数据库
Entity Matching (EM) is a core operation in the data integration pipeline, where records from different sources are compared to determine whether they refer to the same real-world entity. Recent work has incorporated domain information and…
Cloud database systems cannot rely on instance-local disks for write-ahead logging (WAL) durability, forcing WAL onto remote storage. Existing options are unsatisfying: remote block storage like EBS is easy to adopt but adds substantial…
The diverse formats of CSV and Parquet files in data lakes pose a significant challenge to traditional ETL, which relies on data engineers to pre-define a target database schema and build a complex pipeline for data integration. Moreover,…
Spatial pattern matching is the process of matching query entities and constraints with database entities and relations. It has many applications, including similar region search, housing market search, landmark search, and road network…
With the growing adoption of Confidential Computing, running databases in confidential virtual machines (CVMs) such as AMD SEV-SNP has become an attractive way to protect sensitive cloud data with minimal changes to legacy DBMSs. However,…
Modern table formats such as Apache Iceberg compute and store metadata-commit timestamps, record counts, and column-level statistics such as null counts and value bounds at write time as part of file writing. These statistics serve query…
Geo-distributed OLTP databases are widely deployed across cloud regions, yet current evaluation practices do not cover the challenges of this aspect. Existing benchmarks assume stable network conditions; they lack explicit settings for data…
Text-to-Visualization (Text-to-Vis) translates natural language queries into visualization query languages, enabling non-expert users to perform data analysis. However, most existing methods follow a one-shot paradigm that requires users to…
As server CPUs scale to dozens and now hundreds of cores per socket, parallel query engines must rethink how they redistribute data between threads. Partitioned operators such as hash joins and aggregations require frequent data…
In cloud data platforms, developers often encounter performance regressions that occur in specific tenant datasets. However, due to confidentiality constraints, they cannot access the original data, which makes it difficult to reproduce…
Oracle Exadata consolidates thousands of tenant databases onto shared storage infrastructure deployed at hundreds of customer sites worldwide. Oracle Multitenant architecture enables this extreme density, with thousands of tenant databases…
Approximate k-Nearest Neighbor (AKNN) search is widely used in vector databases. When vectors carry additional attributes (e.g., labels or numerical values), filtered AKNN search retrieves the nearest vectors to a query vector under…
Data transformation correctness is a fundamental challenge in data engineering: how can we verify that pipelines produce correct results before executing on production data? Existing practice relies on iterative testing over materialized…
Workload traces from cloud data warehouse providers reveal that standard benchmarks such as TPC-H and TPC-DS fail to capture key characteristics of real-world workloads, including query repetition and string-heavy queries. In this paper, we…
Large Language Model (LLM) serving systems must balance task performance against monetary cost. Two prominent optimization techniques have emerged independently: LLM routing, which directs each query to the most cost-effective model in a…
Recent advancements in large language models (LLMs) have significantly improved Natural Language to SQL (NL2SQL) tasks, yet most NL2SQL systems continue to rely on the autoregressive (AR) paradigm. The highly structured nature of SQL makes…
Real-time OLAP datastores are critical infrastructure for modern enterprises, powering interactive analytics on petabyte-scale datasets with subsecond latency requirements. As these systems become integral to service architectures,…
On-disk graph-based approximate nearest neighbor search (ANNS) is essential for large-scale, high-dimensional vector retrieval, yet its performance is widely recognized to be limited by the prohibitive I/O costs. Interestingly, we observed…
LLM-based agents for industrial asset operations show limited accuracy when reasoning over flat document stores. AssetOpsBench (KDD 2026) establishes that GPT-4 agents achieve 65% on 139 industrial maintenance scenarios backed by CouchDB,…
Reverse k nearest neighbor (RkNN) queries are fundamental in spatial databases, location-based analytics, and recommendation systems. Existing state-of-the-art techniques rely on spatial pruning supported by R-trees and their variants.…