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2016 | OriginalPaper | Chapter

Interactive Visualization of Big Data

Authors : Parke Godfrey, Jarek Gryz, Piotr Lasek, Nasim Razavi

Published in: Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery

Publisher: Springer International Publishing

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Abstract

Data becomes too big to see. Yet visualization is a central way people understand data. We need to learn new ways to accommodate data visualization that scales up and out for large data to enable people to explore visually their data interactively in real-time as a means to understanding it. The five V’s of big data—value, volume, variety, velocity, and veracity—each highlights the challenges of this endeavor.

We present these challenges and a system, Skydive, that we are developing to meet them. Skydive presents an approach that tightly couples a database back-end with a visualization front-end for scaling up and out. We show how hierarchical aggregation can be used to drive this, and the powerful types of interactive visual presentations that can be supported. We are preparing for the day soon when visualization becomes the sixth V of big data.

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next chapter Big Data Management in the Cloud: Evolution or Crossroad?

This includes U.S.A. with 319 million, Mexico with 122 million, and Canada with 35 million, as of 2013.

Though they are cognizant of the need, and are working toward addressing this.

We shall also show ways that categorical data as measures can be accommodated.

We use the same number of divisions—power of two—along each of the dimensions, without loss of generality. It is trivial to allow for different “aspect” ratios with different numbers of divisions for different dimensions, however.

For simplicity, we shall refer to strata \(s_0\), ..., \(s_l\), from the top to the bottom, respectively, forgoing the minus sign when understood in context.

At least not standard versions of these.

https://data.seattle.gov/.

https://snap.stanford.edu/data/.

Or vice versa: the bins of the t-pyramid are then hierarchically aggregated by x,y. This is commutative.

Also called Morton order [22]. This is a one-dimensional, linear ordering for any multi-dimensional data.

“Bins” into which no base data aggregates—“empty bins”—are never created. These numbers in the Z-order are simply skipped over.

This is sometimes referred to as a linear quadtree (for 2-D) [10].

The dataset is over three dimensions—\(X\), \(Y\), and \(T\)—so assume \(B = 2^{3d}\) for some \(d\), without loss of generality.

Andrienko, N., Andrienko, G.: Exploratory analysis of spatial and temporal data: a systematic approach. Springer Science and Business Media, Heidelberg (2006)MATH

Armbrust, M., Xin, R.S., Lian, C., Huai, Y., Liu, D., Bradley, J.K., Meng, X., Kaftan, T., Franklin, M.J., Ghodsi, A., et al.: Spark SQL: relational data processing in spark. In: Proceedings of SIGMOD, pp. 1383–1394. ACM (2015)

Battle, L., Stonebraker, M., Chang, R.: Dynamic reduction of query result sets for interactive visualizaton. In: Proceedings of the International Conference on Big Data, Santa Clara, CA, USA, pp. 1–8 (2013)

Bertin, J.: Semiology of Graphics. University of Wisconsin Press, Madison (1983)

Beyer, M.A., Laney, D.: The importance of “big data”: a definition. Gartner report (2015)

Cable, D.: The racial dot map, demographics Research Group. Weldon Cooper Center for Public Service, University of Virginia, July 2013. www.coopercenter.org/demographics/Racial-Dot-Map

Dijcks, J.P.: Oracle: Big data for the enterprise. Oracle White Paper (2012)

Elmqvist, N., Fekete, J.D.: Hierarchical aggregation for information visualization: overview, techniques, and design guidelines. IEEE Trans. Vis. Comput. Graph. 16(3), 439–454 (2010)CrossRef

Erickson, J.: Private correspondence, conveyed along with permission to use by Tilmann Rabl, May 2015

10.

Gargantini, I.: An effective way to represent quadtrees. Commun. ACM 25(12), 905–910 (1982)CrossRefMATH

11.

Godfrey, P., Gryz, J., Lasek, P., Razavi, N.: Skydive: an interactive data visualization engine. In: IEEE Symposium on Large Data Analytics and Visualization, Chicago, USA, October 25–26, pp. 129–130 (2015)

12.

Godfrey, P., Gryz, J., Lasek, P.: Interactive visualization of large data sets. Technical report EECS-2015-03, York University, March 2015

13.

Godfrey, P., Gryz, J., Lasek, P., Razavi, N.: Visualization through inductive aggregation. In: Proceedings of EDBT, March 2016

14.

Gray, J., Chaudhuri, S., Bosworth, A., Layman, A., Reichart, D., Venkatrao, M., Pellow, F., Pirahesh, H.: Data cube: a relational aggregation operator generalizing group-by, cross-tab, and sub-totals. Data Min. Knowl. Disc. 1(1), 29–53 (1997)CrossRef

15.

Hausenblas, M., Nadeau, J.: Apache drill: interactive ad-hoc analysis at scale. Big Data 1(2), 100–104 (2013)CrossRef

16.

Jugel, U., Jerzak, Z., Hackenbroich, G., Markl, V.: Faster visual analytics through pixel-perfect aggregation. Proc. VLDB Endowment 7(13), 1705–1708 (2014)CrossRef

17.

Jugel, U., Jerzak, Z., Hackenbroich, G., Markl, V.: M4: a visualization-oriented time series data aggregation. Proc. VLDB Endowment 7(10), 797–808 (2014)CrossRef

18.

Laney, D.: Meta Group Res Note 6. META (2001)

19.

Liu, Z., Jiang, B., Heer, J.: imMens: real-time visual querying of big data. Comput. Graph. Forum 32(3), 421–430 (2013)CrossRef

20.

Magdy, A., Aly, A.M., Mokbel, M.F., Elnikety, S., He, Y., Nath, S.: Mars: real-time spatio-temporal queries on microblogs. In: ICDE, pp. 1238–1241 (2014)

21.

Magdy, A., Mokbel, M.F., Elnikety, S., Nath, S., He, Y.: Mercury: a memory-constrained spatio-temporal real-time search on microblogs. In: ICDE, pp. 172–183. IEEE (2014)

22.

Morton, G.M.: A Computer Oriented Geodetic Data Base and A New Technique in File Sequencing. International Business Machines Company, New York (1966)

23.

Sallam, R.L., Hostmann, B., Schlegel, K., Tapadinhas, J., Parenteau, J., Oestreich, T.W.: Magic quadrant for business intelligence and analytics platforms. Gartner report (2015)

24.

Samet, H.: The quadtree and related hierarchical data structures. ACM Comput. Surv. (CSUR) 16(2), 187–260 (1984)MathSciNetCrossRef

25.

Samet, H.: Applications of Spatial Data Structures: Computer Graphics, Image Processing, and GIS. Addison-Wesley Longman Publishing Co., Inc., Boston (1990)

26.

Samet, H.: Foundations of Multidimensional and Metric Data Structures. Morgan Kaufmann, San Francisco (2006)MATH

27.

Schroeck, M., Shockley, R., Smart, J., Romero-Morales, D., Tufano, P.: Analytics: The Real-World Use of Big Data. IBM Global Business Services, Somers (2012)

28.

Shneiderman, B.: The eyes have it: a task by data type taxonomy for information visualizations. In: Proceedings of the 1996 IEEE Symposium on Visual Languages, pp. 336–343. IEEE (1996)

29.

Shneiderman, B.: Extreme visualization: squeezing a billion records into a million pixels. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 3–12. ACM (2008)

30.

Stolte, C., Tang, D., Hanrahan, P.: Polaris: a system for query, analysis, and visualization of multidimensional relational databases. IEEE Trans. Vis. Comput. Graph. 8(1), 52–65 (2002)CrossRef

31.

Thusoo, A., Sarma, J.S., Jain, N., Shao, Z., Chakka, P., Anthony, S., Liu, H., Wyckoff, P., Murthy, R.: Hive: a warehousing solution over a map-reduce framework. Proc. VLDB Endowment 2(2), 1626–1629 (2009)CrossRef

32.

Tigani, J., Naidu, S.: Google BigQuery Analytics. John Wiley & Sons, Hoboken (2014)

33.

Tufte, E.: Envisioning Information. Graphics Press, Cheshire (1990)

34.

Wesley, R., Eldridge, M., Terlecki, P.T.: An analytic data engine for visualization in tableau. In: Proceedings of SIGMOD, pp. 1185–1194. ACM (2011)

35.

Wesley, R.M.G., Terlecki, P.: Leveraging compression in the tableau data engine. In: Proceedings of SIGMOD, pp. 563–573. ACM (2014)

36.

White, T.: Hadoop: The definitive guide. O’Reilly Media Inc, Sebastopol (2012)

37.

Wu, E., Battle, L., Madden, S.R.: The case for data visualization management systems: vision paper. Proc. VLDB Endowment 7(10), 903–906 (2014)CrossRef

38.

Zikopoulos, P.C., Eaton, C., DeRoos, D., Deutsch, T., Lapis, G.: Understanding Big Data. McGraw-Hill, New York (2012)

Title: Interactive Visualization of Big Data
Authors: Parke Godfrey
Jarek Gryz
Piotr Lasek
Nasim Razavi
Publisher: Springer International Publishing
Book: Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery
Print ISBN: 978-3-319-34098-2

Electronic ISBN: 978-3-319-34099-9

Copyright Year: 2016
DOI: https://doi.org/10.1007/978-3-319-34099-9_1

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