不确定数据流上的并行Skyline查询算法

不确定数据流上的Skyline查询技术逐步引起研究者的关注,传统的集中式流处理算法难以满足海量数据的查询需求,并且云计算所提供的海量计算资源和有效的存储管理模式,为研究并行Skyline查询技术提供了充足的条件。基于上述事实,提出了一种不确定数据流上的并行Skyline查询算法(parallel Skyline over uncertain data streams,PSUDS)。该算法通过交叉划分滑动窗口的方式,将集中式流查询转化为并行处理,以并行执行的方式来解决集中式算法处理性能不足的问题。大量实验结果表明,该算法具有较好的并行可扩展性。     

知识图谱划分算法研究综述

知识图谱是人工智能的重要基石,因其包含丰富的图结构和属性信息而受到广泛关注.知识图谱可以精确语义描述现实世界中的各种实体及其联系,其中顶点表示实体,边表示实体间的联系.知识图谱划分是大规模知识图谱分布式处理的首要工作,对知识图谱分布式存储、查询、推理和挖掘起基础支撑作用.随着知识图谱数据规模及分布式处理需求的不断增长,...     

Scaling XML query processing: distribution, localization and pruning

Distributing data collections by fragmenting them is an effective way of improving the scalability of a database system. While the distribution of relational data is well understood, the unique characteristics of the XML data and query model present challenges that require different distribution techniques. In this paper, we show how XML data can be fragmented horizontally and vertically. Based on this, we propose solutions to two of the problems encountered in distributed query processing and optimization on XML data, namely localization and pruning. Localization takes a fragmentation-unaware query plan and converts it to a distributed query plan that can be executed at the sites that hold XML data fragments in a distributed system. We then show how the resulting distributed query plan can be pruned so that only those sites are accessed that can contribute to the query result. We demonstrate that our techniques can be integrated into a real-life XML database system and that they significantly improve the performance of distributed query execution.     

基于Storm的连续范围查询优化技术

移动大数据环境下,传统基于位置服务LBS技术面临来自系统扩展性、性能等方面的挑战。首先针对LBS应用的特点,提出了基于Storm的查询框架。然后结合基于Storm的LBS查询框架,设计并实现了并行连续范围查询算法,优化查询性能。针对分布式环境中的一致性问题,设计使用基于ZooKeeper的分布式锁服务,保证查询结果的正确性。进一步,针对基于Storm并行连续范围查询算法中存在访问数据库开销较大的问题,提出了基于TimeCacheMap的缓存优化算法及两种缓存策略,减少了访问数据库的开销,提高了查询效率。     

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.     

Data storage practices and query processing in XML databases: A survey

With the rapid emergence of XML as a data exchange standard over the Web, storing and querying XML data have become critical issues. The two main approaches to storing XML data are (1) to employ traditional storage such as relational database, object-oriented database and so on, and (2) to create an XML-specific native storage. The storage representation affects the efficiency of query processing. In this paper, firstly, we review the two approaches for storing XML data. Secondly, we review various query optimization techniques such as indexing, labeling and join algorithms to enhance query processing in both approaches. Next, we suggest an indexing classification scheme and discuss some of the current trends in indexing methods, which indicate a clear shift towards hybrid indexing.     

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.     

Matching twigs in fuzzy XML

A considerable amount of twig pattern matching algorithms have been proposed to holistically process a twig query. Those algorithms mainly focus on twig pattern query with the AND-logic. However, there is often a need to process a twig query with the OR-predicates. Furthermore, the existing algorithms fall short in their ability to support twig query with OR-logic in fuzzy XML. To overcome this limitation, in this paper, we first introduce a novel encoding scheme to represent node information in fuzzy XML. Based on the encoding scheme, we then propose an effective algorithm for matching a twig pattern query with the AND/OR-logic in fuzzy XML. Our approach adopts a compact stack technique to process the complicated twig query consisting of both AND-logic and OR-logic. More importantly, our method eliminates re-scanning unnecessary portions of XML documents and redundant intermediate results. Finally, the experimental results demonstrate the performance advantages of our 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.     

一种并行XML数据分片算法的设计与实现

随着XML在各个领域的广泛应用，XML数据规模急剧增加，查询处理也日趋复杂。集中式环境由于单机存在的I／O瓶颈问题，在处理日益复杂的查询操作时已渐渐不能满足应用需求。因此，高性能的并行数据库在XML处理方面的研究，必将为XML数据库的。存储和查询处理带来新的曙光。本文讨论了一种对XML文档的基于节点轮循法进行分片的设计原理及实现算法。实验结果证明该方法具有很好的加速比和缩放比性能。     

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.     

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.     

A pipeline N-way join algorithm based on the 2-waysemijoin program

The semijoin has been used as an effective operator in reducing data transmission and processing over a network that allows forward size reduction of relations and intermediate results generated during the processing of a distributed query. The authors propose a relational operator, two-way semijoin, which enhanced the semijoin with backward size reduction capability for more cost-effective query processing. A pipeline N-way join algorithm for joining the reduced relations residing on N sites is introduced. The main advantage of this algorithm is that it eliminates the need for transferring and storing intermediate results among the sites. A set of experiments showing that the proposed algorithm outperforms all known conventional join algorithms that generate intermediate results is included     

一种基于层次栈的XML数据小枝查询算法研究

当前针对小枝模式的XML查询是XML文档查询的研究热点。文章在分析XML数据小枝查询处理常用算法的基础上,提出了一种高灵活性的、易确定结点对之间结构关系的EDiezt-P编码,并基于EDiezt-P编码和层次栈结构提出了一种自底向上的小枝查询算法。实验表明,该算法在一定程度上减少了查询处理时间,提高了查询效率。     

基于Spark的实时视频分析系统

视频监控技术在交通管理、公共安全、智慧城市等方面有着广泛的应用前景,且向着智能识别、实时处理、大数据分析的方向发展. 本文针对大规模实时视频监控提出了新的解决方案. 基于Spark streaming流式计算、分布式存储及OLAP框架,使多路视频处理在可扩展性、容错性及数据多维聚合分析上具有明显的优势. 系统根据视频处理算法划分为单机处理与分布式处理. 并将视频图像处理与数据分析耦合,利用Kafka消息队列与Spark streaming完成对多路视频输出数据的进一步操作. 结合分布式存储方案,并利用OLAP框架实现对海量数据实时多维聚合分析与高效实时查询.     

XML数据库更新语言的定义和实现

目前人们对XML数据的管理系统研究的焦点主要集中在XML数据的存储和查询,但作为完整的数据管理系统,还需要提供对XML数据库系统的更新操作。论文提出一种称为U_XMLSQL的针对XML数据库的更新语言,接着设计了一种高效的物理存储结构对XML数据进行关系存储,在此存储结构的基础上设计并实现了更新语言中定义的基本操作,最后通过实验验证此存储方法和更新算法的高效性。     

对双亲／孩子结构连接算法的研究与改进

结合区间编码和结点模型映射方法提出一种用于关系数据库的扩展存储模式.通过按广度优先遍历XML树实现对双亲/孩子关系结构连接算法的改进.改进后的算法降低了内存空间的开销,缩小了列表的扫描范围,明显提高了查找匹配速度,达到了查询优化的目的.     

Efficient Mining of Frequent Closed XML Query Pattern

Previous research works have presented convincing arguments that a frequent pattern mining algorithm should not mine all frequent but only the closed ones because the latter leads to not only more compact yet complete result set but also better efficiency. Upon discovery of frequent closed XML query patterns, indexing and caching can be effectively adopted for query performance enhancement. Most of the previous algorithms for finding frequent patterns basically introduced a straightforward generate-and-test strategy. In this paper, we present SOLARIA＊, an efficient algorithm for mining frequent closed XML query patterns without candidate maintenance and costly tree-containment checking. Efficient algorithm of sequence mining is involved in discovering frequent tree-structured patterns, which aims at replacing expensive containment testing with cheap parent-child checking in sequences. SOLARIA＊ deeply prunes unrelated search space for frequent pattern enumeration by parent-child relationship constraint. By a thorough experimental study on various real-life data, we demonstrate the efficiency and scalability of SOLARIA＊ over the previous known alternative. SOLARIA＊ is also linearly scalable in terms of XML queries＇ size.     

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.     

Sliding window top-k dominating query processing over distributed data streams

Preference query processing is important for a wide range of applications involving distributed databases, such as network monitoring, web-based systems, and market analysis. In such applications, data objects are generated frequently and massively, which presents an important and challenging problem of continuous query processing over distributed data stream environments. A top-k dominating query, which has been receiving much research attention recently, returns the k data objects that dominate the highest number of data objects in a given dataset, and due to its dominance-based ranking function, we can easily obtain superior data objects. An emerging requirement in distributed stream environments is an efficient technique for continuously monitoring top-k dominating data objects. Despite of this fact, no study has addressed this problem. In this paper, therefore, we address the problem of continuous top-k dominating query processing over distributed data stream environments. We present two algorithms that monitor the exact top-k dominating data and efficiently eliminate unqualified data objects for the result, which reduces both communication and computation costs. In addition to these algorithms, we present an approximate algorithm that further reduces both communication and computation costs. Extensive experiments on both synthetic and real data have demonstrated the efficiency and scalability of our algorithms.