Difference between revisions of "2019YFE0198600"
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# Deep Attributed Network Representation Learning of Complex Coupling and Interaction [https://www.sciencedirect.com/science/article/pii/S0950705120307474] | # Deep Attributed Network Representation Learning of Complex Coupling and Interaction [https://www.sciencedirect.com/science/article/pii/S0950705120307474] | ||
# WATuning: A Workload-Aware Tuning System with Attention-Based Deep Reinforcement Learning [https://link.springer.com/content/pdf/10.1007/s11390-021-1350-8.pdf] | # WATuning: A Workload-Aware Tuning System with Attention-Based Deep Reinforcement Learning [https://link.springer.com/content/pdf/10.1007/s11390-021-1350-8.pdf] | ||
| − | # 知识图谱划分算法研究综述 | + | # 知识图谱划分算法研究综述 [http://cjc.ict.ac.cn/online/cre/wx-202049111757.pdf] |
# KGDB：统一模型和语言的知识图谱数据库管理系统 [https://www.ccf.org.cn/ccfdl/ccf_dl_focus/Computer_Research/volume5/zllb3/2022-04-15/760763.shtml] | # KGDB：统一模型和语言的知识图谱数据库管理系统 [https://www.ccf.org.cn/ccfdl/ccf_dl_focus/Computer_Research/volume5/zllb3/2022-04-15/760763.shtml] | ||
| − | # 开放领域知识图谱问答研究综述 | + | # 开放领域知识图谱问答研究综述 [http://fcst.ceaj.org/CN/10.3778/j.issn.1673-9418.2106095] |
# HET-KG：Communication-Efficient Knowledge Graph Embedding Training via Hotness-Aware Cache [https://ieeexplore.ieee.org/abstract/document/9835364] | # HET-KG：Communication-Efficient Knowledge Graph Embedding Training via Hotness-Aware Cache [https://ieeexplore.ieee.org/abstract/document/9835364] | ||
# R2B: High-Efficiency and Fair I/O Allocation for Multiple tenants with Differentiated Demands | # R2B: High-Efficiency and Fair I/O Allocation for Multiple tenants with Differentiated Demands | ||
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# Mass Screening for Low Bone Density Using Basic Check-Up Items | # Mass Screening for Low Bone Density Using Basic Check-Up Items | ||
# Field-aware attentive neural factorization with fuzzy mutual information for company investment valuation | # Field-aware attentive neural factorization with fuzzy mutual information for company investment valuation | ||
| − | # Auto-tuning; Database optimization; Correlation expert rules; reinforcement learning; | + | # Auto-tuning; Database optimization; Correlation expert rules; reinforcement learning; Training time reduction |
| − | Training time reduction | ||
=== 专利 === | === 专利 === | ||
Revision as of 08:40, 5 November 2022
成果
论文
- OntoCSM：Ontology-Aware Characteristic Set Merging for RDF Type Discovery [1]
- vRaft: Accelerating the Distributed Consensus Under Virtualized Environments
- Optimal Subgraph Matching Queries over Distributed Knowledge Graphs Based on Partial Evaluation [2]
- OntoSP: Ontology-Based Semantic-Aware Partitioning on RDF Graphs [3]
- XTuning: Expert Database Tuning System Based on Reinforcement Learning [4]
- PAIRPQ: An Efficient Path Index for Regular Path Queries on Knowledge Graphs [5]
- Rethink the Linearizability Constraints of Raft for Distributed Key-Value Stores [6]
- UniKG：A Unified Interoperable Knowledge Graph Database System [7]
- Deep Attributed Network Representation Learning of Complex Coupling and Interaction [8]
- WATuning: A Workload-Aware Tuning System with Attention-Based Deep Reinforcement Learning [9]
- 知识图谱划分算法研究综述 [10]
- KGDB：统一模型和语言的知识图谱数据库管理系统 [11]
- 开放领域知识图谱问答研究综述 [12]
- HET-KG：Communication-Efficient Knowledge Graph Embedding Training via Hotness-Aware Cache [13]
- R2B: High-Efficiency and Fair I/O Allocation for Multiple tenants with Differentiated Demands
- OntoCA: Ontology-Aware Caching for Distributed SPARQL Query
- LightGC
- KGVQL：A knowledge graph visual query language with bidirectional transformations [14]
- Knowledge-enhanced attentive learning for answer selection in community question answering systems.pdf [15]
- Locally weighted factorization machine with fuzzy partition for elderly readmission prediction [16]
- Optimizing subgraph matching over distributed knowledge graphs using partial evaluation [17]
- FPIRPQ: Accelerating Regular Path Queries on Knowledge Graphs
- MacroTrend
- DB4Trans：数据库内置知识图谱嵌入模型训练引擎
- 新一代知识图谱关键技术综述 [18]
- Mass Screening for Low Bone Density Using Basic Check-Up Items
- Field-aware attentive neural factorization with fuzzy mutual information for company investment valuation
- Auto-tuning; Database optimization; Correlation expert rules; reinforcement learning; Training time reduction
专利
软著
技术
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
