Title: Preference Top-k Search Based on Multidimensional B-Tree Author: RNDr. Matúš Ondreička Department: Department of Software Engineering Faculty of Mathematics and Physics Charles University in Prague Supervisor: Prof. RNDr. Jaroslav Pokorný, CSc. Author's e-mail address: ondreicka@ksi.mff.cuni.cz Supervisor's e-mail address: pokorny@ksi.mff.cuni.cz Abstract: In this thesis, we focus on the top-k search according to user pref- erences by using B+ -trees and the multidimensional B-tree (MDB-tree). We use model of user preferences based on fuzzy functions, which enable us to search according to a non-monotone ranking function. We propose model of sorted list based on the B+ -tree, which enables Fagin's algorithms to search for the top-k objects according to a non-monotone ranking function. We apply this model in the Internet environment with data on different remote servers. Furthermore, we designed novel dynamic tree-based data structures, namely, MDB-tree composed of B+ -trees, MDB-tree with lists, MDB-tree with groups of B+ -trees and multiple-ordered MDB-tree. Concurrently, we have developed novel top-k algorithms, namely, the MD algorithm, the MXT algorithm and their variants which are able search for the top-k objects ac- cording to a non-monotone ranking function. These top-k algorithms are efficient...

Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2017-10-02T15:28:01Z No. of bitstreams: 2 Dissertação - Mateus Ferreira e Freitas - 2017.pdf: 4269845 bytes, checksum: e84e69d8747a21125170793812384a98 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2017-10-02T15:30:07Z (GMT) No. of bitstreams: 2 Dissertação - Mateus Ferreira e Freitas - 2017.pdf: 4269845 bytes, checksum: e84e69d8747a21125170793812384a98 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2017-10-02T15:30:07Z (GMT). No. of bitstreams: 2 Dissertação - Mateus Ferreira e Freitas - 2017.pdf: 4269845 bytes, checksum: e84e69d8747a21125170793812384a98 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2017-08-30 / Several applications process documents in different ways, aiming to filter, organize or learn with them. Nowadays, a great computational power is necessary in order to do that efficiently, due to the large and increasing number of documents. Usually, documents are independent of each other, which facilitates the use of parallelism to speed up this processing. This work explores three problems: active learning, learning to rank (L2R) and top-k search. Using the parallelism on multicore CPUs and manycore GPUs (Graphics Processing Unit), parallel algorithms were proposed and evaluated for each problem, and implemented with the OpenMP and CUDA APIs. For the active learning problem a multicore algorithm was proposed, which obtained 10.8x of speedup in the best case with 12 threads. The proposed manycore version obtained 128x of speedup over the serial version, and a solution with 4 GPUs achieved 3.5x of speedup over 1 GPU. For the L2R problem a manycore algorithm was proposed, which follows a thread-block approach using the concept of Combinadic, and uses a cache with fingerprint to speed up the processing. The best case speedups were 508x over the serial, 9x over a GPU baseline, and 4x over our solution when using 4 GPUs. When comparing with a version without combinadic, the speedup over it was 4.4x with both versions using 1 GPU and 3.9x with 4. These solutions used bitmap structures to speed up the association rules creation. In the top-k search a serial and multicore solutions were implemented from a state of the art manycore algorithm for exact searches. These implementations served as baselines for our extension of this algorithm, which includes the use of multi-GPU, group searches and an intra-block load balancing. The speedups were 2.7x over the original algorithm, 17x over the serial, 4x over the multicore, and 4x over our version when using 4 GPUs. / Diversas aplicações processam documentos de diferentes maneiras, visando filtrá-los, organizá-los ou aprender com eles. Atualmente, é necessário um grande poder computacional para que isso seja feito eficientemente, devido ao número grande e crescente de documentos. Geralmente os documentos são independentes entre si, o que facilita o uso de paralelismo para acelerar esse processamento. Este trabalho explora três problemas: aprendizado ativo, learning to rank (L2R) e busca top-k. Usando o paralelismo em CPUs multicore e GPUs (Graphics Processing Unit) manycore, algoritmos paralelos foram propostos e avaliados para cada problema, e implementados com as APIs OpenMP e CUDA. Para problema de aprendizado ativo foi proposto um algoritmo multicore, que obteve speedup de 10,8x no melhor caso com 12 threads. A versão manycore proposta obteve speedup de 128x em relação ao serial, e uma solução com 4 GPUs atingiu 3,5x de speedup sobre 1 GPU. Para o problema de L2R foi proposto um algoritmo manycore, que segue uma abordagem por bloco de threads} usando o conceito de Combinadic, e usa uma cache} com fingerprint para acelerar o processamento. Os speedups nos melhores casos foram de 508x sobre o serial, 9x sobre uma baseline em GPU, e 4x sobre nossa solução com 1 GPU ao usar 4 GPUs. Ao comparar com uma versão sem o combinadic, o speedup sobre ela foi de 4,4x com ambas versões usando 1 GPU e 3,9x usando 4. Estas soluções usaram estruturas de mapa de bits para acelerar a criação de regras de associação. Na busca top-k foram implementadas uma solução serial e uma multicore de um algoritmo manycore estado da arte para buscas exatas. Estas implementações serviram de baseline para nossa extensão desse algoritmo, que inclui o uso de multi-GPU, buscas em grupos e um balanceamento de carga intra-bloco. Os speedups obtidos foram de 2,7x sobre o algoritmo original, 17x sobre o serial, 4x sobre o multicore, e 4x sobre nossa versão ao usar 4 GPUs.