提出了一种可扩展的局部敏感哈希索引(SLSH),以解决高维动态数据索引中,由于数据集大小及分布特征无法确定而导致索引效率降低的问题.SLSH架构于E2LSH之上,继承了其对高维数据索引速度快,并可直接对欧式空间上的数据点进行索引的特点.为了使得哈希索引具有动态的相似性区分能力,SLSH修改了E2LSH的哈希族,通过哈希桶容量约束自适应调节哈希参数.因此对于分布密度动态变化的数据空间,SLSH也能够给出鲁棒的划分.

A scalable locality sensitive hashing (SLSH) scheme is proposed to solve the problem of indexing high-dimensional data for dynamic datasets. The dynamic property destabilizes the size of the dataset, fuzzes up the tendency of data distribution, and conduces to the retrogression of retrieval performance. SLSH inherits two very convenient properties from the novel E2LSH that SLSH can rapidly work on data that is extremely high-dimensional and directly works on Euclidean space. For the purpose of adaptively fit the dynamic data distribution, the original hash family in E2LSH is altered for SLSH. A constraint of hash bucket capacity is applied for the hash parameters adjustment. As a result, SLSH provides robust partitions in the high-dimensional space for the dynamic data.

国家自然科学基金(61370121);国家高技术研究发展计划(863)(2014AA015102)