Demo Data from SIGIR 2026 / reviewers in the wild / expert
Y. Dora Cai
dblp:97/2305
· DBLP profile ↗
4ranked-venue papers
2as first author
0since 2021 · last 2005
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 3 · 1 first-authorArtificial intelligence and machine learning · 1Systems, architecture and hardware · 1 · 1 first-author
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 |
Data mining · 88% Data stream processing · 12% |
Topics — the 9 heaviest of 10, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Data mining
pattern mining |
0.1 | 2 | 2004 | MAIDS: Mining Alarming Incidents from Data Streams · SIGMOD Conference 2004 CoMine: Efficient Mining of Correlated Patterns · ICDM 2003 |
Data mining
clustering |
0.0 | 1 | 2004 | MAIDS: Mining Alarming Incidents from Data Streams · SIGMOD Conference 2004 |
Data mining › clustering › online clustering
data stream clustering |
0.0 | 1 | 2004 | MAIDS: Mining Alarming Incidents from Data Streams · SIGMOD Conference 2004 |
Data mining › pattern mining
frequent pattern mining |
0.0 | 1 | 2004 | MAIDS: Mining Alarming Incidents from Data Streams · SIGMOD Conference 2004 |
Data stream processing
stream mining |
0.0 | 1 | 2004 | MAIDS: Mining Alarming Incidents from Data Streams · SIGMOD Conference 2004 |
Data mining › pattern mining
association rule mining |
0.0 | 1 | 2003 | CoMine: Efficient Mining of Correlated Patterns · ICDM 2003 |
Data mining › pattern mining › association analysis
correlated pattern mining |
0.0 | 1 | 2003 | CoMine: Efficient Mining of Correlated Patterns · ICDM 2003 |
Data mining › predictive modeling
classification |
0.0 | 1 | 2004 | MAIDS: Mining Alarming Incidents from Data Streams · SIGMOD Conference 2004 |
Data mining › predictive modeling › classification › incremental classification
online classification |
0.0 | 1 | 2004 | MAIDS: Mining Alarming Incidents from Data Streams · SIGMOD Conference 2004 |
Methods — techniques the papers use, named apart from their topics
tilted time window · 0.0data cube · 0.0pattern growth · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2005 | Optimized Data Loading for a Multi-Terabyte Sky Survey RepositoryabstractAdvanced instruments in a variety of scientific domains are collecting massive amounts of data that must be postprocessed and organized to support research activities. Astronomers have been pioneers in the use of databases to host sky survey data. Increasing data volumes from more powerful telescopes pose enormous challenges to state-ofthe- art database systems and data-loading techniques. In this paper we present SkyLoader, our novel framework for data loading that is being used to populate a multi-table, multi-terabyte database repository for the Palomar-Quest sky survey. SkyLoader consists of an efficient algorithm for bulk loading, an effective data structure to support data integrity, optimized parallelism, and guidelines for system tuning. Performance studies show the positive effects of these techniques, with load time for a 40-gigabyte data set reduced from over 20 hours to less than 3 hours. Our framework offers a promising approach for loading other large and complex scientific databases. Y. Dora Cai, Ruth A. Aydt, Robert Brunner |
SC | 1 |
| 2005 | Stream Cube: An Architecture for Multi-Dimensional Analysis of Data Streams
Jiawei Han 0001, Yixin Chen 0001, Guozhu Dong, Jian Pei 0001, Benjamin W. Wah, Jianyong Wang 0001, Y. Dora Cai |
Distributed Parallel Databases | 7 |
| 2004 | MAIDS: Mining Alarming Incidents from Data StreamsabstractThis paper presents a demonstration of our recent research and development of the MAIDS system, which mines alarming incidents from data streams, with the following major analysis functions: (1) multi-resolution modeling using a tilted time window framework, (2) multi-dimensional analysis using a stream “data cube” model, (3) online stream classification, (4) online frequent pattern mining, (5) online clustering of data streams, and (6) stream mining visualization. Y. Dora Cai, David Clutter, Greg Pape, Jiawei Han 0001, Michael Welge, Loretta Auvil |
SIGMOD Conference | 1 |
| 2003 | CoMine: Efficient Mining of Correlated PatternsabstractAssociation rule mining often generates a huge number of rules, but a majority of them either are redundant or do not reflect the true correlation relationship among data objects. We re-examine this problem and show that two interesting measures, all-confidence (denoted as /spl alpha/) and coherence (denoted as /spl gamma/), both disclose genuine correlation relationships and can be computed efficiently. Moreover, we propose two interesting algorithms, CoMine(/spl alpha/) and CoMine(/spl gamma/), based on extensions of a pattern-growth methodology. Our performance study shows that the CoMine algorithms have high performance in comparison with their Apriori-based counterpart algorithms. Young-Koo Lee, Won-Young Kim, Y. Dora Cai, Jiawei Han 0001 |
ICDM | 3 |