Open Access
Subscription Access
Approximation Query Layer (AQLayer):Design and Architecture
Data Scientists need to manipulate with data (retrieve, aggregate, join, ....) - when they do their tasks - for that it will be very useful to build a layer which prepare the data to be convenient for analysing step; with an approximation processing and some error tolerant defined by the user, that layer will handle both inserting records or collections to the database and retrieving the information from that database.
In this paper we will focus on the structure and the design of this layer, and dig deeper how this layer will translate the user’s inquiring manner to a SQL statement suitable for approximation processing.
Keywords
Big Data, Approximation Query Processing.
User
Font Size
Information
- A. Corbellini, C. Mateos, A. Zunino, D. Godoy, and S. Schiaf, “Persisting big-data/ : The NoSQL landscape,” vol. 63, pp. 1–23, 2017.
- Microsoft, “standard_query_operators.” 2007.
- K. Chakrabarti, M. Garofalakis, R. Rastogi, and K. Shim, “Approximate query processing using wavelets,” pp. 199–223, 2001.
- B. Babcock, S. CHaudhuri, and G. Das, “Dynamic Sample Selection for Approximate Query Processing,” 2001.
- Y. S. Mehanna, M. Mahmuddin, and H. S. Abdelaziz, “Approximate Query Processing Concepts and Techniques,” pp. 11–19, 2015.
- S. Río, V. López, J. M. Benítez, and F. Herrera, “On the use of MapReduce for imbalanced big data using Random Forest,” Inf. Sci. (Ny)., vol. 285, pp. 112–137, 2014.
- A. Aboulnaga and S. CHaudhuri, “Self-tuning Histograms/ : Building Histograms Without Looking at Data,” 1999.
- H. Mousavi and C. Zaniolo, “Fast and Accurate Computation of Equi-Depth Histograms over Data Streams,” 2011.
- B. Yýldýz and B. Tolga, “Equi-depth Histogram Construction for Big Data with Quality Guarantees,” pp. 1–13, 2016.
- L. Chen and A. Dobra, “Histograms as statistical estimators for aggregate queries,” Inf. Syst., vol. 38, no. 2, pp. 213–230, 2013.
- J. Myung, J. Shim, J. Yeon, and S. Lee, “Handling data skew in join algorithms using MapReduce,” vol. 51, pp. 286–299, 2016.
- P. Porwik and A. Lisowska, “The Haar – Wavelet Transform in Digital Image Processing/ : Its Status and Achievements,” pp. 79–97, 2004.
- M. Hartmann, “Building Wavelet Histograms on Large Data in MapReduce,” no. 1, pp. 1–12, 2013.
- M. Hariharan, C. Y. Fook, R. Sindhu, A. Hamid, and S. Yaacob, “Objective evaluation of speech dysfluencies using wavelet packet transform with sample entropy,” Digit. Signal Process., vol. 23, no. 3, pp. 952–959, 2013.
- R. Maidstone, “Wavelets in a Two-Dimensional Context,” pp. 1–17, 2012.
- R. Angi, “WaveletComp/ : A guided tour through the R-package,” pp. 1–38, 2014.
Abstract Views: 310
PDF Views: 0