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Video database Modeling and Temporal Pattern Retrieval using Hierarchical Markov Model Mediator 22

Video Database Modeling and Temporal Pattern Retrieval using...

引用

22nd international conference on data engineering Workshops, ICDEW 2006

作者： Zhao, Na Chen, Shu-Ching Shyu, Mei-Ling Distributed Multimedia Information System Laboratory School of Computing and Information Sciences Florida International University MiamiFL33199 United States Department of Electrical and Computer Engineering University of Miami Coral GablesFL33124 United States

ISBN: (纸本)0769525717

The dream of pervasive multimedia retrieval and reuse will not be realized without incorporating semantics in the multimedia database. As video data is penetrating many information systems, the need for database support for video data evolves. Hence, we propose an innovative database modeling mechanism called Hierarchical Markov Model Mediator (HMMM) which integrates lowlevel features, semantic concepts, and high-level user perceptions for modeling and indexing multiple-level video objects to facilitate temporal pattern retrieval. Different from the existing database modeling methods, our approach carries a stochastic and dynamic process in both search and similarity calculation. In the retrieval of semantic event patterns, HMMM always tries to traverse the right path and therefore it can assist in retrieving more accurate patterns quickly with lower computational costs. Moreover, HMMM supports feedbacks and learning strategies, which can proficiently assure the continuous improvements of the overall performance. © 2006 IEEE.

关键词： Semantics

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WebIQ: learning from the Web to match Deep-Web query interfaces

WebIQ: Learning from the Web to match Deep-Web query interfa...

引用

22nd international conference on data engineering, ICDE '06

作者： Wu, Wensheng Doan, AnHai Yu, Clement University of Illinois Urbana United States University of Illinois Chicago IL United States

ISBN: (纸本)0769525709

Integrating Deep Web sources requires highly accurate semantic matches between the attributes of the source query interfaces. These matches are usually established by comparing the similarities of the attributes' labels and instances. However, attributes on query interfaces often have no or very few data instances. The pervasive lack of instances seriously reduces the accuracy of current matching techniques. To address this problem, we describe WebIQ, a solution that learns from both the Surface Web and the Deep Web to automatically discover instances for interface attributes. WebIQ extends question answering techniques commonly used in the AI community for this purpose. We describe how to incorporate WebIQ into current interface matching systems. Extensive experiments over five realworld domains show the utility of WebIQ. In particular, the results show that acquired instances help improve matching accuracy from 89.5% F-1 to 97.5%, at only a modest runtime overhead. © 2006 IEEE.

关键词： World Wide Web

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A PCA-Based Vehicle Classification Framework

A PCA-Based Vehicle Classification Framework

引用

international conference on data engineering Workshops

作者： Chengcui Zhang Xin Chen Wei-bang Chen Computer and Information Sciences Department University of Alabama Birmingham Birmingham AL USA

Due to its great practical importance, intelligent Transportation System has been an active research area in recent years. In this paper, we present a framework that incorporates various aspects of an intelligent transportation system with its ultimate goal being vehicle classification. Given a traffic video sequence, the proposed system first proceeds to segment individual vehicles. Then the extracted vehicle objects are normalized so that all vehicles are aligned along the same direction and measured at the same scale. Following the preprocessing step, two classification algorithms - Eigenvehicle and PCA-SVM, are proposed and implemented to classify vehicle objects into trucks, passenger cars, vans, and pick-ups. These two methods exploit the distinguishing power of Principal Component Analysis (PCA) at different granularities with different learning mechanisms. Experiments are conducted to compare these two methods and the results demonstrate the effectiveness of the proposed framework.

关键词： intelligent transportation systems Vehicle detection intelligent vehicles Principal component analysis Humans Surveillance intelligent sensors Military computing Road vehicles Face detection

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learning from Aggregate Views

Learning from Aggregate Views

引用

international conference on data engineering

作者： Bee-Chung Chen Lei Chen R. Ramakrishnan D.R. Musicant University of Wisconsin Madison WI USA Carleton College Northfield MN USA

ISBN: (纸本)9781424429448

In this paper, we introduce a new class of data mining problems called learning from aggregate views. In contrast to the traditional problem of learning from a single table of training examples, the new goal is to learn from multiple aggregate views of the underlying data, without access to the un-aggregated data. We motivate this new problem, present a general problem framework, develop learning methods for RFA (Restriction-Free Aggregate) views defined using COUNT, SUM, AVG and STDEV, and offer theoretical and experimental results that characterize the proposed methods.

关键词： Aggregates Predictive models data privacy learning systems data mining Educational institutions Classification tree analysis Decision trees Labeling Filters

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Automatic sales lead generation from web data

Automatic sales lead generation from web data

引用

22nd international conference on data engineering, ICDE '06

作者： Ramakrishnan, Ganesh Joshi, Sachindra Negi, Sumit Krishnapuram, Raghu Balakrishnan, Sreeram IBM India Research Lab

ISBN: (纸本)0769525709

Speed to market is critical to companies that are driven by sales in a competitive market. The earlier a potential customer can be approached in the decision making process of a purchase, the higher are the chances of converting that prospect into a customer. Traditional methods to identify sales leads such as company surveys and direct marketing are manual, expensive and not scalable. Over the past decade the World Wide Web has grown into an information-mesh, with most important facts being reported through Web sites. Several news papers, press releases, trade journals, business magazines and other related sources are on-line. These sources could be used to identify prospective buyers automatically. In this paper, we present a system called ETAP (Electronic Trigger Alert Program) that extracts trigger events from Web data that help in identifying prospective buyers. Trigger events are events of corporate relevance and indicative of the propensity of companies to purchase new products associated with these events. Examples of trigger events are change in management, revenue growth and mergers & acquisitions. The unstructured nature of information makes the extraction task of trigger events difficult. We pose the problem of trigger events extraction as a classification problem and develop methods for learning trigger event classifiers using existing classification methods. We present methods to automatically generate the training data required to learn the classifiers. We also propose a method of feature abstraction that uses named entity recognition to solve the problem of data sparsity. We score and rank the trigger events extracted from ETAP for easy browsing. Our experiments show the effectiveness of the method and thus establish the feasibility of automatic sales lead generation using the Web data. © 2006 IEEE.

关键词： data reduction

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Content and Context Aware Networking Using Semantic Tagging

Content and Context Aware Networking Using Semantic Tagging

引用

international conference on data engineering Workshops

作者： S.B. Kodeswaran A. Joshi Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD USA

Today’s model of networking primarily concentrates intelligence at the end hosts with the network itself offering a simple"best-effor","data agnostic" communication medium. However, this paradigm has proven to be insufficient to meet todays needs considering the diversity of applications and devices that are networked. To offer value added services to these end users and applications, more and more intelligence needs to be migrated away from the edges and into the network in a controlled and tractable manner. In this paper, we present our approach of utilizing semantic data tagging to provide content level information for data streams flowing through a network. A policy based management mechanism is utilized within the network fabric allowing routers to reason over the content and make intelligent decisions regarding the handling of data packets. Service differentiation, in-network content adaptation, traffic monitoring and control etc. are some of the new services that can now be offered by the network in a generic and flexible manner. By deploying our proposed architecture, a network need no longer be viewed as a simple data transport medium but rather as a policy-controlled intelligent packet/stream processor that can offer specialized handling based on application needs.

关键词： Context awareness Tagging intelligent networks Resource description framework Routing Labeling Communication system traffic control Computer science Content management Fabrics

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Mining, Indexing, and Similarity Search in Graphs and Complex Structures

Mining, Indexing, and Similarity Search in Graphs and Comple...

引用

international conference on data engineering

作者： Jiawei Han Xifeng Yan P.S. Yu University of Illinois Urbana-Champaign USA T. J. Watson Research Center IBM USA

Scalable methods for mining, indexing, and similarity search in graphs and other complex structures, such as trees, lattices, and networks, have become increasingly important in data mining and database management. This is because a large set of emerging applications need to handle new kinds of objects with complex structures, such as trees (e.g., XML data), graphs (e.g., Web, chemical structures and biological graphs) and networks (e.g., social and biological networks). Such complicated data structures pose many new challenging research problems related to data mining, data management, and similarity search that do not exist in the traditional database and data mining studies.

关键词： Indexing data mining Tree graphs Clustering algorithms Search methods Protein sequence Spatial databases Application software intelligent networks Lattices

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Ant Algorithms for Search in Unstructured Peer-to-Peer Networks

Ant Algorithms for Search in Unstructured Peer-to-Peer Netwo...

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international conference on data engineering Workshops

作者： E. Michlmayr Womens Postgraduate College for Internet Technologies WIT Institute of Software Technology and Interactive Systems University of Technology Vienna Austria

Although the ant metaphor has been successfully applied to routing of data packets both in wireless and fixed networks, little is yet known about its applicability to the task of query routing in peer-to-peer environments. This work presents SemAnt, an algorithm for distributed query routing based on the Ant Colony Optimization meta-heuristic. The experimental results show that the algorithm produces robust results and converges fast. Based on the results gained so far, the goal for the Ph.D. thesis is to extend the algorithm to include strategies for self-adaptation to volatile networks where nodes may leave or join at any time.

关键词： intelligent networks Peer to peer computing Routing Query processing Ant colony optimization Costs IP networks Educational institutions Interactive systems Robustness

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Searching Local Information in Mobile databases

Searching Local Information in Mobile Databases

引用

international conference on data engineering

作者： O. Wolfson Bo Xu Huabei Yin Hu Cao University of Illinois Chicago USA

A mobile ad-hoc network (MANET) is a set of moving objects that communicate with each other via unregulated, short-range wireless technologies such as IEEE 802.11, Bluetooth, or Ultra Wide Band (UWB). No fixed infrastructure is assumed or relied upon. An important application domain of MANET’s is local resource discovery. In a local resource discovery application, a user finds local resources that satisfy specified criteria. For example, a driver finds an available parking slot in a region by receiving information generated by the parking meter, or gets the traffic conditions on a highway segment a mile ahead; a cab driver finds a near-by customer, or a participant at a convention finds another participant with a matching profile.

关键词： databases Broadcasting Routing Mobile ad hoc networks Network topology Bandwidth intelligent networks Ad hoc networks Bluetooth Ultra wideband communication

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Integrating Unstructured data into Relational databases

Integrating Unstructured Data into Relational Databases

引用

international conference on data engineering

作者： I.R. Mansuri S. Sarawagi IIT Bombay India

In this paper we present a system for automatically integrating unstructured text into a multi-relational database using state-of-the-art statistical models for structure extraction and matching. We show how to extend current highperforming models, Conditional Random Fields and their semi-markov counterparts, to effectively exploit a variety of recognition clues available in a database of entities, thereby significantly reducing the dependence on manually labeled training data. Our system is designed to load unstructured records into columns spread across multiple tables in the database while resolving the relationship of the extracted text with existing column values, and preserving the cardinality and link constraints of the database. We show how to combine the inference algorithms of statistical models with the database imposed constraints for optimal data integration.

关键词： Relational databases data mining Resumes Bridges Training data Inference algorithms database systems Machine learning Web mining Portals

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