloud technology, thereby accelerating the collection of big data for social networks.
Big data visualization can be achieved through a variety of methods, such as displaying data from multiple perspectives, focusing on dynamic changes in large amounts of data, and filtering information (including dynamic query filtering, star map display, and tight coupling), etc. The following visualization methods are analyzed and classified according to different data types (large-scale volume data, variable data, and dynamic data):
Dendrogram: A space-filling visualization method based on hierarchical data.
Circular Filler: A drop-in replacement for the tree schema. It uses circles as primitive shapes and can bring in more circles from higher-level hierarchies.
Sunburst: Convert to polar coordinate system based on dendrogram visualization. The variable parameters are changed from width and height to radius and arc length.
Parallel Coordinate: Through visual analysis, the multiple data factors of different imperial towns are expanded.
Vapor Schema: A type of stacked area graph in which data is spread around a central axis, with flow and organic form.
Circular network schema: The data are arranged around a circle and are connected to each other by curves according to their own correlation ratios. Correlation of data objects is usually measured with different line widths or color saturation
Traditional data visualization tools are not sufficient to handle big data . Here are a few ways to visualize interactive big data. First, multiple types of changing data can be visualized using a design space consisting of an extensible population of intuitive data summaries derived from data reduction methods such as aggregation or sampling. Interactive query methods (such as association and update techniques) applied to specific intervals are thus developed by combining multivariate data blocks and parallel queries. More advanced methods are used on a browser-based visual analysis system, imMens, to process data and render to the GPU (graphics processor).
Many big data visualization tools run on the Hadoop platform. Common modules in the platform are: Hadoop Common, HDFS (Hadoop Distributed File System), Hadoop YARN and Hadoop MapReduce. These modules can efficiently analyze big data information, but lack sufficient visualization process. The following will introduce some software with visualization functions and interactive data visualization:
Pentaho: a software that supports business intelligence (BI) functions such as analytics, dashboards, enterprise-level reporting, and data mining;
Flare: implements data visualization running in Adobe Video Player;
JasperReports: has a new software layer capable of generating reports from large databases;
Dygraphs: A fast and flexible collection of open source Java description language graphs that can discover and process opaque data.
Datameer Analytics Solution and Cloudera: Using both Datameer and Cloudera software at the same time makes it faster and easier for us to use the Hadoop platform.
Platfora: Transform raw big data in Hadoop into an interactive data processing engine. Platfora also has the ability to modularize the in-memory data engine.
ManyEyes: Visualization tool developed by IBM. It is a public website where users can upload data and implement interactive visualizations.
Cognos : A business intelligence (BI) software that supports interactive and intuitive data analysis, with a built-in in-memory data engine to accelerate visualization processing.
IBM uses powerful Cognos business intelligence software to help clients solve these challenges. Cognos BI is a tool for predicting, tracking, analyzing, and presenting quantitative indicators related to business performance. Through the collection, management, analysis and transformation of data, the data becomes usable information, and the necessary insight and understanding are obtained from it. power to better assist decision-making and guide actions to help business decision-makers make informed decisions at the right time and at the right place.
Big data analytics tools can handle ZB (one trillion terabytes) and petabytes (petabytes) of data with ease, but they often fail to visualize this data. Today, the main big data processing tools are Hadoop, High Performance Computing and Communications, Storm, Apache Drill, RapidMiner and Pentaho BI. Data visualization tools have NodeBox, R, Weka, Gephi, Google Chart API, Flot, D3 and many more. A big data visualization algorithm analysis integration model formed on the basis of RHadoop has been proposed to process ZB and PB data and provide us with high-value analysis results in a visual way. It also fits with the design of the ZB and PB data parallel algorithms.
Interactive visual cluster analysis is the most straightforward method we use to explore cluster patterns. One of the most challenging is to visualize multidimensional data so that users can interactively analyze the data and understand the cluster structure. Now we have developed an optimized star coordinate visualization model to effectively analyze big data interaction clusters. Compared with other multi-dimensional visualization methods (such as parallel coordinates and scatter plot matrix), it is very likely to be the most scalable big data. Visualization Technology:
Parallel coordinates and scatterplot matrices are often used to analyze data up to ten dimensions, while star coordinates can handle dozens of dimensions.
With the help of density-based representation, the star coordinate visualization itself is extended.
The star-coordinate-based cluster visualization is not used to compute pairwise distances in data records; instead, it leverages the performance of the underlying mapping model to partially preserve this positional relationship. This is very useful when dealing with big data.
Direct visualization of big data sources is neither possible nor effective, so it is important to reduce the volume and complexity of big data by analyzing the data. Therefore, the integration of visualization and analysis can maximize efficiency. The RAVE software developed by IBM has been able to apply visualization to the field of business analysis to analyze and solve problems. RAVE and scalable visualization capabilities allow us to better understand big data with effective visualization. Meanwhile, other IBM products, such as IBM® InfoSphere® BigInsights™ and IBM SPSS® Analytic Catalyst, work with RAVE to enrich users' insights into big data with interactive visualizations. For example, InfoSphere BigInsights can help analyze and discover business information hidden in big data. SPSS Analytic Catalyst automates the preparation of big data, selects the appropriate analysis process, and finally presents the final result through interactive visualization.
Scientific data visualization on immersive VR (virtual reality) platforms is still in the research stage, including software and cheap commodity hardware. These potentially valuable and innovative multidimensional data visualization tools undoubtedly facilitate collaborative data visualization. Immersive visualization has obvious advantages over traditional "desktop" visualization because it can better represent the structure of the data landscape and perform more intuitive data analysis. It should also be one of the bases for our exploration of higher-dimensional, more abstract big data. Humans' inherent cognitive model (or visual cognition) skills can be maximized through the use of new types of data related to immersive VR.
Table 5 is a summary of the SWOT analysis of the above big data visualization software, in which the competitive advantages (Strengths) and opportunities (Opportunities) are positive factors; competitive disadvantages (Weaknesses) and threats (Threats) are negative factors.
in conclusion
Visualizations can be either static or dynamic. Interactive visualizations often lead to new discoveries and work better than tools with static data. So interactive visualization brings infinite prospects for big data . The entire scientific process is aided by the associated and updated technologies that interact between visualization tools and the web (or web browser tools) . Web-based visualization allows us to acquire dynamic data in time and realize real-time visualization.
Some extensions of traditional big data visualization tools are not practical. For different big data applications, we should develop more new methods. This article introduces some of the latest big data visualization methods and conducts a SWOT analysis of these software to help us be able to innovate on this basis. Big data analysis and visualization, the integration of the two also makes big data applications better for people to use. In addition, immersive VR, which can effectively help the big data visualization process, is also a powerful new method for us to deal with high-dimensional and abstract information.