Demo Data from EDBT 2026 / reviewers in the wild / expert
Yunrui Li
dblp:93/1581
· DBLP profile ↗
9ranked-venue papers
3as first author
2since 2021 · last 2026
0009-0006-6052-2304ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 4 · 1 first-author · 1 since 2021Artificial intelligence and machine learning · 3 · 1 first-author · 2 since 2021Computer networks · 1 · 1 first-authorHuman-computer interaction and ubiquitous computing · 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.
| Databases, data mining, and information retrieval
2 papers |
Database system architecture and tuning · 40% Distributed and cloud data management · 29% Indexing and storage engines · 24% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Indexing and storage engines
column store |
0.4 | 1 | 2020 | Oracle Database In-Memory on Active Data Guard: Real-time Analytics on a Standby Database · ICDE 2020 |
Distributed and cloud data management
data replication |
0.4 | 1 | 2020 | Oracle Database In-Memory on Active Data Guard: Real-time Analytics on a Standby Database · ICDE 2020 |
Database system architecture and tuning
main-memory database |
0.4 | 1 | 2020 | Oracle Database In-Memory on Active Data Guard: Real-time Analytics on a Standby Database · ICDE 2020 |
Database system architecture and tuning › parallel database system
shared-disk clusters |
0.3 | 1 | 2017 | Improving Predictable Shared-Disk Clusters Performance for Database Clouds · ICDE 2017 |
Transaction processing and concurrency control
OLTP |
0.1 | 1 | 2020 | Oracle Database In-Memory on Active Data Guard: Real-time Analytics on a Standby Database · ICDE 2020 |
Distributed and cloud data management
high availability |
0.1 | 1 | 2017 | Improving Predictable Shared-Disk Clusters Performance for Database Clouds · ICDE 2017 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | EEG-driven natural image reconstruction with regional semantic awareness
Wenhui Zhou 0001, Yunrui Li, Guojun Dai, Lili Lin |
Pattern Recognit. | 4 |
| 2025 | Autism Detection by Analyzing Handwriting Characteristics of Chinese Characters via Deep Learning Models
Yunrui Li, Jasin Wong, Eva E. Chen, Syauki Aulia Thamrin, Arbee L. P. Chen |
DaWaK | 1 |
| 2020 | Oracle Database In-Memory on Active Data Guard: Real-time Analytics on a Standby DatabaseabstractOracle Database In-Memory (DBIM) provides orders of magnitude speedup for analytic queries with its highly compressed, transactionally consistent, memory-optimized Column Store. Customers can use Oracle DBIM for making real-time decisions by analyzing vast amounts of data at blazingly fast speeds. Active Data Guard (ADG) is Oracle's comprehensive solution for high-availability and disaster recovery for the Oracle Database. Oracle ADG eliminates the high cost of idle redundancy by allowing reporting applications, ad-hoc queries and data extracts to be offloaded to the synchronized, physical Standby database replicated using Oracle ADG. In Oracle 12.2, we extended the DBIM advantage to Oracle ADG architecture. DBIM-on-ADG significantly boosts the performance of analytic, read-only workloads running on the physical Standby database, while the Primary database continues to process high-speed OLTP workloads. Customers can partition their data across the In-Memory Column Stores on the Primary and Standby databases based on access patterns, and reap the benefits of fault-tolerance as well as workload isolation without compromising on critical performance SLAs. In this paper, we explore and address the key challenges involved in building the DBIM-on-ADG infrastructure, including synchronized maintenance of the In-Memory Column Store on the Standby database, with high-speed OLTP activity continuously modifying data on the Primary database. Sukhada Pendse, Vasudha Krishnaswamy, Kartik Kulkarni, Yunrui Li, Tirthankar Lahiri, Vivekanandhan Raja, Mahesh Girkar, Akshay Kulkarni |
ICDE | 4 |
| 2020 | Channel fading measurement and statistics of the eCall minimum set of data transmissionabstractThis study presents a testbed for the emergency call (eCall) system satisfying the 3GPP TS 26.267/268/269 standard. Experiments are performed with the in‐vehicle system testbed in a laboratory or a car travelling in city, suburb, countryside or freeway. Sufficient data of the emergency communication signal is recorded and processed. Fading statistics of the received signal after power control are found and discussed with cumulative distribution function of the received signal power, level crossing rate and average fade duration. It is found that with probability less or equal to , fading and attenuation can vary from dB for the continuous wave (CW) signal at 500 Hz to dB for the CW signal at 2000 Hz. It is recommended to move the CW signals at 500 and 800 Hz for detection and synchronisation in the 3GPP standard to be at 1500 and 2000 Hz, respectively. This will give 9.5 dB improvement in detection and synchronisation. The longest fade duration observed is 540 ms. The 64 ms synchronisation signal in the current standard is insufficient to handle the fading. It is shown that fading is minimised in the frequency range [1000, 2500] Hz for the 3GPP TS 26.267 system. Yunrui Li, John Q. Liu, Jacob C. Brandenburg |
IET Commun. | 1 |
| 2017 | Buddy Instance - A Mechanism for Increasing Availability in Shared-Disk Clusters
Anjan Kumar Amirishetty, Yunrui Li, Tolga Yurek, Mahesh Girkar, Wilson Chan, Graham Ivey, Vsevolod Panteleenko |
EDBT | 2 |
| 2017 | Improving Predictable Shared-Disk Clusters Performance for Database CloudsabstractOracle's Real Application Cluster (RAC) allows multiple database instances to run on different server nodes in a cluster against a shared set of data files. A critical aspect of an Oracle RAC system is that of instance recovery. When a node suffers from a hardware failure, or a database instance suffers from a software failure, instance recovery is performed by a surviving instance to ensure that the database remains in a consistent state. High-availability comes from the surviving database instances, each running on a surviving node, that are still able to provide database services. During instance recovery, the set of database resources that are in need of recovery must be identified, locked and then repaired. Until such time as the identification and locking of these resources has been done, Oracle needs to block any requests by database clients to all database resources. The whole database appears to be frozen during this time, a period that is called application brown-out. In the interests of availability it is therefore important that instance recovery endeavors to keep this period of identification and locking as short as possible. In doing so, not only is the brown-out period reduced, but also the overall time to make available those resources that need repair, is reduced. This paper describes the use of a Buddy Instance, a mechanism that significantly reduces the brown-out time and therefore also, the duration of instance recovery. Each database instance has a buddy database instance whose purpose is to construct in-memory metadata that describes the resources needing recovery, on a continuous basis at run-time. In the event of node or instance failure, the buddy instance for the failed instance uses the in-memory metadata in performing instance recovery. The buddy instance mechanism was introduced in the 12.2.0.1 release of Oracle Database to minimize and potentially eliminate the period of identification of resources needing recovery. This mechanism was enhanced in the 12.2.0.2 release of Oracle Database by the construction of a Bloom filter that temporarily serves the purpose of locking the identified resources, thereby enabling the database to be made available sooner than was previously the case. Anjan Kumar Amirishetty, Yunrui Li, Tolga Yurek, Mahesh Girkar, Wilson Chan, Graham Ivey, Vsevolod Panteleenko |
ICDE | 2 |
| 2016 | Fading Statistics of Voice Channel for the European Union Emergency CallabstractThe European Union (EU) emergency call (eCall) employs the GSM voice channel for vehicle emergency data transmission. However, fading statistics of the GSM voice channel after power control has not been discussed previously. This paper reports the power measurements for the end-to-end GSM voice channel with an in-vehicle system (IVS) transmitter in laboratory or on freeway. The data reveals that strong fading still exists in the GSMvoice channel after power control, which can cause severe packet loss and delay in the emergency call. Yunrui Li, John Q. Liu |
VTC Fall | 1 |
| 2016 | A novel multiobjective particle swarm optimization algorithm for signed network community detection
Zhaoxing Li, Lile He, Yunrui Li |
Appl. Intell. | 3 |
| 2014 | A collaborative production model for products with components of different colorsabstractIn traditional manufacture enterprises, a popular production mode is parallel lines within a department and sequential between departments. Such production mode may cause a large buffer between departments, long and complex transportation routes, and components mismatched at the assembly department. Based on the concept of collaborative production, this paper proposes a mathematical model and relative algorithm for collaborative production of products with components of different colors. A case study has been used to validate the proposed model, and the related future value stream mapping has been reached, compared with the current value stream mapping. The analysis results show that the proposed approach is promising. Wenfeng Li 0001, Ye Zhong, Yunrui Li |
CSCWD | 4 |