The sheer volume of contents generated by today’s Internet services is stored in the cloud. The effective indexing method is important to provide the content to users on demand. The indexing method associating the user-generated metadata with the content is vulnerable to the inaccuracy caused by the low quality of the metadata. While the content-based indexing does not depend on the error-prone metadata, the state-of-the-art research focuses on developing descriptive features and misses the system-oriented considerations when incorporating these features into the practical cloud computing systems. We propose an Update-Efficient and Parallel-Friendly content-based indexing system, called Partitioned Hash Forest (PHF). The PHF system incorporates the state-of-the-art content-based indexing models and multiple system-oriented optimizations. PHF contains an approximate content-based index and leverages the hierarchical memory system to support the high volume of updates. Additionally, the content-aware data partitioning and lock-free concurrency management module enable the parallel processing of the concurrent user requests. We evaluate PHF in terms of indexing accuracy and system efficiency by comparing it with the state-of-the-art content-based indexing algorithm and its variances. We achieve the significantly better accuracy with less resource consumption, around 37% faster in update processing and up to 2.5[Formula: see text] throughput speedup in a multi-core platform comparing to other parallel-friendly designs.
GeoIRIS: Geospatial Information Retrieval and Indexing System—Content Mining, Semantics Modeling, and Complex Queries
