11411776
Holistic Twig Joins on Indexed XML Documents
Finding all the occurrences of a twig pattern specified by a selection predicate on multiple elements in an XML document is a core operation for efficient evaluation of XML queries. Holistic twig join algorithms were proposed recently as an optimal solution when the twig pattern only involves ancestor-descendant relationships. In this paper, we address the problem of efficient processing of holistic twig joins on all/partly indexed XML documents. In particular, we propose an algorithm that utilizes available indices on element sets. While it can be shown analytically that the proposed algorithm is as efficient as the existing state-of-the-art algorithms in terms of worst case I/O and CPU cost, experimental results on various datasets indicate that the proposed index-based algorithm performs significantly better than the existing ones, especially when binary structural joins in the twig pattern have varying join selectivities.
11973783
Answering Approximate Range Aggregate Queries on OLAP Data Cubes with Probabilistic Guarantees
Approximate range aggregate queries are one of the most frequent and useful kinds of queries for Decision Support Systems (DSS). Traditionally, sampling- based techniques have been proposed to tackle this problem. However, its effectiveness will degrade when the underlying data distribution is skewed. Another approach based on the outlier management can limit the effect of data skew but fails to address other requirements of approximate range aggregate queries, such as error guarantees and query processing efficiency. In this paper, we present a technique that provide approximate answers to range aggregate queries on OLAP data cubes efficiently with theoretical error guarantees. Our basic idea is to build different data structures for outliers and the rest of the data. Experimental results verified the effectiveness of our proposed methods.
2266606
Space-efficient Relative Error Order Sketch over Data Streams
We consider the problem of continuously maintaining order sketches over data streams with a relative rank error guarantee ∊. Novel space-efficient and one-scan randomised techniques are developed. Our first randomised algorithm can guarantee such a relative error precision ∊ with confidence 1 - \delta using O( 1\_ \in \frac{1} {2}2 log 1d log ∊^2N) space, where N is the number of data elements seen so far in a data stream. Then, a new one-scan space compression technique is developed. Combined with the first randomised algorithm, the one-scan space compression technique yields another one-scan randomised algorithm that guarantees the space requirement is O( 1\frac{1} { \in } log(1\frac{1}{ \in } log 1\begin{gathered} \frac{1}{\delta } \hfill \\ \hfill \\ \end{gathered} )\frac{{\log ^{2 + \alpha } \in N}} {{1 - 1/2^\alpha }} (for\alpha \gt 0) on average while the worst case space remains O( \frac{1}{{ \in ^2 }}\log \frac{1} {\delta }\log \in ^2 N). These results are immediately applicable to approximately computing quantiles over data streams with a relative error guarantee \in and significantly improve the previous best space bound O( \frac{1} {{ \in ^3 }}\log \frac{1}{\delta }\log N). Our extensive experiment results demonstrate that both techniques can support an on-line computation against high speed data streams.
1993887
Locating Motifs in Time-Series Data
Finding motifs in time-series is proposed to make clustering of time-series subsequences meaningful, because most existing algorithms of clustering time-series subsequences are reported meaningless in recent studies. The existing motif finding algorithms emphasize the efficiency at the expense of quality, in terms of the number of time-series subsequences in a motif and the total number of motifs found. In this paper, we formalize the problem as a continuous top-k motif balls problem in an m-dimensional space, and propose heuristic approaches that can significantly improve the quality of motifs with reasonable overhead, as shown in our experimental studies.
11539721
XParent: an efficient RDBMS-Based XML database system
Presents, XParent, an XML document management system built on top of RDBMS. It is based on an efficient, model-mapping-based approach that uses a fixed database schema to store any XML documents without assistance of DTD. The visual query interface of XParent provides both expressive power for professionals and user friendliness for naive users. The proposed multi-level query translation scheme makes it possible to develop a generic XML application that supports multiple XML query languages and mapping schemas.
15045713
Visual Specification and Optimization of XQuery Using VXQ
As the popularity of XML increases, the need for querying collections of XML data from various systems becomes imperative. Proposed by W3C, XQuery is becoming a standard for querying such systems. However, the complexity of XQuery prevents its usage by broad audience. This paper proposes a visual XQuery specification language called VXQ to address this issue. By intuitive abstractions of XML and XQuery, the proposed system can generate XQueries for users that have little knowledge about the language. We show that our visual language is more expressive than previous proposals. Finally, we extend our proposed visual XQuery to support query rewriting and optimization for multiple XQuery systems.
3845821
Performance Analysis of Three Text-Join Algorithms
When a multidatabase system contains textual database systems (i.e., information retrieval systems), queries against the global schema of the multidatabase system may contain a new type of joins-joins between attributes of textual type. Three algorithms for processing such a type of joins are presented and their I/O costs are analyzed in this paper. Since such a type of joins often involves document collections of very large size, it is very important to find efficient algorithms to process them. The three algorithms differ on whether the documents themselves or the inverted files on the documents are used to process the join. Our analysis and the simulation results indicate that the relative performance of these algorithms depends on the input document collections, system characteristics, and the input query. For each algorithm, the type of input document collections with which the algorithm is likely to perform well is identified. An integrated algorithm that automatically selects the best algorithm to use is also proposed.
29035045
Genetic algorithms for determining fuzzy measures from data
A synthetic evaluation of a given object in terms of multiple factors that contribute to some feature of the object (quality, performance, etc.) may be regarded as a system with multiple inputs and one output. Traditionally, the output is expressed as the weighted average of the inputs. Unfortunately, this method is severely limited as it cannot capture any inherent relation among the factors involved. This limitation can be overcome by using the Choquet integral or the fuzzy integral with respect to a fuzzy measure that captures the relation among the factors. The crux of this method is to determine the right fuzzy measure. In this paper, we describe an efficient genetic algorithm for constructing a suitable fuzzy measure from relevant input-output data. This algorithm has a broad applicability in various problem areas, such as decision making, cluster analysis, pattern recognition, image and speech processing, and expert systems.
12798864
BASS: approximate search on large string databases
In this paper, we study the problem on how to build an index structure for large string databases to efficiently support various types of string matching without the necessity of mapping the substrings to a numerical space (e.g., string B-tree and MRS-index) nor the restriction of in-memory practice (e.g., suffix tree and suffix array). Towards this goal, we propose a new indexing scheme, BASS-tree, to efficiently support general approximate substring match (in terms of certain symbol substitutions and misalignments) in sublinear time on a large string database. The key idea behind the design is that all positions in each string are grouped recursively into a fully balanced tree according to the similarities of the subsequent segments starting at those positions. Each node is labeled with a regular expression that describes the commonality of the substrings indexed through the subtree. Any search can then be properly directed to the portion in the database with a high potential of matching quickly. With the BASS-tree in place, wild card(s) in the query pattern can also be handled in a seamless way. In addition, search of a long pattern can be decomposed into a series of searches of short segments followed by a process to join the results. It has been demonstrated in our experiments that the potential performance improvement brought by BASS-tree is in an order of magnitude over alternative methods.
36057715
Guest Editors' Introduction: Special Issue on Mining Biological Data
MINING biological data is an emerging area of intersection between data mining and bioinformatics. Bioinformaticians have been working on the research and development of computational methodologies and tools for expanding the use of biological, medical, behavioral, or health-related data. Data mining researchers have been making substantial contribution to the development of models and algorithms to meet challenges posed by the bioinformatics research. Some successful examples are frequent pattern discovery on biological molecules, text mining in biomedical literature, information integration, probabilistic modeling of genome sequences, etc. This special issue of the IEEE Transactions on Knowledge and Data Engineering features a collection of 11 papers, selected from 54 submissions, representing recent advances at the frontier of mining biological data. Mining frequent trees is very useful in bioinformatics applications. The first paper, “Efficiently Mining Frequent Trees in a Forest: Algorithms and Applications” by Mohammed J. Zaki, formulates the problem of mining (embedded) subtrees in a forest of rooted, labeled, and ordered trees. The author presents TreeMiner, a novel algorithm to discover all frequent subtrees in a forest, using a new data structure called scope-list. TreeMiner has been proven to be superior to previous methods such as PatternMatcher and has shown promising results in analyzing RNA structure and phylogenetics data sets. In the second paper, “Frequent Substructure-Based Approaches for Classifying Chemical Compounds” by Mukund Deshpande, Michihiro Kuramochi, Nikil Wale, and George Karypis, the authors devise a substructurebased classification algorithm that decouples the substructure discovery process from the classification model construction and uses frequent subgraph discovery algorithms to find all topological and geometric substructures present in the data set. The advantage of this approach is that, during classification model construction, all relevant substructures are available and thus allow the classifier to intelligently select the most discriminating ones. This approach is employed to build models to correctly assign chemical compounds to various classes of interests, which have many applications in pharmaceutical research and are used extensively at various phases during the drug development process. Glycans, or carbohydrate sugar chains, are regarded as the third class of biological molecules, subsequent to DNA and proteins, and the recent advent of glycome informatics has generated an increasing number of glycan structures and annotation data. Glycans play important roles in the development and functioning of multicellular organisms and their structures can be represented by labeled ordered trees. The third paper, “A Probabilistic Model for Mining Labeled Ordered Trees: Capturing Patterns in Carbohydrate Sugar Chains” byNobuhisaUeda, Kiyoko F. Aoki-Kinoshita, Atsuko Yamaguchi, Tatsuya Akustu, and Hiroshi Mamitsuka, proposes a probabilistic model for mining labeled ordered trees and an EM algorithm for efficient learning. Proteins are the machinery of life. A number of techniques have been developed to classify proteins according to important features in their sequences, secondary structures, or three-dimensional structures. The fourth paper, “Finding Patterns on Protein Surfaces: Algorithms and Applications to Protein Classification” by Xiong Wang, introduces a novel approach to protein classification based on significant geometric patterns on the surface of a protein. The binding in protein-protein interactions exhibits a kind of biochemical stability in cells, which can be described by the mathematical notion of the fixed points. In the fifth paper, “Using Fixed Point Theorems to Model the Binding in Protein-Protein Interactions” by Jinyan Li and Haiquan Li, the authors define a point as a protein motif pair consisting of two traditional protein motifs. They propose a method to discover stable motif pairs of a given function from a large protein interaction sequence data set. With the rapid growth of articles on genomics research, it has become a challenge for biomedical researchers to access this ever-increasing quantity of information to understand the newest discovery of functions of proteins they are studying. To facilitate functional annotation of proteins by utilizing the huge amounts of biomedical literature and transforming the knowledge into easily accessible database formats, the text mining technique thus becomes essential. The sixth paper, “Literature Extraction of Protein Functions Using Sentence Pattern Mining” by Jung-Hsien Chiang and Hsu-Chun Yu, proposes the method of sentence pattern mining to extract protein functions from biomedical literature. Identifying concepts that have already been patented is essential for undertaking new biomedical research. Traditional keyword-based search on patent databases may not be sufficient enough to retrieve all the relevant information, especially for the biomedical domain. The seventh paper, “Information Retrieval and Knowledge Discovery Utilizing a BioMedical Patent Semantic Web” by Sougata Mukherjea, Bhuvan Bamba, and Pankaj Kankar, presents BioPatentMiner, a system that facilitates information retrieval and knowledge discovery from biomedical patents. BioPatentMiner first identifies biological terms IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 17, NO. 8, AUGUST 2005 1019
8024755
On Demand Phenotype Ranking through Subspace Clustering
High throughput biotechnologies have enabled scientists to collect a large number of genetic and phenotypic attributes for a large collection of samples. Computational methods are in need to analyze these data for discovering genotype-phenotype associations and inferring possible phenotypes from genotypic attributes. In this paper, we study the problem of on demand phenotype ranking. Given a query sample, for which only its genetic information is available, we want to predict the possible phenotypes it may have, ranked in descending order of their likelihood. This problem is challenging since genotype-phenotype databases are updated often and explicitly mine and maintain all patterns is impractical. We propose an on-demand ranking algorithm that uses a modified pattern-based subspace clustering algorithm to effectively identify the subspaces where these relevant clusters may reside. Using this algorithm, we can compute the clusters and their prediction significance for any phenotypes on the fly. Our experiments demonstrate the efficiency and effectiveness of our algorithm.