5504923
Algorithmics and applications of tree and graph searching
Modern search engines answer keyword-based queries extremely efficiently. The impressive speed is due to clever inverted index structures, caching, a domain-independent knowledge of strings, and thousands of machines. Several research efforts have attempted to generalize keyword search to keytree and keygraph searching, because trees and graphs have many applications in next-generation database systems. This paper surveys both algorithms and applications, giving some emphasis to our own work.
16978334
Evaluating a class of distance-mapping algorithms for data mining and clustering
A distance-mapping algorithm takes a set of objects and a distance metric and then maps those objects to a Euclidean or pseudoEuclidean space in such a way that the distances among objects are approximately preserved. Distance mapping algorithms are a useful tool for clustering and visualization in data intensive applications, because they replace expensive distance calculations by sum-of-square calculations. This can make clustering in large databases with expensive distance metrics practical. In this paper we present five distance-mapping algorithms and conduct experiments to compare their performance in data clustering applications. These include two algorithms called FastMap and MetricMap, and three hybrid heuristics that combine the two algorithms in different ways. Experimental results on both synthetic and RNA data show the superiority of the hybrid algorithms. The results imply that FastMap and MetricMap capture complementary information about distance metrics and therefore can be used together to great benefit. The net effect is that multi-day computations may be done in minutes.
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.
17699044
Automated Discovery of Active Motifs in Multiple RNA Secondary Structures
In this paper we present a method for discovering approximately common motifs (also known as active motifs) in multiple RNA secondary structures. The secondary structures can be represented as ordered trees (i.e., the order among siblings matters). Motifs in these trees are connected subgraphs that can differ in both substitutions and deletions/insertions. The proposed method consists of two steps: (1) find candidate motifs in a small sample of the secondary structures; (2) search all of the secondary structures to determine how frequently these motifs occur (within the allowed approximation) in the secondary structures. To reduce the running time, we develop two optimization heuristics based on sampling and pattern matching techniques. Experimental results obtained by running these algorithms on both generated data and RNA secondary structures show the good performance of the algorithms. To demonstrate the utility of our algorithms, we discuss their applications to conducting the phylogenetic study of RNA sequences obtained from GenBank.
46369194
Mining Frequent Agreement Subtrees in Phylogenetic Databases
32133517
Information Extraction from the Structured Part of Office Documents
The contents of office documents can be divided into structured and unstructured parts. In this paper, we present a sample-based approach to analyzing a document to form its layout and conceptual structures, and then extracting information from the structured part of the office documents. We represent a document's layout structure as an ordered labeled tree structure using nested segmentation, and its conceptual structure as a set of attribute type pairs. The layout similarities between the document to be processed and samples are identified by employing an approximate tree matching method. The conceptual similarities are identified by analyzing document and sample contents, and by measuring the degree of conceptual closeness. Finally, the information is extracted by instantiating the attributes specified in the conceptual structure based on the result of document structure analysis.
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.
8673951
An iterative optimization approach for unified image segmentation and matting
Separating a foreground object from the background in a static image involves determining both full and partial pixel coverages, also known as extracting a matte. Previous approaches require the input image to be presegmented into three regions: foreground, background and unknown, which are called a trimap. Partial opacity values are then computed only for pixels inside the unknown region. This presegmentation based approach fails for images with large portions of semitransparent foreground where the trimap is difficult to create even manually. In this paper, we combine the segmentation and matting problem together and propose a unified optimization approach based on belief propagation. We iteratively estimate the opacity value for every pixel in the image, based on a small sample of foreground and background pixels marked by the user. Experimental results show that compared with previous approaches, our method is more efficient to extract high quality mattes for foregrounds with significant semitransparent regions
6695976
Precise Environmental Searches: Integrating Hierarchical Information Search with EnviroDaemon
Information retrieval has evolved from searches of references, to abstracts, to documents. Search on the Web involves search engines that promise to parse full-text and other files: audio, video, and multimedia. With the indexable Web at 320 million pages and growing, difficulties with locating relevant information have become apparent. The most prevalent means for information retrieval relies on syntax-based methods: keywords or strings of characters are presented to a search engine, and it returns all the matches in the available documents. This method is satisfactory and easy to implement, but it has some inherent limitations that make it unsuitable for many tasks. Instead of looking for syntactical patterns, the user often is interested in keyword meaning or the location of a particular word in a title or header. This paper describes some precise search approaches in the environmental domain that locate information according to syntactic criteria, augmented by the utilization of information in a certain context. The main emphasis of this paper lies in the treatment of structured knowledge, where essential aspects about the topic of interest are encoded not only by the individual items, but also by their relationships among each other. Examples for such structured knowledge are hypertext documents, diagrams, logical and chemical formulae. Benefits of this approach are enhanced precision and approximate search in an already focused, context-specific search engine for the environment: EnviroDaemon.
206339726
Soap-HT-BLAST: high throughput BLAST based on Web services
SUMMARY
A high throughput Basic Local Alignment Search Tool (BLAST) system based on Web services is implemented. It provides an alternative BLAST service and allows users to perform multiple BLAST queries at one run in a distributed, parallel environment through the Internet.


AVAILABILITY
It is available at http://mammoth.bii.a-star.edu.sg/webservices/htblast/index.html and at http://www.bii.a-star.edu.sg/jiren/download.html
6919037
A systems engineering approach for constructing certifiable real-time distributed systems
In this paper, we present a systems engineering methodology for constructing certifiable realtime distributed systems. In the proposed approach, an architectural and algorithmic solution to an application problem is designed by considering the "weakest" models including the weakest asynchronous computational model and multimodal arrival model. Furthermore, timeliness properties are described using Jensen's benefit accrual predicates. Once a system solution is designed, timeliness properties are established by constructing necessary feasibility conditions that are expressed as non-valued predicates. The predicates are quantified and verified to produce the specification of a certified solution. We illustrate the approach by considering a packet transmission problem that desire soft timeliness. We present a certifiable solution to this problem that consists of switched Ethernet, a soft real-time packet scheduling algorithm (that was previously developed), and feasibility conditions.