Efficient Similarity Search with Cache-Conscious Data Traversal

Similarity search is important for many data-intensive applications to identify a set of similar objects. Examples of such applications include near-duplicate detection and clustering, collaborative filtering for similarity-based recommendations, search query suggestion, and data cleaning. Conducting similarity search is a time-consuming process, especially when a massive amount of data is involved, and when all pairs are compared. Previous work has used comparison filtering, inverted indexing, and parallel accumulation of partial intermediate results to expedite its execution. However, shuffling intermediate results can incur significant communication overhead as data scales up.We have developed a fast two-stage partition-based approach for all-pairs similarity search which incorporates static partitioning, optimized load balancing, and cache-conscious data traversal. Static partitioning places dissimilar documents into different groups to eliminate unnecessary comparison between their content. To overcome the challenges introduced by skewed distribution of data partition sizes and irregular dissimilarity relationship in large datasets, we conduct computation load balancing for partitioned similarity search, with competitiveness analysis. These techniques can improve performance by one to two orders of magnitude with less unnecessary I/O and data communication and better load balance. We also discuss how to further accelerate similarity search by incorporating incremental computing and approximation methods such as Locality Sensitive Hashing. Because of data sparsity and irregularity, accessing feature vectors in memory for runtime comparison incurs significant overhead in modern memory hierarchy. We have designed and implemented cache-conscious algorithms to improve runtime efficiency in similarity search. The idea of optimizing data layout and traversal patterns is also applied to the search result ranking problem in runtime with multi-tree ensemble models

Wide baseline stereo matching with convex bounded-distortion constraints

Finding correspondences in wide baseline setups is a challenging problem. Existing approaches have focused largely on developing better feature descriptors for correspondence and on accurate recovery of epipolar line constraints. This paper focuses on the challenging problem of finding correspondences once approximate epipolar constraints are given. We introduce a novel method that integrates a deformation model. Specifically, we formulate the problem as finding the largest number of corresponding points related by a bounded distortion map that obeys the given epipolar constraints. We show that, while the set of bounded distortion maps is not convex, the subset of maps that obey the epipolar line constraints is convex, allowing us to introduce an efficient algorithm for matching. We further utilize a robust cost function for matching and employ majorization-minimization for its optimization. Our experiments indicate that our method finds significantly more accurate maps than existing approaches

Distributed top-k aggregation queries at large

Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper introduces novel optimization methods for top-k aggregation queries in such distributed environments. The optimizations can be applied to all algorithms that fall into the frameworks of the prior TPUT and KLEE methods. The optimizations address three degrees of freedom: 1) hierarchically grouping input lists into top-k operator trees and optimizing the tree structure, 2) computing data-adaptive scan depths for different input sources, and 3) data-adaptive sampling of a small subset of input sources in scenarios with hundreds or thousands of query-relevant network nodes. All optimizations are based on a statistical cost model that utilizes local synopses, e.g., in the form of histograms, efficiently computed convolutions, and estimators based on order statistics. The paper presents comprehensive experiments, with three different real-life datasets and using the ns-2 network simulator for a packet-level simulation of a large Internet-style network