Xuhui Li

Web Information Systems – WISE 2006

Keynote Papers.- Internet-Scale Data Distribution: Some Research Problems.- Towards Next-Generation Search Engines and Browsers - Search Beyond Media Types and Places.- Building a Domain Independent Platform for Collecting Domain Specific Data from the Web.- Session 1: Web Search.- A Web Search Method Based on the Temporal Relation of Query Keywords.- Meta-search Based Web Resource Discovery for Object-Level Vertical Search.- PreCN: Preprocessing Candidate Networks for Efficient Keyword Search over Databases.- Searching Coordinate Terms with Their Context from the Web.- Session 2: Web Retrieval.- A Semantic Matching of Information Segments for Tolerating Error Chinese Words.- Block-Based Similarity Search on the Web Using Manifold-Ranking.- Design and Implementation of Preference-Based Search.- Topic-Based Website Feature Analysis for Enterprise Search from the Web.- Session 3: Web Workflows.- Fault-Tolerant Orchestration of Transactional Web Services.- Supporting Effective Operation of E-Governmental Services Through Workflow and Knowledge Management.- DOPA: A Data-Driven and Ontology-Based Method for Ad Hoc Process Awareness in Web Information Systems.- A Transaction-Aware Coordination Protocol for Web Services Composition.- Session 4: Web Services.- Unstoppable Stateful PHP Web Services.- Quantified Matchmaking of Heterogeneous Services.- Pattern Based Property Specification and Verification for Service Composition.- Detecting the Web Services Feature Interactions.- Session 5: Web Mining.- Exploiting Rating Behaviors for Effective Collaborative Filtering.- Exploiting Link Analysis with a Three-Layer Web Structure Model.- Concept Hierarchy Construction by Combining Spectral Clustering and Subsumption Estimation.- Automatic Hierarchical Classification of Structured Deep Web Databases.- Session 6: Performant Web Systems.- A Robust Web-Based Approach for Broadcasting Downward Messages in a Large-Scaled Company.- Buffer-Preposed QoS Adaptation Framework and Load Shedding Techniques over Streams.- Cardinality Computing: A New Step Towards Fully Representing Multi-sets by Bloom Filters.- An Efficient Scheme to Completely Avoid Re-labeling in XML Updates.- Session 7: Web Information Systems.- Modeling Portlet Aggregation Through Statecharts.- Calculation of Target Locations for Web Resources.- Efficient Bid Pricing Based on Costing Methods for Internet Bid Systems.- An Enhanced Super-Peer Model for Digital Library Construction.- Offline Web Client: Approach, Design and Implementation Based on Web System.- Session 8: Web Document Analysis.- A Latent Image Semantic Indexing Scheme for Image Retrieval on the Web.- Hybrid Method for Automated News Content Extraction from the Web.- A Hybrid Sentence Ordering Strategy in Multi-document Summarization.- Document Fragmentation for XML Streams Based on Query Statistics.- A Heuristic Approach for Topical Information Extraction from News Pages.- Session 9: Quality, Security and Trust.- Defining a Data Quality Model for Web Portals.- Finding Reliable Recommendations for Trust Model.- Self-Updating Hash Chains and Their Implementations.- Modeling Web-Based Applications Quality: A Probabilistic Approach.- Monitoring Interactivity in a Web-Based Community.- Session 10: Semantic Web and Integration.- A Metamodel-Based Approach for Extracting Ontological Semantics from UML Models.- Deeper Semantics Goes a Long Way: Fuzzified Representation and Matching of Color Descriptions for Online Clothing Search.- Semantically Integrating Portlets in Portals Through Annotation.- A Self-organized Semantic Clustering Approach for Super-Peer Networks.- Using Categorial Context-SHOIQ(D+) DL to Migrate Between the Context-Aware Scenes.- Session 11: XML Query Processing.- SCEND: An Efficient Semantic Cache to Adequately Explore Answerability of Views.- Clustered Chain Path Index for XML Document: Efficiently Processing Branch Queries.- Region-Based Coding for Queries over Streamed XML Fragments.- PrefixTreeESpan: A Pattern Growth Algorithm for Mining Embedded Subtrees.- Evaluating Interconnection Relationship for Path-Based XML Retrieval.- Session 12: Multimedia and User Interface.- User Models: A Contribution to Pragmatics of Web Information Systems Design.- XUPClient - A Thin Client for Rich Internet Applications.- 2D/3D Web Visualization on Mobile Devices.- Web Driving: An Image-Based Opportunistic Web Browser That Visualizes a Peripheral Information Space.- Blogouse: Turning the Mouse into a Copy&Blog Device.

Deploying Mobile Computation in Cloud Service

Cloud computing advocates a service-oriented computing par- adigm where various kinds of resources are organized in a virtual way. How to specify and execute tasks to make use of the resources efficiently thus becomes an important problem in cloud computing. Mobile computation is often regarded as a good alternative to conventional RPC-based technology for situations where resources can be dynamically bound to computations. In this paper, we propose a middleware framework for cloud computing to deploy mobile computation, especially mobile agent technology, in cloud services. The major issues to enable mobile agent-based services in the service-oriented computing are discussed and the corresponding mechanisms in the framework are introduced.

The Formal Design Model of an Automatic Teller Machine ATM

An Automated Teller Machine ATM is a safety-critical and real-time system that is highly complicated in design and implementation. This article presents the formal design, specification, and modeling of the ATM system using a denotational mathematics known as Real-Time Process Algebra RTPA. The conceptual model of the ATM system is introduced as the initial requirements for the system. The architectural model of the ATM system is created using RTPA architectural modeling methodologies and refined by a set of Unified Data Models UDMs, which share a generic mathematical model of tuples. The static behaviors of the ATM system are specified and refined by a set of Unified Process Models UPMs for the ATM transition processing and system supporting processes. The dynamic behaviors of the ATM system are specified and refined by process priority allocation, process deployment, and process dispatch models. Based on the formal design models of the ATM system, code can be automatically generated using the RTPA Code Generator RTPA-CG, or be seamlessly transformed into programs by programmers. The formal models of ATM may not only serve as a formal design paradigm of real-time software systems, but also a test bench for the expressive power and modeling capability of exiting formal methods in software engineering.

Tri-Training for authorship attribution with limited training data

Authorship attribution (AA) aims to identify the authors of a set of documents. Traditional studies in this area often assume that there are a large set of labeled documents available for training. However, in the real life, it is often difficult or expensive to collect a large set of labeled data. For example, in the online review domain, most reviewers (authors) only write a few reviews, which are not enough to serve as the training data for accurate classification. In this paper, we present a novel three-view Tri-Training method to iteratively identify authors of unlabeled data to augment the training set. The key idea is to first represent each document in three distinct views, and then perform Tri-Training to exploit the large amount of unlabeled documents. Starting from 10 training documents per author, we systematically evaluate the effectiveness of the proposed Tri-Training method for AA. Experimental results show that the proposed approach outperforms the state-of-the-art semi-supervised method CNG+SVM and other baselines.

Using object deputy model to prepare data for data warehousing

Providing integrated access to multiple, distributed, heterogeneous databases and other information sources has become one of the leading issues in database research and the industry. One of the most effective approaches is to extract and integrate information of interest from each source in advance and store them in a centralized repository (known as a data warehouse). When a query is posed, it is evaluated directly at the warehouse without accessing the original information sources. One of the techniques that this approach uses to improve the efficiency of query processing is materialized view(s). Essentially, materialized views are used for data warehouses, and various methods for relational databases have been developed. In this paper, we first discuss an object deputy approach to realize materialized object views for data warehouses which can also incorporate object-oriented databases. A framework has been developed using Smalltalk to prepare data for data warehousing, in which an object deputy model and database connecting tools have been implemented. The object deputy model can provide an easy-to-use way to resolve inconsistency and conflicts while preparing data for data warehousing, as evidenced by our empirical study.

A Direct Execution Approach to Simulating Mobile Agent Algorithms

Mobile agent technology has been applied to develop the solutions for various kinds of parallel and distributed computing problems. However, performance evaluation of mobile agent algorithms remains a difficult task, mainly due to the characteristics of mobile agents such as distributed and asynchronous execution, autonomy and mobility. This paper proposes a general approach based on direct execution simulation for evaluating the performance of mobile agent algorithms by collecting and analyzing the information about the agents during their execution. We describe the proposed generic simulation model, named MADES, the architecture of a software environment based on MADES, and a prototype implementation. A mobile agent-based distributed load balancing algorithm has been used for experiments with the prototype.

Towards a More declarative XML query language

To extract and restructure information in XML documents, various query languages have been proposed in the past decade. These languages take navigational or pattern-based approach to data extraction and often claim to be declarative. However, declarativeness in them is not as prominent as in SQL because they often exhibit a procedural style in handling heterogeneity and presenting tree-like document structure. In this paper, a new XML query language called XTQ is proposed to address this challenge. XTQ is a pattern-based language which introduces disjunction as well as conjunction operators in composing treelike patterns named LXT (Logic XML Tree) for data extraction. LXT can expressively handle heterogeneity common in XML queries. Based on a hierarchically structured pattern with considerate restructuring rules, XTQ deploys a flexible hierarchically grouping mechanism in data construction so that complex tree-like structure can be intuitively presented. Examples from common query request show that XTQ can present XML queries more declaratively than existing studies.

Mining Event-Oriented Topics in Microblog Stream with Unsupervised Multi-View Hierarchical Embedding

This article presents an unsupervised multi-view hierarchical embedding (UMHE) framework to sufficiently reveal the intrinsic topical knowledge in social events. Event-oriented topics are highly related to such events as it can provide explicit descriptions of what have happened in social community. In many real-world cases, however, it is difficult to include all attributes of microblogs, more often, textual aspects only are available. Traditional topic modelling methods have failed to generate event-oriented topics with the textual aspects, since the inherent relations between topics are often overlooked in these methods. Meanwhile, the metrics in original word vocabulary space might not effectively capture semantic distances. Our UMHE framework overcomes the severe information deficiency and poor feature representation. The UMHE first develops a multi-view Bayesian rose tree to preliminarily generate prior knowledge for latent topics and their relations. With such prior knowledge, we design an unsupervised translation-based hierarchical embedding method to make a better representation of these latent topics. By applying self-adaptive spectral clustering on the embedding space and the original space concomitantly, we eventually extract event-oriented topics in word distributions to express social events. Our framework is purely data-driven and unsupervised, without any external knowledge. Experimental results on TREC Tweets2011 dataset and Sina Weibo dataset demonstrate that the UMHE framework can construct hierarchical structure with high fitness, but also yield topic embeddings with salient semantics; therefore, it can derive event-oriented topics with meaningful descriptions.

Coherent Topic Hierarchy: A Strategy for Topic Evolutionary Analysis on Microblog Feeds

Topic evolutionary analysis on microblog feeds can help reveal users’ interests and public concerns in a global perspective. However, it is not easy to capture the evolutionary patterns since the semantic coherence is usually difficult to be expressed and the timeline structure is always intractable to be organized. In this paper, we propose a novel strategy, in which a coherent topic hierarchy is designed to deal with these challenges. First, we incorporate the sparse biterm topic model to extract some coherent topics from microblog feeds. Then the topology of these topics is constructed by the basic Bayesian rose tree combined with topic similarity. Finally, we devise a cross-tree random walk with restart model to bond each pair of sequential trees into a timeline hierarchy. Experimental results on microblog datasets demonstrate that the coherent topic hierarchy is capable of providing meaningful topic interpretations, achieving high clustering performance, as well as presenting motivated patterns for topic evolutionary analysis.

A Pattern-Based Temporal XML Query Language

The need to store large amount of temporal data in XML documents makes temporal XML document query an interesting and practical challenge. Researchers have proposed various temporal XML query languages with specific data models, however, these languages just extend XPath or XQuery with simple temporal operations, thus lacking both declarativeness and consistency in terms of usability and reasonability. In this paper we introduce TempXTQ, a pattern-based temporal XML query language, with a Set-based Temporal XML (STX) data model which uses hierarchically-grouped data sets to uniformly represent both temporal information and common XML data. TempXTQ deploys various patterns equipped with certain pattern restructuring mechanism to present requests on extracting and constructing temporal XML data. These patterns are hierarchically composed with certain operators like logic connectives, which enables TempXTQ to specify temporal queries consistently with the STX model and declaratively present various kinds of data manipulation requests. We further demonstrate that TempXTQ can present complicated temporal XML queries clearly and efficiently.