面向不确定图的概率可达查询

图的可达性查询被广泛应用于生物网络、社会网络、本体网络、RDF数据库和XML数据库等.由于对数据操作时引入的噪声和错误使这些图数据具有不确定性,已经有大量的针对不确定RDF和XML数据库的研究.文中使用可能世界语义模型构建不确定图,基于该模型,研究了概率可达查询(PR).处理PR查询是#P完全问题,对此文中首先给出一个基本随机算法,可快速地估算出可达概率,并且该值有很高的精确度.进一步,文中为随机算法引入条件分布(称为"条件随机算法"),采用图的不相交路径集和割集作为条件概率分布,因此改进的随机算法可准确地并且是在多项式时间内处理查询.最后基于真实不确定图数据的大量实验结果验证了文中的设计.     

基于阈值的概率图可达查询

图的可达性查询被广泛应用于生物网络、社会网络、本体网络、RDF网络等.由于对数据操作时引入的噪声和错误使这些图数据具有不确定性,而确定图的可达查询不能有效地处理不确定性,因此该文研究用概率语义描述的图可达性查询.具体的,该文使用可能世界概率模型定义不确定图(称为概率图),基于该模型,研究了基于阈值的概率可达查询(T-PR).首先为避免枚举所有可能世界,给出一个基本算法可精确求解T-PR查询.其次为进一步加速基本算法,给出3种改进方法,它们是不确定事件界、同构图的缩减、基于不相交路径和割集的界.通过合理的组合给出3种方法的合并算法.最后基于真实概率图数据的大量实验验证了该文的设计.     

不确定图数据库中高效查询处理

近年来,在多种领域中产生的大量数据都可以自然地建模为图结构,比如蛋白质交互网络、社会网络等.测量手段的不准确性以及数据本身的性质导致不确定性在很多图数据中普遍存在.文中研究不确定图数据库中的高效查询处理方法.首先给出一种数据模型来表示图的不确定性.鉴于对用户提交的查询图通常会产生大量匹配结果,高效得到概率最大的k个匹配常常更具有现实意义.因此文中形式化提出概率top-k子图匹配查询的问题.为了解决提出的查询问题,以附带概率信息的邻居子图为基础,设计了一种有效的索引结构.另外,提出一种高效的基于索引的查询处理方法.该查询处理方法的核心是一个基于搜索树的匹配算法,其中运用了一种概率剪枝技术来提高性能.实验结果表明,所提出方法具有良好的效率和可扩展性.     

不确定图数据上的不确定查询处理

图数据结构广泛应用于各种领域的数据建模.由于测量手段和问题特性的限制,数据的不确定性普遍存在.这种不确定性表现在图结构数据中,形成不确定图.之前对于不确定图数据上查询处理的研究,主要是在不确定的图结构数据上查找某一结构确定的图.然而,针对不确定的图数据,其查询很可能也是不确定的.该项工作主要是实现查询过程中的双向匹配,即对于一个不确定的查询,在不确定的图上,得到查询与图的一个可能性最大的匹配组合.这样的研究是具有现实意义的,通过不确定图上对于不确定查询的匹配,可以找到两个不确定结构间存在的最大相似结构,并度量其相似性.     

面向不确定数据流的近似ER-Topk查询处理

随着移动互联网的快速发展以及信息技术的普遍应用,在许多应用中都产生了海量、不确定性数据,包括金融、军事、位置服务、医疗以及气象等。然而,传统的确定性数据管理方法很难管理不确定数据,亟需开发新型数据管理方法。可能世界模型被广泛用于为不确定数据建模,通过该模型可以衍生出诸多确定性的可能世界实例。不确定性数据流是指高速到达的海量不确定元组序列,因而不确定数据流管理比不确定性静态数据管理更具挑战性。面向于不确定数据流的ER-Topk查询是一个典型问题,但是处理复杂度高。提出一种近似算法来处理该查询,具有较小的空间复杂度;同时,还通过搜索策略优化来进一步提升查询处理效率。实验结果验证了所提方法的有效性和高效性。     

基于不确定理论的不确定性数据Top-k查询计算

在不确定性数据集中,基于参数化排名函数的Top-k查询研究近年来备受关注。给出了一种新的解决方法,该方法将不确定性数据集中的元组建模为不确定网络,将有序元组的Top-k查询等价转化为相应样本图中边的不确定测度关系,并对样本图依据所包含边的排序位置进行分类,从而 将不确定性数据中基于参数化排名函数的Top-k查询等价转换为依Top-k值不同的有限查询。本算法避免了计算所有元组在样本图中的排名不确定测度值,提高了不确定图的Top-k查询计算效率。 理论分析和实验结果表明,提出的Top-k查询算法能够从非确定角度解决不确定性数据的Top-k查询计算问题。     

基于x-tuple的概率阈值top-k查询算法

不确定数据库中的概率阈值top-k查询是计算元组排在前k位的概率和,返回概率和不小于p的元组,但现有的查询语义没有将x-tuple内的元组进行整体处理.针对该情况,定义一种新的查询语义——概率阈值x-top-k查询,并给出查询处理算法.在该查询语义下采用动态规划方法求取x-tuple内每个元组排在前k位的概率和,对其进行聚集后做概率阈值top-k查询,并利用观察法、最大上限值等剪枝方法进行优化.实验结果表明,该算法平均扫描全体数据集中60％的数据即可返回正确结果集,证明其查询处理效率较高.     

不确定数据上两种查询的分布式聚集算法

不确定数据查询技术在军事、金融、电信等领域中起到了越来越重要的作用.不确定性数据在传感器网络、分布式Web Server及P2P系统等分布式系统中广泛存在.从这些系统中收集所有数据进行集中式查询将带来巨大的通信开销、时间延迟和存储代价.同时,由于不确定数据的特点,大多数集中式不确定查询算法在分布式环境下并不适用.给出不确定数据的最大值和Top-k聚集查询定义,并分别提出了基于过滤策略的分布式聚集算法.算法根据给出的3个过滤策略,利用数据的分布区间和概率进行筛选概率上限的计算,尽可能将不影响查询结果的数据抛弃.同时,算法以相对较小的代价归并保存并传输了计算最终查询结果所需要的不可丢弃数据.实验结果表明,在各类系统和数据条件下,过滤算法都能够正确地得到查询结果并显著降低系统的数据通信开销.     

不确定数据库中减小可能世界的RPW—kBest查询

不确定数据普遍存在于大量应用之中,如在传感器网络、P2P系统、移动计算及RFID（Radio Frequency IDentification）等,研究者已经提出了多种针对不确定数据库的数据模型,其核心思想都源自于可能世界模型。针对可能世界模型能够演化出数量远大于不确定数据库规模的可能世界实例,文中提出一种减小可能世界的RPW—kBest算法,此算法利用概率和评定条件进行筛选,尽可能将不影响查询结果的数据抛弃,使之在最小的搜索空间内完成查询处婵过程,以降低存储开销。实验结果表明,此算法能正确的得到查询结果并显著提高查淘效率和降低内存使用。     

面向存在不确定对象的组最近邻查询方法

组最近邻查询是空间对象查询领域的一类重要查询,通过该查询可找到距离给定查询点集最近的空间对象.由于图像分辨率或解析度的限制等因素,空间对象的存在不确定性广泛存在于某些涉及图像处理的查询应用中.这些对象位置数据的存在不确定性会对组最近邻查询结果产生影响.本文给出面向存在不确定对象的概率阈值组最近邻查询定义,设计了高效的查询处理机制,通过剪枝优化等手段提高概率阈值组最近邻查询效率,并进一步提出了高效概率阈值组最近邻查询算法.采用多个真实数据集对概率阈值组最近邻算法进行了实验验证,结果表明所提算法具有良好的查询效率.     

Hybrid query execution engine for large attributed graphs

Graphs are widely used for modeling complicated data such as social networks, bibliographical networks and knowledge bases. The growing sizes of graph databases motivate the crucial need for developing powerful and scalable graph-based query engines. We propose a SPARQL-like language, G-SPARQL, for querying attributed graphs. The language enables the expression of different types of graph queries that are of large interest in the databases that are modeled as large graph such as pattern matching, reachability and shortest path queries. Each query can combine both structural predicates and value-based predicates (on the attributes of the graph nodes/edges). We describe an algebraic compilation mechanism for our proposed query language which is extended from the relational algebra and based on the basic construct of building SPARQL queries, the Triple Pattern. We describe an efficient hybrid Memory/Disk representation of large attributed graphs where only the topology of the graph is maintained in memory while the data of the graph are stored in a relational database. The execution engine of our proposed query language splits parts of the query plan to be pushed inside the relational database (using SQL) while the execution of other parts of the query plan is processed using memory-based algorithms, as necessary. Experimental results on real and synthetic datasets demonstrate the efficiency and the scalability of our approach and show that our approach outperforms native graph databases by several factors.     

Aggregate nearest neighbor queries in uncertain graphs

Most recently, uncertain graph data begin attracting significant interests of database research community, because uncertainty is the intrinsic property of the real-world and data are more suitable to be modeled as graphs in numbers of applications, e.g. social network analysis, PPI networks in biology, and road network monitoring. Meanwhile, as one of the basic query operators, aggregate nearest neighbor (ANN) query retrieves a data entity whose aggregate distance, e.g. sum, max, to the given query data entities is smaller than those of other data entities in a database. ANN query on both certain graph data and high dimensional data has been well studied by previous work. However, existing ANN query processing approaches cannot handle the situation of uncertain graphs, because topological structures of an uncertain graph may vary in different possible worlds. Motivated by this, we propose the aggregate nearest neighbor query in uncertain graphs (UG-ANN) in this paper. First of all, we give the formal definition of UG-ANN query and the basic UG-ANN query algorithm. After that, to improve the efficiency of UG-ANN query processing, we develop two kinds of pruning approaches, i.e. structural pruning and instance pruning. The structural pruning takes advantages the monotonicity of the aggregate distance to derive the upper and lower bounds of the aggregate distance for reducing the graph size. Whereas, the instance pruning decreases the number of possible worlds to be checked in the searching tree. Comprehensive experimental results on real-world data sets demonstrate that the proposed method significantly improves the efficiency of the UG-ANN query processing.     

路网中位置不确定的二元反kNN查询

针对路网限制和物体位置的不确定性,提出了路网中位置不确定的二元反kNN查询（PBRkNN）,旨在查找一组位置不确定的点,使得每个不确定点的kNN包含给定查询点的概率大于一个阈值。为了解决该问题,首先提出一种基于Dijkstra进行剪枝处理的基本算法,即PE算法;接着在PE算法的基础上通过预处理计算出每个点的kNN从而加快查询速度,即PPE算法;而为了进一步减小PPE算法中范围查询的开销,提出PPEE算法,利用网格索引来索引范围查询中要查询的不确定空间点,从而提升算法的效率。最后,在北京和加州路网数据集上进行了大量实验,结果表明通过一些预处理的策略确实可以有效地处理路网中位置不确定的二元反kNN查询。     

不确定图间α-β子图同构匹配算法

子图查询返回图数据集合中所有包含查询图的数据图。在查询图和数据图同时为不确定性图的前提下,提出了不确定图间的期望子图同构定义和α-β子图同构匹配定义。不确定图间的期望子图同构是确定图上子图同构在概率图模型上的直接推广,不确定图间α-β子图同构利用两个限制阈值来衡量查询图和数据图间的匹配质量。文章详细阐述了α-β子图同构匹配的语义特点,分析了其和期望子图同构的联系和差别,设计实现α-β子图同构匹配判定算法。     

Semantics preserving SPARQL-to-SQL translation

Most existing RDF stores, which serve as metadata repositories on the Semantic Web, use an RDBMS as a backend to manage RDF data. This motivates us to study the problem of translating SPARQL queries into equivalent SQL queries, which further can be optimized and evaluated by the relational query engine and their results can be returned as SPARQL query solutions. The main contributions of our research are: (i) We formalize a relational algebra based semantics of SPARQL, which bridges the gap between SPARQL and SQL query languages, and prove that our semantics is equivalent to the mapping-based semantics of SPARQL; (ii) Based on this semantics, we propose the first provably semantics preserving SPARQL-to-SQL translation for SPARQL triple patterns, basic graph patterns, optional graph patterns, alternative graph patterns, and value constraints; (iii) Our translation algorithm is generic and can be directly applied to existing RDBMS-based RDF stores; and (iv) We outline a number of simplifications for the SPARQL-to-SQL translation to generate simpler and more efficient SQL queries and extend our defined semantics and translation to support the bag semantics of a SPARQL query solution. The experimental study showed that our proposed generic translation can serve as a good alternative to existing schema dependent translations in terms of efficient query evaluation and/or ensured query result correctness.     

Extending SPARQL with regular expression patterns (for querying RDF)

RDF is a knowledge representation language dedicated to the annotation of resources within the framework of the semantic web. Among the query languages for RDF, SPARQL allows querying RDF through graph patterns, i.e., RDF graphs involving variables. Other languages, inspired by the work in databases, use regular expressions for searching paths in RDF graphs. Each approach can express queries that are out of reach of the other one. Hence, we aim at combining these two approaches. For that purpose, we define a language, called PRDF (for “Path RDF”) which extends RDF such that the arcs of a graph can be labeled by regular expression patterns. We provide PRDF with a semantics extending that of RDF, and propose a correct and complete algorithm which, by computing a particular graph homomorphism, decides the consequence between an RDF graph and a PRDF graph. We then define the PSPARQL query language, extending SPARQL with PRDF graph patterns and complying with RDF model theoretic semantics. PRDF thus offers both graph patterns and path expressions. We show that this extension does not increase the computational complexity of SPARQL and, based on the proposed algorithm, we have implemented a correct and complete PSPARQL query engine.     

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.