Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/135831
Title: TOWARDS EFFICIENT PROCESSING OF NEIGHBOURHOOD ANALYTICS FOR ADVANCED APPLICATIONS
Authors: FAN QI
Keywords: Data management, Big data system, Neighborhood query, Query optimization, Data aggregation
Issue Date: 16-Jan-2017
Source: FAN QI (2017-01-16). TOWARDS EFFICIENT PROCESSING OF NEIGHBOURHOOD ANALYTICS FOR ADVANCED APPLICATIONS. ScholarBank@NUS Repository.
Abstract: With the increasing variety and volume of the data produced by today's applications, the adoption of effective analytics becomes remarkably demanding. Window functions, being an important part of SQL family, have proven numerous successes in relational analytics. A window function assigns each tuple a set of related tuples, on which analytics can be applied. However, the window function de nes the related tuples based on sorting which limits its usage in the domains where sorting may not be meaningful. In this thesis, we generalize the concept the window function to neighborhood analytics which eliminates the stringent sorting requirement. We propose three domain-specifi c queries tailored for advanced applications on the basis of two simple neighborhood functions. Then, we study how to process these queries efficiently given today's data scale.
URI: http://scholarbank.nus.edu.sg/handle/10635/135831
Appears in Collections:Ph.D Theses (Open)

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