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作者： Zhang, Jing Ye, Liuqi Business School Hohai University Nanjing Jiangsu 210098 China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Renmin University of China Beijing 100872 China

ISBN: (纸本)9781424453269

knowledge resource is becoming the most important in an organization, Its exploring degree has direct influence on the core competitive power of the organization. System is also an integral part, the basis and protection for keeping an organization working well, thus the organization realizes its knowledge sharing and exchange and knowledge resource exploring by establishing the group learning system, regular meeting system, the knowledge report system and the knowledge publicity system. Then knowledge can be transmitted widely in the organization and generate knowledge contributions, innovations and promote the core competitive power. © 2010 IEEE.

关键词： knowledge management

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VID Join： Mapping Trajectories to Points of Interest to Support Location-Based Services

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Journal of Computer Science & Technology 2015年第4期30卷 725-744页

作者：商烁谢珂心郑凯刘家俊文继荣 Department of Computer Science China University of Petroleum Beijing 102249 China School of Information Technology and Electrical Engineering The University of Queensland Brisbane QLD 4072 Australia School of Computer Science and Technology Soochow University Suzhou 215006 China Commonwealth Scientific and Industrial Research Organisation Kenmore QLD 4069 Australia Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Beijing 100080 China

Variable influence duration （VID） join is a novel spatio-temporal join operation between a set T of trajectories and a set P of spatial points. Here, trajectories are traveling histories of moving objects （e.g., travelers）, and spatial points are points of interest （POIs, e.g., restaurants）. VID join returns all pairs of （τs, p） if τs is spatially close to p for a long period of time, where τs is a segment of trajectory τ ∈ T and p ∈ P. Each returned （τs, p） implies that the moving object associated with τs stayed at p （e.g., having dinner at a restaurant）. Such information is useful in many aspects, such as targeted advertising, social security, and social activity analysis. The concepts of influence and influence duration are introduced to measure the spatial closeness between τ and p, and the time spanned, respectively. Compared to the conventional spatio-temporal join, the VID join is more challenging since the join condition varies for different POIs, and the additional temporal requirement cannot be indexed effectively. To process the VID join e？ciently, three algorithms are developed and several optimization techniques are applied, including spatial duplication reuse and time duration based pruning. The performance of the developed algorithms is verified by extensive experiments on real spatial data.

关键词： trajectory spatial database spatial join spatio-temporal join

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DB Facade: A web cache with improved data freshness

DB Facade: A web cache with improved data freshness

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2nd International Symposium on Electronic Commerce and Security, ISECS 2009

作者： Xiongpai, Qin Zhou, Xiaoyun Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing 100872 China School of Information and Electrical Engineering China University of Mining Technology Xuzhou Jiangsu 221008 China

ISBN: (纸本)9780769536439

Traditional web database cache techniques have a major disadvantage, namely poor data freshness, because they employ an asynchronous data refresh strategy. A novel web database cache, DB Façade, is proposed in this paper. DB Facade uses a main memory database to cache result sets of previous queries for subsequent reusing. Updates on backend database system are managed by delta tables, and then propagated to web database cache in a near real-time manner, hence guarantee the freshness of data. DB Facade offloads query burden from backend database systems, and exploits the power of main memory database system to boost query performance. TPC-W testing result shows that the system's performance increases by about 17 percent. © 2009 IEEE.

关键词： Facades

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Discovering Reliable Information Extraction Patterns with Pre-Trained Model for Text with Writing Style

Discovering Reliable Information Extraction Patterns with Pr...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bu, Chenyang Liu, Jiacheng Liu, Jiaxuan Ji, Shengwei Yang, Hongbin School of Computer Science and Information Engineering Ministry of Education Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Anhui Hefei230027 China School of Artificial Intelligence and Big Data Hefei University China

ISBN: (纸本)9798350337020

Large-scale pre-trained models such as GPT and BERT have demonstrated remarkable performance in information extraction tasks. However, their black-box nature poses challenges for reliability and interpretability. In contrast, rule- based extraction methods have better interpretability, but typically require domain experts to manually establish rules, limiting their generalization ability. In industry, there is often a demand for reliable knowledge extraction to reduce the time spent on manual verification of each piece of knowledge. In this paper, we explore the idea of combining GPT and symbolic-based methods to automatically discover reliable extraction patterns in text with a particular writing style. This method leverages the characteristics of high information density and similar writing patterns in text with a specific writing style to generate verifiable and reliable patterns. We conduct experiments on two datasets with a specific writing style to demonstrate its effectiveness, validating the idea of combining large models for reliable information extraction pattern discovery in the tested datasets. © 2023 IEEE.

关键词： Information extraction Reliable pattern Writing style data

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Inductive Relation Prediction with Logical Reasoning Using Contrastive Representations

Inductive Relation Prediction with Logical Reasoning Using C...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Pan, Yudai Liu, Jun Zhang, Lingling Zhao, Tianzhe Lin, Qika Hu, Xin Wang, Qianying School of Computer Science and Technology Xi'an Jiaotong University China National Engineering Lab for Big Data Analytics China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering China Lenovo Research Beijing China

Relation prediction in knowledge graphs (KGs) aims at predicting missing relations in incomplete triples, whereas the dominant embedding paradigm has a restriction on handling unseen entities during testing. In the real-world scenario, the inductive setting is more common because entities in the training process are finite. Previous methods capture an inductive ability by implicit logic in KGs. However, it would be challenging to preciously acquire entity-independent relational semantics of compositional logic rules and to deal with the deficient supervision of logic caused by the scarcity of relational semantics. To this end, we propose a novel graph convolutional network (GCN)-based model LogCo with logical reasoning by contrastive representations. LogCo firstly extracts enclosing subgraphs and relational paths between two entities to supply the entity-independence. Then a contrastive strategy for relational path instances and the subgraph is proposed for the issue of deficient supervision. The contrastive representations are learned for a joint training regime. Finally, prediction results and logic rules for reasoning are attained. Comprehensive experiments on twelve inductive datasets show that LogCo achieves outstanding performance comparing with SOTA inductive baselines. © 2022 Association for Computational Linguistics.

关键词： Forecasting

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ALOR: Adaptive layout optimization of raft groups for heterogeneous distributed key-value stores 1

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15th IFIP International Conference on Network and Parallel Computing, NPC 2018

作者： Wang, Yangyang Chai, Yunpeng Wang, Xin Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing China School of Information Renmin University of China Beijing China College of Intelligence and Computing Tianjin University Tianjin China

ISBN: (数字)9783030056773

ISBN: (纸本)9783030056766

Many distributed key-value storage systems employ the simple and effective Raft protocol to ensure data consistency. They usually assume a homogeneous node hardware configuration for the underlying cluster and thus adopt even data distribution schemes. However, today’s distributed systems tend to be heterogeneous in nodes’ I/O devices due to the regular worn I/O device replacement and the emergence of expensive new storage media (e.g., non-volatile memory). In this paper, we propose a new data layout scheme called Adaptive Layout Optimization of Raft groups (ALOR), considering the hardware heterogeneity of the cluster. ALOR aims to optimize the data layout of Raft groups to achieve a better practical load balance, which leads to higher performance. ALOR consists of two components: leader migration in Raft groups and skewed data layout based on cold data migration. We conducted experiments on a practical heterogeneous cluster, and the results indicate that, on average, ALOR improves throughput by 36.89%, reduces latency and 99th percentile tail latency by 24.54% and 21.32%, respectively. © IFIP International Federation for Information Processing 2018.

关键词： Digital storage

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A Metric Approach to Hot Topics in Biomedicine via Keyword Co-occurrence

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Journal of data and Information Science 2019年第4期4卷 13-25页

作者： Jane H.Qin Jean J.Wang Fred Y.Ye Jiangsu Key Laboratory of Data Engineering and Knowledge Service School of Information ManagementNanjing UniversityNanjing 210023China International Joint Informatics Laboratory(IJIL) Nanjing University-University of IllinoisNanjing-ChampaignChina-USA

Purpose:To reveal the research hotpots and relationship among three research hot topics in b iomedicine,namely CRISPR,iPS(induced Pluripotent Stem)cell and Synthetic ***/methodology/approach:We set up their keyword co-occurrence networks with using three indicators and information visualization for metric ***:The results reveal the main research hotspots in the three topics are different,but the overlapping keywords in the three topics indicate that they are mutually integrated and interacted each *** limitations:All analyses use keywords,without any other *** implications:We try to find the information distribution and structure of these three hot topics for revealing their research status and interactions,and for promoting biomedical ***/value:We chose the core keywords in three research hot topics in biomedicine by using h-index.

关键词： Keyword co-occurrence Network analysis Information visualization Biomedicine Hot topics CRISPR-Cas iPS cell Synthetic biology

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A method for measuring journal discriminative capacity and its application in WOS 15th

A method for measuring journal discriminative capacity and i...

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15th International Conference on Sustainable Digital Communities, iConference 2020

作者： Wang, Hao Zhang, Baolong Deng, Sanhong Su, Xinning School of Information Management Nanjing University Nanjing210023 China Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing210023 China

ISBN: (纸本)9783030436865

Journal discriminative capacity refers to the degree of difference between the journals in research subjects, and is of great significance for detecting the level of journal differentiation. Current research on journal discrepancies is predominantly focused on the quantitative analysis of journal content, and rarely measures the degree of journal difference. To address this lapse in research, a method from the perspective of difference is proposed in this paper to quantitatively measure and analyze the discriminative capacity of journals. Using the bibliographic of Science Citation Index (SCI), Social Science Citation Index (SSCI), and Arts & Humanities Citation Index (A&HCI) journals from 2015–2017 as the data source, the contents differences of 23 Library and Information Science (LIS) journals are analyzed through hierarchical clustering and journal discriminative capacity (JDC) measurement. The discriminative capacity of disciplines is then calculated and combined with multidimensional scaling analysis to detect the difference characteristics of journals in ten disciplines, and the mean JDC is used to explore the overall differences of disciplines. Results show that the discriminative capacity of LIS journals is obviously stratified, the journals of library science are the most discriminating, and the journals of evaluation and review are the weakest. In addition, the journals of different disciplines have distinct disciplinary characteristics, with the discriminative capacity of SCI journals as the most notable, followed by SSCI journals, and with A&HCI journals displaying the weakest discriminative capacity. © Springer Nature Switzerland AG 2020.

关键词： Digital libraries

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Extracting multi-records from web pages

Extracting multi-records from web pages

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4th International Conference on Semantics, knowledge, and Grid, SKG 2008

作者： Tian, Xia Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE Beijing 100872 China School of Information Resource Management Renmin University of China Beijing 100872 China

ISBN: (纸本)9780769534015

Extracting multi-records from web pages is useful, it allows us to integrate information from multiple sources to provide value-added services. Existing techniques still have some limitations because of their several restrictions and accuracy. This paper proposes a new method to perform multi-records extraction task automatically. Firstly, the HTML tag tree is build based on an embedded browser interface to solve the AJAX problem. Secondly, data regions are found out by data chunk comparison, and simple tree matching method is proposed to compute the chunk similarity. Finally, the main data region is determined and the multi-records are extracted out. Experimental results show that our method dramatically outperforms other existing methods, and it can extract multi-records from pages very accurately. © 2008 IEEE.

关键词： Websites

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Visual group identification method of technical competitors using LinLog graph clustering algorithm

Visual group identification method of technical competitors ...

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IEEE International Conference on Computer Science and Automation engineering

作者： Han, Hong-Qi An, Xiaomi Zhu, Donghua Wang, Xuefeng Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Ministry of Education Beijing 100872 China School of Management and Economics Beijing Institute of Technology Beijing 100872 China

ISBN: (纸本)9781424487257

Visualization technique is a powerful method used by science and technology intelligence analysis experts to identify technical competitor groups. Common visualization methods tend to create graphs meeting the aesthetic criteria instead of finding better clusters, and their analysis results may provide misleading information. A process model of technical group identification method was presented using LinLog graph clustering algorithm to find better competitor groups. In the model, technical similarity value of each pair of competitors is measured based on their R&D output in sub-fields, and two competitors have a link when they have high similarity value;LinLog algorithm, which is aimed at producing better clusters, was employed to layout graph with competitors as nodes, their links as edges and technology similarity values as weights of edges. Experiment results show the efficiency of presented method. © 2011 IEEE.

关键词： Visualization

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