Thank you for Subscribing to CIO Applications Weekly Brief
A Guide to IoT Data Visualization
Visual analytics is a type of data analysis that combines data mining, statistics, and visualization. Aside from automated analysis, visual analytics tool implementations combine human interaction, allowing user control and judgment during data analysis to produce valuable insight for decision making.
Fremont, CA: As the Internet of Things (IoT) expands, massive amounts of wireless sensor networks emerge from monitoring a wide range of infrastructure in various domains such as healthcare, energy, smart city, transportation, building automation, agriculture, and industry, producing continuous data streams. As visual analytics tools, Big Data technologies play an important role in IoT processes, generating valuable knowledge in real-time to support critical decision making. This article discusses important IoT visualization methods.
Visual analytics is a type of data analysis that combines data mining, statistics, and visualization. Aside from automated analysis, visual analytics tool implementations combine human interaction, allowing user control and judgment during data analysis to produce valuable insight for decision making. Numerous visual analytics research studies have been conducted over the years. The majority of them deal with a traditional visual analytics pipeline, which was first presented by Keim et al and depicted the visual analytics process. The visual analytics process begins by performing data transformation sub processes such as filtering and sampling, which transform the data set into representations that can be explored further. Depending on the use case, the pipeline uses either a visual exploration method or an automatic analysis method to generate knowledge. In the case of automatic analysis, data mining methods are used to help with data characterization. Analysts and decision-makers use the visual interface to explore and analyze data. The Visual Analytics Pipeline framework is comprised of four core concepts: models, data, visualization, and knowledge. The Data module is in charge of gathering and pre-processing raw and heterogeneous data. Because data is collected in real-time via sensors, raw data sets are frequently incomplete, noisy, or inconsistent, making them unsuitable for use directly in the Visualization or Models modules. To overcome these challenges, some data pre-processing must be performed on the original data sets. Depending on the quality of the raw data, data pre-processing is a flexible process. This module includes data integration, data parsing, data cleaning (elimination of redundancy, errors, and invalid data), data transformation (normalization), and data reduction techniques. The Models module is in charge of converting data into information. Conversion methods such as feature selection and generation, model building, selection, and validation are included in this module.
Visual Analytics Pipeline
The Visualization module is in charge of visually representing and abstractly transforming the data. Visual mapping techniques (parallel coordinates, force-directed graphs, scatter matrices, chord graphs), view generation and coordination, and human-computer interaction are all covered in this module. The Knowledge module is in charge of driving the process of transforming information into meaningful insight through the use of human-machine interaction methods. Rules and Tools for Visualization Charts Data visualization places data in a visual context that prompts people to understand its significance. This decreases the overall effort required to manually analyze the data. As a result, visualization and pattern recognition within IoT-generated data play an important role in gaining insight and improving decision-making. Data visualization is important in data analytics because it allows for the presentation of findings and patterns alongside the original data. Data visualization aids in the interpretation of results by correlating the findings to the objectives. It also reveals hidden patterns, trends, and correlations that would otherwise go undetected in a powerful and perceptible way. As a result, in terms of data and data pattern understanding, it aids in the creation of good storytelling. This section will go over various types of data charting. In addition, we will look at chart selection rules that take into account special conditions that apply to a specific use case. In addition, we will discuss the most popular IoT visualization tools.