18635626
Storing Normalized XML Documents in Normalized Relations
This paper studies the XML storage in relations. Unlike traditional techniques, it considers the semantics expressed by functional dependencies. We propose an algorithm for mapping DTD to relational schema, which preserves not only the content and structure but also the semantics of original XML documents. To tackle the problem of constraints expression, we introduce a way to define functional dependencies and normalization for DTD. In a normalized DTD, every constraints expressed by functional dependencies can be concluded to keys. So we use the key definitions for XML as the foundation for relation generation, and maintain the keys in relations. After investigating the relationship between functional dependencies in XML documents with the corresponding ones in relations, we further prove that, if the original DTD is normalized, the generated relations will be in BCNF. So our method keeps the good properties of normalized DTD, and can fully leverage the relational technology
10409272
Repairing Inconsistent XML Documents
XML document may contain inconsistencies that violate predefined integrity constraints, and there are two basic concepts for this problem: Repair is the data consistent with the integrity constraints, and also minimally differs from the original one. Consistent data is the data common for every possible repair. In this paper, first we give a general constraint model for XML, which can express functional dependencies, keys and multivalued dependencies. Next we provide a repair framework for inconsistent XML document with three basic update operations: node insertion, node deletion and value modification. Following this approach, we introduce the concept of repair for inconsistent XML document, discuss the chase process to generate repairs, and prove some important properties of the chase process. Finally we give a method to obtain the greatest lower bound of all possible repairs, which is sufficient for consistent data.
8249361
SUDEPHIC: Self-Tuning Density-Based Partitioning and Hierarchical Clustering
Clustering is one of the primary techniques in data mining, for which to find the user expecting result is a major issue. However, to dynamically specify the parameters for clustering algorithms presents an obstacle for users. This paper firstly introduces a novel density-based partitioning and hierarchical algorithm, which makes it easy to employ synthetic feedback mechanism in clustering. Additionally, by investigating into the relation between parameters and the clustering result, we propose a self-tuning technique for the setting of parameters. Meanwhile, the density distribution within a cluster can be expressed in the result for the user to specify the cluster’s feature. The algorithm is both evaluated in theory and practice. It outperforms many existing algorithms both in efficiency and quality.
30881426
Constraint-Based Graph Mining in Large Database
Currently, constraints are increasingly considered as a kind of means of user- or expert-control for filtering those unsatisfied and redundant patterns rapidly during the web mining process. Recent work has highlighted the importance of constraint-based mining paradigm in the context of frequent itemsets, sequences, and many other interesting patterns in large database. However, it is still not clear how to push various constraints systematically into graph mining process. In this paper, we categorize various graph-based constraints into several major classes and develop a framework CabGin (i.e. C onstr a int- b ased G raph M in ing) to push them into mining process by their categories. Non-monotonic aggregates like average also can be pushed into CabGin with minor revision. Experimental results show that CabGin can prunes a large search space effectively by pushing graph-based constraints into mining process.
207162632
Utility-based anonymization for privacy preservation with less information loss
Privacy becomes a more and more serious concern in applications involving microdata. Recently, efficient anonymization has attracted much research work. Most of the previous methods use global recoding, which maps the domains of the quasi-identifier attributes to generalized or changed values. However, global recoding may not always achieve effective anonymization in terms of discernability and query answering accuracy using the anonymized data. Moreover, anonymized data is often used for analysis. As well accepted in many analytical applications, different attributes in a data set may have different utility in the analysis. The utility of attributes has not been considered in the previous methods.
 In this paper, we study the problem of utility-based anonymization. First, we propose a simple framework to specify utility of attributes. The framework covers both numeric and categorical data. Second, we develop two simple yet efficient heuristic local recoding methods for utility-based anonymization. Our extensive performance study using both real data sets and synthetic data sets shows that our methods outperform the state-of-the-art multidimensional global recoding methods in both discernability and query answering accuracy. Furthermore, our utility-based method can boost the quality of analysis using the anonymized data.
14792668
Personalization as a service: the architecture and a case study
Cloud computing has become a hot topic in the IT industry. Great efforts have been made to establish cloud computing platforms for enterprise users, mostly small businesses. However, there are few researches about the impact of cloud computing over individual users. In this paper we focus on how to provide personalized services for individual users in the cloud environment. We argue that a personalized cloud service shall compose of two parts. The client side program records user activities on personal de-vices such as PC. Besides that, the user model is also computed on the client side to avoid server overhead. The cloud side program fetches the user model periodically and adjusts its results accordingly. We build a personalized cloud data search engine prototype to prove our idea.
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
9042204
CRD: fast co-clustering on large datasets utilizing sampling-based matrix decomposition
The problem of simultaneously clustering columns and rows (co-clustering) arises in important applications, such as text data mining, microarray analysis, and recommendation system analysis. Compared with the classical clustering algorithms, co-clustering algorithms have been shown to be more effective in discovering hidden clustering structures in the data matrix. The complexity of previous co-clustering algorithms is usually O(m X n), where m and n are the numbers of rows and columns in the data matrix respectively. This limits their applicability to data matrices involving a large number of columns and rows. Moreover, some huge datasets can not be entirely held in main memory during co-clustering which violates the assumption made by the previous algorithms. In this paper, we propose a general framework for fast co-clustering large datasets, CRD. By utilizing recently developed sampling-based matrix decomposition methods, CRD achieves an execution time linear in m and n. Also, CRD does not require the whole data matrix be in the main memory. We conducted extensive experiments on both real and synthetic data. Compared with previous co-clustering algorithms, CRD achieves competitive accuracy but with much less computational cost.
15041216
Coding-based Join Algorithms for Structural Queries on Graph-Structured XML Document
In many applications, XML documents need to be modelled as graphs. The query processing of graph-structured XML documents brings new challenges. In this paper, we design a method based on labelling scheme for structural queries processing on graph-structured XML documents. We give each node some labels, the reachability labelling scheme. By extending an interval-based reachability labelling scheme for DAG by Rakesh et al., we design labelling schemes to support the judgements of reachability relationships for general graphs. Based on the labelling schemes, we design graph structural join algorithms to answer the structural queries with only ancestor-descendant relationship efficiently. For the processing of subgraph query, we design a subgraph join algorithm. With efficient data structure, the subgraph join algorithm can process subgraph queries with various structures efficiently. Experimental results show that our algorithms have good performance and scalability.
16343248
iRANK: an interactive ranking framework and its application in query-focused summarization
We address the problem of unsupervised ensemble ranking in this paper. Traditional approaches either combine multiple ranking criteria into a unified representation to obtain an overall ranking score or to utilize certain rank fusion or aggregation techniques to combine the ranking results. Beyond the aforementioned combine-then-rank and rank-then-combine approaches, we propose a novel rank-learn-combine ranking framework, called Interactive Ranking (iRANK), which allows two base rankers to "teach" each other before combination during the ranking process by providing their own ranking results as feedback to the others so as to boost the ranking performance. This mutual ranking refinement process continues until the two base rankers cannot learn from each other any more. The overall performance is improved by the enhancement of the base rankers through the mutual learning mechanism. We apply this framework to the sentence ranking problem in query-focused summarization and evaluate its effectiveness on the DUC 2005 data set. The results are encouraging with consistent and promising improvements.
8102164
XR-tree: indexing XML data for efficient structural joins
XML documents are typically queried with a combination of value search and structure search. While querying by values can leverage traditional database technologies, evaluating structural relationship, specifically parent-child or ancestor-descendant relationship, between XML element sets has imposed a great challenge on efficient XML query processing. We propose XR-tree, namely, XML region tree, which is a dynamic external memory index structure specially designed for strictly nested XML data. The unique feature of XR-tree is that, for a given element, all its ancestors (or descendants) in an element set indexed by an XR-tree can be identified with optimal worst case I/O cost. We then propose a new structural join algorithm that can evaluate the structural relationship between two XR-tree indexed element sets by effectively skipping ancestors and descendants that do not participate in the join. Our extensive performance study shows that the XR-tree based join algorithm significantly outperforms previous algorithms.