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30th International Conference on Software engineering and knowledge engineering, SEKE 2018

作者： Sun, Caihong Zhang, Meina Zuo, Meiyun Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing China

ISBN: (纸本)1891706446

In this paper, we focus on examining the effects of Ad-context on the click-Through rate (CTR) for the online advertising. Many researches have shown that ad-context congruity is a key factor to CTR, but the features of ad-context are rarely introduced in CTR prediction algorithms. By leveraging data from various sources, and using text mining and sentiment analysis techniques, our proposed approach extracts three types of features (i.e. users, advertisements and ad-context features) to predict CTR. User features describe "Who" is browsing the webpage, advertisement features depict "How" an ad serving is, and ad-context features include "What" the product is, "Where" an ad displays, as well as the "Mood" of the context. Experiment results show that our proposed approach outperforms the benchmark models by introducing ad-context features. Novel ad-context features we proposed make good contributions to the prediction of CTR. The research highlights the power of ad-context in online advertising CTR prediction. Moreover it gives an insight into supporting effective advertising and improving users' satisfaction. © 2018 Universitat zu Koln. All rights reserved.

关键词： Sentiment analysis

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Detecting stealthy malware with inter-structure and imported signatures 11

Detecting stealthy malware with inter-structure and imported...

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6th International Symposium on Information, Computer and Communications Security, ASIACCS 2011

作者： Liang, Bin You, Wei Shi, Wenchang Liang, Zhaohui Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9781450305648

Recent years have witnessed an increasing threat from kernel rootkits. A common feature of such attack is hiding malicious objects to conceal their presence, including processes, sockets, and kernel modules. Scanning memory with object signatures to detect the stealthy rootkit has been proven to be a powerful approach only when it is hard for adversaries to evade. However, it is difficult, if not impossible, to select fields from a single data structure as robust signatures with traditional techniques. In this paper, we propose the concepts of inter-structure signature and imported signature, and present techniques to detect stealthy malware based on these concepts. The key idea is to use cross-reference relationships of multiple data structures as signatures to detect stealthy malware, and to import some extra information into regions attached to target data structures as signatures. We have inferred four invariants as signatures to detect hidden processes, sockets, and kernel modules in Linux respectively and implemented a prototype detection system called DeepScanner. Meanwhile, we have also developed a hypervisor-based monitor to protect imported signatures. Our experimental result shows that our DeepScanner can effectively and efficiently detect stealthy objects hidden by seven real-world rootkits without any false positives and false negatives, and an adversary can hardly evade DeepScanner if he/she does not break the normal functions of target objects and the system. Copyright 2011 ACM.

关键词： Malware

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COCA: More accurate multi-dimensional histograms out of more accurate correlations detection

COCA: More accurate multi-dimensional histograms out of more...

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9th International Conference on Web-Age Information Management, WAIM 2008

作者： Wei, Cao Xiongpai, Qin Wang, Shan Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9780769531854

Detecting and exploiting correlations among columns in relational databases are of great value for query optimizers to generate better query execution plans (QEPs). We propose a more robust and informative metric, namely, entropy correlation coefficients, other than chi-square test to detect correlations among columns in large datasets. We introduce a novel yet simple kind of multi-dimensional synopses named COCA-Hist to cope with different correlations in databases. With the aid of the precise metric of entropy correlation coefficients, correlations of various degrees can be detected effectively;when correlation coefficients testify to mutual independence among columns, the AVI (attribute value independence) assumption can be adopted undoubtedly. COCA can also serve as a data-mining tool with superior qualities as CORDS does. We demonstrate the effectiveness and accuracy of our approach by several experiments. © 2008 IEEE.

关键词： Entropy

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Reduce pruning cost to accelerate multimedia kNN search over MBRs based index structures

Reduce pruning cost to accelerate multimedia kNN search over...

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3rd International Conference on Multimedia Information Networking and Security, MINES 2011

作者： Li, Hui Zhang, Xiao Wang, Shan School of Information Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering Beijing China

ISBN: (纸本)9780769545592

MBR (Minimum Bounding Rectangle) has been widely used to represent multimedia data objects for multimedia indexing techniques. In kNN search, MINDIST and MINMAXDIST was the most popular pruning metrics employed by MBR based index structures. However, with the increase of dimensionality, the computation costs of above two metrics are expensive, and the involved distance computations may become the considerable component. In this paper, to improve the performance of kNN search over multimedia data, we proposed an approach to reduce the computation cost of pruning heuristics by use the cheap upper bound of MINMAXDIST instead of its precise value, and then we derive enhanced cheap heuristics based on this upper bound for kNN pruning, which are helpful to avoid visiting unnecessary data objects and MBRs. We conducted an extensive experimental study on a large real-life multimedia dataset called MSRA-MM 2.0, obtained from the commercial Bing Image search engine. The results show that the proposed approach significantly reduced the computation cost and boosted the overall performance of MBR based multimedia kNN search. © 2011 IEEE.

关键词： Cost reduction

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CLASCN： Candidate Network Selection for Efficient Top-κ keyword Queries over databases

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Journal of Computer Science & Technology 2007年第2期22卷 197-207页

作者：张俊彭朝晖王珊聂惠静 School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing 100872 China Computer Science and Technology College Dalian Maritime University Dalian 116026 China

keyword Search Over Relational databases （KSORD） enables casual or Web users easily access databases through free-form keyword queries. Improving the performance of KSORD systems is a critical issue in this area. In this paper, a new approach CLASCN （Classification, Learning And Selection of Candidate Network） is developed to efficiently perform top-κ keyword queries in schema-graph-based online KSORD systems. In this approach, the Candidate Networks （CNs） from trained keyword queries or executed user queries are classified and stored in the databases, and top-κ results from the CNs are learned for constructing CN Language Models （CNLMs）. The CNLMs are used to compute the similarity scores between a new user query and the CNs from the query. The CNs with relatively large similarity score, which are the most promising ones to produce top-κ results, will be selected and performed. Currently, CLASCN is only applicable for past queries and New All-keyword-Used （NAU） queries which are frequently submitted queries. Extensive experiments also show the efficiency and effectiveness of our CLASCN approach.

关键词： relational database keyword search top-κ query candidate network

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Phase spectrum based automatic ship detection in synthetic aperture radar images

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Journal of Ocean engineering and Science 2021年第2期6卷 185-195页

作者： Miaohui Zhang Baojun Qiao Ming Xin Bo Zhang Henan Key Laboratory of Big Data Analysis and Processing Henan UniversityKaifengChina Institute of Data and Knowledge Engineering Henan UniversityKaifengChina School of Computer Science and Engineering Beihang UniversityBeijingChina

This paper proposes an automatic ship detection approach in Synthetic Aperture Radar(SAR)Images using phase *** proposed method mainly contains two stages:Firstly,sea-land segmentation of SAR Images is one of the key stages for SAR image application such as sea-targets detection and recognition,which are easily detected only in sea *** order to eliminate the influence of land regions in SAR images,a novel land removing method is *** removing method employs a Harris corner detector to obtain some image patches belonging to land,and the probability density function(PDF)of land area can be estimated by these ***,an appropriate land segmentation threshold is accordingly ***,an automatic ship detector based on phase spectrum is *** proposed detector is free from various idealized assumptions and can accurately detect ships in SAR *** results demonstrate the efficiency of the proposed ship detection algorithm in diversified SAR images.

关键词： Ship detection saliency detection phase spectrum sea-land segmentation

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RUC @ INEX 2011 data-centric track

RUC @ INEX 2011 data-centric track

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Lecture Notes in Computer Science

作者： Wang, Qiuyue Gan, Yantao Sun, Yu School of Information Renmin University of China China Key Lab. of Data Engineering and Knowledge Engineering MOE Beijing 100872 China

ISBN: (纸本)9783642357336

We report our experiment results on the INEX 2011 data-Centric Track. We participated in both the ad hoc and faceted search tasks. On the ad hoc search task, we employ language modeling approaches to do structured object retrieval, trying to capture both the structure in data and structure in query and unify the structured and unstructured information retrieval in a general framework. However, our initial experimental results using INEX test bed show that the unstructured retrieval model performs better than structured retrieval models. On the faceted search task, we propose a simple user-simulation model to evaluate the effectiveness of a faceted search system's recommending facet-values. We implemented the evaluation system and conducted the evaluations for the track. We also tested basic redundancy and relevance based approaches for recommending facet-values. The results show that our basic approaches of recommending facet-values perform quite well. © Springer-Verlag 2012.

关键词： XML

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FOSS: A Self-Learned Doctor for Query Optimizer 40

FOSS: A Self-Learned Doctor for Query Optimizer

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Zhong, Kai Sun, Luming Ji, Tao Li, Cuiping Chen, Hong Renmin University of China China Key Laboratory of Data Engineering and Knowledge Engineering MOE China Shanghai Yunxi Technology Co. Ltd. China Engineering Research Center of Database and Business Intelligence MOE China

ISBN: (纸本)9798350317152

Various works have utilized deep learning to address the query optimization problem in database system. They either learn to construct plans from scratch in a bottom-up manner or steer the plan generation behavior of traditional optimizer using hints. While these methods have achieved some success, they face challenges in either low training efficiency or limited plan search space. To address these challenges, we introduce FOSS, a novel framework for query optimization based on deep reinforcement learning. FOSS initiates optimization from the original plan generated by a traditional optimizer and incrementally refines suboptimal nodes of the plan through a sequence of actions. Additionally, we devise an asymmetric advantage model to evaluate the advantage between two plans. We integrate it with a traditional optimizer to form a simulated environment. Leveraging this simulated environment, FOSS can bootstrap itself to rapidly generate a large amount of high-quality simulated experiences. FOSS then learns from these experiences to improve its optimization capability. We evaluate the performance of FOSS on Join Order Benchmark, TPC-DS, and Stack Overflow. The experimental results demonstrate that FOSS outperforms the state-of-the-art methods in terms of latency performance. Compared to PostgreSQL, FOSS achieves speedup ranging from 1.15x to 8.33x in total latency across different benchmarks. © 2024 IEEE.

关键词： Reinforcement learning

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Gender Disparity in Expressed Emotions within Health-Related Online Support Groups

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Proceedings of the Association for Information Science and Technology 2021年第1期58卷 883-885页

作者： Zhao, Yuehua Wang, Hao Deng, Sanhong Chen, Ye Nanjing University Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing China Central China Normal University Wuhan China

Online support groups offer a new way to users to communicate with others regarding certain health issues. Taking autism-related support groups on Facebook as an example, we examine whether the expressed emotions differ between female and male users in online health-related support groups and whether such gender disparity varied based on the topics of the groups. Experimental results reveal a significant gender difference of expressed emotions in the groups. We find that female users tended to express more positive emotions in the group discussions than the male group members did. In addition, users appeared to express different sentiments within the groups focused on various topics. Male users tend to convey more negative emotions in the group that related to treatment, while female users were more positive when posted in the research-related group than male users were. This study is beneficial for tracking and moderating the emotional environment in online support groups. 84 Annual Meeting of the Association for Information Science & Technology | Oct. 29 – Nov. 3, 2021 | Salt Lake City, UT. Author(s) retain copyright, but ASIS&T receives an exclusive publication license.

关键词： Sentiment analysis

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Top-k neighborhood dominating query

Top-k neighborhood dominating query

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18th International Conference on database Systems for Advanced Applications, DASFAA 2013

作者： Xie, Xike Lu, Hua Chen, Jinchuan Shang, Shuo Department of Computer Science Aalborg University Denmark Key Labs of Data Engineering and Knowledge Engineering MOE Renmin University of China China

ISBN: (纸本)9783642374869

In many real-life applications, spatial objects are associated with multiple non-spatial attributes. For example, a hotel may have price and rating in addition to its geographic location. In traditional spatial databases, spatial objects are often ranked solely based on their distance to a given query location, e.g., in a nearest neighbor search. In another line of research, domination based skyline queries are used to return best objects according to multi-criteria on non-spatial attributes. In this paper, we study how to rank spatial objects with respect to their non-spatial attributes within their spatial neighborhoods. To enable a general ranking, we design a ranking function that inherits the advantages of dominance relationship and integrates them with spatial proximity. Further, we propose an effective index structure, and a branch and bound solution that executes the ranking efficiently via the index. We conduct extensive empirical studies on real and synthetic datasets. The results demonstrate the high efficiency of our proposal compared to straightforward alternatives. © Springer-Verlag 2013.

关键词： Nearest neighbor search

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