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1. Fine-Grained Provenance for Matching & ETL

   https://doi.org/10.1109/ICDE.2019.00025  

   Zheng, Nan; Alawini, Abdussalam; Ives, Zachary G. (April 2019, 2019 IEEE 35th International Conference on Data Engineering (ICDE))

   Data provenance tools capture the steps used to produce analyses. However, scientists must choose among workflow provenance systems, which allow arbitrary code but only track provenance at the granularity of files; provenance APIs, which provide tuple-level provenance, but incur overhead in all computations; and database provenance tools, which track tuple-level provenance through relational operators and support optimization, but support a limited subset of data science tasks. None of these solutions are well suited for tracing errors introduced during common ETL, record alignment, and matching tasks - for data types such as strings, images, etc. Scientists need new capabilities to identify the sources of errors, find why different code versions produce different results, and identify which parameter values affect output. We propose PROVision, a provenance-driven troubleshooting tool that supports ETL and matching computations and traces extraction of content within data objects. PROVision extends database-style provenance techniques to capture equivalences, support optimizations, and enable selective evaluation. We formalize our extensions, implement them in the PROVision system, and validate their effectiveness and scalability for common ETL and matching tasks. 

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2. RelGNN: Composite Message Passing for Relational Deep Learning

   Chen, Tianlang; Kanatsoulis, Charilaos; Leskovec, Jure (July 2025, International Conference on Machine Learning)

   Predictive tasks on relational databases are critical in real-world applications spanning e-commerce, healthcare, and social media. To address these tasks effectively, Relational Deep Learning (RDL) encodes relational data as graphs, enabling Graph Neural Networks (GNNs) to exploit relational structures for improved predictions. However, existing RDL methods often overlook the intrinsic structural properties of the graphs built from relational databases, leading to modeling inefficiencies, particularly in handling many-tomany relationships. Here we introduce RELGNN, a novel GNN framework specifically designed to leverage the unique structural characteristics of the graphs built from relational databases. At the core of our approach is the introduction of atomic routes, which are simple paths that enable direct single-hop interactions between the source and destination nodes. Building upon these atomic routes, RELGNN designs new composite message passing and graph attention mechanisms that reduce redundancy, highlight key signals, and enhance predictive accuracy. RELGNN is evaluated on 30 diverse real-world tasks from RELBENCH (Fey et al., 2024), and achieves state-of-the-art performance on the vast majority of tasks, with improvements of up to 25%. 

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3. AQUAMAN: An Analytic-Query Offloading Machine

   Xu, Shuotao; Bourgeat, Thomas; Huang, Tianhao; Kim, Hojun; Lee, Sungjin.; Arvind, A. (October 2020, Proceedings of the 53rd Annual IEEE/ACM International Symposium on Microarchitecture)

   null (Ed.)

   Analytic workloads on terabyte data-sets are often run in the cloud, where application and storage servers are separate and connected via network. In order to saturate the storage bandwidth and to hide the long storage latency, such a solution requires an expensive server cluster with sufficient aggregate DRAM capacity and hardware threads. An alternative solution is to push the query computation into storage servers. In this paper we present an in-storage Analytics QUery Offloading MAchiNe (AQUOMAN) to “offload” most SQL operators, including multi-way joins, to SSDs. AQUOMAN executes Table Tasks, which apply a static dataflow graph of SQL operators to relational tables to produce an output table. Table Tasks use a streaming computation model, which allows AQUOMAN to process queries with a reasonable amount of DRAM for intermediate results. AQUOMAN is a general analytic query processor, which can be integrated in the database software stack transparently. We have built a prototype of AQUOMAN in FPGAs, and using TPC-H benchmarks on 1TB data sets, shown that a single instance of 1TB AQUOMAN disk, on average, can free up 70% CPU cycles and reduce DRAM usage by 60%. One way to visualize this saving is to think that if we run queries sequentially and ignore inter-query page cache reuse, MonetDB running on a 4-core, 16GB-DRAM machine with AQUOMAN augmented SSDs performs, on average, as well as a MonetDB running on a 32-core, 128GB-DRAM machine with standard SSDs. 

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4. Relational Diagrams and the Pattern Expressiveness of Relational Languages

   https://doi.org/10.1145/3733620.3733637  

   Gatterbauer, Wolfgang; Dunne, Cody (April 2025, ACM SIGMOD Record)

   Comparing relational languages by their logical expressiveness is well understood. Less understood is how to compare relational languages by their ability to represent relational query patterns. Indeed, what are query patterns other than ''a certain way of writing a query''? And how can query patterns be defined across procedural and declarative languages, irrespective of their syntax? Our SIGMOD 2024 paper proposes a semantic definition of relational query patterns that uses a variant of structure-preserving mappings between the relational tables of queries. This formalism allows us to analyze the relative pattern expressiveness of relational languages. Notably, for the nondisjunctive language fragment, we show that relational calculus (RC) can express a larger class of patterns than the basic operators of relational algebra (RA). We also propose Relational Diagrams, a complete and sound diagrammatic representation of safe relational calculus. These diagrams can represent all query patterns for unions of non-disjunctive queries, in contrast to visual query representations that derive visual marks from the basic operators of algebra. Our anonymously preregistered user study shows that Relational Diagrams allow users to recognize relational patterns meaningfully faster and more accurately than they can with SQL. 

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5. Generating Succinct Descriptions of Database Schemata for Cost-Efficient Prompting of Large Language Models

   https://doi.org/10.14778/3681954.3682017  

   Trummer, Immanuel (July 2024, Proceedings of the VLDB Endowment)

   Using large language models (LLMs) for tasks like text-to-SQL translation often requires describing the database schema as part of the model input. LLM providers typically charge as a function of the number of tokens read. Hence, reducing the length of the schema description saves money at each model invocation. This paper introduces Schemonic, a system that automatically finds concise text descriptions of relational database schemata. By introducing abbreviations or grouping schema elements with similar properties, Schemonic typically finds descriptions that use significantly fewer tokens than naive schema representations. Internally, Schemonic models schema compression as a combinatorial optimization problem and uses integer linear programming solvers to find guaranteed optimal or near-optimal solutions. It speeds up optimization by starting optimization from heuristic solutions and reducing the search space size via pre-processing. The experiments on TPC-H, SPIDER, and Public-BI demonstrate that Schemonic reduces schema description length significantly, along with fees for reading them, without reducing the accuracy in tasks such as text-to-SQL translation. 

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