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Dynamic programming based top-k aggregate queries in uncertain database

Journal of Information and Computational Science 2009年第3期6卷 1589-1596页

作者： Liu, Dexi School of Information Technology Jiangxi University of Finance and Economics Nanchang 330013 China Jiangxi Key Laboratory of Data and Knowledge Engineering Nanchang 330013 China

A Top-k aggregate query ranks groups of tuples by their aggregate values, sum or average for example, and returns k groups with the highest aggregate values. We propose a dynamic programming based method to process uncertain kRanks aggregate queries in uncertain database, where the number of retrieved tuples and group states generated on these tuples are minimized. Our method has two levels, group state generation and U-x-kRanks query processing. In the former level, group states, which satisfy the properties of x-tuple [13], are generated one after the other according to their aggregate values, while in the latter level, dynamic programming based uncertain x-tuple kRanks query processing [15] are employed to return the answers. Comprehensive experiments on different data sets demonstrate the effectiveness of the proposed solutions. 1548-7741/ Copyright © 2009 Binary Information Press.

关键词： Dynamic programming

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Minimizing XML Queries Using a Family of Constraints

Minimizing XML Queries Using a Family of Constraints

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International Conference on Fuzzy Systems and knowledge Discovery (FSKD)

作者： Xiping Liu Changxuan Wan School of Information Technology Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China

An effective way to optimize XML queries is to minimize XML queries. In this paper, we improve redundance elimination in XPath queries greatly by incorporating two novel kinds of constraints: parent constraint and sibling constraint, and by extending the tractable fragment to include descendant-or-self axis. The two novel kinds of constraints, together with child constraint and descendant constraint, form a family of constraints, which complicate the problem but offer possibilities for further minimization. Two techniques, tree augmentation and simulation augmentation, are employed to cope with constraints. We elaborate on the minimizing algorithms and running efficiencies both in the absence and in the presence of various kinds of constraints.

关键词： XML Polynomials Minimization methods Information technology Finance Laboratories knowledge engineering Constraint optimization Fuzzy systems System testing

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Predictable recovery for replication real-time main memory databases using request logs

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Journal of Information and Computational Science 2009年第3期6卷 1607-1614页

作者： Liao, Guoqiong Li, Jing School of Information Technology Jiangxi University of Finance and Economics Nanchang 330013 China Jiangxi Key Laboratory of Data and Knowledge Engineering Nanchang 330013 China

Reliable telecommunication applications in future need the supports from replication real-time main memory databases. In order to improve recovery performance and provide predictable recovery, this paper proposes a new recovery method using request logs for the databases. Firstly, the format and correctness of request logs are discussed. Then, a recovery model based on request logs is given. Thirdly, the recovery algorithms for local recovery and resynchronization are provided in details. At last, a formula to estimate denial-of-service time (DoS) is presented for predictable recovery. 1548-7741/ Copyright © 2009 Binary Information Press.

关键词： Recovery

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FindCredPg: A novel method to find credible pages based on trust web graph

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14th Asia Pacific Web Technology Conference, APWeb 2012

作者： Wang, Teng Zhu, Qing Wang, Shan Liang, Jingfan Key Laboratory of the Ministry of Education for Data Engineering and Knowledge Engineering Renmin University of China Beijing China School of Information Renmin University of China Beijing China

ISBN: (纸本)9783642292521

Finding credible pages is a challenging problem on the Web. Our key observation in this paper is that credible pages usually link to credible content-related pages, which is different from a normal page usually links to normal pages in spam page detection. We propose a novel method to find credible pages based on the trust web graph we define. This method first measures the content correlation between pages connected by hyperlinks, then it combines web link structure with content correlation value of pages to build a trust web graph. At last, credible pages are found successfully by using trust relation of vertices on the trust web graph. We construct a real-world data set by crawling millions of pages on the web and run a set of experiments on this data set. Experiment results show that the accuracy of this method is near 80% and the efficiency is higher. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Hypertext systems

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Can web news media sentiments improve stock trading signal prediction? 18

Can web news media sentiments improve stock trading signal p...

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18th Pacific Asia Conference on Information Systems, PACIS 2014

作者： Lin, Nana Xu, Wei Zhang, Xinwei Lv, Siqi School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China School of Information Beijing China

The technical analysis and machine learning have been integrated in stock trading signal forecasting. And it has been proved that there are some weaknesses in technical analysis because of the complex environment in the stock market. In our prediction system, web news media sentiment analysis is regarded as a supplementary way to cover the shortage of technical analysis. It is considered to bring the stock market sentiment which reflects the subjective information of investors into the prediction system. Web news media sentiment indicators (WNMS) are designed to bring the information about stock market sentiment in our system. The WNMS is generated by analyzing the variance of sentiment elements from the news in the Stock Timely Rain Sector of Sina Finance and Economics Website and it is imported into the prediction system as features combined with common feature indicators (CFI). GMKL is applied to establish the relationship between the trading signals generated by piecewise linear representation (PLR) and the features of the trading signals (SCFI). Comparative experiments are adopted in nine stocks from Shanghai and Shenzhen Stock Exchange to determine the effect of PLR-GMKL and WNMS in prediction. From the aspects of the prediction accuracy and the profit, the final comparative results show that the PLR-GMKL model performs better than the PLR-WSVM model. And the prediction system performs best when adding WNMS into features and using PLRGMKL model.

关键词： Financial markets

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Tracer: Enforcing Mandatory Access Control in commodity OS with the support of light-weight intrusion detection and tracing 11

Tracer: Enforcing Mandatory Access Control in commodity OS w...

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

作者： Shan, Zhiyong Wang, Xin Chiueh, Tzi-Cker Key Laboratory of Data Engineering and Knowledge Engineering Renmin University MOE China Stony Brook University United States Industrial Technology Research Institute Taiwan

ISBN: (纸本)9781450305648

Enforcing a practical Mandatory Access Control (MAC) in a commercial operating system to tackle malware problem is a grand challenge but also a promising approach. The firmest barriers to apply MAC to defeat malware programs are the incompatible and unusable problems in existing MAC systems. To address these issues, we start our work by analyzing the technical details of 2,600 malware samples one by one and performing experiments over two types of MAC enforced operating systems. Based on the preliminary studies, we design a novel MAC model incorporating intrusion detection and tracing in a commercial operating system, named Tracer, in order to disable malware on hosts while offering good compatibility to existing software and good usability to common users who are not system experts. The model conceptually consists of three actions: detecting, tracing and restricting suspected intruders. One novelty is that it leverages light-weight intrusion detection and tracing techniques to automate security label configuration that is widely acknowledged as a tough issue when applying a MAC system in practice. The other is that, rather than restricting information flow as a traditional MAC does, it traces intruders and restricts only their critical malware behaviors, where intruders represent processes and executables that are potential agents of a remote attacker. Our prototyping and experiments on Windows show that Tracer can effectively defeat all malware samples tested via blocking malware behaviors while not causing a significant compatibility problem. Copyright 2011 ACM.

关键词： Malware

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SGSH: Stimulate Large Language Models with Skeleton Heuristics for knowledge Base Question Generation

SGSH: Stimulate Large Language Models with Skeleton Heuristi...

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2024 Findings of the Association for Computational Linguistics: NAACL 2024

作者： Guo, Shasha Liao, Lizi Zhang, Jing Wang, Yanling Li, Cuiping Chen, Hong School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education China Singapore Management University Singapore Zhongguancun Laboratory China

ISBN: (纸本)9798891761193

knowledge base question generation (KBQG) aims to generate natural language questions from a set of triplet facts extracted from KB. Existing methods have significantly boosted the performance of KBQG via pre-trained language models (PLMs) thanks to the richly endowed semantic knowledge. With the advance of pre-training techniques, large language models (LLMs) (e.g., GPT-3.5) undoubtedly possess much more semantic knowledge. Therefore, how to effectively organize and exploit the abundant knowledge for KBQG becomes the focus of our study. In this work, we propose SGSH - a simple and effective framework to Stimulate GPT-3.5 with Skeleton Heuristics to enhance KBQG. The framework incorporates "skeleton heuristics", which provides more fine-grained guidance associated with each input to stimulate LLMs to generate optimal questions, encompassing essential elements like the question phrase and the auxiliary verb. More specifically, we devise an automatic data construction strategy leveraging ChatGPT to construct a skeleton training dataset, based on which we employ a soft prompting approach to train a BART model dedicated to generating the skeleton associated with each input. Subsequently, skeleton heuristics are encoded into the prompt to incentivize GPT-3.5 to generate desired questions. Extensive experiments demonstrate that SGSH derives the new state-of-the-art performance on the KBQG tasks. The code is now available on Github. © 2024 Association for Computational Linguistics.

关键词： knowledge based systems

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Summarizing large-scale database schema using community detection

Summarizing large-scale database schema using community dete...

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作者： Wang, Xue Zhou, Xuan Wang, Shan School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Beijing 100872 China

Schema summarization on large-scale databases is a challenge. In a typical large database schema, a great proportion of the tables are closely connected through a few high degree tables. It is thus dificult to separate these tables into clusters that represent different topics. Moreover, as a schema can be very big, the schema summary needs to be structured into multiple levels, to further improve the usability. In this paper, we introduce a new schema summarization approach utilizing the techniques of community detection in social networks. Our approach contains three steps. First, we use a community detection algorithm to divide a database schema into subject groups, each representing a specific subject. Second, we cluster the subject groups into abstract domains to form a multi-level navigation structure. Third, we discover representative tables in each cluster to label the schema summary. We evaluate our approach on Freebase, a real world large-scale database. The results show that our approach can identify subject groups precisely. The generated abstract schema layers are very helpful for users to explore database. © 2012 Springer Science+Business Media, LLC & Science Press, China.

关键词： database systems

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INK: A cloud-based system for efficient top-k interval keyword search 14

INK: A cloud-based system for efficient top-k interval keywo...

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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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A fast algorithm to building a fuzzy rough classifier 13

A fast algorithm to building a fuzzy rough classifier

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13th International Conference on Machine Learning and Cybernetics, ICMLC 2014

作者： Tsang, Eric C. C. Zhao, Suyun The Macau University of Science and Technology Taipa Macau China Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE Beijing100872 China

ISBN: (纸本)9783662456514

In this paper, by strict mathematic reasoning, we discover the relation between the similarity relation and lower approximation. Based on this relation, we design a fast algorithm to build a rule based fuzzy rough classifier. Finally, the numerical experiments demonstrate the efficiency and the affectivity of the proposed algorithm. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Rough set theory

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