Demo Data from VLDB 2026 / reviewers in the wild / expert
Juseong Yun
dblp:239/8331
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Storage systems · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Storage systems
crash recovery |
0.4 | 1 | 2019 | DASH: Database Shadowing for Mobile DBMS · Proc. VLDB Endow. 2019 |
Storage systems
shadow paging |
0.4 | 1 | 2019 | DASH: Database Shadowing for Mobile DBMS · Proc. VLDB Endow. 2019 |
Storage systems
storage reliability |
0.4 | 1 | 2019 | DASH: Database Shadowing for Mobile DBMS · Proc. VLDB Endow. 2019 |
Storage systems › data management
mobile DBMS |
0.1 | 1 | 2019 | DASH: Database Shadowing for Mobile DBMS · Proc. VLDB Endow. 2019 |
Methods — techniques the papers use, named apart from their topics
write-ahead logging · 0.4shadow paging · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | DASH: Database Shadowing for Mobile DBMSabstractIn this work, we propose Database Shadowing, or DASH, which is a new crash recovery technique for SQLite DBMS. DASH is a hybrid mixture of classical shadow paging and logging. DASH addresses four major issues in the current SQLite journal modes: the performance and write amplification issues of the rollback mode and the storage space requirement and tail latency issues of the WAL mode. DASH exploits two unique characteristics of SQLite: the database files are small and the transactions are entirely serialized. DASH consists of three key ingredients Aggregate Update, Atomic Exchange and Version Reset. Aggregate Update eliminates the redundant write overhead and the requirement to maintain multiple snapshots both of which are inherent in the out-of-place update. Atomic Exchange resolves the overhead of updating the locations of individual database pages exploiting order-preserving nature of the metadata update operation in modern filesystem. Version Reset makes the result of the Atomic Exchange durable without relying on expensive filesystem journaling. The salient aspect of DASH lies in its simplicity and compatibility with the legacy. DASH does not require any modifications in the underlying filesystem or the database organization. It requires only 451 LOC to implement. In Cyclomatic Complexity score, which represents software complexity, DASH renders 33% lower (simpler) mark than PERSIST and WAL modes of SQLite. We implement DASH for SQLite on Android and extensively evaluate it on widely used smartphone devices. DASH yields 4x performance gain over PERSIST mode (default journaling mode). Compared to WAL mode (the fastest journaling mode), DASH uses only 2.5% of the storage space on average. The transaction latency of DASH at 99.9% is one fourth of that of WAL mode. Youjip Won, Sundoo Kim, Juseong Yun, Dam Quang Tuan, Jiwon Seo 0002 |
Proc. VLDB Endow. | 3 |