SAIL: Structure-aware indexing for effective and progressive top-k keyword search over XML documents

Keyword search in XML documents has recently gained a lot of research attention. Given a keyword query, existing approaches first compute the lowest common ancestors (LCAs) or their variants of XML elements that contain the input keywords, and then identify the subtrees rooted at the LCAs as the answer. In this the paper we study how to use the rich structural relationships embedded in XML documents to facilitate the processing of keyword queries. We develop a novel method, called SAIL, to index such structural relationships for efficient XML keyword search. We propose the concept of minimal-cost trees to answer keyword queries and devise structure-aware indices to maintain the structural relationships for efficiently identifying the minimal-cost trees. For effectively and progressively identifying the top-k answers, we develop techniques using link-based relevance ranking and keyword-pair-based ranking. To reduce the index size, we incorporate a numbering scheme, namely schema-aware dewey code, into our structure-aware indices. Experimental results on real data sets show that our method outperforms state-of-the-art approaches significantly, in both answer quality and search efficiency.     

Related Axis: The Extension to XPath Towards Effective XML Search

We investigate the limitations of existing XML search methods and propose a new semantics, related relationship, to effectively capture meaningful relationships of data elements from XML data in the absence of structural constraints. Then we make an extension to XPath by introducing a new axis, related axis, to specify the related relationship between query nodes so as to enhance the flexibility of XPath. We propose to reduce the cost of computing the related relationship by a new schema summary that summarizes the related relationship from the original schema without any loss. Based on this schema summary, we introduce two indices to improve the performance of query processing. Our algorithm shows that the evaluation of most queries can be equivalently transformed into just a few selection and value join operations, thus avoids the costly structural join operations. The experimental results show that our method is effective and efficient in terms of comparing the effectiveness of the related relationship with existing keyword search semantics and comparing the efficiency of our evaluation methods with existing query engines.     

XML keyword search with promising result type recommendations

Keyword search enables inexperienced users to easily search XML database with no specific knowledge of complex structured query languages and XML data schemas. Existing work has addressed the problem of selecting data nodes that match keywords and connecting them in a meaningful way, e.g., SLCA and ELCA. However, it is time-consuming and unnecessary to serve all the connected subtrees to the users because in general the users are only interested in part of the relevant results. In this paper, we propose a new keyword search approach which basically utilizes the statistics of underlying XML data to decide the promising result types and then quickly retrieves the corresponding results with the help of selected promising result types. To guarantee the quality of the selected promising result types, we measure the correlations between result types and a keyword query by analyzing the distribution of relevant keywords and their structures within the XML data to be searched. In addition, relevant result types can be efficiently computed without keyword query evaluation and any schema information. To directly return top-k keyword search results that conform to the suggested promising result types, we design two new algorithms to adapt to the structural sensitivity of the keyword nodes over the keyword search results. Lastly, we implement all proposed approaches and present the relevant experimental results to show the effectiveness of our approach.     

XML 数据流上Top-K 关键字查询处理

利用关键字可以在模式未知的情况下对XML数据进行查询.在当前的XML数据流上的关键字查询处理中,打分函数往往不能都满足各种用户不同的需求.提出了一种基于skyline的XML数据流上的Top-K关键字查询.对于这种查询,不需要考虑影响结果与查询相关性的复杂因素,只需利用skyline挑选与查询最相关的结果.提出了两种XML数据流上的有效的基于skyline的Top-K关键查询处理算法,包括对单查询和多查询的处理算法.通过扩展实验对两种算法的有效性和可扩展性进行了验证.经过实验验证,所提出的查询处理算法的效率几乎不受关键字个数、查询结果数量、查询数量等参数的影响,运行时间和文档大小大致呈线性关系.     

A query refinement framework for xml keyword search

Existing work of XML keyword search focus on how to find relevant and meaningful data fragments for a query, assuming each keyword is intended as part of it. However, in XML keyword search, user queries usually contain irrelevant or mismatched terms, typos etc, which may easily lead to empty or meaningless results. In this paper, we introduce the problem of content-aware XML keyword query refinement, where the search engine should judiciously decide whether a user query Q needs to be refined during the processing of Q, and find a list of promising refined query candidates which guarantee to have meaningful matching results over the XML data, without any user interaction or a second try. To achieve this goal, we build a novel content-aware XML keyword query refinement framework consisting of two core parts: (1) we build a query ranking model to evaluate the quality of a refined query RQ, which captures the morphological/semantical similarity between Q and RQ and the dependency of keywords of RQ over the XML data; (2) we integrate the exploration of RQ candidates and the generation of their matching results as a single problem, which is fulfilled within a one-time scan of the related keyword inverted lists optimally. Finally, an extensive empirical study verifies the efficiency and effectiveness of our framework.     

Keyword query with structure: towards semantic scoring of XML search results

Keyword search is an effective paradigm for information discovery and has been introduced recently to query XML documents. Scoring of XML search results is an important issue in XML keyword search. Traditional “bag-of-words” model cannot differentiate the roles of keywords as well as the relationship between keywords, thus is not proper for XML keyword queries. In this paper, we present a new scoring method based on a novel query model, called keyword query with structure (QWS), which is specially designed for XML keyword query. The method is based on a totally new view taken by the QWS model on a keyword query that, a keyword query is a composition of several query units, each representing a query condition. We believe that this method captures the semantic relevance of the search results. The paper first introduces an algorithm reformulating a keyword query to a QWS. Then, a scoring method is presented which measures the relevance of search results according to how many and how well the query conditions are matched. The scoring method is also extended to clusters of search results. Experimental results verify the effectiveness of our methods.     

Processing keyword search on XML: a survey

Keyword search is a user-friendly approach for users to retrieve information from XML data. Since an XML document can have a large size and contain a lot of information, an XML keyword search result should be a fragment of an XML document dynamically constructed at query time, which is achievable due to the structuredness of XML. Processing keyword searches on XML has several challenges, e.g., what are the elements in the XML document that are relevant to the query? How to generate the results efficiently and rank the results meaningfully? How to present the results to the user in a way such that the user can quickly find the desired information? In this survey, we review the papers in the literature that attempted to address these problems. We divide the existing approaches into several classes based on the problem they tackled, and perform a comprehensive analysis of these works.     

Query Intent Disambiguation of Keyword-Based Semantic Entity Search in Dataspaces

Keyword query has attracted much research attention due to its simplicity and wide applications. The inherent ambiguity of keyword query is prone to unsatisfied query results. Moreover some existing techniques on Web query, keyword query in relational databases and XML databases cannot be completely applied to keyword query in dataspaces. So we propose KeymanticES, a novel keyword-based semantic entity search mechanism in dataspaces which combines both keyword query and semantic query features. And we focus on query intent disambiguation problem and propose a novel three-step approach to resolve it. Extensive experimental results show the effectiveness and correctness of our proposed approach.     

高效的XML关键字查询改写和结果生成技术

用户使用关键字查询时可能不能准确地表达他们的意图,即使用户正确地表达了查询意图,查询引擎也可能不能准确地返回查询结果.针对这一问题,重点研究了在XML关键字查询中如何进行有效的查询改写并生成有意义的结果.提出4种查询改写操作和查询改写代价的概念,给出了动态规划的方法计算查询改写代价.为了找出最优的查询改写,给出了基于栈的查询改写和结果生成算法,并提出了基于划分的优化算法.最后通过丰富的实验对提出的方法进行了验证.     

XQuery speedup by deploying structural redundancy in mapping XML into relations

In designing a relational schema, we often consider that an attribute of a table is replicated into other table to reduce the join cost. Maybe such a possible redundancy will be grasped through E/R model (i.e. semantic analysis). Similarly, in mapping XML into relations, we can consider some redundancies to enhance query performance and they can be grasped through the structural traits of DTD (or XML schema). Several practical structural redundancies are formulated in this paper. If given XML data and queries are very large and complex, finding essential replications may also be difficult, and two efficient search methods are introduced for helping the search. Since the search problem is NP-hard, the methods are heuristically designed. Finally, read and update query costs arising by employing the structural redundancy are analyzed experimentally and the efficiency of two search methods is analyzed. They showed that the replication strategy can be very useful.     

Adaptive relaxation for querying heterogeneous XML data sources

Searching XML data with a structured XML query can improve the precision of results compared with a keyword search. However, the structural heterogeneity of the large number of XML data sources makes it difficult to answer the structured query exactly. As such, query relaxation is necessary. Previous work on XML query relaxation poses the problem of unnecessary computation of a big number of unqualified relaxed queries. To address this issue, we propose an adaptive relaxation approach which relaxes a query against different data sources differently based on their conformed schemas. In this paper, we present a set of techniques that supports this approach, which includes schema-aware relaxation rules for relaxing a query adaptively, a weighted model for ranking relaxed queries, and algorithms for adaptive relaxation of a query and top-k query processing. We discuss results from a comprehensive set of experiments that show the effectiveness and the efficiency of our approach.     

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.     

一种基于节点语义相关性的XML关键字查询算法

现有的XML关键字查询算法,通常只考虑节点间的结构信息,以包含关键字匹配节点的子树作为查询的结果,而节点间的语义相关性一直没有被充分利用。这也是导致现有查询算法的结果中普遍含有大量语义无关的冗余信息的主要原因。在该文中,我们首先对查询关键字的环境语义及节点间的语义相关性进行了定义,在此基础上,提出了一种新的关键字查询算法,寻找语义相关单元作为关键字查询的结果。这样获得的查询结果,一方面不含语义无关的冗余信息,另一方面也与用户的查询意图更加匹配。实验表明,该文提出的算法在查询效率和精确性上都有较大改进。     

安全访问控制的XML关键字检索

XML(extensive makeup language)的关键字检索简单易用,用户不必了解数据库的模式,受到人们的广泛关注。当前的相关研究主要集中于关键字检索的算法以及返回结果的组织和排序,却忽视了其中的安全性问题。结合XML关键字搜索和XML安全控制,研究了基于安全访问控制的XML关键字检索技术。在XML关键字的最小最低公共祖先(smallest lowest common ancestors,SLCA)和基于视图的安全访问控制规则的基础上,确定基于安全访问控制规则的XML关键字检索结果;建立基于安全视图的关键字索引,以及在此基础上的关键字检索算法。实验表明,为了满足安全访问控制规则,该算法虽然需要额外的时间开销但总体上是高效的。     

A novel XML keyword query approach using entity subtree

Keyword query is an important means to find object information in XML document. Most of the existing keyword query approaches adopt the subtrees rooted at the smallest lowest common ancestors of the keyword matching nodes as the basic result units. The structural relationships among XML nodes are excessively emphasized but the semantic relevance is not fully exploited.To change this situation, we propose the concept of entity subtree and emphasis the semantic relevance among different nodes as querying information from XML. In our approach, keyword query cases are improved to a new keyword-based query language, Grouping and Categorization Keyword Expression (GCKE) and the core query algorithm, finding entity subtrees (FEST) is proposed to return high quality results by fully using the keyword semantic meanings exposed by GCKE. We demonstrate the effectiveness and the efficiency of our approach through extensive experiments.     

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.     

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.     

模糊XML关键字近似查询方法研究

在精确XML文档上的关键字查询方法的研究大多是基于LCA语义或者其变种语义(SLCA,ELCA等)开展的,将包含所有关键字的最紧致XML子树片段作为查询结果返回。但是这些基于LCA语义产生的查询结果中通常包含了大量的冗余信息,现实世界中存在着大量的不确定和模糊信息,因而如何从模糊XML文档中搜索到高质量的关键字查询结果是一个需要研究的问题。针对模糊XML文档上的关键字近似查询方法进行研究,通过引入最小连接树(MCT)的概念,提出在模糊XML文档上关键字查询的所有GDMCTs问题,并给出解决这一问题的基于栈的算法All fuzzy GDMCTs,该算法可以得到满足用户指定的子树大小阈值和可能性阈值条件的所有GDMCTs结果。实验表明,该算法在模糊XML文档上能够得到较高质量的关键字查询结果。     

XML文档流上的一种关键字查询方法研究

本文将当前数据库领域的2个研究热点-XML文档和数据流处理一的最新研究结合起来,提出了XML文档流关键字查询的问题。基于最小连通子树的概念。设计了相应的数据结构和基于栈的查询算法,可以有效解决XML文档流上进行关键字查询的问题。具体方法是把XML数据流表示成3类SAX事件：BEGIN（tag）、END（tag）和TEXT0。对每类事件的处理算法进行了详细,并进行了正确性证明。从理论上分析了算法的复杂度,并在XMark和treebank．xml两个数据集上对所提方法进行了广泛的实验。结果验证了本文工作的有效性。