16489994
Holistic Query Interface Matching using Parallel Schema Matching
Using query interfaces of different Web databases, we propose a new complex schema matching approach, Parallel Schema Matching (PSM). A parallel schema is formed by comparing two individual schemas and deleting common attributes. The attribute matching can be discovered from the attribute-occurrence patterns if many parallel schemas are available. A count-based greedy algorithm identifies which attributes are more likely to be matched. Experiments show that PSM can identify both simple matching and complex matching accurately and efficiently.
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.
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).
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.
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.
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.
789503
Exploiting the Web as the multilingual corpus for unknown query translation
target languages. To facilitate a cross-language information retrieval (CLIR) service in digital library systems, it is important to develop a powerful query translation engine. This must be able to automatically translate users’ queries from multiple source languages to the target languages that the systems accept. Conventional approaches to CLIR can be divided into two groups: dictionary-based and corpora-based. A dictionarybased approach (Ballesteros & Croft, 1997) utilizes a simple translation dictionary in which each query term is looked up and translated into its corresponding terms in the target language. However, terms may have multiple ambiguous meanings and many new unknown terms that are not covered in the dictionary cannot be translated. Corpora-based approach (Nie, Isabelle, Simard & Durand, 1999) incorporates multilingual corpus where texts in different languages are collected for translation purpose. Either comparable texts (Fung & Yee, 1998) or parallel texts (Nie et al., 1999) can be used as the multilingual corpus. Parallel texts contain bilingual sentences, from which word or phrase translations can be extracted by appropriate sentence alignment methods (Gale & Church, 1993). Comparable texts share similar concepts in content, but do not necessarily show alignment correspondence for translation. Because there is a fundamental word mismatch problem that queries and documents might not share the same words, the basic assumption of such approaches is that as queries get longer, the severity of the problem tends to decrease. Query expansion techniques (Ballesteros & Croft, 1997) can therefore be used to enrich query terms not covered in documents. However, these approaches present some fundamental difficulties for digital libraries that wish to support practical CLIR services. First, most of existing digital libraries contain only monolingual text collections; hence, there is no bilingual corpus for cross-lingual training. Second, real Exploiting the Web as the Multilingual Corpus for Unknown Query Translation
15308504
Efficient and accurate Ethernet simulation
The Internet is increasingly being called upon to provide different levels of service to different applications and users. A practical problem in doing so is that although Ethernet is one of the hops for nearby all communication in the Internet, it does not provide any QoS guarantees. A natural question, therefore, is the effect of offered load on Ethernet throughput and delay. In this paper, we present several techniques for accurately and efficiently modeling the behavior of a heavily loaded Ethernet link. We propose an efficient distributed simulation model, called Fast Ethernet Simulation, that empirically models an Ethernet link to quickly and accurately simulate it. By eliminating the implementation of the CSMA/CD protocol, our approach reduces computational complexity drastically while still maintaining desirable accuracy. Performance results show that our techniques not only add very little overhead (less than 5% in our tests) to the basic cost of simulating an Ethernet link, but also closely match real-world measurements. We also present efficient techniques for compressing cumulative distributions using hyperbolic curves and for monitoring the load on a heavily loaded link. Finally, we show applications to illustrate the potential usage of the Fast Ethernet Simulation.
28811627
Recognizing depth-first-search trees in parallel
Consider that T. is a given spanning tree of an undirected graph G which contains a vertices and m (/spl ges/n-1) edges. In this paper, we propose an O(m/p+log m)-time parallel algorithm using p processors on the EREW PRAM model to determine whether T is a depth-first-search tree of G. Our algorithm is optimal in time complexity and speed-up.<<ETX>>
6624380
Structural Features of Toxic Chemicals for Specific Toxicity
We have studied the structural features of toxic chemicals from the RTECS database associated with specific toxicity. The frameworks, functional groups, and structure patterns of molecules are taken into account. Potential active frameworks, groups, and structure patterns for specific toxicity are gained by computational chemistry approaches. These structural features of toxic chemicals will be helpful in understanding activities of toxic chemicals and useful in predicting the toxicity of chemicals, especially in the early stage of drug design.
13917172
iCLEF 2003 at Maryland: Translation Selection and Document Selection
Maryland performed two sets of experiments for the 2003 Cross-Language Evaluation Forum’s interactive track, one focused on interactive selection of appropriate translations for query terms, the second focused on interactive selection of relevant documents. Translation selection was supported using possible synonyms discovered through back translation and two techniques for generating KeyWord In Context (KWIC) examples of usage. The results indicate that searchers typically achieved a similar search effectiveness using fewer query iterations when interactive translation selection was available. For document selection, a complete extract of the first 40 words of each news story was compared to a compressed extract generated using an automated parse-and-trim approach that approximates one way in which people can produce headlines. The results indicate that compressed “headlines” result in faster assessment, but with a 20% relative reduction in the F α= 0.8 search effectiveness measure.