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检索条件"机构=Key Laboratory of Data Engineering and Knowledge Engineering and School of Business"

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Modeling User_s Cognitive Structure in Contextual Information Retrieval

Modeling User_s Cognitive Structure in Contextual Informatio...

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International Conference on Fuzzy Systems and knowledge Discovery (FSKD)

作者： Xuan Tian Xiaoyong Du He Hu Haihua Li School of Information Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin University of China Beijing China

In contextual information retrieval, the retrieval of information depends on the time and place of submitting query, history of interaction, task in hand, and many other factors that are not given explicitly but implicitly lie in the interaction and surroundings of searching, namely the context. User's cognition is one of important contextual factors for understanding his or her personal needs. We propose a model called DOSAM to get user's individual cognitive structure on domain knowledge. DOSAM is developed from the spreading-activation model of psychology and is established on the domain ontology. The cost analysis of algorithm shows that it is feasible to get cognitive structure by DOSAM. Personalized search experimental results on digital library indicate that DOSAM can help improve the search effectiveness and user's satisfaction.

关键词： Context modeling Information retrieval Ontologies Cognition History Psychology Helium Laboratories data engineering knowledge engineering

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Cross-Modal knowledge Graph Construction for Multiple Food Additives 18th

Cross-Modal Knowledge Graph Construction for Multiple Food ...

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18th Chinese Intelligent Systems Conference, CISC 2022

作者： Wang, Di Li, Haisheng Wang, Wei Qiao, Libing School of Computer Science and Engineering Beijing Technology and Business University Beijing100048 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing100048 China National Engineering Laboratory for Agri-product Quality Traceability Beijing100048 China

ISBN: (纸本)9789811962257

Aiming at the food safety problems caused by the improper use of food additives, a cross-modal knowledge graph for multiple food additives is constructed based on the data obtained from the Internet. Firstly, we design a model of the Resource Description Framework graph to extract the knowledge from structured text. Subsequently, semantic role tagging and dependency syntactic parsing are used to extract knowledge from unstructured text. Finally, we add images to the knowledge graph as cross-modal attributes by semantic label sets. Overall, we propose a novel cross-modal knowledge graph construction framework, which lays the foundation for intelligent question answering systems and supervision platforms for multiple food additives. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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Unsupervised vehicle re-identification with progressive adaptation 29

Unsupervised vehicle re-identification with progressive adap...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Peng, Jinjia Wang, Yang Wang, Huibing Zhang, Zhao Fu, Xianping Wang, Meng College of Information and Science Technology Dalian Maritime University Liaoning Dalian China Pengcheng Laboratory Shenzhen Guangdong China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology China School of Computer Science and Information Engineering Hefei University of Technology China

ISBN: (纸本)9780999241165

Vehicle re-identification (reID) aims at identifying vehicles across different non-overlapping cameras views. The existing methods heavily relied on well-labeled datasets for ideal performance, which inevitably causes fateful drop due to the severe domain bias between the training domain and the real-world scenes;worse still, these approaches required full annotations, which is labor-consuming. To tackle these challenges, we propose a novel progressive adaptation learning method for vehicle reID, named PAL, which infers from the abundant data without annotations. For PAL, a data adaptation module is employed for source domain, which generates the images with similar data distribution to unlabeled target domain as "pseudo target samples". These pseudo samples are combined with the unlabeled samples that are selected by a dynamic sampling strategy to make training faster. We further proposed a weighted label smoothing (WLS) loss, which considers the similarity between samples with different clusters to balance the confidence of pseudo labels. Comprehensive experimental results validate the advantages of PAL on both VehicleID and VeRi-776 dataset. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Vehicles

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Mining Approximate Closed Frequent Itemsets over Stream

Mining Approximate Closed Frequent Itemsets over Stream

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ACIS International Conference on Software engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD)

作者： Haifeng Li Zongjian Lu Hong Chen School of Information Key Laboratory of Data Engineering and Knowledge EngineeringMOEg Renmin University of China Beijing China

ISBN: (纸本)9780769532636

Frequent itemset mining is a very important problem in data mining. Closed frequent itemsets is the condensed representation of frequent itemsets thus spend less memory, so it is much suitable for stream mining. But on the other hand, when the minimum support is much lower, the size of closed frequent itemsets turns larger, which makes the performance reduced a lot. In this paper, we introduce a threshold to approximately mine closed frequent itemsets with a limited error tolerance. A new algorithm named ACFIM is proposed based on the introduction of the distance conception to mine the sliding window of stream, in which more data are pruned and more computation time are saved, so it much raise the performance in running time and memory comparing to the state-of-art closed frequent itemsets mining methods. Our experimental results over real-life datasets show that ACFIM is effective and efficient.

关键词： Itemsets data mining Distance measurement Pediatrics Approximation algorithms Approximation methods databases

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Geometric Routing Precluding Loops and Dead Ends in 3-D Wireless Sensor Networks

Geometric Routing Precluding Loops and Dead Ends in 3-D Wire...

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2010 IEEE Global Telecommunications Conference GLOBECOM 2010

作者： Jun Duan Deying Li Wenping Chen Key Laboratory of Data Engineering and Knowledge Engineering MOE School of Information Renmin University of China Beijing China

Numerous algorithms on geometric networks has been studied, and most of them were based on 2-dimensional networks. But 2-dimensional geometric routing algorithms cannot be directly adapted to the 3-dimensional networks. In this paper, we propose routing algorithms based on the iteration of specific angles on the networks of Delaunay Triangulation in 3D space, and prove the certainty of data transmission of our routing algorithms. In the algorithms, the messages only need to carry information of O(1) nodes and each node just keeps 1-hop neighbors' information.

关键词： Routing Peer to peer computing Three dimensional displays Algorithm design and analysis Compass Wireless sensor networks Relays

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Cutting Learned Index into Pieces: An In-depth Inquiry into Updatable Learned Indexes

Cutting Learned Index into Pieces: An In-depth Inquiry into ...

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International Conference on data engineering

作者： Jiake Ge Boyu Shi Yanfeng Chai Yuanhui Luo Yunda Guo Yinxuan He Yunpeng Chai Key Laboratory of Data Engineering and Knowledge Engineering MOE China School of Information Renmin University of China China

Numerous high-performance updatable learned indexes have recently been designed to support the writing requirements in practical systems. Researchers have proposed various strategies to improve the availability of updatable learned indexes. However, it is unclear which strategy is more profitable. Therefore, we deconstruct the design of learned indexes into multiple dimensions and in-depth evaluate their impacts on the overall performance, respectively. Through the in-depth exploration of learned indexes, we reckon that the approximation algorithm is the most crucial design dimension for improving the performance of the learned indexes rather than the popular works that focus on the learned index structure. Moreover, this paper makes a comprehensive end-to-end evaluation based on a high-performance key-value store to answer people’s concerns about which learned index is better and whether learned indexes can outperform traditional ones. Finally, according to end-to-end and in-depth evaluation results, we give some constructive suggestions on designing a better learned index in these dimensions, especially how to design an excellent approximate algorithm to improve the lookup and insertion performance of learned indexes.

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Sentiment classification via L2-norm deep belief network 11

Sentiment classification via L2-norm deep belief network

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20th ACM Conference on Information and knowledge Management, CIKM'11

作者： Liu, Tao Li, Minghui Zhou, Shusen Du, Xiaoyong Key Laboratory of Data Engineering and Knowledge Engineering MOE 100872 Beijing China School of Information Renmin University of China 100872 Beijing China Microsoft Asian Research and Development Group 100080 Beijing China Shenzhen Graduate School Harbin Institute of Technology 518055 Shenzhen China

ISBN: (纸本)9781450307178

Automatic analysis of sentiments expressed in large scale online reviews is very important for intelligent business applications. Sentiment classification is the most popular task of sentiment analysis, which is more challenging than traditional topic-based text classification. Basic features, such as vocabulary words, are not enough to classify sentiments well. Deep Belief Network (DBN) is introduced to discover more abstract features of sentiments. To capture full information of the features, large-size network can be constructed, but at the same time, large-size network tends to over fit the training data and even noise, which will reduce the generalization ability of the network. In this paper, L2-norm Deep Belief Network (L2DBN) is proposed, which uses L2-norm regularization to optimize the network parameters of DBN. L2DBN is first initialized by an unsupervised layer-wise training algorithm, and then fine-tuned by a supervised procedure. Network parameters are optimized using both classification loss and network complexity. Experimental results show that the proposed L2DBN outperforms the state-of-the-art method and the basic DBN on golden, noisy and heterogeneous datasets. © 2011 ACM.

关键词： Sentiment analysis

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Optimizing Random Access to Hierarchically-Compressed data on GPU

Optimizing Random Access to Hierarchically-Compressed Data o...

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Supercomputing Conference

作者： Feng Zhang Yihua Hu Haipeng Ding Zhiming Yao Zhewei Wei Xiao Zhang Xiaoyong Du Key Laboratory of Data Engineering and Knowledge Engineering (MOE) School of Information Renmin University of China Beijing China

ISBN: (纸本)9781665454452

GPU's powerful computational capacity holds great potentials for processing hierarchically-compressed data without decompression in data science domain. Unfortunately, existing GPU approaches offer only traversal-based data analytics; random access is extremely inefficient, substantially limiting their utility. To solve this problem, we develop a novel and broadly applicable optimization that enables efficient random access to hierarchically-compressed data without decompression in GPU memory. We address three major challenges for enabling efficient random access to compressed data on GPUs. The first challenge is designing GPU data structures that support random access. The second challenge is efficiently generating data structures on GPU. Generating data structures for random access is costly on the CPU, and the inefficiency increases dramatically when PCIe data transmission is incorporated. The third challenge is query processing on compressed data in GPU memory. Random accesses, including data updates, result in significant conflicts between massive threads. To solve the first challenge, we propose and modify a number of compressed data structures, including indexing within the complicated GPU memory hierarchy. To address the second challenge, we develop a two-phase process for generating these data structures on the GPU. To handle the third challenge, we propose a double-parsing design to avoid data conflicts. We evaluate our solution on two GPU platforms using five real-world datasets. Experiments show that the random access operations on GPU can achieve 65.04x average speedup compared to the state-of-the-art method.

关键词： Limiting Query processing Instruction sets High performance computing Memory management Graphics processing units data science

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Research on Web Services Selection Model Based on AHP

Research on Web Services Selection Model Based on AHP

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2008 IEEE International Conference on Service Operations and Logistics, and Informatics(IEEE/SOLI’2008)(IEEE服务运作、物流与信息年会)

作者： Bei Wu Meiyun Zuo Shijuan Wang Information School and Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin University of China BeijingChina

This paper focuses on the problem that how to select the optimal service among many Web services which all meet the functional needs,establishes an index system for Web services products selection from four aspects,namely the supply side,the user,product and *** on this,we collect the views of 30 experts by Analytic Hierarchy Process (AHP) method and calculate the weight of each index at all levels based on the data collected from questionnaire *** the overall sample data analysis,we put two types of sample data namely business operation experts and academics for comparative *** Web services selection model proposed in this article can provide the reference to Web services managers when they selecting Web services,and also contributes to in-depth research on the adoption of Web services based information system.

关键词： web services selection model analytic hierarchy process information system

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Improve Frequent Closed Itemsets Mining over data Stream with Bitmap

Improve Frequent Closed Itemsets Mining over Data Stream wit...

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ACIS International Conference on Software engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD)

作者： Haifeng Li Hong Chen Key Laboratory of Data Engineering and Knowledge Engineering MOESchool of Information Renmin University of China Beijing China

ISBN: (纸本)9780769532639

Frequent itemsets mining is an important problem in data mining. Frequent closed itemsets mining provides complete and condensed information for frequent pattern analysis thus reduces the memory cost without accuracy loss. More research focus on stream mining with the more application of stream. Stream is fast and unlimited thus data had to be stored in limited memory, how to save running time and memory usage is the most important target. In this paper, we propose an improved frequent closed itemsets mining method based on traditional stream mining algorithm CFI-stream with bitmap coding named CLIMB (closed itemset mining with bitmap) over stream's sliding window. The distinct items are maintained in memory in lexicographic order and each itemset is coded to bit-sequence with the order of items, moreover, the bit-sequence is split into sections to be recoded to reduce the memory cost. The experimental results on real-life show that CLIMB algorithm is effective and efficient.

关键词： Itemsets data mining Encoding Decoding data structures Runtime Algorithm design and analysis

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