Galactica: A Simulation Database for Open Science in Astrophysics
Galactica is an open-source platform designed to facilitate the sharing and reuse of astrophysics simulation data, promoting open science practices within the field.
Comparing Lua API Elegance and Raw Speed in Redis and YottaDB Using a 3n+1 Sequence Benchmark
While both Redis and YottaDB offer Lua APIs for database interaction, YottaDB's approach, particularly its in-process data sharing and elegant transaction handling, results in significantly faster performance, as demonstrated by a 3n+1 sequence benchmark.
Dirigo: A Design Science Approach to Extracting High-Quality Object-Centric Event Logs Using Object-Role Modeling
This paper proposes Dirigo, a novel method for extracting high-quality object-centric event logs (OCEL) that conform to the OCEL 2.0 standard, leveraging Object-Role Modeling (ORM) for conceptual clarity and addressing limitations of existing extraction methods.
데이터 마켓에 대한 포괄적인 조사
데이터 마켓은 데이터 상품의 가치를 극대화하기 위해 데이터 소유자, 구매자, 브로커 및 정책 입안자 등 다양한 주체가 상호 작용하는 복잡한 생태계입니다.
Efficiently Finding Joinable Spatial Datasets Across Multiple Sources: Addressing Overlap, Coverage, and Efficiency
This research paper introduces two novel spatial joinable search problems, Overlap Joinable Search Problem (OJSP) and Coverage Joinable Search Problem (CJSP), and proposes an efficient distributed framework with a new index structure, DITS, to solve them across multiple data sources.
Ultraverse: A System for Faster What-If Analysis in Database-Driven Web Applications
Ultraverse is a novel framework that accelerates what-if analysis in database-intensive web applications by combining application and database layers, using dynamic symbolic execution for code translation, and employing query dependency analysis for efficient replay.
LSMGraph：一種採用多級 CSR 的高效能動態圖儲存系統
LSMGraph 是一種結合了 LSM-tree 的寫入效能和 CSR 的讀取效能優勢，專為高效處理動態圖資料而設計的新型儲存系統。
LSMGraph: Enhancing Dynamic Graph Storage Performance with a Multi-Level CSR Approach
LSMGraph is a novel dynamic graph storage system that addresses the limitations of existing systems by combining the write efficiency of LSM-trees with the read efficiency of CSR, resulting in significant performance improvements for both graph updates and analytical workloads.
학습된 데이터베이스 작업의 오류 보장을 위한 연구: 인덱싱, 카디널리티 추정, 범위 합계 추정에 필요한 모델 크기 분석
학습된 데이터베이스 작업 (인덱싱, 카디널리티 추정, 범위 합계 추정)에서 원하는 정확도를 달성하기 위해 필요한 모델 크기에 대한 이론적 하한선을 제시하고, 최악의 경우와 평균적인 경우의 오류 시나리오를 고려하여 데이터 크기, 데이터 차원 및 정확도 간의 관계를 분석합니다.
Guaranteed Error Bounds for Learned Database Operations: A Theoretical Study of Indexing, Cardinality Estimation, and Range-Sum Estimation
This paper establishes the first theoretical lower bounds on the model size required for learned database operations (indexing, cardinality estimation, and range-sum estimation) to achieve guaranteed error bounds, demonstrating the relationship between model size, data size, and accuracy.
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