Flux capacitors for JavaScript deloreans: approximate caching for physics-based data interaction

Interactive visualizations have become an effective and pervasive mode of allowing users to explore the data in a visual, fluid, and immersive manner. While modern web, mobile, touch, and gesturedriven next-generation interfaces such as Leap Motion allow for highly interactive experiences, they pose unique and unprecedented workloads to the underlying data platform. Usually, these visualizations do not need precise results for most queries generated during an interaction, and the users require the intermediate results as feedback only to guide them towards their goal query. We present a middleware component - Flux Capacitor, that insulates the backend from bursty and query-intensive workloads. Flux Capacitor uses prefetching and caching strategies devised by exploiting the inherent physics-metaphor of UI widgets such as friction and inertia in range sliders, and typical characteristics of user-interaction. This enables low interaction response times while intelligently trading off accuracy

[1]  Orit Shaer,et al.  Reality-based interaction: a framework for post-WIMP interfaces , 2008, CHI.

[2]  Carlos Eduardo Scheidegger,et al.  Hashedcubes: Simple, Low Memory, Real-Time Visual Exploration of Big Data , 2017, IEEE Transactions on Visualization and Computer Graphics.

[3]  Krzysztof Z. Gajos,et al.  Content-aware kinetic scrolling for supporting web page navigation , 2014, UIST.

[4]  Carlos Eduardo Scheidegger,et al.  Nanocubes for Real-Time Exploration of Spatiotemporal Datasets , 2013, IEEE Transactions on Visualization and Computer Graphics.

[5]  Stanley B. Zdonik,et al.  Query Steering for Interactive Data Exploration , 2013, CIDR.

[6]  Helen J. Wang,et al.  Online aggregation , 1997, SIGMOD '97.

[7]  Anastasia Ailamaki,et al.  NoDB: efficient query execution on raw data files , 2012, Commun. ACM.

[8]  Joseph M. Hellerstein,et al.  Data Tweening: Incremental Visualization of Data Transforms , 2017, Proc. VLDB Endow..

[9]  David Firth,et al.  Bradley-Terry Models in R: The BradleyTerry2 Package , 2012 .

[10]  Allen Newell,et al.  The psychology of human-computer interaction , 1983 .

[11]  Arnab Nandi,et al.  Distributed and interactive cube exploration , 2014, 2014 IEEE 30th International Conference on Data Engineering.

[12]  Ravin Balakrishnan,et al.  Keepin' it real: pushing the desktop metaphor with physics, piles and the pen , 2006, CHI.

[13]  Ben Shneiderman Direct manipulation: A step beyond programming languages (abstract only) , 1981, CHI '81.

[14]  Divesh Srivastava,et al.  Semantic Data Caching and Replacement , 1996, VLDB.

[15]  Jock D. Mackinlay,et al.  Automating the design of graphical presentations of relational information , 1986, TOGS.

[16]  Cleotilde Gonzalez,et al.  Does animation in user interfaces improve decision making? , 1996, CHI.

[17]  Abraham Silberschatz,et al.  Invisible loading: access-driven data transfer from raw files into database systems , 2013, EDBT '13.

[18]  Eugene Wu,et al.  PFunk-H: approximate query processing using perceptual models , 2016, HILDA '16.

[19]  Arvind Satyanarayan,et al.  Declarative interaction design for data visualization , 2014, UIST.

[20]  Stanley B. Zdonik,et al.  Interactive data exploration using semantic windows , 2014, SIGMOD Conference.

[21]  Christopher Williamson,et al.  Dynamic queries for information exploration: an implementation and evaluation , 1992, CHI.

[22]  Arnab Nandi,et al.  FluxQuery: An Execution Framework for Highly Interactive Query Workloads , 2016, SIGMOD Conference.

[23]  M.O. Ward,et al.  Prefetching for visual data exploration , 2003, Eighth International Conference on Database Systems for Advanced Applications, 2003. (DASFAA 2003). Proceedings..

[24]  Jeffrey Heer,et al.  imMens: Real‐time Visual Querying of Big Data , 2013, Comput. Graph. Forum.

[25]  Michael Stonebraker,et al.  Dynamic Prefetching of Data Tiles for Interactive Visualization , 2016, SIGMOD Conference.

[26]  Surajit Chaudhuri,et al.  Overview of Data Exploration Techniques , 2015, SIGMOD Conference.

[27]  Ion Stoica,et al.  BlinkDB: queries with bounded errors and bounded response times on very large data , 2012, EuroSys '13.

[28]  Arnab Nandi,et al.  SnapToQuery: Providing Interactive Feedback during Exploratory Query Specification , 2015, Proc. VLDB Endow..

[29]  B. Ahn South Korea's renewed focus on space weather , 2011 .

[30]  Harumi A. Kuno,et al.  Concurrency Control for Adaptive Indexing , 2012, Proc. VLDB Endow..

[31]  Chris Fleizach The R*-Tree: An Efficient and Robust Access Method for Points and Rectangles , 2017 .

[32]  Christian S. Jensen,et al.  Building Accurate 3D Spatial Networks to Enable Next Generation Intelligent Transportation Systems , 2013, 2013 IEEE 14th International Conference on Mobile Data Management.

[33]  Jaehyun Park,et al.  Adaptation of a Neighbor Selection Markov Chain for Prefetching Tiled Web GIS Data , 2002, ADVIS.