Efficient subtree results computation for XML keyword queries

In this paper, we focus on efficient construction of restricted subtree (RSubtree) results for XML keyword queries on amulticore system. We firstly show that the performance bottlenecks for existing methods lie in 1) computing the set of relevant keyword nodes (RKNs) for each subtree root node, 2) constructing the corresponding RSubtree, and 3) parallel execution. We then propose a two-step generic top-down subtree construction algorithm, which computes SLCA/ELCA nodes in the first step, and parallelly gets RKNs and generates RSubtree results in the second step, where genericmeans that 1) our method can be used to compute different kinds of subtree results, 2) our method is independent of the query semantics; top-down means that our method constructs each RSubtree by visiting nodes of the subtree constructed based on an RKN set level-by-level from left to right, such that to avoid visiting as many useless nodes as possible. The experimental results show that our method is much more efficient than existing ones according to various metrics.     

Efficient query processing for XML keyword queries based on the IDList index

Keyword search over XML data has attracted a lot of research efforts in the last decade, where one of the fundamental research problems is how to efficiently answer a given keyword query w.r.t. a certain query semantics. We found that the key factor resulting in the inefficiency for existing methods is that they all heavily suffer from the common-ancestor-repetition problem. In this paper, we propose a novel form of inverted list, namely the IDList; the IDList for keyword $k$ consists of ordered nodes that directly or indirectly contain $k$ . We then show that finding keyword query results based on the smallest lowest common ancestor and exclusive lowest common ancestor semantics can be reduced to ordered set intersection problem, which has been heavily optimized due to its application in areas such as information retrieval and database systems. We propose several algorithms that exploit set intersection in different directions and with or without using additional indexes. We further propose several algorithms that are based on hash search to simplify the operation of finding common nodes from all involved IDLists. We have conducted an extensive set of experiments using many state-of-the-art algorithms and several large-scale datasets. The results demonstrate that our proposed methods outperform existing methods by up to two orders of magnitude in many cases.     

Top-k queries over web applications

The core logic of web applications that suggest some particular service, such as online shopping, e-commerce etc., is typically captured by Business Processes (BPs). Among all the (maybe infinitely many) possible execution flows of a BP, analysts are often interested in identifying flows that are “most important”, according to some weight metric. The goal of the present paper is to provide efficient algorithms for top-k query evaluation over the possible executions of Business Processes, under some given weight function. Unique difficulties in top-k analysis in this settings stem from (1) the fact that the number of possible execution flows of a given BP is typically very large, or even infinite in presence of recursion and (2) that the weights (e.g., likelihood, monetary cost, etc.) induced by actions performed during the execution (e.g., product purchase) may be inter-dependent (due to probabilistic dependencies, combined discount deals etc.). We exemplify these difficulties, and overcome them to provide efficient algorithms for query evaluation where possible. We also describe in details an application prototype that we have developed for recommending optimal navigation in an online shopping web site that is based on our model and algorithms.     

Top-k queries on temporal data

The database community has devoted extensive amount of efforts to indexing and querying temporal data in the past decades. However, insufficient amount of attention has been paid to temporal ranking queries. More precisely, given any time instance t, the query asks for the top-k objects at time t with respect to some score attribute. Some generic indexing structures based on R-trees do support ranking queries on temporal data, but as they are not tailored for such queries, the performance is far from satisfactory. We present the Seb-tree, a simple indexing scheme that supports temporal ranking queries much more efficiently. The Seb-tree answers a top-k query for any time instance t in the optimal number of I/Os in expectation, namely, O(log_B \fracNB+\frackB){O\left({\rm log}_B\,\frac{N}{B}+\frac{k}{B}\right)} I/Os, where N is the size of the data set and B is the disk block size. The index has near-linear size (for constant and reasonable k _max values, where k _max is the maximum value for the possible values of the query parameter k), can be constructed in near-linear time, and also supports insertions and deletions without affecting its query performance guarantee. Most of all, the Seb-tree is especially appealing in practice due to its simplicity as it uses the B-tree as the only building block. Extensive experiments on a number of large data sets, show that the Seb-tree is more than an order of magnitude faster than the R-tree based indexes for temporal ranking queries.     

Correcting queries for XML

It has been observed that queries over XML data sources are often unsatisfiable. Unsatisfiability may stem from several different sources, e.g., the user may be insufficiently familiar with the labels appearing the documents, or may not be intimately aware of the hierarchical structure of the documents. To deal with query and document mismatches, previous research has considered returning answers that maximally satisfy (in some sense) the query, instead of only returning strictly satisfying answers. However, this breaks the golden database rule that only strictly satisfying answers are returned when querying. Indeed, the relationship between the query and answers is no longer clear, when unsatisfying answers are returned. To reinstate the golden database rule, this article proposes a framework for automatically correcting queries over XML. This framework generates similar satisfiable queries, when the user query is unsatisfiable. The user can then choose a satisfiable query of interest, and receive exactly satisfying answers to this query.     

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.     

Top-k dominating queries on incomplete large dataset

Top-k dominating (TKD) query is one of the methods to find the interesting objects by returning the k objects that dominate other objects in a given dataset. Incomplete datasets have missing values in uncertain dimensions, so it is difficult to obtain useful information with traditional data mining methods on complete data. BitMap Index Guided Algorithm (BIG) is a good choice for solving this problem. However, it is even harder to find top-k dominance objects on incomplete big data. When the dataset is too large, the requirements for the feasibility and performance of the algorithm will become very high. In this paper, we proposed an algorithm to apply MapReduce on the whole process with a pruning strategy, called Efficient Hadoop BitMap Index Guided Algorithm (EHBIG). This algorithm can realize TKD query on incomplete datasets through BitMap Index and use MapReduce architecture to make TKD query possible on large datasets. By using the pruning strategy, the runtime and memory usage are greatly reduced. What’s more, we also proposed an improved version of EHBIG (denoted as IEHBIG) which optimizes the whole algorithm flow. Our in-depth work in this article culminates with some experimental results that clearly show that our proposed algorithm can perform well on TKD query in an incomplete large dataset and shows great performance in a Hadoop computing cluster.

     

XML关键字检索中Dewey码存储方式的研究

Dewey码是XML关键字检索中采用的重要编码方式。在目前的研究当中,Dewey码通常以字符形式进行存储,这种方式造成Dewey码存储代价过大,并且在LCA求解过程中也必须通过字符比较才能获得Dewey码各层的数值,影响LCA求解效率。提出采用前缀共享和变长整形编码思路的PSVL存储方式,在消除字符比较操作的同时减少了Dewey码集合的存储代价。实验证明利用该存储方式对Dewey码集合进行存储,可以有效地降低其存储代价,并且减少获取Dewey码各层数值这一步骤花费的时间,间接提高了LCA的求解效率。     

Intensional answers to database queries

In addition to data, database systems store various kinds of information about their data. Examples are: class hierarchies, to define the various data classes and their relationships; integrity constraints, to state required relationships among the data; and inference rules, to define new classes in terms of known classes. This information is often referred to as intensional information (the data are referred to as extensional information). There have been several independent research works that suggested ways by which intensional information may be used to improve the conventional (extensional) database answers. Although each of these efforts developed its own specific methods, they all share a common belief: database answers would be improved if accompanied by intensional statements that describe them more abstractly. We study and compare the various approaches to intensional answers by using various classifications; we examine their relative merits with regard to key aspects; we discuss remaining issues; and we offer new research directions     

有效支持全文本检索的XML索引技术研究

在XML文档上进行全文本检索已经成为很多研究课题的基础问题,例如Web信息检索,信息抽取等。有效的XML索引结构对于加速检索速度是至关重要的,在文献[1]的基础上全面地构建和实现了一个可以有效的支持XML全文本检索的索引结构。实验表明提出的索引结构在索引构建时间、空间等性能指标上均有很好的表现。     

Verifiable searchable symmetric encryption for conjunctive keyword queries in cloud storage

Searchable symmetric encryption (SSE) has been introduced for secure outsourcing the encrypted database to cloud storage, while maintaining searchable features. Of various SSE schemes, most of them assume the server is honest but curious, while the server may be trustless in the real world. Considering a malicious server not honestly performing the queries, verifiable SSE (VSSE) schemes are constructed to ensure the verifiability of the search results. However, existing VSSE constructions only focus on single-keyword search or incur heavy computational cost during verification. To address this challenge, we present an efficient VSSE scheme, built on OXT protocol (Cash et al., CRYPTO 2013), for conjunctive keyword queries with sublinear search overhead. The proposed VSSE scheme is based on a privacy-preserving hash-based accumulator, by leveraging a well-established cryptographic primitive, Symmetric Hidden Vector Encryption (SHVE). Our VSSE scheme enables both correctness and completeness verifiability for the result without pairing operations, thus greatly reducing the computational cost in the verification process. Besides, the proposed VSSE scheme can still provide a proof when the search result is empty. Finally, the security analysis and experimental evaluation are given to demonstrate the security and practicality of the proposed scheme.     

一种基于自然语言生成的XML关键字查询技术

为了解决基于LCA（Lower Common Ancestor）的XML关键字查询丢失语义的问题,提出了一种基于“自然语言生成技术（Natural Language Generation,NLG）”的XML关键字查询技术,将NLG的内容规划应用到XML文档,产生针对用户查询的消息语句集,通过对消息语句集的筛选既可以实现基于语义的XML关键字查询,又可以极大地提高查询效率。     

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.     

FastMatch:一种高效的XML关键字查询算法

现有的XML关键字查询方法包括两步:确定满足特定语义的节点;构建满足特定条件的子树。这种处理方式需要多次扫描关键字倒排表,效率低下。针对这一问题,提出快速分组方法来减少扫描倒排表次数,进而基于快速分组方法提出FastMatch算法。该算法仅需扫描一次关键字倒排表就能构建满足特定条件的子树,从而提高了查询效率。最后通过实验验证了该方法的高效性。     

FastMatch:an efficient algorithm for XML keyword search

现有的XML关键字查询方法包括两步:确定满足特定语义的节点;构建满足特定条件的子树.这种处理方式需要多次扫描关键字倒排表,效率低下.针对这一问题,提出快速分组方法来减少扫描倒排表次数,进而基于快速分组方法提出FastMatch算法.该算法仅需扫描一次关键字倒排表就能构建满足特定条件的子树,从而提高了查询效率.最后通过实验验证了该方法的高效性.     

Finding smallest k-Compact tree set for keyword queries on graphs using mapreduce

Keyword search is integrated in many applications on account of the convenience to convey users’ query intention. Most existing works in keyword search on graphs modeled the query results as individual minimal connected trees or connected graphs that contain the keywords. We observe that significant overlap may exist among those query results, which would affect the result diversification. Besides, most solutions required accessing graph data and pre-built indexes in memory, which is not suitable to process big dataset. In this paper, we define the smallest k-compact tree set as the keyword query result, where no shared graph node exists between any two compact trees. We then develop a progressive A* based scalable solution using MapReduce to compute the smallest k-compact tree set, where the computation process could be stopped once the generated compact tree set is sufficient to compute the keyword query result. We conduct experiments to show the efficiency of our proposed algorithm.     

Efficient keyword search over virtual XML views

Emerging applications such as personalized portals, enterprise search, and web integration systems often require keyword search over semi-structured views. However, traditional information retrieval techniques are likely to be expensive in this context because they rely on the assumption that the set of documents being searched is materialized. In this paper, we present a system architecture and algorithm that can efficiently evaluate keyword search queries over virtual (unmaterialized) XML views. An interesting aspect of our approach is that it exploits indices present on the base data and thereby avoids materializing large parts of the view that are not relevant to the query results. Another feature of the algorithm is that by solely using indices, we can still score the results of queries over the virtual view, and the resulting scores are the same as if the view was materialized. Our performance evaluation using the INEX data set in the Quark (Bhaskar et al. in Quark: an efficient XQuery full-text implementation. In: SIGMOD, 2006) open-source XML database system indicates that the proposed approach is scalable and efficient.