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Chinese morpheme-based emotion detection on financial securities information

Journal of Convergence Information Technology 2012年第20期7卷 406-418页

作者： Hairong, Bian Changxuan, Wan Guolin, Li Li, Yang School of Information Technology Jiangxi University of Finance and Economics Nanchang 330013 China Jiangxi Key Lab of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang 330013 China

With the unceasing improvement of data mining and natural language processing technology, more and more researchers devote themselves to comment resources on the Web. While there are no readymade emotional corpora in the financial and securities domain, emotional analysis applied in this field is still rare. As financial information is usually in the form of unstructured Web Text, this paper fully considers characteristics of financial information, and analyzes the emotion of Web Text by calculating their emotional inclination values based on evaluation scores of morpheme. For each document we compute an emotional value. Its symbol indicates the emotional inclination, and its absolute value reflects the emotional intensity. Thus, this can prevent the limitation of lacking emotional corpus in the financial and securities domain. Experimental results demonstrate that this presented scheme optimizes the existing main schemes to effectively analyze the emotional inclinations of Web financial information.

关键词： Sentiment analysis

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Reducing Uncertainty of Low-Sampling-Rate Trajectories

Reducing Uncertainty of Low-Sampling-Rate Trajectories

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International Conference on data engineering

作者： Kai Zheng Yu Zheng Xing Xie Xiaofang Zhou School of Information Technology and Electrical Engineering University of Queensland Brisbane Australia Microsoft Research Asia Beijing China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China China

ISBN: (纸本)9781467300421

The increasing availability of GPS-embedded mobile devices has given rise to a new spectrum of location-based services, which have accumulated a huge collection of location trajectories. In practice, a large portion of these trajectories are of low-sampling-rate. For instance, the time interval between consecutive GPS points of some trajectories can be several minutes or even hours. With such a low sampling rate, most details of their movement are lost, which makes them difficult to process effectively. In this work, we investigate how to reduce the uncertainty in such kind of trajectories. Specifically, given a low-sampling-rate trajectory, we aim to infer its possible routes. The methodology adopted in our work is to take full advantage of the rich information extracted from the historical trajectories. We propose a systematic solution, History based Route Inference System (HRIS), which covers a series of novel algorithms that can derive the travel pattern from historical data and incorporate it into the route inference process. To validate the effectiveness of the system, we apply our solution to the map-matching problem which is an important application scenario of this work, and conduct extensive experiments on a real taxi trajectory dataset. The experiment results demonstrate that HRIS can achieve higher accuracy than the existing map-matching algorithms for low-sampling-rate trajectories.

关键词： Trajectory Roads Global Positioning System Inference algorithms Uncertainty Artificial neural networks Educational institutions

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Drivers for strategic choice of cloud computing as online service in SMEs

Drivers for strategic choice of cloud computing as online se...

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International Conference on Information Systems, ICIS 2012

作者： Li, Min Yu, Yan Zhao, J. Leon Li, Xin University of Science and Technology China-City University of Hong Kong Joint Advanced Research Center 166 Renai Road Dushu Lake Higher Education Town Suzhou China Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin University of China 59 Zhongguancun Street Haidian District Beijing China Department of Information Systems City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong

ISBN: (纸本)9781627486040

Cloud Computing Service (CCS) paradigm is changing IT strategy of organizations in the digital world. CCS that requires few upfront investments and uses lease-based pricing is especially relevant to the Small and Medium Enterprises (SMEs), which have limited resources and may not know their true valuation for the IT prior to adoption. Thus, this research aims to investigate the influential factors of SMEs' strategic choice of CCS as online service. Relying upon Technology-Organization-Environment (TOE) paradigm, we identify both generic and context-specific factors from the three aspects and explain how the identified factors affect SMEs' CCS strategic choices. We hope this research can make contributions to innovation diffusion theory and IT strategy literature. We also hope the research with progress going on can generate insights for the CCS vendors who care about the sector of SME as well as the government administrators to make appropriate policies or supports for SMEs.

关键词： Cloud computing

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Endless and Scalable knowledge Table Extraction from Semi-structured Websites

Endless and Scalable Knowledge Table Extraction from Semi-st...

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IEEE International Conference on data Mining Workshops (ICDM Workshops)

作者： Yingqin Gu Lei Ji Ziheng Jiang Jun He Key Labs of Data Engineering and Knowledge Engineering University of China Microsoft Research Asia Beijing P.R. China School of Computer Science Beijing Institute of Technology P. R. China

ISBN: (纸本)9781467351645

The problem of scalable knowledge extraction from the Web has attracted much attention in the past decade. However, it is under explored how to extract the structured knowledge from semi-structured Websites in a fully automatic and scalable way. In this work, we define the table-formatted structured data with clear schema as knowledge Tables and propose a scalable learning system, which is named as Kable to extract knowledge from semi-structured Websites automatically in a never ending and scalable way. Kable consists of two major components, which are auto wrapper induction and schema matching respectively. In contrast to the state of the art auto wrappers for semi-structured Web sites, our adopted approach can run around 1'000 times faster, which makes the Web scale knowledge extraction possible. On the other hand, we propose a novel schema matching solution which can work effectively on the auto-extracted structured data. With 3 months' continuous run using ten Web servers, we successfully extracted 427,105,009 knowledge facts. The manual labeling over sampled knowledge extracted show the up to 87% precision for supporting various Web applications.

关键词： data mining knowledge engineering Motion pictures Clustering algorithms knowledge based systems Algorithm design and analysis Manganese

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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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Preventing Recommendation Attack in Trust-Based Recommender Systems

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Journal of Computer Science & Technology 2011年第5期26卷 823-828页

作者：张富国 School of Information and Technology Jiangxi University of Finance and Economics Institute of Information Resource Management Jiangxi University of Finance and Economics Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics

Despite its success,similarity-based collaborative filtering suffers from some limitations,such as scalability,sparsity and recommendation *** work has shown incorporating trust mechanism into traditional collaborative filtering recommender systems can improve these *** argue that trust-based recommender systems are facing novel recommendation attack which is different from the profile injection attacks in traditional recommender *** the best of our knowledge,there has not any prior study on recommendation attack in a trust-based recommender *** analyze the attack problem,and find that ＂victim＂ nodes play a significant role in the ***,we propose a data provenance method to trace malicious users and identify the ＂victim＂ nodes as distrust users of recommender *** study of the defend method is done with the dataset crawled from Epinions website.

关键词： data lineage ＂victim＂ node attack trust propagation

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A Timeline-Based Algorithm for Personalized Tag Recommendation

A Timeline-Based Algorithm for Personalized Tag Recommendati...

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11th International Conference on Web Information Systems engineering

作者： Yu, Zhaohui Wang, Puwei Du, Xiaoyong Cui, Jianwei Xu, Tianren Key Labs. of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9783642243950

Recently, tagging has been a flexible and important way to share and categorize web resources, these user-generated tags are effective to represent user interests because these tags reflect human being's judgments while more concise and closer to human understanding, and the user interests are changing over time. Thus, modeling user interests to meet individual user needs is an important challenge for personalization and information filtering applications, such as recommender systems. In this paper, we apply a distance decay model for modeling user interests in terms of tags based on timeline. We then propose a novel algorithm to measure users' similarities in terms of their tagging activity over a specific time period and provide personalized tag recommendation according to similar users' interests in their next time intervals. Experimental results demonstrate the higher precision and recall with our personalized tag recommendation algorithm than other existing methods.

关键词： timeline user interests distance decay model personalized tag recommendation

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Lawyer information integration and recommendation by multi-source information validation

Lawyer information integration and recommendation by multi-s...

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2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011

作者： Liu, Tao Fan, Biao Hu, He Du, Xiao-Yong Key Laboratory of Data Engineering and Knowledge Engineering MOE School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9781457703065

On the internet, all-round lawyer information is located at separated information sources, which prevent web users from effective information acquisition. In order to build a unified view of separated, heterogeneous, and often redundant lawyer information, we propose a new information integration method using multi-source information cross-validation. Based on the unified integrated data, a lawyer recommendation system is built. Several key technologies are presented and evaluated, including the multi-source information acquisition and validation. Experimental results indicate the key techniques used in the system are effective for lawyer information integration and recommendation. © 2011 IEEE.

关键词： Information retrieval

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Improving performance by creating a native join-index for OLAP

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Frontiers of Computer Science 2011年第2期5卷 236-249页

作者： Yansong ZHANG Shan WANG Jiaheng LU National Survey Research Center at Renmin University of China Beijing 100872 China Key Laboratory of the Ministry of Education for Data Engineering and Knowledge Engineering Renmin University of China Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

The performance of online analytical processing （OLAP） is critical for meeting the increasing requirements of massive volume analytical applications. Typical techniques, such as in-memory processing, column-storage, and join indexes focus on high perfor- mance storage media, efficient storage models, and reduced query processing. While they effectively perform OLAP applications, there is a vital limitation： main- memory database based OLAP （MMOLAP） cannot provide high performance for a large size data set. In this paper, we propose a novel memory dimension table model, in which the primary keys of the dimension table can be directly mapped to dimensional tuple addresses. To achieve higher performance of dimensional tuple access, we optimize our storage model for dimension tables based on OLAP query workload features. We present directly dimensional tuple accessing （DDTA） based join （DDTA- JOIN）, a technique to optimize query processing on the memory dimension table by direct dimensional tuple access. We also contribute by proposing an optimization of the predicate tree to shorten predicate operation length by pruning useless predicate processing. Our experimental results show that the DDTA-JOIN algorithm is superior to both simulated row-store main memory query processing and the open-source column-store main memory database MonetDB, thanks to the reduced join cost and simple yet efficient query processing.

关键词： directly dimensional tuple accessing (DDTA) – DDTA JOIN – native join index – predicate tree

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Efficient Duplicate Detection on Cloud Using a New Signature Scheme

Efficient Duplicate Detection on Cloud Using a New Signature...

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12th International Conference on Web-Age Information Management

作者： Rong, Chuitian Lu, Wei Du, Xiaoyong Zhang, Xiao Key Labs. of Data Engineering and Knowledge Engineering MOE China School of Information Renmin University of China China Shanghai Key Laboratory of Intelligent Information Processing China

ISBN: (纸本)9783642235344;9783642235351

Duplicate detection has been well recognized as a crucial task to improve the quality of data. Related work on this problem mainly aims to propose efficient approaches over a single machine. However, with increasing volume of the data, the performance to identify duplicates is still far from satisfactory. Hence, we try to handle the problem of duplicate detection over MapReduce, a share-nothing paradigm. We argue the performance of utilizing MapReduce to detect duplicates mainly depends on the number of candidate record pairs. In this paper, we proposed a new signature scheme with new pruning strategy over MapReduce to minimize the number of candidate record pairs. Our experimental results over both real and synthetic datasets demonstrate that our proposed signature based method is efficient and scalable.

关键词： duplicate detection MapReduce Cloud

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