A Semantic Cache Framework for Secure XML Queries

Secure XML query answering to protect data privacy and semantic cache to speed up XML query answering are two hot spots in current research areas of XML database systems. While both issues are explored respectively in depth,they have not been studied together,that is,the problem of semantic cache for secure XML query answering has not been addressed yet. In this paper,we present an interesting joint of these two aspects and propose an efficient framework of semantic cache for secure XML query answering,which can improve the performance of XML database systems under secure circumstances. Our framework combines access control,user privilege management over XML data and the state-of-the-art semantic XML query cache techniques,to ensure that data are presented only to authorized users in an efficient way. To the best of our knowledge,the approach we propose here is among the first beneficial efforts in a novel perspective of combining caching and security for XML database to improve system performance. The efficiency of our framework is verified by comprehensive experiments.     

CLASCN： Candidate Network Selection for Efficient Top-κ Keyword Queries over Databases

Keyword Search Over Relational Databases （KSORD） enables casual or Web users easily access databases through free-form keyword queries. Improving the performance of KSORD systems is a critical issue in this area. In this paper, a new approach CLASCN （Classification, Learning And Selection of Candidate Network） is developed to efficiently perform top-κ keyword queries in schema-graph-based online KSORD systems. In this approach, the Candidate Networks （CNs） from trained keyword queries or executed user queries are classified and stored in the databases, and top-κ results from the CNs are learned for constructing CN Language Models （CNLMs）. The CNLMs are used to compute the similarity scores between a new user query and the CNs from the query. The CNs with relatively large similarity score, which are the most promising ones to produce top-κ results, will be selected and performed. Currently, CLASCN is only applicable for past queries and New All-keyword-Used （NAU） queries which are frequently submitted queries. Extensive experiments also show the efficiency and effectiveness of our CLASCN approach.     

CLASCN:Candidate Network Selection for Efficient Top-k Keyword Queries over Databases

Keyword Search Over Relational Databases (KSORD) enables casual or Web users easily access databases through free-form keyword queries. Improving the performance of KSORD systems is a critical issue in this area. In this paper, a new approach CLASCN (Classification, Learning And Selection of Candidate Network) is developed to efficiently perform top-fc keyword queries in schema-graph-based online KSORD systems. In this approach, the Candidate Networks (CNs) from trained keyword queries or executed user queries are classified and stored in the databases, and top-fc results from the CNs are learned for constructing CN Language Models (CNLMs). The CNLMs are used to compute the similarity scores between a new user query and the CNs from the query. The CNs with relatively large similarity score, which are the most promising ones to produce top-fc results, will be selected and performed. Currently, CLASCN is only applicable for past queries and New All-keyword-Used (NAU) queries which are frequently submitted queries. Extensive experiments also show the efficiency and effectiveness of our CLASCN approach.     

Dynamic Query Optimization Approach for Semantic Database Grid

Fundamentally, semantic grid database is about bringing globally distributed databases together in order to coordinate resource sharing and problem solving in which information is given well-defined meaning, and DartGrid II is the implemented database gird system whose goal is to provide a semantic solution for integrating database resources on the Web. Although many algorithms have been proposed for optimizing query-processing in order to minimize costs and/or response time, associated with obtaining the answer to query in a distributed database system, database grid query optimization problem is fundamentally different from traditional distributed query optimization. These differences are shown to be the consequences of autonomy and heterogeneity of database nodes in database grid. Therefore, more challenges have arisen for query optimization in database grid than traditional distributed database. Following this observation, the design of a query optimizer in DartGrid II is presented, and a heuristic, dynamic and parallel query optimization approach to processing query in database grid is proposed. A set of semantic tools supporting relational database integration and semantic-based information browsing has also been implemented to realize the above vision.     

An Effective Semantic Cache for Exploiting XPath Query/View Answerability

Maintaining a semantic cache of materialized XPath views inside or outside the database is a novel,feasible and efficient approach to facilitating XML query processing.However,most of the existing approaches incur the following disadvantages:1) they cannot discover enough potential cached views sufficiently to effectively answer subsequent queries; or 2) they are inefficient for view selection due to the complexity of XPath expressions.In this paper,we propose SCEND, an effective Semantic Cache based on ...     

Fast Smallest Lowest Common Ancestor Computation Based on Stable Match

In this paper, we focus on efficient processing of XML keyword queries based on smallest lowest common ancestor (SLCA) semantics. For a given query Q with m Key words we propose to use stable matches as the basis for SLCA computation, where each stable match M consists of m nodes that belong to the m distinct keyword inverted lists of Q. M satisfies that no other lowest common ancestor (LCA) node of Q can be found to be located after the first node of M and be a descendant of the LCA of M, based on which the operation of locating a stable match can skip more useless nodes. We propose two stable match based algorithms for SLCA computation, i.e., BSLCA and HSLCA. BSLCA processes two keyword inverted lists each time from the shortest to the longest, while HSLCA processes all keyword inverted lists in a holistic way to avoid the problem of redundant computation invoked by BSLCA. Our extensive experimental results verify the performance advantages of our methods according to various evaluation metrics.     

Accomplishing Deterministic XML Query Optimization

As the popularity of XML (extensible Markup Language) keeps growing rapidly, the management of XML compliant structured-document databases has become a very interesting and compelling research area. Query optimization for XML structured-documents stands out as one of the most challenging research issues in this area because of the much enlarged optimization (search) space, which is a consequence of the intrinsic complexity of the underlying data model of XML data. We therefore propose to apply deterministic transformations on query expressions to most aggressively prune the search space and fast achieve a sufficiently improved alternative (if not the optimal) for each incoming query expression. This idea is not just exciting but practically attainable. This paper first provides an overview of our optimization strategy, and then focuses on the key implementation issues of our rule-based transformation system for XML query optimization in a database environment. The performance results we obtained from experimentation show that our approach is a valid and effective one.     

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.     

Searching Databases with Keywords

Traditionally, SQL query language is used to search the data in databases. However, it is inappropriate for end-users, since it is complex and hard to learn. It is the need of end-user, searching in databases with keywords, like in web search engines. This paper presents a survey of work on keyword search in databases. It also includes a brief introduction to the SEEKER system which has been developed.     

XDist： an effective XML keyword search system with re-ranking model based on keyword distribution

Keyword search enables web users to easily access XML data without understanding the complex data schemas. However, the native ambiguity of keyword search makes it arduous to select qualified relevant results matching keywords. To solve this problem, researchers have made much effort on establishing ranking models distinguishing relevant and irrelevant passages, such as the highly cited TF＊IDF and BM25. However, these statistic based ranking methods mostly consider term frequency, inverse document frequency and length as ranking factors, ignoring the distribution and connection information between different keywords. Hence, these widely used ranking methods are powerless on recognizing irrelevant results when they are with high term frequency, indicating a performance limitation. In this paper, a new searching system XDist is accordingly proposed to attack the problems aforementioned. In XDist, we firstly use the semantic query model maximal lowest common ancestor （MAXLCA） to recognize the returned results of a given query, and then these candidate results are ranked by BM25. Especially, XDist re-ranks the top several results by a combined distribution measurement （CDM） which considers four measure criterions： term proximity, intersection of keyword classes, degree of integration among keywords and quantity variance of keywords. The weights of the four measures in CDM are trained by a listwise learning to optimize method. The experimental results on the evaluation platform of INEX show that the re-ranking method CDM can effectively improve the performance of the baseline BM25 by 22% under iP[0.01] and 18% under MAiP. Also the semantic model MAXLCA and the search engine XDist perform the best in their respective related fields.     

一种面向主题的关键词查询扩展方法

提出一种基于局部统计和语义扩展相结合,面向主题的关键词查询扩展方法。该方法通过对给定主题的初始关键词搜索反馈网页进行分析,采用TF*PSF语义加权方法计算主题候选词的权重来进一步筛选主题关键词。在此基础上,设计了面向Web的主题关键词迭代查询扩展算法,采用主题关键词的组合查询策略,迭代扩展出主题的关键词集合。实验证明该方法是有效的。     

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.     

Top-<Emphasis Type="Italic">k</Emphasis> coupled keyword recommendation for relational keyword queries

Providing top-k typical relevant keyword queries would benefit the users who cannot formulate appropriate queries to express their imprecise query intentions. By extracting the semantic relationships both between keywords and keyword queries, this paper proposes a new keyword query suggestion approach which can provide typical and semantically related queries to the given query. Firstly, a keyword coupling relationship measure, which considers both intra- and inter-couplings between each pair of keywords, is proposed. Then, the semantic similarity of different keyword queries can be measured by using a semantic matrix, in which the coupling relationships between keywords in queries are reserved. Based on the query semantic similarities, we next propose an approximation algorithm to find the most typical queries from query history by using the probability density estimation method. Lastly, a threshold-based top-k query selection method is proposed to expeditiously evaluate the top-k typical relevant queries. We demonstrate that our keyword coupling relationship and query semantic similarity measures can capture the coupling relationships between keywords and semantic similarities between keyword queries accurately. The efficiency of query typicality analysis and top-k query selection algorithm is also demonstrated.     

Semantic-aware top-k spatial keyword queries

The fast development of GPS equipped devices has aroused widespread use of spatial keyword querying in location based services nowadays. Existing spatial keyword query methodologies mainly focus on the spatial and textual similarities, while leaving the semantic understanding of keywords in spatial Web objects and queries to be ignored. To address this issue, this paper studies the problem of semantic based spatial keyword querying. It seeks to return the k objects most similar to the query, subject to not only their spatial and textual properties, but also the coherence of their semantic meanings. To achieve that, we propose novel indexing structures, which integrate spatial, textual and semantic information in a hierarchical manner, so as to prune the search space effectively in query processing. Extensive experiments are carried out to evaluate and compare them with other baseline algorithms.     

Indexing dataspaces with partitions

Dataspaces are recently proposed to manage heterogeneous data, with features like partially unstructured, high dimension and extremely sparse. The inverted index has been previously extended to retrieve dataspaces. In order to achieve more efficient access to dataspaces, in this paper, we first introduce our survey of data features in the real dataspaces. Based on the features observed in our study, several partitioning based index approaches are proposed to accelerate the query processing in dataspaces. Specifically, the vertical partitioning index utilizes the partitions on tokens to merge and compress data. We can both reduce the number of I/O reads and avoid aggregation of data inside a compressed list. The horizontal partitioning index supports pruning partitions of tuples in the top-k query. Thus, we can reduce the computation overhead of irrelevant candidate tuples to the query. Finally, we also propose a hybrid index with both vertical and horizontal partitioning. The extensive experiment results in real data sets demonstrate that our approaches outperform the previous techniques and scale well with the large data size.     

基于二分图的RDF关键词扩展查询方法

使用图表示RDF数据可以保持数据间的关联信息和语义信息,越来越多的关键词查询方法基于图结构实现RDF数据的查询处理。将二分图与RDF数据图相结合,定义RDF二分图模型,并提出一种基于二分图的RDF关键词扩展查询方法KERBG。该方法将文本信息封装在二分图顶点标签上,以支持对关系的查询;利用关键词同义词扩展技术对查询关键词进行语义扩展,有效解决同一对象的描述用词的多样性问题,进而提高查准率;利用RDF二分图的反对称邻接矩阵及其幂矩阵构造包含关键顶点的查询结果子图,实现关键词查询处理,并降低查询响应时间。实验结果表明,在查准率和查询响应时间方面,提出的KERBG方法优于当前主流方法。     

Beyond search: Retrieving complete tuples from a text-database

A common task of Web users is querying structured information from Web pages. For realizing this interesting scenario we propose a novel query processor for systematically discovering instances of semantic relations in Web search results and joining these relation instances into complex result tuples with conjunctive queries. Our query processor transforms a structured user query into keyword queries that are submitted to a search engine, forwards search results to a relation extractor, and then combines relations into complex result tuples. The processor automatically learns discriminative and effective keywords for different types of semantic relations. Thereby, our query processor leverages the index of a search engine to query potentially billions of pages. Unfortunately, relation extractors may fail to return a relation for a result tuple. Moreover, user defined data sources may not return at least k complete result tuples. Therefore we propose an adaptive routing model based on information theory for retrieving missing attributes of incomplete result tuples. The model determines the most promising next incomplete tuple and attribute type for returning any-k complete result tuples at any point during the query execution process. We report a thorough experimental evaluation over multiple relation extractors. Our query processor returns complete result tuples while processing only very few Web pages.     

一种基于HBase的高效空间关键字查询策略

随着移动定位技术的发展以及智能手机的普及,互联网中空间文本对象的数量正在急速增长,如何在规模庞大且动态增长的空间文本对象中进行高效的空间关键字查询成为了许多空间关键字查询应用所关心的问题.现有的方法通常利用基于R树和倒排索引的混合索引结构来处理空间关键字查询,然而,面对数量巨大而且不断增长的空间文本对象,这些方法往往难以为空间关键字查询的高效性和扩展性提供支持.对此,提出一种基于HBase的空间文本数据索引结构SK-HBase.SK-HBase以HBase作为数据存储,通过有效的数据分配策略对空间文本对象的空间信息和文本信息同时进行索引.在SK-HBase的基础上,本文提出了两种空间关键字查询算法,以保证不同空间范围下的空间关键字查询的高效性和可扩展性.实验证明,我们的方法能够在海量数据下进行高效的空间关键字查询并具有良好的可扩展性.     

From keywords to semantic queries—Incremental query construction on the semantic web

Constructing semantic queries is a demanding task for human users, as it requires mastering a query language as well as the schema which has been used for storing the data. In this paper, we describe QUICK, a novel system for helping users to construct semantic queries in a given domain. QUICK combines the convenience of keyword search with the expressivity of semantic queries. Users start with a keyword query and then are guided through a process of incremental refinement steps to specify the query intention. We describe the overall design of QUICK, present the core algorithms to enable efficient query construction, and finally demonstrate the effectiveness of our system through an experimental study.     

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.