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Yingmei Qi

dblp:52/10139 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2014
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1 · 1 first-authorApplied, interdisciplinary, general and emerging 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
1 paper
Query processing and optimization · 56% Data stream processing · 44%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data stream processing
complex event processing
0.212014
Complex event analytics: online aggregation of stream sequence patterns · SIGMOD Conference 2014
Query processing and optimization › aggregation
online aggregation
0.212014
Complex event analytics: online aggregation of stream sequence patterns · SIGMOD Conference 2014
Query processing and optimization
shared computation
0.112014
Complex event analytics: online aggregation of stream sequence patterns · SIGMOD Conference 2014
YearPublicationVenuePosition
2014 Complex event analytics: online aggregation of stream sequence patterns
abstract
Complex Event Processing (CEP) is a technology of choice for high performance analytics in time-critical decision-making applications. Yet while effective technologies for complex pattern detection on continuous event streams have been developed, the problem of scalable online aggregation of such patterns has been overlooked. Instead, aggregation is typically applied as a post processing step after CEP pattern detection, leading to an extremely ineffective solution. In this paper, we demonstrate that CEP aggregation can be pushed into the sequence construction process. Based on this insight our A-Seq strategy successfully aggregates sequence pattern online without ever constructing sequence matches. This drives down the complexity of the CEP aggregation problem from polynomial to linear. We further extend our A-Seq strategy to support the shared processing of concurrent CEP aggregation queries. The A-Seq solution is shown to achieve over four orders of magnitude performance improvement for a wide range of tested scenarios compared to the state-of-the-art solution.
Yingmei Qi, Lei Cao 0004, Medhabi Ray, Elke A. Rundensteiner
SIGMOD Conference1
2011 Does Time Matter? Modeling the Effect of Time with Bayesian Knowledge Tracing
Yumeng Qiu, Yingmei Qi, Hanyuan Lu, Zachary A. Pardos, Neil T. Heffernan
EDM2