activity
20192022
most citedOne-Shot Learning on Attributed Sequences

4 citations · 5 across the 3 of their papers we have counts for

collaborators

7 papers

cs.LG20224 cited

One-Shot Learning on Attributed Sequences

Zhongfang Zhuang, Xiangnan Kong, Elke Rundensteiner +2

One-shot learning has become an important research topic in the last decade with many real-world applications. The goal of one-shot learning is to classify unlabeled instances when…

cs.DB2021

To Share, or not to Share Online Event Trend Aggregation Over Bursty Event Streams

Olga Poppe, Chuan Lei, Lei Ma +2

Complex event processing (CEP) systems continuously evaluate large workloads of pattern queries under tight time constraints. Event trend aggregation queries with Kleene patterns a…

cs.LG20201 cited

MLAS: Metric Learning on Attributed Sequences

Zhongfang Zhuang, Xiangnan Kong, Elke Rundensteiner +2

Distance metric learning has attracted much attention in recent years, where the goal is to learn a distance metric based on user feedback. Conventional approaches to metric learni…

cs.DB2020

Sharon: Shared Online Event Sequence Aggregation

Olga Poppe, Allison Rozet, Chuan Lei +2

Streaming systems evaluate massive workloads of event sequence aggregation queries. State-of-the-art approaches suffer from long delays caused by not sharing intermediate results o…

cs.DS2020

GRETA: Graph-based Real-time Event Trend Aggregation

Olga Poppe, Chuan Lei, Elke A. Rundensteiner +1

Streaming applications from algorithmic trading to traffic management deploy Kleene patterns to detect and aggregate arbitrarily-long event sequences, called event trends. State-of…

cs.DB2020

Event Trend Aggregation Under Rich Event Matching Semantics

Olga Poppe, Chuan Lei, Elke A. Rundensteiner +1

Streaming applications from health care analytics to algorithmic trading deploy Kleene queries to detect and aggregate event trends. Rich event matching semantics determine how to…