7588542
Instance-based Schema Matching for Web Databases by Domain-specific Query Probing
In a Web database that dynamically provides information in response to user queries, two distinct schemas, interface schema (the schema users can query) and result schema (the schema users can browse), are presented to users. Each partially reflects the actual schema of the Web database. Most previous work only studied the problem of schema matching across query interfaces of Web databases. In this paper, we propose a novel schema model that distinguishes the interface and the result schema of a Web database in a specific domain. In this model, we address two significant Web database schema-matching problems: intra-site and inter-site. The first problem is crucial in automatically extracting data from Web databases, while the second problem plays a significant role in meta-retrieving and integrating data from different Web databases. We also investigate a unified solution to the two problems based on query probing and instance-based schema matching techniques. Using the model, a cross validation technique is also proposed to improve the accuracy of the schema matching. Our experiments on real Web databases demonstrate that the two problems can be solved simultaneously with high precision and recall.
12680145
Finding Patterns in Three-Dimensional Graphs: Algorithms and Applications to Scientific Data Mining
Presents a method for finding patterns in 3D graphs. Each node in a graph is an undecomposable or atomic unit and has a label. Edges are links between the atomic units. Patterns are rigid substructures that may occur in a graph after allowing for an arbitrary number of whole-structure rotations and translations as well as a small number (specified by the user) of edit operations in the patterns or in the graph. (When a pattern appears in a graph only after the graph has been modified, we call that appearance "approximate occurrence.") The edit operations include relabeling a node, deleting a node and inserting a node. The proposed method is based on the geometric hashing technique, which hashes node-triplets of the graphs into a 3D table and compresses the label-triplets in the table. To demonstrate the utility of our algorithms, we discuss two applications of them in scientific data mining. First, we apply the method to locating frequently occurring motifs in two families of proteins pertaining to RNA-directed DNA polymerase and thymidylate synthase and use the motifs to classify the proteins. Then, we apply the method to clustering chemical compounds pertaining to aromatic compounds, bicyclicalkanes and photosynthesis. Experimental results indicate the good performance of our algorithms and high recall and precision rates for both classification and clustering.
236203
New Techniques for DNA Sequence Classification
DNA sequence classification is the activity of determining whether or not an unlabeled sequence S belongs to an existing class C. This paper proposes two new techniques for DNA sequence classification. The first technique works by comparing the unlabeled sequence S with a group of active motifs discovered from the elements of C and by distinction with elements outside of C. The second technique generates and matches gapped fingerprints of S with elements of C. Experimental results obtained by running these algorithms on long and well conserved Alu sequences demonstrate the good performance of the presented methods compared with FASTA. When applied to less conserved and relatively short functional sites such as splice-junctions, a variation of the second technique combining fingerprinting with consensus sequence analysis gives better results than the current classifiers employing text compression and machine learning algorithms.
14900181
Unordered tree mining with applications to phylogeny
Frequent structure mining (FSM) aims to discover and extract patterns frequently occurring in structural data, such as trees and graphs. FSM finds many applications in bioinformatics, XML processing, Web log analysis, and so on. We present a new FSM technique for finding patterns in rooted unordered labeled trees. The patterns of interest are cousin pairs in these trees. A cousin pair is a pair of nodes sharing the same parent, the same grandparent, or the same great-grandparent, etc. Given a tree T, our algorithm finds all interesting cousin pairs of T in O(|T|/sup 2/) time where |T| is the number of nodes in T. Experimental results on synthetic data and phylogenies show the scalability and effectiveness of the proposed technique. To demonstrate the usefulness of our approach, we discuss its applications to locating co-occurring patterns in multiple evolutionary trees, evaluating the consensus of equally parsimonious trees, and finding kernel trees of groups of phylogenies. We also describe extensions of our algorithms for undirected acyclic graphs (or free trees).
2286283
Query result ranking over e-commerce web databases
To deal with the problem of too many results returned from an E-commerce Web database in response to a user query, this paper proposes a novel approach to rank the query results. Based on the user query, we speculate how much the user cares about each attribute and assign a corresponding weight to it. Then, for each tuple in the query result, each attribute value is assigned a score according to its "desirableness" to the user. These attribute value scores are combined according to the attribute weights to get a final ranking score for each tuple. Tuples with the top ranking scores are presented to the user first. Our ranking method is domain independent and requires no user feedback. Experimental results demonstrate that this ranking method can effectively capture a user's preferences.
11616193
Automatic Hierarchical Classification of Structured Deep Web Databases
We present a method that automatically classifies structured deep Web databases according to a pre-defined topic hierarchy. We assume that there are some manually classified databases, i.e., training databases, in every node of the topic hierarchy. Each training database is probed using queries constructed from the node titles of the topic hierarchy and the query result counts reported by the database are used to represent the content of the database. Hence, when adding a new database it can be probed by the same set of queries and classified to a node whose training databases are most similar to the new one. Specifically, a support vector machine classifier is trained on each internal node of the topic hierarchy with these training databases and the new database can be classified into the hierarchy top-down level by level. A feature extension method is proposed to create discriminant features. Experiments run on real structured Web databases collected from the Internet show that this classification method is quite accurate.
8736539
A hole-filling strategy for reconstruction of smooth surfaces in range images
Creating models of real scenes is a complex task for which the use of traditional modelling techniques is inappropriate. For this task, laser rangefinders are frequently used to sample the scene from several viewpoints, with the resulting range images integrated into a final model. In practice, due to surface reflectance properties, occlusions and accessibility limitations, certain areas of the scenes are usually not sampled, leading to holes and introducing undesirable artifacts in the resulting models. We present an algorithm for filling holes on surfaces reconstructed from point clouds. The algorithm is based on moving least squares and can recover both geometry and shading information, providing a good alternative when the properties to be reconstructed are locally smooth. The reconstruction process is mostly automatic and the sampling rate in the reconstructed areas follows the given samples. We demonstrate the use of the algorithm on both real and synthetic data sets to obtain complete geometry and reasonable shading.
94229
Translating unknown queries with web corpora for cross-language information retrieval
It is crucial for cross-language information retrieval (CLIR) systems to deal with the translation of unknown queries due to that real queries might be short. The purpose of this paper is to investigate the feasibility of exploiting the Web as the corpus source to translate unknown queries for CLIR. We propose an online translation approach to determine effective translations for unknown query terms via mining of bilingual search-result pages obtained from Web search engines. This approach can alleviate the problem of the lack of large bilingual corpora, translate many unknown query terms, provide flexible query specifications, and extract semantically-close translations to benefit CLIR tasks -- especially for cross-language Web search.
14678674
Enhanced intranet management in a DHCP-enabled environment
The DHCP (dynamic host configuration protocol) is widely deployed in resource allocation and intranet management. However the DHCP mechanism is not mandatory and the DHCP server can neither force DHCP clients to release their leases, nor enforce cooperation from externally configured hosts that are DHCP-unaware. Although new DHCP options such as DHCP reconfigure extension have been proposed, the basic problems inherent in DHCP mechanism cannot be solved without first strengthening its operations. In the paper a DHCP-based infrastructure for intranet management is proposed by combining the resource allocation functions of the DHCP server with the packet filtering features of MAC (Medium Access Control) bridges such as Ethernet switches and wireless access points. DHCP clients and DHCP-unaware hosts that do not abide by the DHCP mechanism or our management policy will be denied network accesses by MAC bridges. Resource allocation and access control can be integrated and local configuration conflicts can be reduced to the minimum.
207320252
An efficient kernel discriminant analysis method
Small sample size and high computational complexity are two major problems encountered when traditional kernel discriminant analysis methods are applied to high-dimensional pattern classification tasks such as face recognition. In this paper, we introduce a new kernel discriminant learning method, which is able to effectively address the two problems by using regularization and subspace decomposition techniques. Experiments performed on real face databases indicate that the proposed method outperforms, in terms of classification accuracy, existing kernel methods, such as kernel principal component analysis and kernel linear discriminant analysis, at a significantly reduced computational cost.
11945508
Content-Based Audio Classification Using Support Vector Machines and Independent Component Analysis
In this paper, we present a new audio classification system. First, a frame-based multiclass support vector machine (SVM) for audio classification is proposed. The accuracy rate has significant improvements over conventional file-based SVM audio classifier. In feature selection, this study transforms the log powers of the critical-band filters based on independent component analysis (ICA). This new audio feature is combined with mel-frequency cepstral coefficients (MFCCs) and five perceptual features to form an audio feature set. The superiority of the proposed system has been demonstrated via a 15-class sound database with a 91.7% accuracy rate