Search Results for author: Yuanyuan Tian

Found 6 papers, 0 papers with code

Sibyl: Forecasting Time-Evolving Query Workloads

no code implementations8 Jan 2024 Hanxian Huang, Tarique Siddiqui, Rana Alotaibi, Carlo Curino, Jyoti Leeka, Alekh Jindal, Jishen Zhao, Jesus Camacho-Rodriguez, Yuanyuan Tian

Drawing insights from real-workloads, we propose template-based featurization techniques and develop a stacked-LSTM with an encoder-decoder architecture for accurate forecasting of query workloads.

GEqO: ML-Accelerated Semantic Equivalence Detection

no code implementations2 Jan 2024 Brandon Haynes, Rana Alotaibi, Anna Pavlenko, Jyoti Leeka, Alekh Jindal, Yuanyuan Tian

Detecting common computation is the first and key step for reducing this computational redundancy.

Semantic Similarity Measure of Natural Language Text through Machine Learning and a Keyword-Aware Cross-Encoder-Ranking Summarizer -- A Case Study Using UCGIS GIS&T Body of Knowledge

no code implementations17 May 2023 Yuanyuan Tian, Wenwen Li, Sizhe Wang, Zhining Gu

Initiated by the University Consortium of Geographic Information Science (UCGIS), GIS&T Body of Knowledge (BoK) is a community-driven endeavor to define, develop, and document geospatial topics related to geographic information science and technologies (GIS&T).

Semantic Similarity Semantic Textual Similarity +1

GeoAI for Knowledge Graph Construction: Identifying Causality Between Cascading Events to Support Environmental Resilience Research

no code implementations11 Nov 2022 Yuanyuan Tian, Wenwen Li

Knowledge graph technology is considered a powerful and semantically enabled solution to link entities, allowing users to derive new knowledge by reasoning data according to various types of reasoning rules.

graph construction Knowledge Graphs

Temporally-Biased Sampling Schemes for Online Model Management

no code implementations11 Jun 2019 Brian Hentschel, Peter J. Haas, Yuanyuan Tian

To maintain the accuracy of supervised learning models in the presence of evolving data streams, we provide temporally-biased sampling schemes that weight recent data most heavily, with inclusion probabilities for a given data item decaying over time according to a specified "decay function".

Management

Temporally-Biased Sampling for Online Model Management

no code implementations29 Jan 2018 Brian Hentschel, Peter J. Haas, Yuanyuan Tian

Moreover, time-biasing lets the models adapt to recent changes in the data while -- unlike in a sliding-window approach -- still keeping some old data to ensure robustness in the face of temporary fluctuations and periodicities in the data values.

Databases

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