Visualisation is an integral part of data science process – Exploratory Data Analysis to understand the shape of the data, Model visualisation to unbox the model algorithm and Dashboard visualisation to communicate the insight. This task of visualisation is increasingly shifting from a static & narrative setup to an interactive & reactive setup, which presents a new set of challenges for those designing interactive visualisation applications.
The talk covers the four major areas that impact the architecture design of interactive visualization at scale and will illustrate the different design trade-offs involved using exemplars and case studies for each.
1. Rendering for Data Scale: Envisioning how the visualization can be displayed when data size is small is not hard. But how do you render interactive visualization when data points is in millions or billions of data points?
– Bin-Summarize-Smooth e.g. Datashader, BigVis
– WebGL based Rendering e.g. Deck.gl
2. Computation for Interaction Speed: Making the visualisation reactive requires the user to have the ability to interact, drill-down, brush and link multiple visual views to gain insight. But how do you reduce the latency of the query at the interaction layer, so that the user can interact with the visualisation?.
– Aggregation & In-Memory Cubes e.g. Hashcubes, inMems, Nanocubes
– Approximate Query Processing / Sampling e.g. VerdictDB
– GPU based Databases e.g. MapD
3. Adaptive to Data Complexity: Choosing a good visualisation design for a singular dataset is possible after a few experiments and iteration. But how do you ensure that the visualisation will adapt to the variety, volume and edge cases in the real data?
– Responsive Visualisation to Space & Data
– Handling High Cardinality e.g. Facet-Dive
– Multi-Dimensional Reduction e.g. Embedding Projector
4. Responsive to Data Velocity: Designing for periodic query based visualisation refreshes is one thing. But streaming data adds a whole new level of challenge to interactive visualisation. So how do you trade-offs between real-time vs. near real-time data and its impact on refreshing visualization?
– Optimizing for near real-time visual refreshes
– Handling event / time based streams
Bargava Subramanian is a machine learning engineer based in Bangalore, India. Bargava has 14 years’ experience delivering business analytics solutions to investment banks, entertainment studios, and high-tech companies. He has given talks and conducted numerous workshops on data science, machine learning, deep learning, and optimization in Python and R around the world. He mentors early-stage startups in their data science journey. Bargava holds a master’s degree in statistics from the University of Maryland at College Park. He is an ardent NBA fan.
Amit Kapoor is interested in learning and teaching the craft of telling visual stories with data. At narrativeVIZ Consulting, Amit uses storytelling and data visualization as tools for improving communication, persuasion, and leadership through workshops and trainings conducted for corporations, nonprofits, colleges, and individuals. Amit also teaches storytelling with data as guest faculty in executive courses at IIM Bangalore and IIM Ahmedabad. Amit’s background is in strategy consulting, using data-driven stories to drive change across organizations and businesses. He has more than 12 years of management consulting experience with AT Kearney in India, Booz & Company in Europe, and more recently for startups in Bangalore. Amit holds a BTech in mechanical engineering from IIT, Delhi, and a PGDM (MBA) from IIM, Ahmedabad. Find more about him at Amitkaps.com.
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