The past decade has been marked by the (continued) explosion of diverse data content and the fast development of intelligent data analytics techniques. One problem we identified in the mid-2000s was similarity search of feature-rich data. The challenge here was achieving both high accuracy and high efficiency in high-dimensional spaces. Locality sensitive hashing (LSH), which uses certain random space partitions and hash table lookups to find approximate nearest neighbors, was a promising approach with theoretical guarantees. But LSH alone was insufficient since a large number of hash tables were required to achieve good search quality. Building on an idea of Panigrahy, our multi-probe LSH method introduced the idea of intelligent probing. Given a query object, we strategically probe its neighboring hash buckets (in a query-dependent fashion) by calculating the statistical probabilities of similar objects falling into each bucket. Such intelligent probing can significantly reduce the number of hash tables while achieving high quality. In this paper, we revisit the problem motivation, the challenges, the key design considerations of multi-probe LSH, as well as discuss recent developments in this space and some questions for further research.

We present the design, implementation, and evaluation of Direct File System (DFS) for virtualized flash storage. Instead of using traditional layers of abstraction, our layers of abstraction are designed for directly accessing flash memory devices. DFS has two main novel features. First, it lays out its files directly in a very large virtual storage address space provided by FusionIO's virtual flash storage layer. Second, it leverages the virtual flash storage layer to perform block allocations and atomic updates. As a result, DFS performs better and is much simpler than a traditional Unix file system with similar functionalities. Our microbenchmark results show that DFS can deliver 94,000 I/O operations per second (IOPS) for direct reads and 71,000 IOPS for direct writes with the virtualized flash storage layer on FusionIO's ioDrive. For direct access performance, DFS is consistently better than ext3 on the same platform, sometimes by 20%. For buffered access performance, DFS is also consistently better than ext3, and sometimes by over 149%. Our application benchmarks show that DFS outperforms ext3 by 7% to 250% while requiring less CPU power.

Although Locality-Sensitive Hashing (LSH) is a promising approach to similarity search in high-dimensional spaces, it has not been considered practical partly because its search quality is sensitive to several parameters that are quite data dependent. Previous research on LSH, though obtained interesting asymptotic results, provides little guidance on how these parameters should be chosen, and tuning parameters for a given dataset remains a tedious process.

Using small, pre-computed tables is a well-known technique for improving the performance of expensive computations with small operands. However, as the performance gap between CPU and memory continues to increase, table lookup in main memory may no longer be beneficial. Instead of doing table lookups in memory, this paper proposes table lookup instruction support to accelerate Rabin fingerprinting and Reed-Solomon erasure coding over Galois fields. Both are core computations in emerging main-stream systems such as bandwidth optimized protocol engines, capacity optimized storage systems, and content-distribution networks. We show that the proposed instructions are both beneficial and easy to implement. A simple table lookup instruction that addresses four 256-entry tables in parallel can speed up Rabin fingerprinting and anchoring by a factor of 2.6 and Reed-Solomon coding by a factor of 1.5.

Sketches are compact data structures that can be used to estimate properties of the original data in building large-scale search engines and data analysis systems. Recent theoretical and experimental studies have shown that sketches constructed from feature vectors using randomized projections can effectively approximate L1 distance on the feature vectors with the Hamming distance on their sketches. Furthermore, such sketches can achieve good filtering accuracy while reducing the metadata space requirement and speeding up similarity searches by an order of magnitude. However, it is not clear how to choose the size of the sketches since it depends ondata type, dataset size, and desired filtering quality. In real systems designs, it is necessary to understand how to choose sketch size without the dataset, or at least without the whole datase.

Building content-based search tools for feature-rich data has been a challenging problem because feature-rich data such as audio recordings, digital images, and sensor data are inherently noisy and high dimensional. Comparing noisy data requires comparisons based on similarity instead of exact matches, and thus searching for noisy data requires similarity search instead of exact search.The Ferret toolkit is designed to help system builders quickly construct content-based similarity search systems for feature-rich data types. The key component of the toolkit is a content-based similarity search engine for generic, multi-feature object representations. To solve the similarity search problem in high-dimensional spaces, we have developed approximation methods inspired by recent theoretical results on dimension reduction. The search engine constructs sketches from feature vectors as highly compact data structures for matching, filtering and ranking data objects. The toolkit also includes several other components to help system builders address search system infrastructure issues. We have implemented the toolkit and used it to successfully construct content-based similarity search systems for four data types: audio recordings, digital photos, 3D shape models and genomic microarray data.

At the 1999 Workshop on Hot Topics in Operating Systems (HotOS VII), the attendees reached consensus that the most important issue facing the OS research community was "No-Futz" computing; eliminating the ongoing "futzing" that characterizes most systems today. To date, little research has been accomplished in this area. Our goal in this paper is to focus the research community on the challenges we face if we are to design systems that are truly futz-free, or even low-futz.