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10th International Joint Conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2018

作者： Xie, Sherry L. School of Information Resource Management Renmin University of China 59 Zhongguancun Ave. Beijing China Center for Digital Records Management Research Beijing China Key Laboratory of Knowledge and Data Engineering Ministry of Education of China China

ISBN: (纸本)9789897583308

The Research on Balancing Theories and Mechanisms to Pervasive Information Systems (B2P) is one of the component studies of the Records-centered Digital Information Management Theory and Mechanisms (DI{R}Mtm) Project, which aims pointedly at synchronizing records/information related research interests with the evolution of information technologies and their joint impact on society. Due to the breakthroughs in cloud computing, big data, the Internet of Things and artificial intelligence, ubiquitous/pervasive computing, conceived in 1988, has now been rapidly advanced. As its materialization in organizations, pervasive information systems (PISs) converges information technologies, data, digital operations and human actors and functions as the main channel for digital decisions. Digital decisions, therefore, are autonomous, algorithmic and penetrating, possessing influential abilities that are much greater than any single technology in human history, including the Internet. Such abilities make it a strong force for societal advancement, yet, at the same time, a potential hurdle – even harm – for decision recipients who are unaware of the decision-making methods. The B2P study is conceived as a reaction to this phenomenon. This paper gives an overview of the B2P study and calls for collaborations among researchers from the various fields relevant to the aims of the study, i.e., archival science, records management, information science, pubic administration, law, social sciences, and computer sciences. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： Information management

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Adaptive State Continuity-Based Sparse Inverse Covariance Clustering for Multivariate Time Series

Adaptive State Continuity-Based Sparse Inverse Covariance Cl...

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International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)

作者： Lei Li Wei Li Jianxing Liao Xuegang Hu Ministry of Education Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Hefei China

ISBN: (数字)9781728159287

ISBN: (纸本)9781728159294

Compared with univariate time series clustering, multivariate time series (MTS) clustering has become a challenging research topic on the data mining of time series. In this paper, a novel model-based approach Adaptive State Continuity-Based Sparse Inverse Covariance Clustering (ASCSICC) is proposed for MTS clustering. Specifically, the state continuity is introduced to make the traditional Gaussian mixture model (GMM) applicable to time series clustering. To prevent overfitting, the alternating direction method of multipliers (ADMM) is applied to optimize the parameter of GMM inverse covariance. In addition, the proposed approach simultaneously segments and clusters the time series. Technically, first, the adaptive state continuity is estimated based on the distance similarity of adjacent time series data. Then, a dynamic programming algorithm of cluster assignment by adaptive state continuity is taken as the E-step, and the ADMM for solving sparse inverse covariance is taken as the M-step. E-step and M-step are combined into an Expectation-Maximization (EM) algorithm to conduct the clustering process. Finally, we show the effectiveness of the proposed approach by comparing the ASC-SICC with several state-of-the-art approaches in experiments on two datasets from real applications.

关键词： Time series analysis Clustering algorithms Covariance matrices Security Pattern analysis Feature extraction Cybernetics

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K-nearest neighbors in uncertain graph

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2011年第10期48卷 1850-1858页

作者： Zhang, Yinglong Li, Cuiping Chen, Hong Du, Lingxia Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Ministry of Education Beijing 100872 China School of Computer and Information Engineer Jiangxi Agriculture University Nanchang 330045 China

In many areas, a lot of data have been modeled by graphs which are subject to uncertainties, such as molecular compounds and protein interaction networks. While many real applications, for example, collaborative filtering, fraud detection, and link prediction in social networks etc, rely on efficiently answering k-nearest neighbor queries (kNN), which is the problem of computing the most "similar" k nodes to a given query node. To solve the problem, in this paper a novel method based on measurement of SimRank is proposed. However, because graphs evolve over time and are uncertainly, the computing cost can be very high in practice to solve the problem using the existing algorithms of SimRank. So the paper presents an optimization algorithm. Introducing path threshold, which is suitable in both determined graph and uncertain graph, the algorithm merely considers the local neighborhood of a given query node instead of whole graph to prune the search space. To further improving efficiency, the algorithm adopts sample technology in uncertain graph. At the same time, theory and experiments interpret and verify that the optimization algorithm is efficient and effective.

关键词： Collaborative filtering

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The Approximation Operators Within Formal Contexts

The Approximation Operators Within Formal Contexts

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International Conference on Multimedia and Ubiquitous engineering (MUE)

作者： Ming-Wen Shao Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang Jiangxi China

In this paper, based on concept lattices and dual concept lattices, we introduced a pair of rough set approximation operators within formal contexts. The proposed approximations operators don't require the equival... 详细信息

关键词： Lattices Set theory Finance data analysis Bismuth Information technology Laboratories knowledge engineering Artificial intelligence Uncertainty

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Sampling topic representative users by integrating node degree and edge weight 5

Sampling topic representative users by integrating node degr...

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5th IEEE International Conference on data Science in Cyberspace, DSC 2020

作者： Wang, Jiangwen Ban, Zhijie Inner Mongolia A.R. Key Laboratory of Data Mining and Knowledge Engineering College of Computer Science Inner Mongolia University Huhhot China China Banking and Insurance Information Technology Management Co. Ltd Beijing China

ISBN: (纸本)9781728195582

Finding a subset of users to statistically represent the original social network in terms of attributes has received a lot of attention recently. The existing literature had proved that the problem is NP-Hard and proposed some sampling models, but representative degrees aren't systematically studied. Taking node degree and edge weight into account, we present a topic sampling model for the whole social network in this paper. Firstly, by statistically calculating node degrees for one real dataset, we find that one node is more likely to be a representative user if its degree is high. We then give some definitions about representative degrees. Based on statistical stratified sample, we finally give a sampling model of topic representative users who are from most or all attribute groups in the network. Experimental results show that our algorithm significantly outperforms other six baseline methods and is also effective in terms of time complexity. © 2020 IEEE.

关键词： Sampling

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Co-design of information systems with digital records management: A proposal for research 7

Co-design of information systems with digital records manage...

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7th International Joint Conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2015

作者： Xie, Sherry L. Key Laboratory of Data Enginerring and Knowledge Engineering Ministry of Education Beijing China Center for Electronic Records Management Research Beijing China School of Information Resource Management Renmin Univeristy of China 59 Zhonguancun Ave Beijing China

ISBN: (纸本)9789897581588

This position paper, via rationalizing the potentials of carrying out a carefully crafted research project, argues that the three fields of enterprise information system, business process management, and digital records management should join forces in serving the needs of organizations, regardless of their types. By preliminary findings uncovered during the conceiving process of the project, it advocates that the best point of convergence is the design phase, a basic step for all of the three fields and an increasingly critical one in today's digital environment. © 2015 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.

关键词： Information systems

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Using Ontology to Enhance Collaborative Recommendation Based on Community

Using Ontology to Enhance Collaborative Recommendation Based...

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International Conference on Web-Age Information Management Workshops (WAIM)

作者： Li Yu School of Information Key Laboratory of Data Engineering and Knowledge Engineerin Renmin University of China Beijing China

ISBN: (纸本)9781424432004;9780769531854

Collaborative filtering is an important personalized recommendation technique applied widely in E-commerce. It is not adapted to multi-interest or title recommendation for the 'general neighbourhood' problem which is analyzed in this paper. Based on it, collaborative filtering recommendation based on community is presented by introducing the concept 'community neighbourhood' in the paper. Unfortunately, it results into severer sparsity problem which makes heavy effect on its performance. In order to overcome it, an ontological A-priori score is used to infer user preference and to pre-fill null rating first. After pre-filling using the ontology method, then collaborative filtering based on community is executed based on a dense rating matrix. The experiment shows that collaborative filtering based on community makes generally better performance than traditional method when data is not very sparse, and ontology method can truly enhance collaborative filtering based on community since the sparsity is overcame.

关键词： Communities Ontologies Filtering Collaboration Motion pictures Filling Argon

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Design of Wireless data Center Network Structure Based on ExCCC-DCN 6th

Design of Wireless Data Center Network Structure Based on Ex...

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6th International Conference on data Science, ICDS 2019

作者： Jing, Yanhao Han, Zhijie Du, Xiaoyu Zhang, Qingfang School of Computer and Information Engineering Henan University KaifengHenan475004 China Institute of Data and Knowledge Engineering Henan University KaifengHenan475004 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Network NanjingJiangsu210003 China

ISBN: (纸本)9789811528095

A data center is a cluster of servers, which is typically an organic collection of tens of thousands of servers. The sheer number of servers determines how well its performance is related to how it is interconnected. Just like the topology of Ethernet determines its network capacity and communication characteristics, the network structure of the data center has a great impact on its performance and capacity. The research object ExCCC-DCN in this paper has excellent node capacity, and its construction cost and operating energy consumption are relatively low, which is very suitable for deploying large-scale data centers. However, ExCCC-DCN is deployed on a wired link, and a large number of wired links can make maintenance and upgrade of the data center more difficult. Therefore, this paper utilizes the advantages of high transmission rate, strong anti-interference and high security of 60 GHz millimeter wave to wirelessly transform ExCCC-DCN, making it more flexible in the case of maintaining communication efficiency. © Springer Nature Singapore Pte Ltd 2020.

关键词： Millimeter waves

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knowledge Fragment Cleaning in a Genealogy knowledge Graph

Knowledge Fragment Cleaning in a Genealogy Knowledge Graph

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IEEE International Conference on Big knowledge (ICBK)

作者： Guliu Liu Lei Li Key Laboratory of Knowledge Engineering with Big Data (Heifei Unversity of Technology) Ministry of Education Heifei China

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

As an important topic of artificial intelligence, knowledge graphs have a wide range of applications such as semantic search, intelligent question answering, and visual decision support. Among them, the genealogy knowledge graph, as a kind of domain knowledge graph, has important application value in genetic disease analysis, population behavior analysis, etc. In the case of multiple data sources and multi-person collaboration, the construction of a genealogy knowledge graph involves the techniques of knowledge representation, knowledge acquisition, and knowledge fusion. In the knowledge fusion process, there are many situations such as the lack and chaos of a relationship, redundant entities, the isolation of some entities and knowledge fragments. How to effectively detect and process these problematic knowledge fragments is significant to the construction of a genealogy knowledge graph. In response to this problem, we propose a method for cleaning the problematic knowledge fragments in a genealogy knowledge graph. The method consists of three phases. In phase 1, we propose a method for detecting and analyzing the problematic knowledge fragments. In phase 2, we design a method for supplementing the entity-relationship of people for different error patterns and a method fusion method for the aligned entity. In phase 3, for the cleaning of isolated knowledge fragments, we propose an entity alignment method based on the father-son relationship and people's names and a connection method of isolated knowledge fragments. Finally, we conduct experiments on a family tree dataset of the Huapu System, and the experimental results indicate the effectiveness and practicality of the method.

关键词： Cleaning Big data knowledge acquisition Redundancy Organizations databases

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Neural Collaborative Recommendation with knowledge Graph

Neural Collaborative Recommendation with Knowledge Graph

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IEEE International Conference on Big knowledge (ICBK)

作者： Lei Sang Lei Li Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Hefei China

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

knowledge Graph (KG), which commonly consists of fruitful connected facts about items, presents an unprecedented opportunity to alleviate the sparsity problem in recommender system. However, existing KG based recommendation methods mainly rely on handcrafted meta-path features or simple triple-level entity embedding, which cannot automatically capture entities' long-term relational dependencies for the recommendation. In this paper, a two-channel neural interaction method named knowledge Graph enhanced Neural Collaborative Filtering with Residual Recurrent Network (KGNCF-RRN) is proposed, which leverages both long-term relational dependencies KG context and user-item interaction for recommendation. (1) For the KG context interaction channel, we propose a Residual Recurrent Network (RRN) to construct context-based path embedding, which incorporates residual learning into traditional recurrent neural networks (RNNs) to efficiently encode the long-term relational dependencies of KG. The self-attention network is then applied to the path embedding to capture the polysemy of various user interaction behaviours. (2) For the user-item interaction channel, the user and item embeddings are fed into a newly designed two-dimensional interaction map. (3) Finally, above the two-channel neural interaction matrix, we employ a convolutional neural network to learn complex correlations between user and item. Extensive experimental results on three benchmark datasets show that our proposed approach outperforms existing state-of-the-art approaches for knowledge graph based recommendation.

关键词： Collaboration Recurrent neural networks knowledge engineering Semantics Motion pictures Context modeling Filtering

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