Augmented Reality (AR) has assisted immensely to overcome the issues of inadequate human perception and restrictions from dimensions and screen magnitude. AR helps explains the data produced by computers that are superimposed in the world. With an exceptional blend of filtering visualization approaches, dynamic projection, and motion recognition AR can present a multi-dimensional representation of data visualization thus assisting to discover slices of data hidden in unadorned view, for a better approaching generation. In the world of big data visualization, data scientists often find it tricky to overcome the issue of scaling that is the need to enlarge to another branch of information from a definite point of presenting data which is efficiently solved by AR.
Issues and Solutions of AR in Data Visualization
Tracking and Classification
The constant assessment and reassessment is the explanation of the success in blending AR with data analytics and visualization. Since monitoring is forceful and modifies every nano-second, appropriate infrastructure must be in place to observe and identify the coordinates. Tracking the location and angle values of virtual objects within the 3D canvas and with dynamism re-estimating it during the presentation is the key to improved user engagement and improved conveying of the idea behind data analytics through AR.
Observation
While computational prowess of modern-day systems is far above the ground with modern technologies like machine learning and artificial neural networks, the fact suggests that it still lags behind the raw and cognitive abilities of the human brain. These disconnect between technology and human brainpower is a confrontation in the effective union of data visualization and AR. The human appraoch has its own set of idiosyncrasies and features. It becomes complicated to mimic these features through interface and hardware integration. Also, how the viewers will react to certain associations and change of visual aspect too demand to be taken into account. A great workaround to this issue is to make sure simplicity in the visualization of information.
Incorporating the Two Technologies
AR is equipped with functionalities like motion through 3D spaces, scaling and data slicing beyond the field of vision. These functionalities can be useful with great success to produce insights that drive decision making. Another additional step can be implementing voice and signs to offer a better user experience to the audience during the storytelling phase of data visualization. Applying machine learning algorithms to describe basic gestures would work well to advance the overall effectiveness of data visualization with the help of AR.
Edge of Hardware
The hardware amalgamation with data visualization requires a certain level of skills and learning. With advanced skillset, data scientists can trounce common challenges like limited field vision, deformation of scene’s resolution, time holdup and cost factor. The correct decision will be to formulate a framework that permits for seamless interaction with the 3D model coupled with spontaneous gestures or voice.
Difference between Actual and Virtual Objects
Today AR can handle seamless blending of the real world with virtual objects and scenarios. Executing this beauty is one of the most significant issues of using AR to drive data visualization. A difference between the real and virtual objects leads to numerous problems – vision distortion, an incorrect response, wrong focus to a data visualization picture, or brightness and contrast issues. A counter quantification would be to use advanced equipment with the latest optical technologies that deliver brightness, enhanced saturation, and contrast.
One can say that AR has touched upon an assortment of business sectors, and Big Data remains no exclusion to this trend. We see the impact of Big Data more significant than before the reason being visually stunning and easily intelligible value proposition of AR.
