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Title:A scalable direct manipulation engine for position-aware presentational data management
Author(s):Zhou, Xinyan
Advisor(s):Chang, Kevin Chen-Chuan
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(s):Order
Direct Manipulation
Positional Indexing
Abstract:With the explosion of data, large datasets become more common for data analysis. How- ever, existing analytic tools are lack of scalability and large-scale data management tools are lack of interactivity. A lot of data analysis tasks are based on the order of data, we are proposing the very first positional storage engine supporting persistence and maintenance of orders for large datasets and allow direct manipulation on orders. We introduce a sparse monotonic order statistic structure for persisting and maintaining order. We also show how to support multiple orders and optimize the storage. After that, we demonstrate a buffered storage manager to ensure the direct manipulation interactivity. Last, we show our final system DataSpread which is interactive and scalable. In the end, we hope that our solution can point out a potential direction to support data analysis for large-scale data.
Issue Date:2018-07-20
Type:Thesis
URI:http://hdl.handle.net/2142/101629
Rights Information:Copyright 2018 Xinyan Zhou
Date Available in IDEALS:2018-09-27
Date Deposited:2018-08


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