2 research outputs found
Modifying Hamming Spaces for Efficient Search
We focus on the efficient search for the most similar bit strings to a given query in the Hamming space. The distance of this space can be lower-bounded by a function based on a difference of the number of ones in the compared strings, i.e. their weights. Recently, such property has been successfully used by the Hamming Weight Tree (HWT) indexing structure. We propose modifications of the bit strings that preserve pairwise Hamming distances but improve the tightness of these lower bounds, so the query evaluation with the HWT is several times faster. We also show that the unbalanced bit strings, recently reported to provide similar quality of search as the traditionally used balanced bit strings, are more easy to index with the HWT. Combined with the distance preserving modifications, the HWT query evaluation can be more than one order of magnitude faster than the HWT baseline. Finally, we show that such modifications are useful even for a very complex data where the search with the HWT is slower than a sequential search
Indexing Metric Spaces for Exact Similarity Search
With the continued digitalization of societal processes, we are seeing an
explosion in available data. This is referred to as big data. In a research
setting, three aspects of the data are often viewed as the main sources of
challenges when attempting to enable value creation from big data: volume,
velocity and variety. Many studies address volume or velocity, while much fewer
studies concern the variety. Metric space is ideal for addressing variety
because it can accommodate any type of data as long as its associated distance
notion satisfies the triangle inequality. To accelerate search in metric space,
a collection of indexing techniques for metric data have been proposed.
However, existing surveys each offers only a narrow coverage, and no
comprehensive empirical study of those techniques exists. We offer a survey of
all the existing metric indexes that can support exact similarity search, by i)
summarizing all the existing partitioning, pruning and validation techniques
used for metric indexes, ii) providing the time and storage complexity analysis
on the index construction, and iii) report on a comprehensive empirical
comparison of their similarity query processing performance. Here, empirical
comparisons are used to evaluate the index performance during search as it is
hard to see the complexity analysis differences on the similarity query
processing and the query performance depends on the pruning and validation
abilities related to the data distribution. This article aims at revealing
different strengths and weaknesses of different indexing techniques in order to
offer guidance on selecting an appropriate indexing technique for a given
setting, and directing the future research for metric indexes