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检索条件"机构=Data and Knowledge Engineering Lab."

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Visual summarization of the social image collection using image attractiveness learned from social behaviors

Visual summarization of the social image collection using im...

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2012 13th IEEE International Conference on Multimedia and Expo, ICME 2012

作者： Jeong, Jin-Woo Hong, Hyun-Ki Heu, Jee-Uk Qasim, Iqbal Lee, Dong-Ho Knowledge and Data Engineering Lab. Hanyang University Korea Republic of

ISBN: (纸本)9781467316590

How to effectively summarize a large-scale image collection is still an important and open problem. In this paper, we propose a novel method to effectively generate a summary of the social image collection using image attractiveness learned from the social behaviors conducted in Flickr. To this end, we exploit the note information of Flickr images. The notes of Flickr images are user generated bounding boxes with text annotations assigned on the interesting image regions. Using the visual features extracted from the images that have notes, we have generated the attractiveness models for various concepts. Finally, the attractiveness models are exploited to make a summary of the social image collection. Through various user studies on the image collections from Flickr groups, we show the feasibility of our method and discuss further directions. © 2012 IEEE.

关键词： image samples Flickr bounding boxes region of an image

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Fusing heterogeneous information for social image retrieval

Fusing heterogeneous information for social image retrieval

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Int. Workshops on Web-Age Information Management, WAIM 2012: 1st Int. Workshop on GDMM 2012, 2nd Int. Wireless Sensor Networks Workshop, IWSN 2012, 1st Int. Workshop on MDSP 2012, 3rd Int. Workshop on USDM 2012, 4th Int. Workshop on XMLDM 2012

作者： Li, Xirong MOE Key Lab. of Data Engineering and Knowledge Engineering Renmin University of China China

ISBN: (纸本)9783642330490

Given that millions of images are uploaded to the social web in a single day, how to effectively retrieve such increasing amounts of unstructured data becomes crucial. While current social platforms find images on the base of user-contributed tags, the tags are known to be subjective and noisy, and consequently put the effectiveness of social image retrieval into question. Different from existing work which focuses on analyzing individual sources of information such as textual and visual separately, in this paper we propose to fuse the heterogeneous information. We investigate the state of the art weighting methods within a linear fusion framework. Image retrieval experiments on a present day benchmark with 46 visual concept queries show encouraging results. Compared to the best search results obtained by a single source of information, the Uniform method and the Coordinate Ascent method obtain relative performance gain of 3.9% and 5.7% in terms of mean average precision. This work provides some practical guidelines for fusing multiple sources of information for improving social image retrieval. © 2012 Springer-Verlag.

关键词： Image enhancement

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XRCJ: Supporting keyword search in XML and relation co-occurrence

XRCJ: Supporting keyword search in XML and relation co-occur...

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1st International Workshop on Graph database, IWGD 2010, 1st International Workshop on Advanced Techniques on XML data Management, XMLDM 2010, 2nd International Workshop on WCMT 2010, Held in Conj. with WAIM 2010

作者： Zhang, Song Du, Xiaoyong Key Lab. of Data Engineering and Knowledge Engineering School of Information Renmin University of China China

ISBN: (纸本)3642167195

Keyword search is a proven, user-friendly way to query html documents in the World Wide Web. With the trend of integrating IR technologies into DB community, state-of-the-art studies have introduced keyword search into XML and relation. In this paper we introduce keyword search into a new circumstance called XRC (XML and relation co-occurrence) where XML and relational data exist at the same storage. By exploring semantic relationships between data from relation and XML, we enable users who intend to visit data from XML and relation to access information in an easier manner. To achieve this target, we propose a novel operation called XRCJ which connects data from XML and relation together when answering search query. We also improve traditional inverted index and design a mixed inverted index. Through conducting extensive experiments on synthetic data from real data set DBLP, we find that our method can achieve a good performance in XML and relation co-occurrence circumstance. © 2010 Springer-Verlag.

关键词： XML

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Behavior Informatics and Analytics: Let behavior talk

Behavior Informatics and Analytics: Let behavior talk

引用

IEEE International Conference on data Mining Workshops, ICDM Workshops 2008

作者： Cao, Longbing Data Sciences and Knowledge Discovery Lab. Faculty of Engineering and Information Technology University of Technology Sydney Australia

ISBN: (纸本)9780769535036

Behavior is increasingly recognized as a key component in business intelligence and problem-solving. Different from traditional behavior analysis, which mainly focus on implicit behavior and explicit business appearance as a result of business usage and customer demographics, this paper proposes the field of Behavior Informatics and Analytics (BIA), to support explicit behavior involvement through a conversion from transactional data to behavioral data, and further genuine analysis of native behavior patterns and impacts. BIA consists of key components including behavior modeling and representation, behavioral data construction, behavior impact modeling, behavior pattern analysis, and behavior presentation. BIA can greatly complement the existing means for combined, more informative and social patterns and solutions for critical problem-solving in areas such as dealing with customer-officer interaction, counterterrorism and monitoring online communities. © 2008 IEEE.

关键词： Problem solving

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Domain Driven data Mining (D3 M)

Domain Driven Data Mining (D3 M)

引用

IEEE International Conference on data Mining Workshops, ICDM Workshops 2008

作者： Cao, Longbing Data Sciences and Knowledge Discovery Lab. Faculty of Engineering and Information Technology University of Technology Sydney Australia

ISBN: (纸本)9780769535036

In deploying data mining into the real-world business, we have to cater for business scenarios, organizational factors, user preferences and business needs. However, the current data mining algorithms and tools often stop at the delivery of patterns satisfying expected technical interestingness. Business people are not informed about how and what to do to take over the technical deliverables. The gap between academia and business has seriously affected the widespread employment of advanced data mining techniques in greatly promoting enterprise operational quality and productivity. To narrow down the gap, cater for realworld factors relevant to data mining, and make data mining workable in supporting decision-making actions in the real world, we propose the methodology of Domain Driven data Mining (D3M for short). D3M aims to construct next-generation methodologies, techniques and tools for a possible paradigm shift from data-centered hidden pattern mining to domain-driven actionable knowledge delivery. In this talk, we address the concept map of D3M, theoretical underpinnings, several general and flexible frameworks, research issues, possible directions, application areas etc. related to D3M. Real-world case studies in financial data mining and social security mining are demonstrated to show the effectiveness and applicability of D3M in both research and development of real-world challenging problems. © 2008 IEEE.

关键词： data mining

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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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F-trade 3.0: An agent-based integrated framework for data mining experiments

F-trade 3.0: An agent-based integrated framework for Data mi...

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2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2008

作者： Moemeng, Peerapol Cao, Longbing Zhang, Chengqi Data Sciences and Knowledge Discovery Research Lab. Faculty of Engineering and Information Technology University of Technology Sydney Australia

ISBN: (纸本)9780769534961

data mining researches focus on algorithms that mine valuable patterns from particular domain. Apart from the theoretical research, experiments take a vast amount of effort to build. In this paper, we propose an integrated framework that utilises a multi-agent system to support the researchers to rapidly develop experiments. Moreover, the proposed framework allows extension and integration for future researches in mutual aspects of agent and data mining. The paper describes the details of the framework and also presents a sample implementation. © 2008 IEEE.

关键词： data mining

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LODDO: Using linked open data description overlap to measure semantic relatedness between named entities

LODDO: Using linked open data description overlap to measure...

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Joint International Semantic Technology Conference, JIST 2011

作者： Zhou, Wenlei Wang, Haofen Chao, Jiansong Zhang, Weinan Yu, Yong APEX Data and Knowledge Management Lab. Department of Computer Science Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9783642299223

Measuring semantic relatedness plays an important role in information retrieval and Natural Language Processing. However, little attention has been paid to measuring semantic relatedness between named entities, which is also very significant. As the existing knowledge based approaches have the entity coverage issue and the statistical based approaches have unreliable result to low frequent entities, we propose a more comprehensive approach by leveraging Linked Open data (LOD) to solve these problems. LOD consists of lots of data sources from different domains and provides rich a priori knowledge about the entities in the world. By exploiting the semantic associations in LOD, we propose a novel algorithm, called LODDO, to measure the semantic relatedness between named entities. The experimental results show the high performance and robustness of our approach. © 2012 Springer-Verlag.

关键词： knowledge based systems

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Emergent semantics from folksonomies: A quantitative study

引用

Journal on data Semantics VI - Special Issue on Emergent Semantics

作者： Zhang, Lei Wu, Xian Yu, Yong APEX Data and Knowledge Management Lab. Department of Computer Science and Engineering Shanghai JiaoTong University Shanghai 200030 China

ISBN: (纸本)3540367128

Defining and using ontology to annotate web resources with semantic markups is generally perceived as the primary way to implement the vision of the Semantic Web. The ontology provides a shared and machine understandable semantics for web resources that agents and applications can utilize. This top-down approach (in the sense that an ontology is defined first on top of existing web resources and then used later to markup them), however, has a high barrier to entry and is difficult to scale up. In this paper, we investigate using a bottom-up approach for semantically annotating web resources as supported by the now widely popular social bookmarks services on the web where users can annotate and categorize web resources using "tags" freely choosen by the user without any pre-existing global semantic model. This kind of informal social categories is coined as "folksonomies". We show how global semantics can be statistically inferred from the folksonomies to semantically annotate the web resources. The global semantic model also disambiguate the tags and group synonymous tags together. Finally, we show that there indeed are hierarchical relations among the emerged concepts in the folksonomy and it is plausible to further identify them if we use more advanced probabilistic models. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Semantics

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Neural network classifier of time series: A case study of symbolic representation preprocessing for Control Chart Patterns

Neural network classifier of time series: A case study of sy...

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2012 8th International Conference on Natural Computation, ICNC 2012

作者： Lavangnananda, K. Sawasdimongkol, P. Data and Knowledge Engineering Lab. School of Information Technology King Mongkut's University of Technology Thonburi Bangkok Thailand

ISBN: (纸本)9781457721311

Detecting abnormalities in manufacturing process at an early stage is advantageous. This can be done by monitoring its Control Chart Patterns (CCPs). These patterns can be considered as time series data. Therefore, accurate classification of CCPs is vital in the process control. There have been several types of CCPs classifiers with various degrees of accuracy. This work is concerned with implementation of a CCPs classifier based on neural networks and symbolic representation as its preprocessing method. CCPs are generated by the commonly used Generalized Autoregressive Conditional Heteroskedasticity (GARH) Model. Several of both feed forward and recurrent networks are investigated for the classifier. The symbolic representation of time series known as Symbolic Aggregate Approximation (SAX) is selected as its application was satisfactory in numerous similar works. In feed forward network, the Multilayer Perceptron network yields the best performance while Time-lag network yields the best performance for recurrent network. The results of neural network classifier which utilizes SAX in preprocessing in this work are superior than previous works which used the same CCPs model. © 2012 IEEE.

关键词： Process control

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