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1. ThalamusDB: Approximate Query Processing on Multi-Modal Data

   https://doi.org/10.1145/3654989  

   Jo, Saehan; Trummer, Immanuel (May 2024, Proceedings of the ACM on Management of Data)

   We introduce ThalamusDB, a novel approximate query processing system that processes complex SQL queries on multi-modal data. ThalamusDB supports SQL queries integrating natural language predicates on visual, audio, and text data. To answer such queries, ThalamusDB exploits a collection of zero-shot models in combination with relational processing. ThalamusDB utilizes deterministic approximate query processing, harnessing the relative efficiency of relational processing to mitigate the computational demands of machine learning inference. For evaluating a natural language predicate, ThalamusDB requests a small number of labels from users. User can specify their preferences on the performance objective regarding the three relevant metrics: approximation error, computation time, and labeling overheads. The ThalamusDB query optimizer chooses optimized plans according to user preferences, prioritizing data processing and requested labels to maximize impact. Experiments with several real-world data sets, taken from Craigslist, YouTube, and Netflix, show that ThalamusDB achieves an average speedup of 35.0x over MindsDB, an exact processing baseline, and outperforms ABAE, a sampling-based method, in 78.9% of cases. 

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2. RAGtime-PIANO: Efficient Secure Remote RAG

   Januszewicz, Antonia; Zhao, Jiachen; Jiang, Meng; Jung, Taeho (February 2026, IACR ePrint Archive)

   Bos, Joppe W; Celi, Sofia; Kannwischer, Matthias J (Ed.)

   Retrieval Augmented Generation (RAG) can enhance the performance of Large Language Models (LLMs) when used in conjunction with a comprehensive knowledge database. However, the space required to store the necessary information can be taxing when RAG is used locally. As such, the concept of RAG-as-a-Service (RaaS) has emerged, in which third-party servers can be used to process client queries via an external database. Unfortunately, using such a service would expose the client's query to a third party, making the product unsuitable for processing sensitive queries. Our scheme ensures that throughout the entire RAG processing, neither the query, any distances, nor retrieval information is known to the database hosting server. Using a two-pronged approach, we employ Fully Homomorphic Encryption (FHE) and Private Information Retrieval (PIR) to ensure complete security during RAG processing. FHE is used to maintain privacy during initial query processing, during which the query embedding is encrypted and sent to the server for k-means centroid scoring to obtain a similarity ranking. Then, a series of PIR queries is used to privately retrieve the centroid-associated embeddings and the top-ranked documents. A first-of-its-kind, lightweight, fully secure RAG protocol, RAGtime-PIANO, enables efficient secure RAG. 

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3. Demonstration of accelerating machine learning inference queries with correlative proxy models

   https://doi.org/10.14778/3554821.3554887  

   Yang, Zhihui; Huang, Yicong; Wang, Zuozhi; Gao, Feng; Lu, Yao; Li, Chen; Wang, X. Sean (August 2022, Proceedings of the VLDB Endowment)

   We will demonstrate a prototype query-processing engine, which utilizes correlations among predicates to accelerate machine learning (ML) inference queries on unstructured data. Expensive operators such as feature extractors and classifiers are deployed as user-defined functions (UDFs), which are not penetrable by classic query optimization techniques such as predicate push-down. Recent optimization schemes (e.g., Probabilistic Predicates or PP) build a cheap proxy model for each predicate offline, and inject proxy models in the front of expensive ML UDFs under the independence assumption in queries. Input records that do not satisfy query predicates are filtered early by proxy models to bypass ML UDFs. But enforcing the independence assumption may result in sub-optimal plans. We use correlative proxy models to better exploit predicate correlations and accelerate ML queries. We will demonstrate our query optimizer called CORE, which builds proxy models online, allocates parameters to each model, and reorders them. We will also show end-to-end query processing with or without proxy models. 

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4. 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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5. Shrinkwrap: efficient SQL query processing in differentially private data federations

   https://doi.org/10.14778/3291264.3291274  

   Bater, Johes; He, Xi; Ehrich, William; Machanavajjhala, Ashwin; Rogers, Jennie (November 2018, Proceedings of the VLDB Endowment)

   null (Ed.)

   A private data federation is a set of autonomous databases that share a unified query interface offering in-situ evaluation of SQL queries over the union of the sensitive data of its members. Owing to privacy concerns, these systems do not have a trusted data collector that can see all their data and their member databases cannot learn about individual records of other engines. Federations currently achieve this goal by evaluating queries obliviously using secure multiparty computation. This hides the intermediate result cardinality of each query operator by exhaustively padding it. With cascades of such operators, this padding accumulates to a blow-up in the output size of each operator and a proportional loss in query performance. Hence, existing private data federations do not scale well to complex SQL queries over large datasets. We introduce Shrinkwrap, a private data federation that offers data owners a differentially private view of the data held by others to improve their performance over oblivious query processing. Shrinkwrap uses computational differential privacy to minimize the padding of intermediate query results, achieving up to a 35X performance improvement over oblivious query processing. When the query needs differentially private output, Shrinkwrap provides a trade-off between result accuracy and query evaluation performance. 

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