Faster visual analytics through pixel-perfect aggregation

Abstract

State-of-the-art visual data analysis tools ignore bandwidth limitations. They fetch millions of records of high-volume time series data from an underlying RDBMS to eventually draw only a few thousand pixels on the screen.In this work, we demonstrate a pixel-aware big data visualization system that dynamically adapts the number of data points transmitted and thus the data rate, while preserving pixel-perfect visualizations. We show how to carefully select the data points to fetch for each pixel of a visualization, using a visualization-driven data aggregation that models the visualization process. Defining all required data reduction operators at the query level, our system trades off a few milliseconds of query execution time for dozens of seconds of data transfer time. The results are significantly reduced response times and a near real-time visualization of millions of data points.Using our pixel-aware system, the audience will be able to enjoy the speed and ease of big data visualizations and learn about the scientific background of our system through an interactive evaluation component, allowing the visitor to measure, visualize, and compare competing visualizationrelated data reduction techniques.

User Group Analytics Survey and Research Opportunities

“…Lumira and VAS. In [165], [166], an inter-connection mechanism between exploration and visualization is proposed. Lumira is pixel-wise, i.e., explored groups will be pruned to match available pixels to visualize [166].…”

“…In [165], [166], an inter-connection mechanism between exploration and visualization is proposed. Lumira is pixel-wise, i.e., explored groups will be pruned to match available pixels to visualize [166]. VAS is samplingbased, i.e., it picks a sampling rate that fits the visualization criteria defined by the analyst [165].…”

A hierarchical aggregation framework for efficient multilevel visual exploration and analysis

“…data reduction techniques) in which partial results are computed. Existing approaches are mostly based on: (1) sampling and filtering [40,81,2,55,13] and/or (2) aggregation (e.g., binning, clustering) [37,59,58,77,111,12,76,1,57]. Similarly, some modern database-oriented systems adopt approximation techniques using querybased approaches (e.g., query translation, query rewriting) [13,59,58,106,112].…”

Εξατομικευμένη, Σημασιολογική Και Διερευνητική Ανάλυση Δεδομένων

“…Οι υφιστάμενες προσεγγίσεις βασίζονται κυρίως: (1) στη δειγματοληψία (sampling) και στο φιλτράρισμα (filtering) [155,271,212,20,190,46] και/ή στη (2) συνάθροιση (aggregation) (π.χ. binning, clustering) [144,198,197,167,239,344,45,236,18,195]. Ομοίως, μερικά σύγχρονα συστήματα βάσεων δεδομένων (database-oriented systems) υιοθετούν τεχνικές προσεγγίσεις χρησιμοποιώντας προσεγγίσεις ερωτήσεων (π.χ.query translation, query rewriting) [46,198,197,337,346].…”

“…binning, clustering) [144,198,197,167,239,344,45,236,18,195]. Ομοίως, μερικά σύγχρονα συστήματα βάσεων δεδομένων (database-oriented systems) υιοθετούν τεχνικές προσεγγίσεις χρησιμοποιώντας προσεγγίσεις ερωτήσεων (π.χ.query translation, query rewriting) [46,198,197,337,346]. Πρόσφατα, σταδιακές τεχνικές προσεγγίσεων (incremental approximation techniques) έχουν υιοθετηθεί: σε αυτές τις μεθόδους υπολογίζονται προσεγγιστικές απαντήσεις σε προοδευτικά μεγαλύτερα σύνολα δεδομένων [155,20,190].…”