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1. mmLSH: A Practical and Efficient Technique for Processing Approximate Nearest Neighbor Queries on Multimedia Data

   https://doi.org/10.1007/978-3-030-60936-8_4  

   Jafari, O ; Nagarkar, P ; Montano, J ( October 2020 , Similarity Search and Applications. SISAP 2020)

   null (Ed.)

   Many large multimedia applications require efficient processing of nearest neighbor queries. Often, multimedia data are represented as a collection of important high-dimensional feature vectors. Existing Locality Sensitive Hashing (LSH) techniques require users to find top-k similar feature vectors for each of the feature vectors that represent the query object. This leads to wasted and redundant work due to two main reasons: 1) not all feature vectors may contribute equally in finding the top-k similar multimedia objects, and 2) feature vectors are treated independently during query processing. Additionally, there is no theoretical guarantee on the returned multimedia results. In this work, we propose a practical and efficient indexing approach for finding top-k approximate nearest neighbors for multimedia data using LSH called mmLSH, which can provide theoretical guarantees on the returned multimedia results. Additionally, we present a buffer-conscious strategy to speed up the query processing. Experimental evaluation shows significant gains in performance time and accuracy for different real multimedia datasets when compared against state-of-the-art LSH techniques. 

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2. Efficient Bitmap-based Indexing and Retrieval of Similarity Search Image Queries

   https://doi.org/10.1109/SSIAI49293.2020.9094616  

   Jafari, Omid ; Nagarkar, Parth ; Montano, Jonathan ( March 2020 , 2020 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI))

   null (Ed.)

   Finding similar images is a necessary operation in many multimedia applications. Images are often represented and stored as a set of high-dimensional features, which are extracted using localized feature extraction algorithms. Locality Sensitive Hashing is one of the most popular approximate processing techniques for finding similar points in high-dimensional spaces. Locality Sensitive Hashing (LSH) and its variants are designed to find similar points, but they are not designed to find objects (such as images, which are made up of a collection of points) efficiently. In this paper, we propose an index structure, Bitmap-Image LSH (bImageLSH), for efficient processing of high-dimensional images. Using a real dataset, we experimentally show the performance benefit of our novel design while keeping the accuracy of the image results high. 

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3. Continuously Adaptive Similarity Search

   https://doi.org/10.1145/3318464.3380601  

   Zhang, Huayi ; Cao, Lei ; Yan, Yizhou ; Madden, Samuel ; Rundensteiner, Elke A. ( June 2020 , Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data)

   Similarity search is the basis for many data analytics techniques, including k-nearest neighbor classification and outlier detection. Similarity search over large data sets relies on i) a distance metric learned from input examples and ii) an index to speed up search based on the learned distance metric. In interactive systems, input to guide the learning of the distance metric may be provided over time. As this new input changes the learned distance metric, a naive approach would adopt the costly process of re-indexing all items after each metric change. In this paper, we propose the first solution, called OASIS, to instantaneously adapt the index to conform to a changing distance metric without this prohibitive re-indexing process. To achieve this, we prove that locality-sensitive hashing (LSH) provides an invariance property, meaning that an LSH index built on the original distance metric is equally effective at supporting similarity search using an updated distance metric as long as the transform matrix learned for the new distance metric satisfies certain properties. This observation allows OASIS to avoid recomputing the index from scratch in most cases. Further, for the rare cases when an adaption of the LSH index is shown to be necessary, we design an efficient incremental LSH update strategy that re-hashes only a small subset of the items in the index. In addition, we develop an efficient distance metric learning strategy that incrementally learns the new metric as inputs are received. Our experimental study using real world public datasets confirms the effectiveness of OASIS at improving the accuracy of various similarity search-based data analytics tasks by instantaneously adapting the distance metric and its associated index in tandem, while achieving an up to 3 orders of magnitude speedup over the state-of-art techniques. 

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4. FoggyCache: Cross-Device Approximate Computation Reuse

   https://doi.org/10.1145/3241539.3241557  

   Guo, Peizhen ; Hu, Bo ; Li, Rui ; Hu, Wenjun ( October 2018 , Mobicom'18)

   Mobile and IoT scenarios increasingly involve interactive and computation intensive contextual recognition. Existing optimizations typically resort to computation offloading or simplified on-device processing. Instead, we observe that the same application is often invoked on multiple devices in close proximity. Moreover, the application instances often process similar contextual data that map to the same outcome. In this paper, we propose cross-device approximate computation reuse, which minimizes redundant computation by harnessing the “equivalence” between different input values and reusing previously computed outputs with high confidence. We devise adaptive locality sensitive hashing (A-LSH) and homogenized k nearest neighbors (H-kNN). The former achieves scalable and constant lookup, while the latter provides high-quality reuse and tunable accuracy guarantee. We further incorporate approximate reuse as a service, called FoggyCache, in the computation offloading runtime. Extensive evaluation shows that, when given 95% accuracy target, FoggyCache consistently harnesses over 90% of reuse opportunities, which translates to reduced computation latency and energy consumption by a factor of 3 to 10. 

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5. Arrays of (locality-sensitive) Count Estimators (ACE): Anomaly Detection on the Edge

   https://doi.org/10.1145/3178876.3186056  

   Luo, Chen ; Shrivastava, Anshumali ( January 2018 , Proceedings of the 2018 World Wide Web Conference)

   Anomaly detection is one of the frequent and important subroutines deployed in large-scale data processing applications. Even being a well-studied topic, existing techniques for unsupervised anomaly detection require storing significant amounts of data, which is prohibitive from memory, latency and privacy perspectives, especially for small mobile devices which has ultra-low memory budget and limited computational power. In this paper, we propose ACE (Arrays of (locality-sensitive) Count Estimators) algorithm that can be 60x faster than most state-of-the-art unsupervised anomaly detection algorithms. In addition, ACE has appealing privacy properties. Our experiments show that ACE algorithm has significantly smaller memory footprints (∠ 4MB in our experiments) which can exploit Level 3 cache of any modern processor. At the core of the ACE algorithm, there is a novel statistical estimator which is derived from the sampling view of Locality Sensitive Hashing (LSH). This view is significantly different and efficient than the widely popular view of LSH for near-neighbor search. We show the superiority of ACE algorithm over 11 popular baselines on 3 benchmark datasets, including the KDD-Cup99 data which is the largest available public benchmark comprising of more than half a million entries with ground truth anomaly labels. 

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