commit24_27

content/publication/24-FineStream/cite.bib

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+@inproceedings{DBLP:conf/usenix/0007YZHL020,
+  author       = {Feng Zhang and
+                  Lin Yang and
+                  Shuhao Zhang and
+                  Bingsheng He and
+                  Wei Lu and
+                  Xiaoyong Du},
+  editor       = {Ada Gavrilovska and
+                  Erez Zadok},
+  title        = {FineStream: Fine-Grained Window-Based Stream Processing on {CPU-GPU}
+                  Integrated Architectures},
+  booktitle    = {Proceedings of the 2020 {USENIX} Annual Technical Conference, {USENIX}
+                  {ATC} 2020, July 15-17, 2020},
+  pages        = {633--647},
+  publisher    = {{USENIX} Association},
+  year         = {2020},
+  url          = {https://www.usenix.org/conference/atc20/presentation/zhang-feng},
+  timestamp    = {Tue, 16 Jul 2024 09:12:32 +0200},
+  biburl       = {https://dblp.org/rec/conf/usenix/0007YZHL020.bib},
+  bibsource    = {dblp computer science bibliography, https://dblp.org}
+}
+

content/publication/24-FineStream/index.md

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+---
+title: 'FineStream: Fine-Grained Window-Based Stream Processing on CPU-GPU Integrated Architectures'
+profile: false
+# Authors
+# If you created a profile for a user (e.g. the default `admin` user), write the username (folder name) here
+# and it will be replaced with their full name and linked to their profile.
+authors:
+  - Feng Zhang
+  - Lin Yang
+  - Shuhao Zhang
+  - Bingsheng He
+  - Wei Lu
+  - Xiaoyong Du
+# Author notes (optional)
+
+# 论文发表时间, 格式为xxxx-xx-xxTxx:xx:xxZ, 需要完整填写，否则可能导致网站显示异常
+date: "2020-07-15T00:00:00Z"
+doi: ''
+
+# Schedule page publish date (NOT publication's date).
+publishDate: "2020-07-15T00:00:00Z"
+
+# Publication type.
+# Accepts a single type but formatted as a YAML list (for Hugo requirements).
+# Enter a publication type from the CSL standard.
+# 论文类型，会议为paper-conference，期刊为journal-article
+publication_types: ['paper-conference']
+
+# Publication name and optional abbreviated publication name.
+publication: In *ATC*
+publication_short: ""
+
+# 文章摘要
+abstract: Accelerating SQL queries on stream processing by utilizing heterogeneous coprocessors, such as GPUs, has shown to be an effective approach. Most works show that heterogeneous processors bring significant performance improvement because of their high parallelism and computation capacity. However, the discrete memory architectures with relatively low PCI-e bandwidth and high latency have dragged down the benefits of heterogeneous coprocessors. Recently, hardware vendors propose CPU-GPU integrated architectures that integrate CPU and GPU on the same chip. This integration provides new opportunities for fine-grained cooperation be-tween CPU and GPU for optimizing SQL queries on stream processing. In this paper, we propose a data stream system, called FineStream, for efficient window-based stream pro-cessing on integrated architectures. Particularly, FineStreamperforms fine-grained workload scheduling between CPU and GPU to take advantage of both architectures, and also targets at dynamic stream query co-processing with window handling. Our experimental results show that 1) on integrated architectures, FineStream achieves an average 52% throughput improvement and 36% lower latency over the state-of-the-art stream processing engine; 2) compared to the stream processing engine on the discrete architecture, FineStream on the integrated architecture achieves 10.4x price-throughput ratio, 1.8x energy efficiency, and can enjoy lower latency benefits.
+
+# Summary. An optional shortened abstract.
+summary: ""
+
+tags: []
+
+# Display this page in the Featured widget?
+featured: false
+
+# Custom links (uncomment lines below)
+# links:
+# - name: Custom Link
+#   url: http://example.org
+
+# 论文和代码链接，如果没有代码链接则直接将url_code一行删除
+url_pdf: 'https://www.usenix.org/system/files/atc20-zhang-feng.pdf'
+url_code: ''
+
+# Featured image
+# To use, add an image named `featured.jpg/png` to your page's folder.
+#image:
+#  caption: 'Image credit: [**Unsplash**](https://unsplash.com/photos/pLCdAaMFLTE)'
+#  focal_point: ''
+#  preview_only: false
+
+# Associated Projects (optional).
+#   Associate this publication with one or more of your projects.
+#   Simply enter your project's folder or file name without extension.
+#   E.g. `internal-project` references `content/project/internal-project/index.md`.
+#   Otherwise, set `projects: []`.
+# 可以不填
+projects:
+  - example
+# Slides (optional).
+#   Associate this publication with Markdown slides.
+#   Simply enter your slide deck's filename without extension.
+#   E.g. `slides: "example"` references `content/slides/example/index.md`.
+#   Otherwise, set `slides: ""`.
+# 可以不填
+slides: example
+---

content/publication/25-LERI_Local_Exploration_for_Rare-Category_Identification/cite.bib

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+@article{DBLP:journals/tkde/HuangYLLGC20,
+  author       = {Hao Huang and
+                  Qian Yan and
+                  Wei Lu and
+                  Huaizhong Lin and
+                  Yunjun Gao and
+                  Lei Chen},
+  title        = {{LERI:} Local Exploration for Rare-Category Identification},
+  journal      = {{IEEE} Trans. Knowl. Data Eng.},
+  volume       = {32},
+  number       = {9},
+  pages        = {1761--1772},
+  year         = {2020},
+  url          = {https://doi.org/10.1109/TKDE.2019.2911941},
+  doi          = {10.1109/TKDE.2019.2911941},
+  timestamp    = {Mon, 04 Jan 2021 16:15:13 +0100},
+  biburl       = {https://dblp.org/rec/journals/tkde/HuangYLLGC20.bib},
+  bibsource    = {dblp computer science bibliography, https://dblp.org}
+}
+

content/publication/25-LERI_Local_Exploration_for_Rare-Category_Identification/index.md

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+---
+title: 'LERI: Local Exploration for Rare-Category Identification'
+profile: false
+# Authors
+# If you created a profile for a user (e.g. the default `admin` user), write the username (folder name) here
+# and it will be replaced with their full name and linked to their profile.
+authors:
+  - Hao Huang
+  - Qian Yan
+  - Wei Lu
+  - Huaizhong Lin
+  - Yunjun Gao
+  - Lei Chen 
+# Author notes (optional)
+
+# 论文发表时间, 格式为xxxx-xx-xxTxx:xx:xxZ, 需要完整填写，否则可能导致网站显示异常
+date: "2019-04-17T00:00:00Z"
+doi: ''
+
+# Schedule page publish date (NOT publication's date).
+publishDate: "2019-04-17T00:00:00Z"
+
+# Publication type.
+# Accepts a single type but formatted as a YAML list (for Hugo requirements).
+# Enter a publication type from the CSL standard.
+# 论文类型，会议为paper-conference，期刊为journal-article
+publication_types: ['journal-article']
+
+# Publication name and optional abbreviated publication name.
+publication: In *TKDE*
+publication_short: ""
+
+# 文章摘要
+abstract: To identify the data examples of rare categories that form small compact clusters in large data sets, existing approaches mostly require enough labeled data examples as a training set to learn a classifier, assuming that the rare-category clusters are spherical or nearly spherical. Nonetheless, a large enough training set is usually difficult to obtain in practice, and rare categories in many real-world applications often form small compact clusters with arbitrary shapes. In this paper, we investigate how to identify all data examples of a rare category with an arbitrary shape based on only one seed (i.e., a labeled rare-category data example). Instead of finding a compact and spherical local region around the seed, we locally explore the data set from the seed by continuously searching and visiting the k-nearest neighbors of each newly visited data example. The local exploration connects the data examples in the objective rare category by the relationship of k-nearest neighbors, and meanwhile, suspected external data examples are filtered out if they are not close enough to any visited data example. Experimental results on both synthetic and real-world data sets are conducted, and the results verify the effectiveness and efficiency of our approach.
+
+# Summary. An optional shortened abstract.
+summary: ""
+
+tags: []
+
+# Display this page in the Featured widget?
+featured: false
+
+# Custom links (uncomment lines below)
+# links:
+# - name: Custom Link
+#   url: http://example.org
+
+# 论文和代码链接，如果没有代码链接则直接将url_code一行删除
+url_pdf: 'https://ieeexplore.ieee.org/abstract/document/8693540'
+url_code: ''
+
+# Featured image
+# To use, add an image named `featured.jpg/png` to your page's folder.
+#image:
+#  caption: 'Image credit: [**Unsplash**](https://unsplash.com/photos/pLCdAaMFLTE)'
+#  focal_point: ''
+#  preview_only: false
+
+# Associated Projects (optional).
+#   Associate this publication with one or more of your projects.
+#   Simply enter your project's folder or file name without extension.
+#   E.g. `internal-project` references `content/project/internal-project/index.md`.
+#   Otherwise, set `projects: []`.
+# 可以不填
+projects:
+  - example
+# Slides (optional).
+#   Associate this publication with Markdown slides.
+#   Simply enter your slide deck's filename without extension.
+#   E.g. `slides: "example"` references `content/slides/example/index.md`.
+#   Otherwise, set `slides: ""`.
+# 可以不填
+slides: example
+---

content/publication/26-Effective Medical Archives Processing Using Knowledge Graphs/cite.bib

@@ -0,0 +1,26 @@
+@inproceedings{DBLP:conf/sigir/WangWBLL19,
+  author       = {Xiaoli Wang and
+                  Rongzhen Wang and
+                  Zhifeng Bao and
+                  Jiayin Liang and
+                  Wei Lu},
+  editor       = {Benjamin Piwowarski and
+                  Max Chevalier and
+                  {\'{E}}ric Gaussier and
+                  Yoelle Maarek and
+                  Jian{-}Yun Nie and
+                  Falk Scholer},
+  title        = {Effective Medical Archives Processing Using Knowledge Graphs},
+  booktitle    = {Proceedings of the 42nd International {ACM} {SIGIR} Conference on
+                  Research and Development in Information Retrieval, {SIGIR} 2019, Paris,
+                  France, July 21-25, 2019},
+  pages        = {1141--1144},
+  publisher    = {{ACM}},
+  year         = {2019},
+  url          = {https://doi.org/10.1145/3331184.3331350},
+  doi          = {10.1145/3331184.3331350},
+  timestamp    = {Tue, 08 Dec 2020 00:32:56 +0100},
+  biburl       = {https://dblp.org/rec/conf/sigir/WangWBLL19.bib},
+  bibsource    = {dblp computer science bibliography, https://dblp.org}
+}
+

content/publication/26-Effective Medical Archives Processing Using Knowledge Graphs/index.md

@@ -0,0 +1,74 @@
+---
+title: 'Effective Medical Archives Processing Using Knowledge Graphs'
+profile: false
+# Authors
+# If you created a profile for a user (e.g. the default `admin` user), write the username (folder name) here
+# and it will be replaced with their full name and linked to their profile.
+authors:
+  - Xiaoli Wang
+  - Rongzhen Wang
+  - Zhifeng Bao
+  - Jiayin Liang
+  - Wei Lu
+# Author notes (optional)
+
+# 论文发表时间, 格式为xxxx-xx-xxTxx:xx:xxZ, 需要完整填写，否则可能导致网站显示异常
+date: "2019-07-18T00:00:00Z"
+doi: ''
+
+# Schedule page publish date (NOT publication's date).
+publishDate: "2019-07-18T00:00:00Z"
+
+# Publication type.
+# Accepts a single type but formatted as a YAML list (for Hugo requirements).
+# Enter a publication type from the CSL standard.
+# 论文类型，会议为paper-conference，期刊为journal-article
+publication_types: ['paper-conference']
+
+# Publication name and optional abbreviated publication name.
+publication: In *SIGIR*
+publication_short: ""
+
+# 文章摘要
+abstract: Medical archives processing is a very important task in a medical information system. It generally consists of three steps medical archives recognition, feature extraction and text classification. In this paper, we focus on empowering the medical archives processing with knowledge graphs. We first build a semantic-rich medical knowledge graph. Then, we recognize texts from medical archives using several popular optical character recognition (OCR) engines, and extract keywords from texts using a knowledge graph based feature extraction algorithm. Third, we define a semantic measure based on knowledge graph to evaluate the similarity between medical texts, and perform the text classification task. This measure can value semantic relatedness between medical documents, to enhance the text classification. We use medical archives collected from real hospitals for validation. The results show that our algorithms can significantly outperform typical baselines that employs only term statistics.
+
+# Summary. An optional shortened abstract.
+summary: ""
+
+tags: []
+
+# Display this page in the Featured widget?
+featured: false
+
+# Custom links (uncomment lines below)
+# links:
+# - name: Custom Link
+#   url: http://example.org
+
+# 论文和代码链接，如果没有代码链接则直接将url_code一行删除
+url_pdf: 'https://dl.acm.org/doi/abs/10.1145/3331184.3331350'
+url_code: ''
+
+# Featured image
+# To use, add an image named `featured.jpg/png` to your page's folder.
+#image:
+#  caption: 'Image credit: [**Unsplash**](https://unsplash.com/photos/pLCdAaMFLTE)'
+#  focal_point: ''
+#  preview_only: false
+
+# Associated Projects (optional).
+#   Associate this publication with one or more of your projects.
+#   Simply enter your project's folder or file name without extension.
+#   E.g. `internal-project` references `content/project/internal-project/index.md`.
+#   Otherwise, set `projects: []`.
+# 可以不填
+projects:
+  - example
+# Slides (optional).
+#   Associate this publication with Markdown slides.
+#   Simply enter your slide deck's filename without extension.
+#   E.g. `slides: "example"` references `content/slides/example/index.md`.
+#   Otherwise, set `slides: ""`.
+# 可以不填
+slides: example
+---

content/publication/27-A lightweight and efficient temporal database management system in TDSQL/27cite.bib

@@ -0,0 +1,24 @@
+@article{DBLP:journals/pvldb/LuZWLZSYPD19,
+  author       = {Wei Lu and
+                  Zhanhao Zhao and
+                  Xiaoyu Wang and
+                  Haixiang Li and
+                  Zhenmiao Zhang and
+                  Zhiyu Shui and
+                  Sheng Ye and
+                  Anqun Pan and
+                  Xiaoyong Du},
+  title        = {A Lightweight and Efficient Temporal Database Management System in
+                  {TDSQL}},
+  journal      = {Proc. {VLDB} Endow.},
+  volume       = {12},
+  number       = {12},
+  pages        = {2035--2046},
+  year         = {2019},
+  url          = {http://www.vldb.org/pvldb/vol12/p2035-lu.pdf},
+  doi          = {10.14778/3352063.3352122},
+  timestamp    = {Sat, 30 Sep 2023 10:24:09 +0200},
+  biburl       = {https://dblp.org/rec/journals/pvldb/LuZWLZSYPD19.bib},
+  bibsource    = {dblp computer science bibliography, https://dblp.org}
+}
+