基于自动机的XML流多查询处理

XML流数据处理在研究领域引起广泛关注,该文针对XML流上的多查询处理提出一种算法,把多个查询合并为一个共享前缀的查询树,应用自动机和运行时栈相结合的方法,单遍扫描XML流处理数据流上的多个查询。该算法采用一种分层栈结构保存查询模式匹配候选集,利用XML节点的区间编码来确定节点之间的关系,返回整条匹配路径。     

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.     

Efficient evaluation of generalized tree-pattern queries on XML streams

The streaming evaluation is a popular way of evaluating queries on XML documents. Besides its many advantages, it is also the only option for a number of important XML applications. Unfortunately, existing algorithms focus almost exclusively on tree-pattern queries (TPQs). Requirements for flexible querying of XML data have motivated recently the introduction of query languages that are more general and flexible than TPQs. These languages are not supported by existing algorithms. In this paper, we consider a partial tree-pattern query (PTPQ) language which generalizes and strictly contains TPQs. PTPQs can express a fragment of XPath which comprises reverse axes and the node identity equality (is) operator, in addition to forward axes, wildcards and predicates. They constitute an important subclass of XPath, which is very useful in practice. Unfortunately, previous streaming algorithms for TPQs cannot be applied to PTPQs. PTPQs can be represented as dags enhanced with constraints. We explore this representation to design an original polynomial time streaming algorithm for PTPQs. Our algorithm aggressively filters incoming data that is irrelevant to the query and wisely avoids processing redundant query matches (i.e., matches of the query dag that do not contribute to new solutions). Our algorithm is the first one to support the streaming evaluation of such a broad fragment of XPath. We provide an analysis of it, and conduct an extensive experimental evaluation of its performance and scalability. Compared to the only known streaming algorithm that supports TPQs extended with reverse axes, our algorithm performs better by orders of magnitude while consuming a much smaller fraction of memory space. Current streaming applications have stringent requirements on query response time and memory consumption because of the large (possibly unbounded) size of data they handle. In order to keep memory usage and CPU consumption low for the PTPQ streaming evaluation, we design another streaming algorithm called Eager PSX for PTPQs. Its key feature is that it applies an eager evaluation strategy to quickly determine when node matches should be returned as solutions to the user and also to proactively detect redundant matches. We theoretically analyze Eager PSX, and experimentally test its time and space performance and scalability. We compare it with PSX. Our results show that Eager PSX not only achieves better space performance without compromising time performance, but it also greatly improves query response time for both simple and complex queries, in many cases, by orders of magnitude.     

XMin: Minimizing Tree Pattern Queries with Minimality Guarantee

Due to wide use of XPath, the problem of efficiently processing XPath queries has recently received a lot of attention. In particular, a considerable effort has been devoted to minimizing XPath queries since the efficiency of query processing greatly depends on the size of the query. Research work in this area can be classified into two categories: constraint-independent minimization and constraint-dependent minimization. The former minimizes queries in the absence of integrity constraints while the latter in the presence of them. For a linear path query, which is an XPath query without branching predicates, existing constraint-independent minimization methods are generally known to be unable to minimize the query without processing the query itself. Most recently, however, by using the DataGuide, a representative structural summary of XML data, a constraint-independent method that minimizes linear path queries in a top-down fashion has been proposed. Nevertheless, this method can fail to find a minimal query since it minimizes a query by merely erasing labels from the original query whereas a minimal query could include labels that are not present in the original query. In this paper, we propose a bottom-up approach called XMin that guarantees finding a minimal query for a given tree pattern query by using the DataGuide without processing the query itself. For the linear path query, we first show that the sequence of labels occurring in the minimal query is a subsequence of every schema label sequence that matches the original query. Here, the schema label sequence for a node is the sequence of labels from the root of XML data to the node. We then propose iterative subsequence generation that iteratively generates subsequences from the shortest schema label sequence matching the original query in a bottom-up fashion and tests query equivalence. Using iterative subsequence generation, we can always find a minimal query and we formally prove this guarantee. We also propose an extended algorithm that guarantees the minimality for the tree pattern query, which is a linear path query with branching predicates. These methods have been prototyped in a full-fledged object-relational DBMS. The experimental results using real and synthetic data sets show the practicality of our method.     

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.     

Optimal and efficient generalized twig pattern processing: a combination of preorder and postorder filterings

Searching for occurrences of a twig pattern query (TPQ) in an XML document is a core task of all XML database query languages. The generalized twig pattern (GTP) extends the TPQ model to include semantics related to output nodes, optional nodes, and boolean expressions which are part of the XQuery language. Preorder filtering holistic algorithms such as TwigStack represent a significant class of TPQ processing approaches with a linear worst-case I/O complexity with respect to the sum of the input and output sizes for some query classes. Another important class of holistic approaches is represented by postorder filtering holistic algorithms such as $\text{ Twig}^2$ Stack which introduced a linear output enumeration time with respect to the result size. In this article, we introduce a holistic algorithm called GTPStack which is the first approach capable of processing a GTP with a linear worst-case I/O complexity with respect to the GTP result size. This is achieved by using a combination of the preorder and postorder filterings before storing nodes in an intermediate storage. Additionally, another contribution of this article is an introduction of a new perspective of holistic algorithm optimality. We show that the optimality depends not only on a query class but also on XML document characteristics. This new view on the optimality extends the general knowledge about the type of queries for which the holistic algorithms are optimal. Moreover, it allows us to determine that GTPStack is optimal for any GTP when a specific XML document is considered. We present a comprehensive experimental study of the state-of-the-art holistic algorithms showing under which conditions GTPStack outperforms the other holistic approaches.     

A survey on XML streaming evaluation techniques

XML is currently the most popular format for exchanging and representing data on the web. It is used in various applications and for different types of data including structured, semistructured, and unstructured heterogeneous data types. During the period, XML was establishing itself, data streaming applications have gained increased attention and importance. Because of these developments, the querying and efficient processing of XML streams has became a central issue. In this study, we survey the state of the art in XML streaming evaluation techniques. We focus on both the streaming evaluation of XPath expressions and of XQuery queries. We classify the XPath streaming evaluation approaches according to the main data structure used for the evaluation into three categories: automaton-based approach, array-based approach, and stack-based approach. We review, analyze, and compare the major techniques proposed for each approach. We also review multiple query streaming evaluation techniques. For the XQuery streaming evaluation problem, we identify and discuss four processing paradigms adopted by the existing XQuery stream query engines: the transducer-based paradigm, the algebra-based paradigm, the automata-algebra paradigm, and the pull-based paradigm. In addition, we review optimization techniques for XQuery streaming evaluation. We address the problem of optimizing XQuery streaming evaluation as a buffer optimization problem. For all techniques discussed, we describe the research issues and the proposed algorithms and we compare them with other relevant suggested techniques.     

有效支持XML结构化连接的索引——CATI

结构化连接的效率直接影响着XML查询的性能,目前对XML的结构化连接大多都是基于编码的方法.介绍了一种全新的有效支持XML结构化连接的树索引CATI(compact ancestor tree index)CATI的基本思想是,对于给定的一个祖先后代查询(A-D查询)或Twig查询,遍历XML文档,找出所有的祖先A的实例,用以建立CATI的主干;对于每个A实例,找出它的直接后代D的实例链接在它的后面.因为经典的结构连接算法Stack-Tree算法效率较高且使用较广,因此应用基于CATI的结构连接算法和基于Stack-Tree的结构连接算法就A-D查询和Twig查询做了大量实验.实验结果表明,基于CATI的结构化连接在一般查询情况下性能明显优于基于Stack-Tree的结构化连接.     

一种基于结构索引的XML模式匹配方法

XML文档采用了树型的数据模型,对其查询通常是用带有选择谓词的模式树在XML数据中进行匹配.因此,找出XML文档中所有符合模式树结构的元素集,是XML查询处理的核心操作.本文提出了结构索引JoinGuide,并在此基础上提出了一种新的XML模式匹配方法.它使用JoinGuide来对模式树进行预匹配,这样在XML文档上查询时可以利用索引上的匹配结果来忽略部分连接谓词和不必要的候选XML元素序列.本文还提出了三种具体算法来利用索引匹配结果进行进一步的查询.实验结果表明本文中的模式树匹配方法优于以往的匹配方法,并且索引所需的空间很小.     

A syntactic approach to twig-query matching on XML streams

Query matching on XML streams is challenging work for querying efficiency when the amount of queried stream data is huge and the data can be streamed in continuously. In this paper, the method Syntactic Twig-Query Matching (STQM) is proposed to process queries on an XML stream and return the query results continuously and immediately. STQM matches twig queries on the XML stream in a syntactic manner by using a lexical analyzer and a parser, both of which are built from our lexical-rules and grammar-rules generators according to the user's queries and document schema, respectively. For query matching, the lexical analyzer scans the incoming XML stream and the parser recognizes XML structures for retrieving every twig-query result from the XML stream. Moreover, STQM obtains query results without a post-phase for excluding false positives, which are common in many streaming query methods. Through the experimental results, we found that STQM matches the twig query efficiently and also has good scalability both in the queried data size and the branch degree of the twig query. The proposed method takes less execution time than that of a sequence-based approach, which is widely accepted as a proper solution to the XML stream query.     

Indexing useful structural patterns for XML query processing

Queries on semistructured data are hard to process due to the complex nature of the data and call for specialized techniques. Existing path-based indexes and query processing algorithms are not efficient for searching complex structures beyond simple paths, even when the queries are high-selective. We introduce the definition of minimal infrequent structures (MIS), which are structures that 1) exist in the data, 2) are not frequent with respect to a support threshold, and 3) all substructures of them are frequent. By indexing the occurrences of MIS, we can efficiently locate the high-selective substructures of a query, improving search performance significantly. An efficient data mining algorithm is proposed, which finds the minimal infrequent structures. Their occurrences in the XML data are then indexed by a lightweight data structure and used as a fast filter step in query evaluation. We validate the efficiency and applicability of our methods through experimentation on both synthetic and real data.     

概率XML文件树结点概率的查询算法

概率XML文件是概率数据的网络数据交换和表示标准,元素取值及其概率的查询与计算是概率XML文件的重要研究内容.概率XML文件树是一种有效的概率XML文件的数据模型,定义了概率XML文件树的基本路径和扩展路径,提出了根据可能世界原理将概率XML文件树分解为普通子XML树的集合的算法,根据路径分析原理将概率XML文件树分解为子概率XML树的集合的算法和相应的查询与计算结点及结点集合概率的算法,并通过实验进行了比较分析.实验结果表明:这两种方法是有效的;与前一种方法比较,后一种方法适合较大的概率XML文件树、结点及结点集合的概率的查询,计算过程较简单.     

Querying and ranking incomplete twigs in probabilistic XML

As the next generation language of the Internet, XML has been the de-facto standard of information exchange over the web. A core operation for XML query processing is to find all the occurrences of a twig pattern in an XML database. In addition, the study of probabilistic data has become an emerging topic for various applications on the Web. Therefore, researching the combination of XML twig pattern and probabilistic data is quite significant. In prior work of probabilistic XML, the answers of a given twig query are always complete. However, complete answers with low probabilities may be deemed irrelevant while incomplete answers with high probabilities are of great significance because incomplete answers may be the potential answers that interest the users. Different from complete evaluation, evaluating incomplete twigs in probabilistic XML introduces some new challenges. On one hand, incomplete queries do not only obtain complete matches, but also return answers that contain considerable incomplete matches. On the other hand, the processing of incomplete evaluation is more complicated. It is obvious that a ranking approach should be adopted along with evaluating incomplete answers. In this paper, we propose an efficient algorithm to handle the problem of querying incomplete twigs over the probabilistic XML database. We also present a novel algorithm for ranking the incomplete answers. The experimental results show that our proposed algorithms can improve the performance of querying and ranking incomplete twigs significantly.     

Object-stack: An object-oriented approach for top-k keyword querying over fuzzy XML

Keyword search is the most popular technique of searching information from XML (eXtensible markup language) document. It enables users to easily access XML data without learning the structure query language or studying the complex data schemas. Existing traditional keyword query methods are mainly based on LCA (lowest common ancestor) semantics, in which the returned results match all keywords at the granularity of elements. In many practical applications, information is often uncertain and vague. As a result, how to identify useful information from fuzzy data is becoming an important research topic. In this paper, we focus on the issue of keyword querying on fuzzy XML data at the granularity of objects. By introducing the concept of “object tree”, we propose the query semantics for keyword query at object-level. We find the minimum whole matching result object trees which contain all keywords and the partial matching result object trees which contain partial keywords, and return the root nodes of these result object trees as query results. For effectively and accurately identifying the top-K answers with the highest scores, we propose a score mechanism with the consideration of tf*idf document relevance, users’ preference and possibilities of results. We propose a stack-based algorithm named object-stack to obtain the top-K answers with the highest scores. Experimental results show that the object-stack algorithm outperforms the traditional XML keyword query algorithms significantly, and it can get high quality of query results with high search efficiency on the fuzzy XML document.     

ELCA evaluation for keyword search on probabilistic XML data

As probabilistic data management is becoming one of the main research focuses and keyword search is turning into a more popular query means, it is natural to think how to support keyword queries on probabilistic XML data. With regards to keyword query on deterministic XML documents, ELCA (Exclusive Lowest Common Ancestor) semantics allows more relevant fragments rooted at the ELCAs to appear as results and is more popular compared with other keyword query result semantics (such as SLCAs). In this paper, we investigate how to evaluate ELCA results for keyword queries on probabilistic XML documents. After defining probabilistic ELCA semantics in terms of possible world semantics, we propose an approach to compute ELCA probabilities without generating possible worlds. Then we develop an efficient stack-based algorithm that can find all probabilistic ELCA results and their ELCA probabilities for a given keyword query on a probabilistic XML document. Finally, we experimentally evaluate the proposed ELCA algorithm and compare it with its SLCA counterpart in aspects of result probability, time and space efficiency, and scalability.     

一种基于有序对的小枝模式匹配算法

随着半结构化的数据在信息交换中越来越重要,近年来,在XML数据库中,研究工作者提出了很多匹配小枝查询的算法.这些算法对仅含祖先后裔边的查询是很有效的,但是当查询中同时含祖先后裔和父子边时,以前算法仍可能产生大量中间结果,尤其是输入和输出的规模很大时.为避免中间结果的产生,提出了一种新的算法OPTwig,它是基于有序对的,通过查询树和文档树中结点有序对的匹配来进行查询,且不需要进行归并操作.结果表明,该算法优于以前算法.     

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.     

Efficient processing of XPath queries using indexes

A number of indexing techniques have been proposed in recent times for optimizing the queries on XML and other semi-structured data models. Most of the semi-structured models use tree-like structures and query languages (XPath, XQuery, etc.) which make use of regular path expressions to optimize the query processing. In this paper, we propose two algorithms called Entry-point algorithm (EPA) and Two-point Entry algorithms that exploit different types of indices to efficiently process XPath queries. We discuss and compare two approaches namely, Root-first and Bottom-first in implementing the EPA. We present the experimental results of the algorithms using XML benchmark queries and data and compare the results with that of traditional methods of query processing with and without the use of indexes, and ToXin indexing approach. Our algorithms show improved performance results than the traditional methods and Toxin indexing approach.     

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. Support by the Key Program of the National Natural Science Foundation of China under Grant No.60533110; the National Grand Fundamental Research 973 Program of China under Grant No. 2006CB303000; the National Natural Science Foundation of China under Grant No. 60773068 and No. 60773063.     

一种基于关键字的XML文档查询算法

对XML文档查询的常用方法有两种：一种是使用查询语言;另一种是使用关键字,而使用关键字查询XML文档比使用查询语言更为简单方便。给出了一种使用关键字查询XML文档的索引查找算法。该算法只需要扫描一次关键字对应的编码列,就可以找到需要的编码,提高了查询效率。实验表明该算法是可行的和有效的。