All pairs similarity search (APSS) is used in many web search and data mining applications. Previous work has used techniques such as comparison filtering, inverted indexing, and parallel accumulation of partial results. However, shuffling intermediate results can incur significant communication overhead as data scales up. This paper studies a scalable two-phase approach called Partition-based Similarity Search (PSS). The first phase is to partition the data and group vectors that are potentially similar. The second phase is to run a set of tasks where each task compares a partition of vectors with other candidate partitions. Due to data sparsity and the presence of memory hierarchy, accessing feature vectors during the partition comparison phase incurs significant overhead. This paper introduces a cache-conscious design for data layout and traversal to reduce access time through size-controlled data splitting and vector coalescing, and it provides an analysis to guide the choice of optimization parameters. The evaluation results show that for the tested datasets, the proposed approach can lead to an early elimination of unnecessary I/O and data communication while sustaining parallel efficiency with one order of magnitude of performance improvement and it can also be integrated with LSH for approximated APSS.

The previous two-phase method for searching versioned documents seeks a cost tradeoff by using non-positional information to rank document versions first. The second phase then re-ranks top document versions using positional information with fragment-based index compression. This paper proposes an alternative approach that uses cluster-based retrieval to quickly narrow the search scope guided by version representatives at Phase 1 and develops a hybrid index structure with adaptive runtime data traversal to speed up Phase 2 search. The hybrid scheme exploits the advantages of forward index and inverted index based on the term characteristics to minimize the time in extracting positional and other feature information during runtime search. This paper compares several indexing and data traversal options with different time and space tradeoffs and describes evaluation results to demonstrate their effectiveness. The experiment results show that the proposed scheme can be up-to about 4x as fast as the previous work on solid state drives while retaining good relevance.

Machine-learned classification and ranking techniques often use ensembles to aggregate partial scores of feature vectors for high accuracy and the runtime score computation can become expensive when employing a large number of ensembles. The previous work has shown the judicious use of memory hierarchy in a modern CPU architecture which can effectively shorten the time of score computation. However, different traversal methods and blocking parameter settings can exhibit different cache and cost behavior depending on data and architectural characteristics. It is very time-consuming to conduct exhaustive search for performance comparison and optimum selection. This paper provides an analytic comparison of cache blocking methods on their data access performance with an approximation and proposes a fast guided sampling scheme to select a traversal method and blocking parameters for effective use of memory hierarchy. The evaluation studies with three datasets show that within a reasonable amount of time, the proposed scheme can identify a highly competitive solution that significantly accelerates score calculation.

All pairs similarity search, used in many data mining and information retrieval applications, is a time consuming process. Although a partition-based approach accelerates this process by simplifying parallelism management and avoiding unnecessary I/O and comparison, it is still challenging to balance the computation load among parallel machines with a distributed architecture. This is mainly due to the variation in partition sizes and irregular dissimilarity relationship in large datasets. This paper presents a two-stage heuristic algorithm to improve the load balance and shorten the overall processing time. We analyze the optimality and competitiveness of the proposed algorithm and demonstrates its effectiveness using several datasets. We also describe a static partitioning algorithm to even out the partition sizes while detecting more dissimilar pairs. The evaluation results show that the proposed scheme outperforms a previously developed solution by up to 41% in the tested cases.

Multi-tree ensemble models have been proven to be effective for document ranking. Using a large number of trees can improve accuracy, but it takes time to calculate ranking scores of matched documents. This paper investigates data traversal methods for fast score calculation with a large ensemble. We propose a 2D blocking scheme for better cache utilization with simpler code structure compared to previous work. The experiments with several benchmarks show significant acceleration in score calculation without loss of ranking accuracy.