A method for improving graph queries processing using positional inverted index (P.I.I) idea in search engines and parallelization techniques

The idea of positional inverted index is exploited for indexing of graph database. The main idea is the use of hashing tables in order to prune a considerable portion of graph database that cannot contain the answer set. These tables are implemented using column-based techniques and are used to store graphs of database, frequent sub-graphs and the neighborhood of nodes. In order to exact checking of remaining graphs, the vertex invariant is used for isomorphism test which can be parallel implemented. The results of evaluation indicate that proposed method outperforms existing methods.

Processing Constrained K Closest Pairs Query in Spatial Databases

In this paper, constrained K closest pairs query is introduced, wbich retrieves the K closest pairs satisfying the given spatial constraint from two datasets. For data sets indexed by R trees in spatial databases, three algorithms are presented for answering this kind of query. Among of them, two-phase Range＋Join and Join＋Range algorithms adopt the strategy that changes the execution order of range and closest pairs queries, and constrained heap-based algorithm utilizes extended distance functions to prune search space and minimize the pruning distance. Experimental results show that constrained heap-base algorithm has better applicability and performance than two-phase algorithms.

Monitoring Median Queries over Moving Objects

The k-median problem has attracted a number of researchers. However,few of them have considered both the dynamic environment and the issue of accuracy. In this paper,a new type of query is studied,called continuous median monitoring （CMM） query. It considers the k-median problem under dynamic environment with an accuracy guarantee. A continuous group nearest neighbor based （CGB） algorithm and an average distance medoid （ADM） algorithm are proposed to solve the CMM problem. ADM is a hill climbing schemed algorithm and achieves a rapid converging speed by checking only qualified candidates. Experiments show that ADM is more efficient than CGB and outperforms the classical PAM （partitioning around medoids） and CLARANS （clustering large applications based on randomized search） algorithms with various parameter settings.

An Optimized Labeling Scheme for Reachability Queries

Answering reachability queries is one of the fundamental graph operations.Existing approaches either accelerate index construction by constructing an index that covers only partial reachability relationship,which may result in performing cost traversing operation when answering a query;or accelerate query answering by constructing an index covering the complete reachability relationship,which may be inefficient due to comparing the complete node labels.We propose a novel labeling scheme,which covers the complete reachability relationship,to accelerate reachability queries processing.The idea is to decompose the given directed acyclic graph(DAG)G into two subgraphs,G1 and G2.For G1,we propose to use topological labels consisting of two integers to answer all reachability queries.For G2,we construct 2-hop labels as existing methods do to answer queries that cannot be answered by topological labels.The benefits of our method lie in two aspects.On one hand,our method does not need to perform the cost traversing operation when answering queries.On the other hand,our method can quickly answer most queries in constant time without comparing the whole node labels.We confirm the efficiency of our approaches by extensive experimental studies using 20 real datasets.

A Shallow Parsing Approach to Natural Language Queries of a Database

The performance and reliability of converting natural language into structured query language can be problematic in handling nuances that are prevalent in natural language. Relational databases are not designed to understand language nuance, therefore the question why we must handle nuance has to be asked. This paper is looking at an alternative solution for the conversion of a Natural Language Query into a Structured Query Language (SQL) capable of being used to search a relational database. The process uses the natural language concept, Part of Speech to identify words that can be used to identify database tables and table columns. The use of Open NLP based grammar files, as well as additional configuration files, assist in the translation from natural language to query language. Having identified which tables and which columns contain the pertinent data the next step is to create the SQL statement.

Efficient Pr-Skyline Query Processing and Optimization in Wireless Sensor Networks

As one of the commonly used queries in modern databases, skyline query has received extensive attention from database research community. The uncertainty of the data in wireless sensor networks makes the corresponding skyline uncertain and not unique. This paper investigates the Pr-Skyline problem, i.e., how to compute the skyline with the highest existence probability in a computational and energy-efficient way. We formulate the problem and prove that it is NP-Complete and cannot be approximated in a given expression. However, the proposed algorithm SKY-SEARCH with pruning techniques can guarantee the computational efficiency given relatively large input size, while the filter-based distributed optimization strategy significantly reduces the transmission cost and the required storage space of the sensor nodes. Extensive experiments verify the efficiency and scalability of SKY-SEARCH and the distributed optimizing strategy.

Storage optimization for query processing over data streams

A defining characteristic of continuous queries over on-line data streams,possibly bounded by sliding windows,is the potentially infinite and time-evolving nature of their inputs and outputs.For different update patterns of continuous queries,suitable data structures bring great query processing efficiency.In this paper,we proposed a data structure suitable for weak nonmonotonic update pattern in which the lifetime of each tuple is known at generation time,but the length of lifetime is not necessarily the same.The new data structure combined the ladder queue with the feature of weak non-monotonic update pattern.The experiment results show that the new data structure performs much better than the traditional calendar queue in many cases.

Research on Welding Quality Traceability Model of Offshore Platform Block Construction Process

Quality traceability plays an essential role in assembling and welding offshore platform blocks.The improvement of the welding quality traceability system is conducive to improving the durability of the offshore platform and the process level of the offshore industry.Currently,qualitymanagement remains in the era of primary information,and there is a lack of effective tracking and recording of welding quality data.When welding defects are encountered,it is difficult to rapidly and accurately determine the root cause of the problem from various complexities and scattered quality data.In this paper,a composite welding quality traceability model for offshore platform block construction process is proposed,it contains the quality early-warning method based on long short-term memory and quality data backtracking query optimization algorithm.By fulfilling the training of the early-warning model and the implementation of the query optimization algorithm,the quality traceability model has the ability to assist enterprises in realizing the rapid identification and positioning of quality problems.Furthermore,the model and the quality traceability algorithm are checked by cases in actual working conditions.Verification analyses suggest that the proposed early-warningmodel for welding quality and the algorithmfor optimizing backtracking requests are effective and can be applied to the actual construction process.

Distributed location-based query processing on large volumes of moving items

Recently, new techniques to efficiently manage current and past location information of moving objects have received significant interests in the area of moving object databases and location based service systems. In this paper, we exploit query processing schemes for location management systems, which consist of multiple data processing nodes to handle massive volume of moving objects such as cellular phone users. To show the usefulness of the proposed schemes, some experimental results showing performance factors regarding distributed query processing are explained. In our experiments, we use two kinds of data set: one is generated by the extended GSTD simulator and another is generated by the real time data generator which generates location sensing reports of various types of users having different movement patterns.

面向云边端协同的多模态数据建模技术及其应用

云边端协同架构中数据类型多样,各级存储资源与计算资源存在差异,给数据管理带来新的挑战.现有数据模型或者数据模型的简单叠加,都难以同时满足云边端中多模态数据管理和协同管理需求.因此,研究面向云边端协同的多模态数据建模技术成为重要问题.其核心在于,如何高效地从云边端三层架构中得到满足应用所需的查询结果.从云边端三层数据的数据类型出发,提出了面向云边端协同的多模态数据建模技术,给出了基于元组的多模态数据模型定义,设计了6种基类,解决多模态数据统一表征困难的问题;提出了云边端协同查询的基本数据操作体系,以满足云边端业务场景的查询需求;给出了多模态数据模型的完整性约束,为查询优化奠定了理论基础.最后,给出了面向云边端协同多模态数据模型的示范应用,并从数据存储时间、存储空间和查询时间这3个方面对所提出的数据模型存储方法进行了验证.实验结果表明,所提方案能够有效地表示云边端协同架构中的多模态数据.

不确定性Top-K查询处理

高效Top-K查询处理在涉及大量数据交互的应用中是一项重要技术,随着应用中不确定性数据的大量涌现,不确定性数据的管理逐渐引起人们的重视.不确定性数据上Top-K查询从语义和处理上都呈现出与传统Top-K查询不同的特点.在主流不确定性数据模型和可能世界语义模型下,学者们已经提出了多种不确定性Top-K查询的语义和处理方法.介绍了当前不确定性Top-K查询的研究工作,并对其进行分类,讨论包括语义、排序标准、算法以及应用等方面的技术.最后提出不确定性Top-K查询面临的挑战和下一步的发展方向.

支持混合事务和分析处理的数据库管理系统综述

数据库管理系统根据应用场景分为事务型(OLTP)系统和分析型(OLAP)系统.随着实时数据分析需求增长,OLTP任务和OLAP任务混合的场景越来越普遍,业界开始重视支持混合事务和分析处理(HTAP)的数据库管理系统.这种HTAP数据库系统除了需要满足高性能的事务处理外,还需要满足实时分析对数据新鲜度的要求.因此,对数据库系统的设计与实现提出了新的挑战.近年来,在工业界和学术界涌现了一批架构多样、技术各异的原型和产品.综述HTAP数据库的背景和发展现状,并且从存储和计算的角度对现阶段的HTAP数据库进行分类.在此基础上,按照从下往上的顺序分别总结HTAP系统在存储和计算方面采用的关键技术.在此框架下介绍各类系统的设计思想、优劣势以及适用的场景.此外,结合HTAP数据库的评测基准和指标,分析各类HTAP数据库的设计与其呈现出的性能与数据新鲜度的关联.最后,结合云计算、人工智能和新硬件技术为HTAP数据库的未来研究和发展提供思路.

不确定数据Top-k查询算法

不确定数据普遍存在于大量应用之中,如移动计算、RFID技术和传感器网络等。针对不确定数据的各种查询算法是数据库领域近年来的热点研究课题。其中,基于不确定数据的Top-k查询和排序查询提出了很多有价值的查询语义和查询算法。详细分析这一最新领域提出的各种查询算法、数据模型、算法复杂度和不同算法所适应的应用场景,并用实验从多个方面比较不同查询算法执行效率、查询语义以及结果集的关联程度。

基于不确定数据的分布式Top-k查询算法

目前基于不确定数据的Top-k查询算法仅考虑了集中式的环境,为了解决分布式系统中节省系统带宽的问题,在此基础上,提出了在分布式环境中基于不确定数据的Top-k查询算法UDTopk.该算法定义了一个候选集(candidate set),仅使用候选集中的数据,而不用访问数据集中所有数据,就可以得到正确的Top-k查询答案.算法通过动态维护候选集、仅传输少量数据,达到减少网络中数据传输的目的.实验结果表明,该算法可以有效地节省网络带宽.

基于变分自编码器的近似聚合查询优化方法

针对偏态数据分布不平衡,传统近似聚合查询方法难以抽样生成偏态分布数据的问题,提出基于优化的变分自编码器的近似聚合查询方法,研究近似聚合查询方法对偏态分布数据的近似聚合查询准确率的影响.在预处理阶段对偏态分布数据进行分层分组,对变分自编码器生成模型的网络结构和损失函数进行优化,降低近似聚合查询相对误差.实验结果表明,与基准方法相比,近似聚合查询对偏态分布数据的查询相对误差更小,且随着偏态系数的提高,查询相对误差的上升趋势更平缓.

GPU加速的分段Top-k查询算法

现有Top-k查询优化算法无法充分利用图形处理器(GPU)强大的并行吞吐量及时获取查询结果,为此提出了一种基于统一计算设备架构(CUDA)模型的大规模分段查询算法。通过划分查询过程以及采用分段并行处理策略,该算法可最大限度地提升查询过程中的计算和比较效率。实验结果表明,与4线程多核优化算法相比,所提算法具有明显的性能优势,当有序列表数量为6,遍历步长为120时,性能达到最优,此时比多核算法快40倍。

健壮且自适应的学习型近似查询处理方法研究

由于在大规模数据集上执行精确查询耗时较长,因此近似查询处理(AQP)技术常被用于在线分析处理,目的是以较短的交互延迟返回查询结果,并尽可能地降低查询误差。现有的学习型AQP方法与底层数据解耦,将I/O密集型计算转化为CPU密集型计算,但是由于计算资源的限制,该类方法通常基于随机的数据样本进行模型训练,此类训练数据会引起稀有群组缺失问题,导致模型预测准确性不高。针对上述问题,提出一种基于分层样本学习的混合型和积网络模型,并基于该模型设计一种AQP框架。分层样本能够有效避免稀有群组缺失现象,基于该样本训练的模型预测准确性大幅提升。此外,针对数据动态更新的情况,提出一种模型自适应更新策略,使得模型能够及时检测数据偏移现象并自适应地执行更新。实验结果表明,与基于抽样和基于机器学习的AQP方法相比,该模型在真实数据集和合成数据集上的平均相对误差分别约降低18.3%和2.2%,在数据动态更新的场景下,其准确性和查询时延均呈现出良好的稳定性。

面向查询的文本摘要关键技术研究综述

面向查询的文本摘要是自动文摘中的一个特殊领域,可以根据用户个性化的查询需求,从原始文档或文档集中提取有价值的摘要信息。目前,该技术已经在面向查询的搜索引擎、智能化信息检索、问答系统等领域得到广泛应用,并受到越来越多的关注。文章基于面向查询的文本摘要任务的典型技术框架,从查询理解、文档处理和信息组织三个方面对其国内外研究方法的现状进行对比和分析,对不同业务场景的应用进行了举例,归纳了面向查询的文本摘要面临的挑战及发展趋势。

RDF图的Top-k最短路径查询

最短路径查询是图数据管理与复杂关系挖掘的基本操作之一．本文针对资源描述框架图上的top-k最短路径查询，构造基于组件的索引，并在该索引的基础上实现查询的响应．查询优化阶段，针对查询效率问题，提出频繁路径以及结构剪枝策略，并给出有效性证明．实验表明，本文方法准确返回top-k最短路径并提高92％的查询速率．索引构造时间相比已有方法，提高约56％．同时，索引所占空间仅为原始数据大小的1～1．2倍．

基于不确定数据的top-k概率相互最近邻查询

不确定数据上的概率相互最近邻查询具有重要的实际应用,针对目前关于这方面的研究尚少,提出了不确定数据上的概率相互最近邻的top-k查询算法。首先对问题进行描述与定义,其次总结可行的裁剪规则,从而裁剪查询对象中未计算的实例点。通过实验表明,该算法能有效地降低最近邻查询中的I/O开销,提高查询的响应速度。