Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/35235
Title: Solving big data problems from sequences to tables and graphs
Authors: FELIX HALIM
Keywords: Big Data Problems, Sequence Segmentation, Database Cracking, Adaptive Indexing, Maximum Flow, MapReduce
Issue Date: 20-Jun-2012
Source: FELIX HALIM (2012-06-20). Solving big data problems from sequences to tables and graphs. ScholarBank@NUS Repository.
Abstract: Big Data problems arise when the existing solutions become impractical to run because the amount of resources needed to process the ever increasing amount of data exceeds the available resources. Problems whose solutions consume more than a linear amount of resources will face the big data problem sooner. Thus, such problems that were considered solved need to be revisited in the context of big data. This thesis provides solutions to three big data problems that exists in different scales in terms of number of available resources and data size. In the limited scale such as sensor networks, we have the sequence segmentation problem. In desktop/server scale, we have database indexing problem. In cloud computing scale, we have large graph processing problem. We conclude that these seemingly unrelated problems share similar solution ideas such as the use of stochasticity, robustness property, exploitation of the inherent data properties, and algorithm-system optimizations.
URI: http://scholarbank.nus.edu.sg/handle/10635/35235
Appears in Collections:Ph.D Theses (Open)

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