Efficient Mining of Frequent Closed XML Query Pattern

Previous research works have presented convincing arguments that a frequent pattern mining algorithm should not mine all frequent but only the closed ones because the latter leads to not only more compact yet complete result set but also better efficiency. Upon discovery of frequent closed XML query patterns, indexing and caching can be effectively adopted for query performance enhancement. Most of the previous algorithms for finding frequent patterns basically introduced a straightforward generate-and-test strategy. In this paper, we present SOLARIA＊, an efficient algorithm for mining frequent closed XML query patterns without candidate maintenance and costly tree-containment checking. Efficient algorithm of sequence mining is involved in discovering frequent tree-structured patterns, which aims at replacing expensive containment testing with cheap parent-child checking in sequences. SOLARIA＊ deeply prunes unrelated search space for frequent pattern enumeration by parent-child relationship constraint. By a thorough experimental study on various real-life data, we demonstrate the efficiency and scalability of SOLARIA＊ over the previous known alternative. SOLARIA＊ is also linearly scalable in terms of XML queries＇ size.     

Finding hot query patterns over an XQuery stream

Caching query results is one efficient approach to improving the performance of XML management systems. This entails the discovery of frequent XML queries issued by users. In this paper, we model user queries as a stream of XML query pattern trees and mine the frequent query patterns over the query stream. To facilitate the one-pass mining process, we devise a novel data structure called DTS to summarize the pattern trees seen so far. By grouping the incoming pattern trees into batches, we can dynamically mark the active portion of the current batch in DTS and limit the enumeration of candidate trees to only the currently active pattern trees. We also design another summary data structure called ECTree that provides for the incremental computation of the frequent tree patterns over the query stream. Based on the above two constructs, we present two mining algorithms called XQSMinerI and XQSMinerII. XQSMinerI is fast, but it tends to overestimate, while XQSMinerII adopts a filter-and-refine approach to minimize the amount of overestimation. Experimental results show that the proposed methods are both efficient and scalable and require only small memory footprints.Received: 17 October 2003, Accepted: 16 April 2004, Published online: 14 September 2004Edited by: J. Gehrke and J. Hellerstein.     

An efficient algorithm of frequent XML query pattern mining for ebXML applications in e-commerce

Providing efficient query to XML data for ebXML applications in e-commerce is crucial, as XML has become the most important technique to exchange data over the Internet. ebXML is a set of specifications for companies to exchange their data in e-commerce. Following the ebXML specifications, companies have a standard method to exchange business messages, communicate data, and business rules in e-commerce. Due to its tree-structure paradigm, XML is superior for its capability of storing and querying complex data for ebXML applications. Therefore, discovering frequent XML query patterns has become an interesting topic for XML data management in ebXML applications. In this paper, we present an efficient mining algorithm, namely ebXMiner, to discover the frequent XML query patterns for ebXML applications. Unlike the existing algorithms, we propose a new idea by collecting the equivalent XML queries and then enumerating the candidates from infrequent XML queries in our ebXMiner. Furthermore, our simulation results show that ebXMiner outperforms other algorithms in its execution time.     

Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach

Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist a large number of patterns and/or long patterns.In this study, we propose a novel frequent-pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develop an efficient FP-tree-based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth. Efficiency of mining is achieved with three techniques: (1) a large database is compressed into a condensed, smaller data structure, FP-tree which avoids costly, repeated database scans, (2) our FP-tree-based mining adopts a pattern-fragment growth method to avoid the costly generation of a large number of candidate sets, and (3) a partitioning-based, divide-and-conquer method is used to decompose the mining task into a set of smaller tasks for mining confined patterns in conditional databases, which dramatically reduces the search space. Our performance study shows that the FP-growth method is efficient and scalable for mining both long and short frequent patterns, and is about an order of magnitude faster than the Apriori algorithm and also faster than some recently reported new frequent-pattern mining methods.     

Efficient mining of frequent XML query patterns with repeating-siblings

A recent approach to improve the performance of XML query evaluation is to cache the query results of frequent query patterns. Unfortunately, discovering these frequent query patterns is an expensive operation. In this paper, we develop a two-pass mining algorithm 2PXMiner that guarantees the discovery of frequent query patterns by scanning the database at most twice. By exploiting a transaction summary data structure, and an enumeration tree, we are able to determine the upper bounds of the frequencies of the candidate patterns, and to quickly prune away the infrequent patterns. We also design an index to trace the repeating candidate subtrees generated by sibling repetition, thus avoiding redundant computations. Experiments results indicate that 2PXMiner is both efficient and scalable.     

Indexing and querying XML using extended Dewey labeling scheme

Finding all the occurrences of a tree pattern in an XML database is a core operation for efficient evaluation of XML queries. The Dewey labeling scheme is commonly used to label an XML document to facilitate XML query processing by recording information on the path of an element. In order to improve the efficiency of XML tree pattern matching, we introduce a novel labeling scheme, called extended Dewey, which effectively extends the existing Dewey labeling scheme to combine the types and identifiers of elements in a label, and to avoid the scan of labels for internal query nodes to accelerate query processing (in I/O cost). Based on extended Dewey, we propose a series of holistic XML tree pattern matching algorithms. We first present TJFast to answer an XML twig pattern query. To efficiently answer a generalized XML tree pattern, we then propose GTJFast, an optimization that exploits the non-output nodes. In addition, we propose TJFastTL and GTJFastTL based on the tag + level data partition scheme to further reduce I/O costs by level pruning. Finally, we report our comprehensive experimental results to show that our set of XML tree pattern matching algorithms are superior to existing approaches in terms of the number of elements scanned, the size of intermediate results and query performance.     

基于频繁叶模式的XML最大频繁查询模式挖掘算法

在XML频繁查询模式挖掘稠密数据集、长数据集中,为克服项目集挖掘过程中挖掘的项目过多、不利于结果利用等问题,提出基于频繁叶模式的最大频繁查询模式挖掘算法MFRSTMiner。该算法通过构造频繁模式扩展森林,在扩展森林的叶节点中挖掘出最大频繁子树。试验结果表明该算法能够有效地挖掘动态事务集的最大频繁查询模式。     

A Pattern Decomposition Algorithm for Data Mining of Frequent Patterns

Efficient algorithms to mine frequent patterns are crucial to many tasks in data mining. Since the Apriori algorithm was proposed in 1994, there have been several methods proposed to improve its performance. However, most still adopt its candidate set generation-and-test approach. In addition, many methods do not generate all frequent patterns, making them inadequate to derive association rules. We propose a pattern decomposition (PD) algorithm that can significantly reduce the size of the dataset on each pass, making it more efficient to mine all frequent patterns in a large dataset. The proposed algorithm avoids the costly process of candidate set generation and saves time by reducing the size of the dataset. Our empirical evaluation shows that the algorithm outperforms Apriori by one order of magnitude and is faster than FP-tree algorithm. Received 14 May 2001 / Revised 5 September 2001 / Accepted in revised form 26 October 2001 Correspondence and offprint requests to: Qinghua Zou, Department of Computer Science, California University–Los Angeles, CA 90095, USA. Email: zou@cs.ucla.eduau     

RRSi: indexing XML data for proximity twig queries

Twig query pattern matching is a core operation in XML query processing. Indexing XML documents for twig query processing is of fundamental importance to supporting effective information retrieval. In practice, many XML documents on the web are heterogeneous and have their own formats; documents describing relevant information can possess different structures. Therefore some “user-interesting” documents having similar but non-exact structures against a user query are often missed out. In this paper, we propose the RRSi, a novel structural index designed for structure-based query lookup on heterogeneous sources of XML documents supporting proximate query answers. The index avoids the unnecessary processing of structurally irrelevant candidates that might show good content relevance. An optimized version of the index, oRRSi, is also developed to further reduce both space requirements and computational complexity. To our knowledge, these structural indexes are the first to support proximity twig queries on XML documents. The results of our preliminary experiments show that RRSi and oRRSi based query processing significantly outperform previously proposed techniques in XML repositories with structural heterogeneity.

The dynamic predicate: integrating access control with query processing in XML databases

Recently, access control on XML data has become an important research topic. Previous research on access control mechanisms for XML data has focused on increasing the efficiency of access control itself, but has not addressed the issue of integrating access control with query processing. In this paper, we propose an efficient access control mechanism tightly integrated with query processing for XML databases. We present the novel concept of the dynamic predicate (DP), which represents a dynamically constructed condition during query execution. A DP is derived from instance-level authorizations and constrains accessibility of the elements. The DP allows us to effectively integrate authorization checking into the query plan so that unauthorized elements are excluded in the process of query execution. Experimental results show that the proposed access control mechanism improves query processing time significantly over the state-of-the-art access control mechanisms. We conclude that the DP is highly effective in efficiently checking instance-level authorizations in databases with hierarchical structures.     

Constructing ontologies by mining deep semantics from XML Schemas and XML instance documents

With the development of the Semantic Web and Artificial Intelligence techniques, ontology has become a very powerful way of representing not only knowledge but also their semantics. Therefore, how to construct ontologies from existing data sources has become an important research topic. In this paper, an approach for constructing ontologies by mining deep semantics from eXtensible Markup Language (XML) Schemas (including XML Schema 1.0 and XML Schema 1.1) and XML instance documents is proposed. Given an XML Schema and its corresponding XML instance document, 34 rules are first defined to mine deep semantics from the XML Schema. The mined semantics is formally stored in an intermediate conceptual model and then is used to generate an ontology at the conceptual level. Further, an ontology population approach at the instance level based on the XML instance document is proposed. Now, a complete ontology is formed. Also, some corresponding core algorithms are provided. Finally, a prototype system is implemented, which can automatically generate ontologies from XML Schemas and populate ontologies from XML instance documents. The paper also classifies and summarizes the existing work and makes a detailed comparison. Case studies on real XML data sets verify the effectiveness of the approach.     

A weighted common structure based clustering technique for XML documents

XML has recently become very popular as a means of representing semistructured data and as a standard for data exchange over the Web, because of its varied applicability in numerous applications. Therefore, XML documents constitute an important data mining domain. In this paper, we propose a new method of XML document clustering by a global criterion function, considering the weight of common structures. Our approach initially extracts representative structures of frequent patterns from schemaless XML documents using a sequential pattern mining algorithm. Then, we perform clustering of an XML document by the weight of common structures, without a measure of pairwise similarity, assuming that an XML document is a transaction and frequent structures extracted from documents are items of the transaction. We conducted experiments to compare our method with previous methods. The experimental results show the effectiveness of our approach.     

使用序列模式精简基挖掘序列模式

传统的序列模式挖掘方法在挖掘由短的频繁序列模式组成的数据库时有良好的性能．但在挖掘长的序列模式或支持度阈值很低时，这些方法可能遇到固有的困难，因为产生的频繁序列模式的数量经常太大．在许多情况下，用户可能只需要那些覆盖许多短模式的长模式．此外，在很多应用中，只要得到产生的频繁序列模式的近似支持度就已足够，而不需要它们的精确支持度．介绍了能将误差控制在确定范围内的频繁序列模式精简基的概念，并开发了一个挖掘这种序列模式精简基的算法．实验结果显示计算频繁序列模式精简基是很有前途的．     

FP-growth算法的实现方法研究

事务数据库中频繁模式的挖掘研究作为关联规则等许多数据挖掘问题的核心工作,已经研究了许多年。早期算法大都是Apriori型算法,即首先产生候选集,然后在候选集的基础上找出频繁模式,候选集的产生往往是耗时的,特别是挖掘富模式或长模式时。JianweiHan等人提出了一种新颖的数据结构FP-tree及基于其上的FP-growth算法,用于有效的富模式与长模式挖掘。由于不同的实现方法可能会导致不同的挖掘效率,该文在讨论FP-growth算法的基础上,采用了几种不同的方法来实现它,并用几个数据库对它们的性能进行了比较。     

PS+Pre/Post: A novel structure and access mechanism for wireless XML stream supporting twig pattern queries

XML data broadcast is an efficient way to disseminate XML data to a large number of mobile clients in mobile wireless networks. Recently, several indexing methods have been proposed to improve the performance of XML query processing in terms of access time and tuning time over XML streams. However, existing indexing methods cannot process twig pattern XML queries. In this paper, we propose a novel structure for streaming XML data called PS+Pre/Post by integrating the path summary technique and the pre/post labeling scheme. Our proposed XML stream structure exploits the benefits of the path summary technique and the pre/post labeling scheme to efficiently process different types of XML queries over the broadcast stream. Experimental results show that our proposed XML stream structure improves the performance of access time and tuning time in processing different types of XML queries.     

Efficient mining of maximal frequent itemsets from databases on a cluster of workstations

In this paper, we propose two parallel algorithms for mining maximal frequent itemsets from databases. A frequent itemset is maximal if none of its supersets is frequent. One parallel algorithm is named distributed max-miner (DMM), and it requires very low communication and synchronization overhead in distributed computing systems. DMM has the local mining phase and the global mining phase. During the local mining phase, each node mines the local database to discover the local maximal frequent itemsets, then they form a set of maximal candidate itemsets for the top-down search in the subsequent global mining phase. A new prefix tree data structure is developed to facilitate the storage and counting of the global candidate itemsets of different sizes. This global mining phase using the prefix tree can work with any local mining algorithm. Another parallel algorithm, named parallel max-miner (PMM), is a parallel version of the sequential max-miner algorithm (Proc of ACM SIGMOD Int Conf on Management of Data, 1998, pp 85–93). Most of existing mining algorithms discover the frequent k-itemsets on the kth pass over the databases, and then generate the candidate (k + 1)-itemsets for the next pass. Compared to those level-wise algorithms, PMM looks ahead at each pass and prunes more candidate itemsets by checking the frequencies of their supersets. Both DMM and PMM were implemented on a cluster of workstations, and their performance was evaluated for various cases. They demonstrate very good performance and scalability even when there are large maximal frequent itemsets (i.e., long patterns) in databases.

支持XML查询代数和树模式查询的XQuery系统框架

XQuery语言的高性能实现需要利用XML查询代数提供的查询优化方法,也需要采取高效的树模式整体匹配算法。为了将这两种XML查询处理技术有效地结合在XQuery语言处理系统中,提出了一种通用系统框架来支持XQuery语言的高性能实现。在这个框架内,提供开放式XML数据源连接,并且通过作为中间语言的一种函数式查询计划描述语言FXQL来支持各种查询代数算子和树查询模式的表示,既允许采用各种XML查询代数,又允许采用各种树模式查询算法;进而,通过这种中间层的程序变换可以实现基于各种查询代数的查询重写,并从查询计划中分离出独立的树模式查询计算,使两种查询处理技术适当地统一在同一系统框架中,有效地支持了多种环境下XQuery语言的实现。     

Mining sequential patterns across multiple sequence databases

In this paper, given a set of sequence databases across multiple domains, we aim at mining multi-domain sequential patterns, where a multi-domain sequential pattern is a sequence of events whose occurrence time is within a pre-defined time window. We first propose algorithm Naive in which multiple sequence databases are joined as one sequence database for utilizing traditional sequential pattern mining algorithms (e.g., PrefixSpan). Due to the nature of join operations, algorithm Naive is costly and is developed for comparison purposes. Thus, we propose two algorithms without any join operations for mining multi-domain sequential patterns. Explicitly, algorithm IndividualMine derives sequential patterns in each domain and then iteratively combines sequential patterns among sequence databases of multiple domains to derive candidate multi-domain sequential patterns. However, not all sequential patterns mined in the sequence database of each domain are able to form multi-domain sequential patterns. To avoid the mining cost incurred in algorithm IndividualMine, algorithm PropagatedMine is developed. Algorithm PropagatedMine first performs one sequential pattern mining from one sequence database. In light of sequential patterns mined, algorithm PropagatedMine propagates sequential patterns mined to other sequence databases. Furthermore, sequential patterns mined are represented as a lattice structure for further reducing the number of sequential patterns to be propagated. In addition, we develop some mechanisms to allow some empty sets in multi-domain sequential patterns. Performance of the proposed algorithms is comparatively analyzed and sensitivity analysis is conducted. Experimental results show that by exploring propagation and lattice structures, algorithm PropagatedMine outperforms algorithm IndividualMine in terms of efficiency (i.e., the execution time).     

Fast Frequent Free Tree Mining in Graph Databases

Free tree, as a special undirected, acyclic and connected graph, is extensively used in computational biology, pattern recognition, computer networks, XML databases, etc. In this paper, we present a computationally efficient algorithm F3TM (Fast Frequent Free Tree Mining) to find all frequently-occurred free trees in a graph database, . Two key steps of F3TM are candidate generation and frequency counting. The frequency counting step is to compute how many graphs in containing a candidate frequent free tree, which is proved to be the subgraph isomorphism problem in nature and is NP-complete. Therefore, the key issue becomes how to reduce the number of false positives in the candidate generation step. Based on our observations, the cost of false positive reduction can be prohibitive itself. In this paper, we focus ourselves on how to reduce the candidate generation cost and minimize the number of infrequent candidates being generated. We prove a theorem that the complete set of frequent free trees can be discovered from a graph database by growing vertices on a limited range of positions of a free tree. We propose two pruning algorithms, namely, automorphism-based pruning and canonical mapping-based pruning, which significantly reduce the candidate generation cost. We conducted extensive experimental studies using a real application dataset and a synthetic dataset. The experiment results show that our algorithm F3TM outperforms the up-to-date algorithms by an order of magnitude in mining frequent free trees in large graph databases.     

XML data mining

With the spreading of XML sources, mining XML data can be an important objective in the near future. This paper presents a project focussed on designing a general‐purpose query language in support of mining XML data. In our framework, raw data, mining models and domain knowledge are represented by way of XML documents and stored inside native XML databases. Data mining (DM) tasks are expressed in an extension of XQuery. Special attention is given to the frequent pattern discovery problem, and a way of exploiting domain‐dependent optimizations and efficient data structures as deeper as possible in the extraction process is presented. We report the results of a first bunch of experiments, showing that a good trade‐off between expressiveness and efficiency in XML DM is not a chimera. Copyright © 2009 John Wiley & Sons, Ltd.