dbgroup - User contributions [en]

Conferences

2023-03-30T12:24:39Z

2021244137: /* 重要时间 */

{{DISPLAYTITLE:科学研究}}
</big>
== 重要时间 ==

{| class="wikitable"
|-
! CCF !! Conference !! Abstract !! Full Paper !! Short Paper !! Demo !! Poster !! Link
|-
| A || SIGMOD-24 || 2023.8.23/2023.11.23 || 2023.8.23/2023.11.23 || || TBD || || http://2024.sigmod.org/
|-
| A || VLDB-24 || 2024.3.1 || 2024.3.1 || || TBD || || https://vldb.org/2024/
|-
| A || ICDE-24 || TBD || TBD || || TBD || || TBD
|-
| A || WWW-22 || 2021.10.14 || 2021.10.21 || || 2022.02.03 || 2022.02.03 || https://www2022.thewebconf.org/
|-
| A || IJCAI-22 || TBD || || || || || https://ijcai-22.org/
|-
| A || AAAI-22 || 2021.8.30 || 2021.9.8 || || || || https://aaai.org/Conferences/AAAI-22/
|-
| A || KDD-22 || TBD || || || || ||
|-
| A || SIGIR-22 || TBD || || || || || https://sigir.org/sigir2022/
|-
| A || ACL-22 || || 2022.1.7 || 2022.1.7 || || || https://2022.aclweb.org
|-
| A || NeurIPS-22 ||TBD || || || || ||
|-
| A || ICML-22 || TBD || || || || ||
|-
| B || EMNLP-22 || TBD || || || || ||
|-
| B || CIKM-22 || 2022.5.9 || 2022.5.16 || || || || https://www.cikm2022.org/calls
|-
| B || EDBT-24 || TBD || TBD || || || || TBD
|-
| B || ISWC-23 || 2022.5.2 || 2022.5.9 || || 2022.7.12 || 2022.7.19 || https://iswc2023.semanticweb.org/dates/
|-
| B || DASFAA-24 || TBD || TBD || || TBD || || TBD
|-
| B || 软件学报 || 2023.7.17 || 2023.7.17 || || || || http://www.jos.org.cn/jos/news/view/20230214120222001
|-
| C || WISA-23 || 2023.5.1 || 2023.5.8 || || || || https://ccf-wisa.github.io/2023/
|-
| C || APWEB-23 || 2023.6.3 || 2023.6.10 || || 2023.6.10 || || http://www.apweb-waim2023.com/
|-
| C || ADMA-23 || 2023.4.8 || 2023.4.15 || || TBD || || https://adma2023.uqcloud.net/
|}

== 学术写作 ==
[[ Writing | 学术写作指南 ]]

== 经费报销 ==
[[ reimbursement | 报销指南 ]]

Conferences

2023-03-30T12:23:42Z

2021244137: /* 重要时间 */

{{DISPLAYTITLE:科学研究}}
</big>
== 重要时间 ==

{| class="wikitable"
|-
! CCF !! Conference !! Abstract !! Full Paper !! Short Paper !! Demo !! Poster !! Link
|-
| A || SIGMOD-24 || 2023.8.23/2023.11.23 || 2023.8.23/2023.11.23 || || TBD || || http://2024.sigmod.org/
|-
| A || VLDB-24 || 2024.3.1 || 2024.3.1 || || TBD || || https://vldb.org/2024/
|-
| A || ICDE-24 || TBD || TBD || || TBD || || TBD
|-
| A || WWW-22 || 2021.10.14 || 2021.10.21 || || 2022.02.03 || 2022.02.03 || https://www2022.thewebconf.org/
|-
| A || IJCAI-22 || TBD || || || || || https://ijcai-22.org/
|-
| A || AAAI-22 || 2021.8.30 || 2021.9.8 || || || || https://aaai.org/Conferences/AAAI-22/
|-
| A || KDD-22 || TBD || || || || ||
|-
| A || SIGIR-22 || TBD || || || || || https://sigir.org/sigir2022/
|-
| A || ACL-22 || || 2022.1.7 || 2022.1.7 || || || https://2022.aclweb.org
|-
| A || NeurIPS-22 ||TBD || || || || ||
|-
| A || ICML-22 || TBD || || || || ||
|-
| B || EMNLP-22 || TBD || || || || ||
|-
| B || CIKM-22 || 2022.5.9 || 2022.5.16 || || || || https://www.cikm2022.org/calls
|-
| B || EDBT-24 || || TBD || TBD|| || || TBD
|-
| B || ISWC-23 || 2022.5.2 || 2022.5.9 || || 2022.7.12 || 2022.7.19 || https://iswc2023.semanticweb.org/dates/
|-
| B || DASFAA-24 || TBD || TBD || || TBD || || TBD
|-
| B || 软件学报 || 2023.7.17 || 2023.7.17 || || || || http://www.jos.org.cn/jos/news/view/20230214120222001
|-
| C || WISA-23 || 2023.5.1 || 2023.5.8 || || || || https://ccf-wisa.github.io/2023/
|-
| C || APWEB-23 || 2023.6.3 || 2023.6.10 || || 2023.6.10 || || http://www.apweb-waim2023.com/
|-
| C || ADMA-23 || 2023.4.8 || 2023.4.15 || || TBD || || https://adma2023.uqcloud.net/
|}

== 学术写作 ==
[[ Writing | 学术写作指南 ]]

== 经费报销 ==
[[ reimbursement | 报销指南 ]]

Conferences

2023-03-30T11:53:19Z

2021244137: /* 重要时间 */

{{DISPLAYTITLE:科学研究}}
</big>
== 重要时间 ==

{| class="wikitable"
|-
! CCF !! Conference !! Abstract !! Full Paper !! Short Paper !! Demo !! Poster !! Link
|-
| A || SIGMOD-24 || || 2023.8.23/2022.11.23 || 2023.8.23/2022.11.23 || TBD || TBD || https://2022.sigmod.org/
|-
| A || VLDB-22 || || 20th of each month || || || || https://vldb.org/2022/
|-
| A || ICDE-24 || || 2023.7.31 || || || || https://icde2023.ics.uci.edu/research-papers-track/
|-
| A || WWW-22 || 2021.10.14 || 2021.10.21 || || 2022.02.03 || 2022.02.03 || https://www2022.thewebconf.org/
|-
| A || IJCAI-22 || TBD || || || || || https://ijcai-22.org/
|-
| A || AAAI-22 || 2021.8.30 || 2021.9.8 || || || || https://aaai.org/Conferences/AAAI-22/
|-
| A || KDD-22 || TBD || || || || ||
|-
| A || SIGIR-22 || TBD || || || || || https://sigir.org/sigir2022/
|-
| A || ACL-22 || || 2022.1.7 || 2022.1.7 || || || https://2022.aclweb.org
|-
| A || NeurIPS-22 ||TBD || || || || ||
|-
| A || ICML-22 || TBD || || || || ||
|-
| B || EMNLP-22 || TBD || || || || ||
|-
| B || CIKM-22 || 2022.5.9 || 2022.5.16 || || || || https://www.cikm2022.org/calls
|-
| B || EDBT-22 || || 2021.10.1 || 2021.10.1|| || || https://conferences.inf.ed.ac.uk/edbticdt2022/
|-
| B || ISWC-22 || 2022.4.28 || 2022.5.5 || || 2022.7.13 || 2022.7.13 || https://iswc2022.semanticweb.org/index.php/
|-
| B || DASFAA-22 || 2021.10.28 || 2021.11.4 || 2021.11.4 || 2021.11.28 || || https://www.dasfaa2022.org/
|-
| C || WISE-22 || 2022.3.1 || 2022.3.8 || || || || https://wise2022.sigappfr.org/
|}

== 学术写作 ==
[[ Writing | 学术写作指南 ]]

== 经费报销 ==
[[ reimbursement | 报销指南 ]]

Conferences

2023-03-30T11:49:06Z

2021244137:

{{DISPLAYTITLE:科学研究}}
</big>
== 重要时间 ==

{| class="wikitable"
|-
! CCF !! Conference !! Abstract !! Full Paper !! Short Paper !! Demo !! Poster !! Link
|-
| A || SIGMOD-24 || || 2021.9.15 / 2022.10.8 || 2021.9.15 / 2022.10.8 || || || https://2022.sigmod.org/
|-
| A || VLDB-22 || || 20th of each month || || || || https://vldb.org/2022/
|-
| A || ICDE-24 || || 2023.7.31 || || || || https://icde2023.ics.uci.edu/research-papers-track/
|-
| A || WWW-22 || 2021.10.14 || 2021.10.21 || || 2022.02.03 || 2022.02.03 || https://www2022.thewebconf.org/
|-
| A || IJCAI-22 || TBD || || || || || https://ijcai-22.org/
|-
| A || AAAI-22 || 2021.8.30 || 2021.9.8 || || || || https://aaai.org/Conferences/AAAI-22/
|-
| A || KDD-22 || TBD || || || || ||
|-
| A || SIGIR-22 || TBD || || || || || https://sigir.org/sigir2022/
|-
| A || ACL-22 || || 2022.1.7 || 2022.1.7 || || || https://2022.aclweb.org
|-
| A || NeurIPS-22 ||TBD || || || || ||
|-
| A || ICML-22 || TBD || || || || ||
|-
| B || EMNLP-22 || TBD || || || || ||
|-
| B || CIKM-22 || 2022.5.9 || 2022.5.16 || || || || https://www.cikm2022.org/calls
|-
| B || EDBT-22 || || 2021.10.1 || 2021.10.1|| || || https://conferences.inf.ed.ac.uk/edbticdt2022/
|-
| B || ISWC-22 || 2022.4.28 || 2022.5.5 || || 2022.7.13 || 2022.7.13 || https://iswc2022.semanticweb.org/index.php/
|-
| B || DASFAA-22 || 2021.10.28 || 2021.11.4 || 2021.11.4 || 2021.11.28 || || https://www.dasfaa2022.org/
|-
| C || WISE-22 || 2022.3.1 || 2022.3.8 || || || || https://wise2022.sigappfr.org/
|}

== 学术写作 ==
[[ Writing | 学术写作指南 ]]

== 经费报销 ==
[[ reimbursement | 报销指南 ]]

2019YFE0198600

2022-11-05T04:57:17Z

2021244137: /* 财务 */

{{DISPLAYTITLE:分布式知识图谱数据管理关键技术与系统 (国家重点研发计划项目)}}
== 技术 ==
=== 论文+专利+软著汇总 ===
=== 技术指标 ===
形成技术报告

=== 测试 ===
第三方测试报告

=== 数据汇交 ===
通过数据汇交网站，打电话问清楚我们这样的项目需要交什么

== 财务 ==
需要形成财务报告
=== 专家评定 ===
=== 测试 ===
测试费用任务书中是2万元
=== 审计 ===
建议找北京审计事务所，审计费用占项目总经费5‰，约合1.07万元

2019YFE0198600

2022-11-05T04:57:02Z

2021244137: /* 财务 */

{{DISPLAYTITLE:分布式知识图谱数据管理关键技术与系统 (国家重点研发计划项目)}}
== 技术 ==
=== 论文+专利+软著汇总 ===
=== 技术指标 ===
形成技术报告

=== 测试 ===
第三方测试报告

=== 数据汇交 ===
通过数据汇交网站，打电话问清楚我们这样的项目需要交什么

== 财务 ==
形成财务报告
=== 专家评定 ===
=== 测试 ===
测试费用任务书中是2万元
=== 审计 ===
建议找北京审计事务所，审计费用占项目总经费5‰，约合1.07万元

2019YFE0198600

2022-11-05T04:56:33Z

2021244137: /* 审计 */

{{DISPLAYTITLE:分布式知识图谱数据管理关键技术与系统 (国家重点研发计划项目)}}
== 技术 ==
=== 论文+专利+软著汇总 ===
=== 技术指标 ===
形成技术报告

=== 测试 ===
第三方测试报告

=== 数据汇交 ===
通过数据汇交网站，打电话问清楚我们这样的项目需要交什么

== 财务 ==
=== 专家评定 ===
=== 测试 ===
测试费用任务书中是2万元
=== 审计 ===
建议找北京审计事务所，审计费用占项目总经费5‰，约合1.07万元

2019YFE0198600

2022-11-05T04:56:10Z

2021244137: /* 数据汇交 */

{{DISPLAYTITLE:分布式知识图谱数据管理关键技术与系统 (国家重点研发计划项目)}}
== 技术 ==
=== 论文+专利+软著汇总 ===
=== 技术指标 ===
形成技术报告

=== 测试 ===
第三方测试报告

=== 数据汇交 ===
通过数据汇交网站，打电话问清楚我们这样的项目需要交什么

== 财务 ==
=== 专家评定 ===
=== 测试 ===
测试费用任务书中是2万元
=== 审计 ===
建议找北京公司，审计费用占项目总经费5‰，约合1.07万元

2019YFE0198600

2022-11-05T04:55:16Z

2021244137: /* 测试 */

{{DISPLAYTITLE:分布式知识图谱数据管理关键技术与系统 (国家重点研发计划项目)}}
== 技术 ==
=== 论文+专利+软著汇总 ===
=== 技术指标 ===
形成技术报告

=== 测试 ===
第三方测试报告

=== 数据汇交 ===

== 财务 ==
=== 专家评定 ===
=== 测试 ===
测试费用任务书中是2万元
=== 审计 ===
建议找北京公司，审计费用占项目总经费5‰，约合1.07万元

2019YFE0198600

2022-11-05T04:54:52Z

2021244137: /* 技术指标 */

{{DISPLAYTITLE:分布式知识图谱数据管理关键技术与系统 (国家重点研发计划项目)}}
== 技术 ==
=== 论文+专利+软著汇总 ===
=== 技术指标 ===
形成技术报告

=== 测试 ===
=== 数据汇交 ===

== 财务 ==
=== 专家评定 ===
=== 测试 ===
测试费用任务书中是2万元
=== 审计 ===
建议找北京公司，审计费用占项目总经费5‰，约合1.07万元

2019YFE0198600

2022-11-05T03:30:56Z

2021244137: /* 审计 */

{{DISPLAYTITLE:分布式知识图谱数据管理关键技术与系统 (国家重点研发计划项目)}}
== 技术 ==
=== 论文+专利+软著汇总 ===
=== 技术指标 ===
=== 测试 ===
=== 数据汇交 ===

== 财务 ==
=== 专家评定 ===
=== 测试 ===
测试费用任务书中是2万元
=== 审计 ===
建议找北京公司，审计费用占项目总经费5‰，约合1.07万元

2019YFE0198600

2022-11-05T03:30:41Z

2021244137: /* 财务 */

{{DISPLAYTITLE:分布式知识图谱数据管理关键技术与系统 (国家重点研发计划项目)}}
== 技术 ==
=== 论文+专利+软著汇总 ===
=== 技术指标 ===
=== 测试 ===
=== 数据汇交 ===

== 财务 ==
=== 专家评定 ===
=== 测试 ===
测试费用任务书中是2万元
=== 审计 ===
建议找北京专家，审计费用占项目总经费5‰，约合1.07万元

2019YFE0198600

2022-11-05T03:25:49Z

2021244137:

{{DISPLAYTITLE:分布式知识图谱数据管理关键技术与系统 (国家重点研发计划项目)}}
== 技术 ==
=== 论文+专利+软著汇总 ===
=== 技术指标 ===
=== 测试 ===
=== 数据汇交 ===

== 财务 ==
=== 专家评定费用 ===
=== 测试费用 ===
=== 审计费用 ===

2019YFE0198600

2022-11-05T03:24:22Z

2021244137: /* 技术指标 */

{{DISPLAYTITLE:分布式知识图谱数据管理关键技术与系统 (国家重点研发计划项目)}}
== 技术 ==
=== 论文+专利+软著汇总 ===
=== 技术指标 ===
=== 测试 ===
=== 数据汇交 ===

== 财务指标 ==

2019YFE0198600

2022-11-05T03:20:40Z

2021244137: /* 技术指标 */

{{DISPLAYTITLE:分布式知识图谱数据管理关键技术与系统 (国家重点研发计划项目)}}
== 技术指标 ==
'''报告题目：'''

== 财务指标 ==

2019YFE0198600

2022-11-05T03:19:11Z

2021244137:

{{DISPLAYTITLE:分布式知识图谱数据管理关键技术与系统 (国家重点研发计划项目)}}
== 技术指标 ==

== 财务指标 ==

2019YFE0198600

2022-11-05T03:18:34Z

2021244137:

{{DISPLAYTITLE:分布式知识图谱数据管理关键技术与系统 (国家重点研发计划项目)}}
== 重要时间 ==

Reports

2022-09-23T07:15:19Z

2021244137:

{{DISPLAYTITLE:学术报告}}
== 北洋智算论坛 ==

'''报告题目：'''Querying Geo-Textual Data Using Keywords and User Location.

'''报告人：'''Christian Jensen, Aalborg University.

'''报告纪实：'''
[[File:Christian Jensen教授.jpeg| 500px]]

== APWeb-WAIM 2020 国际会议 (2020.9.18 - 2020.9.20) ==

'''报告题目：'''The tragedy of the (Data) Commons.

'''报告人：'''James Hendler, Rensselaer Polytechnic Institute

'''Abstract: '''The tragedy of the commons, first proposed by William Lloyd in 1833, is an economic problem in which every individual has an incentive to consume a resource at the expense of every other individual with no way to exclude anyone from consuming. It results in overconsumption, under investment, and ultimately depletion of the resource. While the direct application of these principles to data seems like a bit of a reach, it becomes clear that data sharing risks much of the same problem - people wishing to protect their own data while having access to other people’s. Motivation for sharing is thus weak, until incentives and policies are in place. However, now that these incentives and policies are coming into practice, the implementation will have high impact on the benefits. Thus, as we work to make the world a FAIRer place, we must consider how the way data, and especially metadata, is represented and shared.

'''报告纪实：'''
[[File:apweb_keynote_1.png | 500px]]

'''报告题目：'''The tragedy of the (Data) Commons.

'''报告人：'''Xuemin Lin, The University of New South Wales

'''Abstract: ''' With the emergence of large-scale networks, the study of network structural stability has recently received a great deal of attention in different areas such as social networks, the world wide web, and biology. The stability of a network indicates the ability of the network to maintain an acceptable level of service and/or to defend the attacks from the competitors. In this talk, we first introduce the stability models in different domains, their applications, and unique challenges that need to be addressed. We focus on three fundamental problems: (a) efficiently computing the stability of a given network, (b) motivating critical nodes and edges to enhance the network stability, and (c) defending critical nodes and edges against the attacks to network stability. Due to the fast evolvement of real-life networks, we also discuss the stability problems and their computation on dynamic graphs. We will explore the nature and lay the scientific foundation of these problems. Subsequently, we introduce novel computing paradigms and algorithms, indexing techniques, batch processing techniques, and distributed solutions. Finally, we discuss the future research directions in this important and growing research area.

'''报告纪实：'''
[[File:apweb_keynote_2.png | 500px]]

'''报告题目：'''Distributed Graph Processing Systems.

'''报告人：'''Yanfeng Zhang, Northeastern University

'''Abstract: '''During the past 10 years, there has been a surging interest in developing distributed graph processing systems. This tutorial provides a comprehensive review of existing distributed graph processing systems. We firstly review the programming models for distributed graph processing and then summarize the common optimization techniques for improving graph execution performance, including graph partitioning methods, communication mechanisms, parallel processing models, hardware-specific optimizations, and incremental graph processing. We also present an emerging hot topic, distributed Graph Neural Networks (GNN) frameworks, and review recent progress on this topic.

'''报告纪实：'''
[[File:apweb_tutorial_2.png | 500px]]

== 组会报告 ==

{| class="wikitable"
|-
! 报告人!! 报告题目 || 出处 !! 时间 !! Link
|-
| 董思聪 || Translating Embeddings for Modeling Multi-relational Data || NIPS-2013 || 2021.10.24 ||
|-
| 刘鑫 || Embedding Entities and Relations for Learning and Inference in Knowledge Bases || ICLR-2015 || 2021.11.28 ||
|-
| 陈子睿 || Inductive Representation Learning on Large Graphs || NIPS-2017 || 2021.12.13 ||
|-
| 张少伟 || Inductive representation learning on temporal graphs || ICLR-2020 || 2022.12.27 ||
|-
| 曹舒珊 || RDF graph summarization for first-sight structure discovery || VLDB-2020 || 2022.3.4 ||
|-
| 宋妍妍 || Time- and Space-Efficient Regular Path Queries on Graphs || ICDE-2022 || 2022.4.1 || https://mp.weixin.qq.com/s/42nxB6Sp_9u1dTVFxrXCvQ
|-
| 柳鹏凯 || Adaptive Low-level Storage of Very Large Knowledge Graphs || WWW-2020 || 2022.4.17 || https://mp.weixin.qq.com/s/42nxB6Sp_9u1dTVFxrXCvQ
|-
| 秦雨舟 || Structural Indexing for Conjunctive Path Queries || ICDE-2022 || 2022.6.3 || 待定
|-
| 王晨旭 || Knowledge Hypergraphs: Prediction Beyond Binary Relations || arXiv || 2022.6.10 || https://mp.weixin.qq.com/s/GXPJLtvLJp8FMzdcQ5QnYA
|-
| 郝文琦 || Answering Regular Path Queries through Exemplars || VLDB 2021 || 2022.6.27 ||
|-
| 王翔 || Introducing PathQuery, Google’s Graph Query Language|| arXiv 2021 || 2022.09.13 ||
|}

郝文琦

2022-04-09T06:44:06Z

2021244137:

天津大学智能与计算学部电子信息硕士研究生（全日制）

'''研究方向'''：图数据库、知识图谱数据管理

'''通讯地址'''：天津市津南区海河教育园区雅观路135号天津大学北洋园校区智能与计算学部

'''邮政编码'''：300354

'''办公地址'''：55教学楼B区324室

'''电子邮箱'''：haowenqi@tju.edu.cn

== 简历 ==
2021年7月于江南大学获得物联网工程学位

2021年9月至今天津大学智能与计算学部硕士研究生在读

郝文琦

2022-04-09T06:43:19Z

2021244137: Created page with "天津大学智能与计算学部电子信息硕士研究生（全日制） '''研究方向'''：图数据库、知识图谱数据管理 '''通讯地址'''：天津市..."

天津大学智能与计算学部电子信息硕士研究生（全日制）

'''研究方向'''：图数据库、知识图谱数据管理

'''通讯地址'''：天津市津南区海河教育园区雅观路135号天津大学北洋园校区智能与计算学部

'''邮政编码'''：300354

'''办公地址'''：55教学楼B区324室

'''电子邮箱'''：yuzhou_qin@tju.edu.cn

== 简历 ==
2021年7月于江南大学获得物联网工程学位

2021年9月至今天津大学智能与计算学部硕士研究生在读

数据管理小组

2022-04-09T06:40:22Z

2021244137: /* 分布式数据管理 */

== 综述 ==
# Z. Kaoudi and I. Manolescu. RDF in the Clouds: A Survey. VLDB J., 24(1):67–91, 2015. [https://link.springer.com/article/10.1007/s00778-014-0364-z]
# Özsu, M. Tamer. "A survey of RDF data management systems." Frontiers of Computer Science 10(3): 418-432, 2016.[https://link.springer.com/article/10.1007/s11704-016-5554-y]
# 邹磊, 彭鹏. 分布式 RDF 数据管理综述[J]. 计算机研究与发展, 54(6):1213-1224, 2017.[http://crad.ict.ac.cn/CN/abstract/abstract3420.shtml]
# I. Abdelaziz, R. Harbi, Z. Khayyat, P. Kalnis, A survey and experimental comparison of distributed SPARQL engines for very large RDF data, Proceedings of the Vldb Endowment 10(13):2049–2060,2017. [https://dl.acm.org/doi/abs/10.14778/3151106.3151109]
# Wylot, Marcin, et al. "RDF Data Storage and Query Processing Schemes: A Survey." ACM Computing Surveys (CSUR) 51(4):84, 2018.[https://dl.acm.org/doi/abs/10.1145/3177850]
# 王鑫, 邹磊, 王朝坤, 彭鹏, 冯志勇. 知识图谱数据管理研究综述. 软件学报, 30(7):2139-2174, 2019.[http://jos.org.cn/html/2019/7/5841.htm]
# Besta M, Peter E, Gerstenberger R, et al. Demystifying graph databases: Analysis and taxonomy of data organization, system designs, and graph queries[J]. arXiv preprint arXiv:1910.09017, 2019. [https://arxiv.org/abs/1910.09017]

== 子图匹配查询相关 ==
# A. K. Chandra, P. M. Merlin, Optimal implementation of conjunctive queries in relational databases, in ACM Symposium on Theory of Computing, pp.77–90, 1997. [https://dl.acm.org/doi/abs/10.1145/800105.803397]
# Z. Sun, H. Wang, H. Wang, B. Shao, J. Li, Efficient subgraph matching on billion node graphs, Proceedings of the VLDB Endowment 5(9):788–799, 2012.[https://arxiv.org/abs/1205.6691]
# L. Lai, L. Qin, X. Lin, L. Chang, Scalable subgraph enumeration in MapReduce, Proceedings of the VLDB Endowment 8(10):974–985, 2015. [https://dl.acm.org/doi/abs/10.14778/2794367.2794368]
# # P.Peng,L.Zou, M.T.Özsu, L.Chen, and D.Zhao.Processing SPARQL Queries over Distributed RDF Graphs. VLDB J., 25(2):243–268, 2016. [https://link.springer.com/article/10.1007/S00778-015-0415-0]
# L. Lai, L. Qin, X. Lin, Y. Zhang, L. Chang, S. Yang, Scalable distributed subgraph enumeration, Proceedings of the Vldb Endowment 10(3)217–228, 2016. [https://dl.acm.org/doi/abs/10.14778/3021924.3021937]
# I. Abdelaziz, R. Harbi, Z. Khayyat, P. Kalnis, A survey and experimental comparison of distributed SPARQL engines for very large RDF data, Proceedings of the Vldb Endowment 10(13):2049–2060, 2017. [https://dl.acm.org/doi/abs/10.14778/3151106.3151109]
# I. Abdelaziz, R. Harbi, Z. Khayyat, and P. Kalnis. A Survey and Experimental Comparison of Distributed SPARQL Engines for Very Large RDF Data. PVLDB, 10(13):2049–2060, 2017. [https://dl.acm.org/doi/abs/10.14778/3151106.3151109]
# Huang C, Zhang Q, Guo D, et al. GQARDF: An Efficient SPARQL Query Answering Engine on RDF Graphs[J], 2020.[http://www.semantic-web-journal.net/system/files/swj2922.pdf]
# Yang Z, Lai L, Lin X, et al. Huge: An efficient and scalable subgraph enumeration system[J]. arXiv preprint arXiv:2103.14294, 2021.[https://arxiv.org/pdf/2103.14294.pdf]
# Mehmood Q, Saleem M, Jha A, et al. Efficient distributed path computation on RDF knowledge graphs using partial evaluation[J]. World Wide Web, 2021: 1-32.[https://link.springer.com/article/10.1007/s11280-021-00965-5]
# Lassila O, Schmidt M, Bebee B, et al. Graph? Yes! Which one? Help![J]. arXiv preprint arXiv:2110.13348, 2021.[https://arxiv.org/pdf/2110.13348.pdf]
# Arroyuelo D, Hogan A, Navarro G, et al. Time-and Space-Efficient Regular Path Queries on Graphs[J]. arXiv preprint arXiv:2111.04556, 2021.[https://arxiv.org/pdf/2111.04556.pdf]
# Wu J, Chen R, Xia Y. Fast and Accurate Optimizer for Query Processing over Knowledge Graphs[C]//Proceedings of the ACM Symposium on Cloud Computing. 2021: 503-517.[https://dl.acm.org/doi/abs/10.1145/3472883.3486991]

== 编码 ==
# Urbani J, Dutta S, Gurajada S, et al. KOGNAC: efficient encoding of large knowledge graphs[J]. arXiv preprint arXiv:1604.04795, 2016.[https://arxiv.org/pdf/1604.04795.pdf]
# Singh G, Upadhyay D, Atre M. Efficient RDF dictionaries with B+ trees[C]//Proceedings of the ACM India Joint International Conference on Data Science and Management of Data. 2018: 128-136.[https://cse.iitk.ac.in/users/atrem/papers/cods-comad2018.pdf]

== 分布式数据管理 ==
# Zeng K, Yang J, Wang H, et al. A distributed graph engine for web scale RDF data[J]. Proceedings of the VLDB Endowment, 2013, 6(4): 265-276.
# Shao B, Wang H, Li Y. Trinity: A distributed graph engine on a memory cloud[C]//Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data. 2013: 505-516.[https://dl.acm.org/doi/pdf/10.1145/2463676.2467799][https://www.graphengine.io/downloads/papers/Trinity.RDF.pdf]
# Zou L, Özsu M T, Chen L, et al. gStore: a graph-based SPARQL query engine[J]. The VLDB journal, 2014, 23(4): 565-590.[https://link.springer.com/article/10.1007/s00778-013-0337-7]
# Gurajada S, Seufert S, Miliaraki I, et al. TriAD: a distributed shared-nothing RDF engine based on asynchronous message passing[C]//Proceedings of the 2014 ACM SIGMOD international conference on Management of data. 2014: 289-300.[http://resources.mpi-inf.mpg.de/d5/triad/mod375-gurajada.pdf]
# Schätzle A, Przyjaciel-Zablocki M, Skilevic S, et al. S2RDF: RDF querying with SPARQL on spark[J]. arXiv preprint arXiv:1512.07021, 2015.[https://arxiv.org/pdf/1512.07021.pdf]

== 存储相关 ==
# Yuan P, Liu P, Wu B, et al. TripleBit: a fast and compact system for large scale RDF data[J]. Proceedings of the VLDB Endowment, 2013, 6(7): 517-528.[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.308.3012&rep=rep1&type=pdf]
# Urbani J, Jacobs C. Adaptive low-level storage of very large knowledge graphs[C]//Proceedings of The Web Conference 2020. 2020: 1761-1772.[https://arxiv.org/pdf/2001.09078.pdf]
# Li G, Zhou X, Sun J, et al. opengauss: An autonomous database system[J]. Proceedings of the VLDB Endowment, 2021, 14(12): 3028-3042.[https://www.vldb.org/pvldb/vol14/p3028-li.pdf]

数据管理小组

2022-04-09T06:39:26Z

2021244137: /* 编码 */

== 综述 ==
# Z. Kaoudi and I. Manolescu. RDF in the Clouds: A Survey. VLDB J., 24(1):67–91, 2015. [https://link.springer.com/article/10.1007/s00778-014-0364-z]
# Özsu, M. Tamer. "A survey of RDF data management systems." Frontiers of Computer Science 10(3): 418-432, 2016.[https://link.springer.com/article/10.1007/s11704-016-5554-y]
# 邹磊, 彭鹏. 分布式 RDF 数据管理综述[J]. 计算机研究与发展, 54(6):1213-1224, 2017.[http://crad.ict.ac.cn/CN/abstract/abstract3420.shtml]
# I. Abdelaziz, R. Harbi, Z. Khayyat, P. Kalnis, A survey and experimental comparison of distributed SPARQL engines for very large RDF data, Proceedings of the Vldb Endowment 10(13):2049–2060,2017. [https://dl.acm.org/doi/abs/10.14778/3151106.3151109]
# Wylot, Marcin, et al. "RDF Data Storage and Query Processing Schemes: A Survey." ACM Computing Surveys (CSUR) 51(4):84, 2018.[https://dl.acm.org/doi/abs/10.1145/3177850]
# 王鑫, 邹磊, 王朝坤, 彭鹏, 冯志勇. 知识图谱数据管理研究综述. 软件学报, 30(7):2139-2174, 2019.[http://jos.org.cn/html/2019/7/5841.htm]
# Besta M, Peter E, Gerstenberger R, et al. Demystifying graph databases: Analysis and taxonomy of data organization, system designs, and graph queries[J]. arXiv preprint arXiv:1910.09017, 2019. [https://arxiv.org/abs/1910.09017]

== 子图匹配查询相关 ==
# A. K. Chandra, P. M. Merlin, Optimal implementation of conjunctive queries in relational databases, in ACM Symposium on Theory of Computing, pp.77–90, 1997. [https://dl.acm.org/doi/abs/10.1145/800105.803397]
# Z. Sun, H. Wang, H. Wang, B. Shao, J. Li, Efficient subgraph matching on billion node graphs, Proceedings of the VLDB Endowment 5(9):788–799, 2012.[https://arxiv.org/abs/1205.6691]
# L. Lai, L. Qin, X. Lin, L. Chang, Scalable subgraph enumeration in MapReduce, Proceedings of the VLDB Endowment 8(10):974–985, 2015. [https://dl.acm.org/doi/abs/10.14778/2794367.2794368]
# # P.Peng,L.Zou, M.T.Özsu, L.Chen, and D.Zhao.Processing SPARQL Queries over Distributed RDF Graphs. VLDB J., 25(2):243–268, 2016. [https://link.springer.com/article/10.1007/S00778-015-0415-0]
# L. Lai, L. Qin, X. Lin, Y. Zhang, L. Chang, S. Yang, Scalable distributed subgraph enumeration, Proceedings of the Vldb Endowment 10(3)217–228, 2016. [https://dl.acm.org/doi/abs/10.14778/3021924.3021937]
# I. Abdelaziz, R. Harbi, Z. Khayyat, P. Kalnis, A survey and experimental comparison of distributed SPARQL engines for very large RDF data, Proceedings of the Vldb Endowment 10(13):2049–2060, 2017. [https://dl.acm.org/doi/abs/10.14778/3151106.3151109]
# I. Abdelaziz, R. Harbi, Z. Khayyat, and P. Kalnis. A Survey and Experimental Comparison of Distributed SPARQL Engines for Very Large RDF Data. PVLDB, 10(13):2049–2060, 2017. [https://dl.acm.org/doi/abs/10.14778/3151106.3151109]
# Huang C, Zhang Q, Guo D, et al. GQARDF: An Efficient SPARQL Query Answering Engine on RDF Graphs[J], 2020.[http://www.semantic-web-journal.net/system/files/swj2922.pdf]
# Yang Z, Lai L, Lin X, et al. Huge: An efficient and scalable subgraph enumeration system[J]. arXiv preprint arXiv:2103.14294, 2021.[https://arxiv.org/pdf/2103.14294.pdf]
# Mehmood Q, Saleem M, Jha A, et al. Efficient distributed path computation on RDF knowledge graphs using partial evaluation[J]. World Wide Web, 2021: 1-32.[https://link.springer.com/article/10.1007/s11280-021-00965-5]
# Lassila O, Schmidt M, Bebee B, et al. Graph? Yes! Which one? Help![J]. arXiv preprint arXiv:2110.13348, 2021.[https://arxiv.org/pdf/2110.13348.pdf]
# Arroyuelo D, Hogan A, Navarro G, et al. Time-and Space-Efficient Regular Path Queries on Graphs[J]. arXiv preprint arXiv:2111.04556, 2021.[https://arxiv.org/pdf/2111.04556.pdf]
# Wu J, Chen R, Xia Y. Fast and Accurate Optimizer for Query Processing over Knowledge Graphs[C]//Proceedings of the ACM Symposium on Cloud Computing. 2021: 503-517.[https://dl.acm.org/doi/abs/10.1145/3472883.3486991]

== 编码 ==
# Urbani J, Dutta S, Gurajada S, et al. KOGNAC: efficient encoding of large knowledge graphs[J]. arXiv preprint arXiv:1604.04795, 2016.[https://arxiv.org/pdf/1604.04795.pdf]
# Singh G, Upadhyay D, Atre M. Efficient RDF dictionaries with B+ trees[C]//Proceedings of the ACM India Joint International Conference on Data Science and Management of Data. 2018: 128-136.[https://cse.iitk.ac.in/users/atrem/papers/cods-comad2018.pdf]

== 分布式数据管理 ==
# Gurajada S, Seufert S, Miliaraki I, et al. TriAD: a distributed shared-nothing RDF engine based on asynchronous message passing[C]//Proceedings of the 2014 ACM SIGMOD international conference on Management of data. 2014: 289-300.[http://resources.mpi-inf.mpg.de/d5/triad/mod375-gurajada.pdf]
# Schätzle A, Przyjaciel-Zablocki M, Skilevic S, et al. S2RDF: RDF querying with SPARQL on spark[J]. arXiv preprint arXiv:1512.07021, 2015.[https://arxiv.org/pdf/1512.07021.pdf]
# Shao B, Wang H, Li Y. Trinity: A distributed graph engine on a memory cloud[C]//Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data. 2013: 505-516.[https://dl.acm.org/doi/pdf/10.1145/2463676.2467799]
# Zeng K, Yang J, Wang H, et al. A distributed graph engine for web scale RDF data[J]. Proceedings of the VLDB Endowment, 2013, 6(4): 265-276.[https://www.graphengine.io/downloads/papers/Trinity.RDF.pdf]
# Zou L, Özsu M T, Chen L, et al. gStore: a graph-based SPARQL query engine[J]. The VLDB journal, 2014, 23(4): 565-590.[https://link.springer.com/article/10.1007/s00778-013-0337-7]
== 存储相关 ==
# Yuan P, Liu P, Wu B, et al. TripleBit: a fast and compact system for large scale RDF data[J]. Proceedings of the VLDB Endowment, 2013, 6(7): 517-528.[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.308.3012&rep=rep1&type=pdf]
# Urbani J, Jacobs C. Adaptive low-level storage of very large knowledge graphs[C]//Proceedings of The Web Conference 2020. 2020: 1761-1772.[https://arxiv.org/pdf/2001.09078.pdf]
# Li G, Zhou X, Sun J, et al. opengauss: An autonomous database system[J]. Proceedings of the VLDB Endowment, 2021, 14(12): 3028-3042.[https://www.vldb.org/pvldb/vol14/p3028-li.pdf]

数据管理小组

2022-04-09T06:39:05Z

2021244137: /* 子图匹配查询相关 */

== 综述 ==
# Z. Kaoudi and I. Manolescu. RDF in the Clouds: A Survey. VLDB J., 24(1):67–91, 2015. [https://link.springer.com/article/10.1007/s00778-014-0364-z]
# Özsu, M. Tamer. "A survey of RDF data management systems." Frontiers of Computer Science 10(3): 418-432, 2016.[https://link.springer.com/article/10.1007/s11704-016-5554-y]
# 邹磊, 彭鹏. 分布式 RDF 数据管理综述[J]. 计算机研究与发展, 54(6):1213-1224, 2017.[http://crad.ict.ac.cn/CN/abstract/abstract3420.shtml]
# I. Abdelaziz, R. Harbi, Z. Khayyat, P. Kalnis, A survey and experimental comparison of distributed SPARQL engines for very large RDF data, Proceedings of the Vldb Endowment 10(13):2049–2060,2017. [https://dl.acm.org/doi/abs/10.14778/3151106.3151109]
# Wylot, Marcin, et al. "RDF Data Storage and Query Processing Schemes: A Survey." ACM Computing Surveys (CSUR) 51(4):84, 2018.[https://dl.acm.org/doi/abs/10.1145/3177850]
# 王鑫, 邹磊, 王朝坤, 彭鹏, 冯志勇. 知识图谱数据管理研究综述. 软件学报, 30(7):2139-2174, 2019.[http://jos.org.cn/html/2019/7/5841.htm]
# Besta M, Peter E, Gerstenberger R, et al. Demystifying graph databases: Analysis and taxonomy of data organization, system designs, and graph queries[J]. arXiv preprint arXiv:1910.09017, 2019. [https://arxiv.org/abs/1910.09017]

== 子图匹配查询相关 ==
# A. K. Chandra, P. M. Merlin, Optimal implementation of conjunctive queries in relational databases, in ACM Symposium on Theory of Computing, pp.77–90, 1997. [https://dl.acm.org/doi/abs/10.1145/800105.803397]
# Z. Sun, H. Wang, H. Wang, B. Shao, J. Li, Efficient subgraph matching on billion node graphs, Proceedings of the VLDB Endowment 5(9):788–799, 2012.[https://arxiv.org/abs/1205.6691]
# L. Lai, L. Qin, X. Lin, L. Chang, Scalable subgraph enumeration in MapReduce, Proceedings of the VLDB Endowment 8(10):974–985, 2015. [https://dl.acm.org/doi/abs/10.14778/2794367.2794368]
# # P.Peng,L.Zou, M.T.Özsu, L.Chen, and D.Zhao.Processing SPARQL Queries over Distributed RDF Graphs. VLDB J., 25(2):243–268, 2016. [https://link.springer.com/article/10.1007/S00778-015-0415-0]
# L. Lai, L. Qin, X. Lin, Y. Zhang, L. Chang, S. Yang, Scalable distributed subgraph enumeration, Proceedings of the Vldb Endowment 10(3)217–228, 2016. [https://dl.acm.org/doi/abs/10.14778/3021924.3021937]
# I. Abdelaziz, R. Harbi, Z. Khayyat, P. Kalnis, A survey and experimental comparison of distributed SPARQL engines for very large RDF data, Proceedings of the Vldb Endowment 10(13):2049–2060, 2017. [https://dl.acm.org/doi/abs/10.14778/3151106.3151109]
# I. Abdelaziz, R. Harbi, Z. Khayyat, and P. Kalnis. A Survey and Experimental Comparison of Distributed SPARQL Engines for Very Large RDF Data. PVLDB, 10(13):2049–2060, 2017. [https://dl.acm.org/doi/abs/10.14778/3151106.3151109]
# Huang C, Zhang Q, Guo D, et al. GQARDF: An Efficient SPARQL Query Answering Engine on RDF Graphs[J], 2020.[http://www.semantic-web-journal.net/system/files/swj2922.pdf]
# Yang Z, Lai L, Lin X, et al. Huge: An efficient and scalable subgraph enumeration system[J]. arXiv preprint arXiv:2103.14294, 2021.[https://arxiv.org/pdf/2103.14294.pdf]
# Mehmood Q, Saleem M, Jha A, et al. Efficient distributed path computation on RDF knowledge graphs using partial evaluation[J]. World Wide Web, 2021: 1-32.[https://link.springer.com/article/10.1007/s11280-021-00965-5]
# Lassila O, Schmidt M, Bebee B, et al. Graph? Yes! Which one? Help![J]. arXiv preprint arXiv:2110.13348, 2021.[https://arxiv.org/pdf/2110.13348.pdf]
# Arroyuelo D, Hogan A, Navarro G, et al. Time-and Space-Efficient Regular Path Queries on Graphs[J]. arXiv preprint arXiv:2111.04556, 2021.[https://arxiv.org/pdf/2111.04556.pdf]
# Wu J, Chen R, Xia Y. Fast and Accurate Optimizer for Query Processing over Knowledge Graphs[C]//Proceedings of the ACM Symposium on Cloud Computing. 2021: 503-517.[https://dl.acm.org/doi/abs/10.1145/3472883.3486991]

== 编码 ==
# Singh G, Upadhyay D, Atre M. Efficient RDF dictionaries with B+ trees[C]//Proceedings of the ACM India Joint International Conference on Data Science and Management of Data. 2018: 128-136.[https://cse.iitk.ac.in/users/atrem/papers/cods-comad2018.pdf]
# Urbani J, Dutta S, Gurajada S, et al. KOGNAC: efficient encoding of large knowledge graphs[J]. arXiv preprint arXiv:1604.04795, 2016.[https://arxiv.org/pdf/1604.04795.pdf]
== 分布式数据管理 ==
# Gurajada S, Seufert S, Miliaraki I, et al. TriAD: a distributed shared-nothing RDF engine based on asynchronous message passing[C]//Proceedings of the 2014 ACM SIGMOD international conference on Management of data. 2014: 289-300.[http://resources.mpi-inf.mpg.de/d5/triad/mod375-gurajada.pdf]
# Schätzle A, Przyjaciel-Zablocki M, Skilevic S, et al. S2RDF: RDF querying with SPARQL on spark[J]. arXiv preprint arXiv:1512.07021, 2015.[https://arxiv.org/pdf/1512.07021.pdf]
# Shao B, Wang H, Li Y. Trinity: A distributed graph engine on a memory cloud[C]//Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data. 2013: 505-516.[https://dl.acm.org/doi/pdf/10.1145/2463676.2467799]
# Zeng K, Yang J, Wang H, et al. A distributed graph engine for web scale RDF data[J]. Proceedings of the VLDB Endowment, 2013, 6(4): 265-276.[https://www.graphengine.io/downloads/papers/Trinity.RDF.pdf]
# Zou L, Özsu M T, Chen L, et al. gStore: a graph-based SPARQL query engine[J]. The VLDB journal, 2014, 23(4): 565-590.[https://link.springer.com/article/10.1007/s00778-013-0337-7]
== 存储相关 ==
# Yuan P, Liu P, Wu B, et al. TripleBit: a fast and compact system for large scale RDF data[J]. Proceedings of the VLDB Endowment, 2013, 6(7): 517-528.[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.308.3012&rep=rep1&type=pdf]
# Urbani J, Jacobs C. Adaptive low-level storage of very large knowledge graphs[C]//Proceedings of The Web Conference 2020. 2020: 1761-1772.[https://arxiv.org/pdf/2001.09078.pdf]
# Li G, Zhou X, Sun J, et al. opengauss: An autonomous database system[J]. Proceedings of the VLDB Endowment, 2021, 14(12): 3028-3042.[https://www.vldb.org/pvldb/vol14/p3028-li.pdf]