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23rd ACM International Conference on Information and knowledge Management, CIKM 2014

作者： Li, Rui Zhang, Xiao Zhou, Xin Wang, Shan Key Laboratory of Data Engineering and Knowledge Engineering of the Ministry of Education Renmin University of China Beijing China School of Information Renmin University of China Beijing China

ISBN: (纸本)9781450325981

It is insufficient to search temporal text by only focusing on either time attribute or keywords today as we pay close attention to the evolution of event with time. Both temporal and textual constraints need to be considered in one single query, called Top-k Interval keyword Query (TIKQ). In this paper, we presents a cloud-based system named INK that supports efficient execution of TIKQs with appropriate effectiveness on Hadoop and HBase. In INK, an Adaptive Index Selector (AIS) is devised to choose the better execution plan for various TIKQs adaptively based on the proposed cost model, and leverage two novel hybrid index modules (TriI and IS-Tree) to combine keyword and interval filtration seamlessly.

关键词： Search engines

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Measuring semantic association in domain ontology

Measuring semantic association in domain ontology

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3rd International Conference on Semantics, knowledge, and Grid, SKG 2007

作者： Tian, Xuan Li, HaiHua Du, Xiaoyong School of Information Renmin University of China Beijing 100872 China School of Information Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering Beijing 100872 China

ISBN: (纸本)0769530079

In domain ontology, Semantic Association(SA) is used to depict the correlation between two concepts. In this paper, we define Semantic Association Degree(SAD) for measuring SA in the domain ontology. We first present a method to measure SAD of two direct related concepts by evaluating the semantic relationship between them, and then give another method to measure SAD of two indirect related concepts though SAD of two directed neighboring concepts. A set of comparison experiments show the benefit of our approaches. © 2007 IEEE.

关键词： Semantics

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Dynamic balanced storage in wireless sensor networks 07

Dynamic balanced storage in wireless sensor networks

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DMSN '07: 4th Workshop on data Management for Sensor Networks

作者： Lai, Yongxuan Chen, Hong Wang, Yufeng School of Information Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering MOE No.59 Zhongguancun Street Haidian District Beijing China

ISBN: (纸本)9781595939111

data-centric storage is an effective and important technique in the wireless sensor networks. It stores the sensing data according to their values by mapping them to some point in the network in order to avoid routing all the values outside the network and flooding the queries. However, in most data-centric storage schemes, there is a "hotspot" problem due to the skewness of data and randomness of the mapping functions. Large number of sensor readings (events) may be routed to the same point by the predefined hashed function. In this paper, we propose a new Dynamic BAlanced data-centric Storage (DBAS) scheme, a cooperative strategy between the base station and the in-network processing in wireless sensor network. Our scheme, which utilizes the rich resources in the base station and is aware of the data distributions of the network, dynamically adjusts the mappings from readings to the storage points to balance the storage and workload in the network, as well as to reduce the cost of storing these readings. Moreover, it takes advantage of perimeter routing algorithm of the GPSR routing protocol to store multiple copies of readings to improve the robustness of the network with little overhead. Simulation results show that DBAS is more balanced and energy efficient than the traditional data-centric storage mechanism in wireless sensor network.

关键词： Wireless sensor networks

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Bootstrap-Based Layerwise Refining for Causal Structure Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2708-2722页

作者： Xiang, Guodu Wang, Hao Yu, Kui Guo, Xianjie Cao, Fuyuan Song, Yukun Hefei University of Technology Key Laboratory of Knowledge Engineering with the Big Data of Ministry of Education Hefei230601 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China Shanxi University School of Computer and Information Technology Taiyuan030006 China

Learning causal structures from observational data is critical for causal discovery and many machine learning tasks. Traditional constraint-based methods first adopt conditional independence (CI) tests to learn a global skeleton layer by layer and then orient the undirected edges to obtain a causal structure. However, the reliability of these statistical tests largely depends on the quality of data samples. In real-life scenarios, the presence of data noise or limited samples often makes many CI tests unreliable at each layer in the skeleton learning phase, leading to an inaccurate skeleton. As the number of layers increases, the inaccurate skeleton will continue to impair the skeleton construction of subsequent layers. Furthermore, an unreliable skeleton hampers the skeleton orientation procedure, resulting in an unsatisfactory causal structure. In this article, we propose a Bootstrap-based layerwise refining (BLR) algorithm for causal structure learning, which includes two new procedures to solve the above problems. First, BLR utilizes a novel layerwise skeleton refining procedure to construct the global skeleton layer by layer based on the bootstrap sampling. Second, BLR employs a collective skeleton orientation procedure that incorporates scoring techniques to collectively orient the global skeleton. The experimental results show that BLR outperforms the state-of-the-art methods on the benchmark Bayesian Network datasets. © 2020 IEEE.

关键词： Refining

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RETRACTED ARTICLE: knowledge management technologies and applications: A literature review

RETRACTED ARTICLE: Knowledge management technologies and app...

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作者： An, Xiaomi Wang, Wang Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Beijing China School of Information Resource Management Renmin University of China Beijing China

ISBN: (纸本)9781424469291

knowledge management (KM) technologies and applications are fundamental to the success of KM programs. This paper aims to find out linkages between newly-developed KM theories and various technologies and their relevant applications. It explores various understandings on KM technologies, their functions and benefits to organizations, and several types of classification of KM technologies and their applications by reviewing literature. The authors ask and answer the following questions: (1) How are KM technologies defined in literature according to their different perspectives or disciplines? (2) What are the functions, or the roles, of the KM technologies and their applications as enabling KM? and (3) How are KM technologies classified in literature? This study may have implications to mapping, building and operating KM programs that need to have concerns about KM technologies. Different schools of thought on KM technologies, their functions and benefits and their strengths and limitations as solutions to KM problems shall be taken into account. © 2010 IEEE.

关键词： knowledge management

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Using New Fusion Operations to Improve Trust Expressiveness of Subjective Logic

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Wuhan University Journal of Natural Sciences 2011年第5期16卷 376-382页

作者： ZHOU Hongwei1,2,3,SHI Wenchang1,2,LIANG Zhaohui1,2,LIANG Bin1,2 1.key laboratory of data engineering and knowledge engineering,Ministry of Education,Beijing 100872,China 2.school of Information,Renmin University of China,Beijing 100872,China 3.Institute of Electronic Technology,Information engineering University of PLA,Zhengzhou 450004,Henan,China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing China School of Information Renmin University of China Beijing China Institute of Electronic Technology Information Engineering University of PLA Zhengzhou Henan China

Subjective logic provides a means to describe the trust relationship of the realworld. However, existing fusion operations it offers Weal fused opiniotts equally, which makes it impossible to deal with the weighted opinions effectively. A. Jcsang presents a solution, which combines the discounting operator and the fusion operator to produce the consensus to the problem. In this paper, we prove that this approach is unsuitable to deal with the weighted opinions because it increases the uncertainty of the consensus. To address the problem, we propose two novel fusion operators that are capable of fusing opinions according to the weight of opinion in a fair way, and one of the strengths of them is improving the trust expressiveness of subjective logic. Furthermore, we present the justification on their definitions with the mapping between the evidence space and the opinion space. Comparisons between existing operators and the ones we proposed show the effectiveness of our new fusion operations.

关键词： trust subjective logic opinion fusion weizht

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Endurable SSD-Based Read Cache for Improving the Performance of Selective Restore from Deduplication Systems

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Journal of Computer Science & Technology 2018年第1期33卷 58-78页

作者： Jian Liu Yun-Peng Chai Xiao Qin Yao-Hong Liu Division of Computer Science and Engineering Louisiana State University Baton Rouge LA 70803 U.S.A. Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education of China Beijing 100872 China School of Information Renmin University of China Beijing 100872 China Shelby Center for Engineering Technology Department of Computer Science and Software Engineering Samuel Ginn College of Engineering Auburn University Auburn AL 368~9-53~7 U.S.A.

Deduplication has been commonly used in both enterprise storage systems and cloud storage. To overcome the performance challenge for the selective restore operations of deduplication systems, solid-state-drive-based （i.e., SSD-based） re^d cache cm, be deployed for speeding up by caching popular restore contents dynamically. Unfortunately, frequent data updates induced by classical cache schemes （e.g., LRU and LFU） significantly shorten SSDs＇ lifetime while slowing down I/O processes in SSDs. To address this problem, we propose a new solution -- LOP-Cache to greatly improve tile write durability of SSDs as well as I/O performance by enlarging the proportion of long-term popular （LOP） data among data written into SSD-based cache. LOP-Cache keeps LOP data in the SSD cache for a long time period to decrease the number of cache replacements. Furthermore, it prevents unpopular or unnecessary data in deduplication containers from being written into the SSD cache. We implemented LOP-Cache in a prototype deduplication system to evaluate its pertbrmance. Our experimental results indicate that LOP-Cache shortens the latency of selective restore by an average of 37.3% at the cost of a small SSD-based cache with only 5.56% capacity of the deduplicated data. Importantly, LOP-Cache improves SSDs＇ lifetime by a factor of 9.77. The evidence shows that LOP-Cache offers a cost-efficient SSD-based read cache solution to boost performance of selective restore for deduplication systems.

关键词： data deduplication solid state drive (SSD) flash cache endurance

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A study of SQL-on-Hadoop systems

A study of SQL-on-Hadoop systems

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4th and 5th Workshops on Big data Benchmarks, Performance Optimization, and Emerging Hardware, BPOE 2014

作者： Chen, Yueguo Qin, Xiongpai Bian, Haoqiong Chen, Jun Dong, Zhaoan Du, Xiaoyong Gao, Yanjie Liu, Dehai Lu, Jiaheng Zhang, Huijie Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing China School of Information Renmin University of China Beijing100872 China

ISBN: (纸本)9783319130200

Hadoop is now the de facto standard for storing and processing big data, not only for unstructured data but also for some structured data. As a result, providing SQL analysis functionality to the big data resided in HDFS becomes more and more important. Hive is a pioneer system that support SQL-like analysis to the data in HDFS. However, the performance of Hive is not satisfactory for many applications. This leads to the quick emergence of dozens of SQL-on-Hadoop systems that try to support interactive SQL query processing to the data stored in HDFS. This paper firstly gives a brief technical review on recent efforts of SQLon- Hadoop systems. Then we test and compare the performance of five representative SQL-on-Hadoop systems, based on some queries selected or derived from the TPC-DS benchmark. According to the results, we show that such systems can benefit more from the applications of many parallel query processing techniques that have been widely studied in the traditional MPP analytical databases. © Springer International Publishing Switzerland 2014.

关键词： Big data

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An apparel trade quotation architecture based on BPM and SOA

An apparel trade quotation architecture based on BPM and SOA

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13th Pacific Asia Conference on Information Systems: IT Services in a Global Environment, PACIS 2009

作者： Li, Qian Chen, Yu Zhang, Lan Fang Information School Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Beijing 100872 China School of Information Management Science and Engineering Renmin University of China Beijing 100872 China

Based on the analysis of problems and difficulties in apparel quotation system, this paper puts forward the combination of BPM and SOA as a new idea for analysis of apparel quotation system, according to their advantages in business goals and requirements analysis, and the corresponding services' definition, extraction, optimization and integration. Through the combination, system flexibility, rapidity and accuracy could be achieved. The establishment of Service Repository according to the business requirements, is a crucial part in the architecture, however there are no definite rules for service extraction. In this paper, the detailed activities and steps, as well as a specific establishment case is illustrated. At last, architecture based on BPM and SOA for the apparel trade quotation is put forward, and its composition and implement are also analyzed.

关键词： Extraction

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Image Fusion Method Based on NSCT and Adaptive Sparse Representation 4

Image Fusion Method Based on NSCT and Adaptive Sparse Repres...

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4th International Conference on Video and Image Processing, ICVIP 2020

作者： Dong, Zhangyu Wei, Xin Wang, Mengyang School of Computer and Information Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei230009 China

ISBN: (纸本)9781450389075

ABSTRACT: Aiming at the imaging characteristics of multispectral and panchromatic image and the problem that the traditional image fusion methods cannot take into account the spectral quality and spatial resolution of the image at the same time, a novel fusion method based on the Non-subsampled Contourlet Transform (NSCT) domain and adaptive sparse representation (ASR) algorithm is presented in this paper. It combines the advantages of principal component substitution and multi-scale analysis, and uses the ASR model to encode the low-frequency components, which improves the shortcomings of the traditional SR-based approach applications. Firstly, NSCT transform is performed on thefirst principal component of multi-spectral image obtained by PCA transform and panchromatic image,and the low-pass and high-pass coefficients of the source image are obtained. Secondly, the Max-absolute' rule and the ASR model are used to fuse the separated high-pass and low-pass bands respectively. Finally, we obtained the final fused image by the corresponding inverse transformation reconstruction. The results show that the fusion algorithm proposed in this paper performs well in both spatial resolution and spectral performance;it not only improves the subjective visual effect, but also improves the objective evaluation index. © 2020 ACM.

关键词： Image fusion

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