多条件自由组合查询的实现

多条件自由组合查询的实现遵义医学院附属医院王晓华组合查询对查询数据库的工作来说，可以达到查询准确、减少不相关数据记录的目的。我们在病案管理系统的查询子系统中实现了组合查询的功能。１．组合查询方法１）条件相“与”的查询即在条件选择目录中所选择出来的条件...     

VFP下组合查询类和通用查询类的设计及应用

本文介绍了VisualFoxPro3.0环境下组合查询类和通用查询类的设计实现方法，这些类对任何MIS系统都是适用的，并且不需要进行任何修改。文中还给出了组合查询类和通用查询类的详细设计思路和关键的原程序清单，并有具体的应用实例。     

多条件组合式数据库查询的优化

在数据库编程中，需要经常从数据库中查询数据，当查询条件为确定时，我们可以用明确的SQL语句来实现，但是当查询条件为多个条件的动态组合时，查询语句会由于分支太多及IF语句的多重嵌套而变的相当复杂，在此，笔者提出了一种优化方法，运用本方法可以有效的减少查询语句的分支和数量以及IF条件语句的嵌套重数，从而提高程序的运行效率。     

递归下降分析法在组合查询中的应用

组合查询一直是管理信息系统的开发重点。文中论述了用递归下降分析法构造组合查询器的一种方法，首先简要介绍了递归下降分析法的概念，分析了组合查询器的功能，进而提出了利用递归下降分析法对组合查询条件的描述和解释方法，在此基础上讨论了基于MVC模式的设计，并给出了模型层、控制层、表示层的详细实现方案。这种方法可以满足组合查询的条件随机性、输出多样性以及接口友好性等方面的综合要求。     

数据库的多条件组合查询

数据库的多条件组合查询深圳刘学志查询是数据库管理系统中不必不可少的功能，单一条件的查询往往不能满足用户的要求，本文介绍一种动态随机条件组合查询的一种方法。具体方法是：①由原数据库生成一个结构数据库。②把结构数据库中ＦＩＥＬＤ－ＮＡＭＥ字段中的内容（即...     

类汉语组合查询器的查询操作模式构造

讨论类汉语组合查询器ＦＩＭＳＦＩＮＤ的查询操作模式构造方面的有关问题。提出了以查询模式构造、查询模式驱动为基本思想的、面向中文信息处理的查询系统快速原型法生成工具箱，对脚本语言ＦｉｍｓＳｃｒｉｐｔ的语法规则、语义描述、查询单元（项）的重用概念等关键问题进行了详细讨论。     

基于XML的通用组合查询组件的设计与实现

在Web应用系统中,由于用户的业务需求,经常需要进行连接多个表的复杂查询.而由于Web浏览器交互功能极其有限,难以实现多个条件的组合,不同的应用系统数据库结构又千差万别,如何实现能够满足用户高级查询需求而又能快速嵌入不同系统的查询模块成了一个难题.文中通过在Applet中实现条件组合逻辑,在XML文件中描述数据库结构,实现了独立于特定数据库的组合查询模块,能够应用于大部分基于J2EE框架创建的Web应用系统.     

SQL Server的查询优化方法的设计和实现

在分析传统SQLSERVER查询优化技术的基础上,本文用面向对象方法分析了SQL语句中的查询语句SELECT语法结构,概括出基于表达式类等7个抽象类的面向对象模型。使得用户可以根据这个模型创建、删除、组合和拆分这些类所定义的对象,提高了SQLSERVER的查询效率。     

应用ADO技术实现组合查询的设计

本文介绍了管理信息系统设计中常用的组合查询的设计方法．通过具体实例说明了如何在Visual Basic中运用ADO数据库访问技术访问ACCESS数据库，实现多条件组合查询的功能。     

在Visual FoxPro 6.0中实现多条件组合查询

在VFP实现多限制条件查询的步骤，通过使用表格控件及组合框的结合作为输入界面，并将用户的条件保存为临时表，通过临时表来获取查询语句，利用查询语句，使得在VFP中实现多条件组合查询，满足了用户复杂查询的需求。     

采用LINQ预编译技术提高查询数据库性能

比较了利用LINQ查询数据库时未预编译和预编译的执行效率。如果将首次执行时的编译结果保存在一个静态类中,在全局范围内只需要编译一次,再次查询时就可以直接使用。结果表明,采用预编译技术能够在很大程度上提高查询效率。     

Approximate Continuous Top-<Emphasis Type="Italic">k</Emphasis> Query over Sliding Window

Continuous top-k query over sliding window is a fundamental problem in database, which retrieves k objects with the highest scores when the window slides. Existing studies mainly adopt exact algorithms to tackle this type of queries, whose key idea is to maintain a subset of objects in the window, and try to retrieve answers from it. However, all the existing algorithms are sensitive to query parameters and data distribution. In addition, they suffer from expensive overhead for incremental maintenance, and thus cannot satisfy real-time requirement. In this paper, we define a novel query named (ε, δ)-approximate continuous top-k query, which returns approximate answers for top-k query. In order to efficiently support this query, we propose an efficient framework, named PABF (Probabilistic Approximate Based Framework), to support approximate top-k query over sliding window. We firstly maintain a self-adaptive pruning value, which could filter out newly arrived objects who have a probability less than 1 ? δ of being a query result. For those objects that are not filtered, we combine them together, if the score difference among them is less than a threshold. To efficiently maintain these combined results, the framework PABF also proposes a multi-phase merging algorithm. Theoretical analysis indicates that even in the worst case, we require only logarithmic complexity for maintaining each candidate.     

概念驱动的小样本判别特征学习方法

小样本学习旨在让模型能够在仅有少量标记数据的新类中进行分类。基于度量学习的方法是小样本学习的一种有效方法,该类方法利用有标签的支持集样本构建类表示,再基于查询样本和类表示的相似性进行分类。因此,如何构建判别性更强的类表示是这类方法的关键所在。多数工作在构建类表示时,忽略了类概念相关信息的挖掘,这样容易引入样本中类别无关信息,从而降低类表示的判别性。为此本文提出一种概念驱动的小样本判别特征学习方法。该方法首先利用类别的语义信息来指导模型挖掘样本中类概念相关信息,进而构建更具判别性的类表示。其次,设计了随机掩码混合机制增加样本的多样性和识别难度,进一步提升类表示的质量。最后对处于决策边界附近的查询样本赋予更大的权重,引导模型关注难样本,从而更好地进行类表示学习。大量实验的结果表明本文提出的方法能够有效提升小样本分类任务的准确率,并且在多个数据集上优于当前先进的算法。     

QUBiC: An adaptive approach to query-based recommendation

Search engine users often encounter the difficulty of phrasing the precise query that could lead to satisfactory search results. Query recommendation is considered an effective assistant in enhancing keyword-based queries in search engines and Web search software. In this paper, we present a Query-URL Bipartite based query reCommendation approach, called QUBiC. It utilizes the connectivity of a query-URL bipartite graph to recommend related queries and can significantly improve the accuracy and effectiveness of personalized query recommendation systems comparing with the conventional pairwise similarity based approach. The main contribution of the QUBiC approach is its three-phase framework for personalized query recommendations. The first phase is the preparation of queries and their search results returned by a search engine, which generates a historical query-URL bipartite collection. The second phase is the discovery of similar queries by extracting a query affinity graph from the bipartite graph, instead of operating on the original bipartite graph directly using biclique-based approach or graph clustering. The query affinity graph consists of only queries as its vertices and its edges are weighted according to a query-URL vector based similarity (dissimilarity) measure. The third phase is the ranking of similar queries. We devise a novel rank mechanism for ordering the related queries based on the merging distances of a hierarchical agglomerative clustering (HAC). By utilizing the query affinity graph and the HAC-based ranking, we are able to capture the propagation of similarity from query to query by inducing an implicit topical relatedness between queries. Furthermore, the flexibility of the HAC strategy makes it possible for users to interactively participate in the query recommendation process, and helps to bridge the gap between the determinacy of actual similarity values and the indeterminacy of users’ information needs, allowing the lists of related queries to be changed from user to user and query to query, thus adaptively recommending related queries on demand. Our experimental evaluation results show that the QUBiC approach is highly efficient and more effective compared to the conventional query recommendation systems, yielding about 13.3 % as the most improvement in terms of precision.     

Scalable and efficient processing of top-<Emphasis Type="Italic">k</Emphasis> multiple-type integrated queries

In this paper, we define a new class of queries, the top-k multiple-type integrated query (simply, top-k MULTI query). It deals with multiple data types and finds the information in the order of relevance between the query and the object. Various data types such as spatial, textual, and relational data types can be used for the top-k MULTI query. The top-k MULTI query distinguishes itself from the traditional top-k query in that the component scores to calculate final scores are determined dependent of the query. Hence, each component score is calculated only when the query is given for each data type rather than being calculated apriori as in the top-k query. As a representative instance, the traditional top-k spatial keyword query is an instance of the top-k MULTI query. It deals with the spatial data type and text data type and finds the information based on spatial proximity and textual relevance between the query and the object, which is determined only when the query is given. In this paper, we first define the top-k MULTI query formally and define a new specific instance for the top-k MULTI query, the top-k spatial-keyword-relational(SKR) query, by integrating the relational data type into the traditional top-k spatial keyword query. Then, we investigate the processing approaches for the top-k MULTI query. We discuss the scalability of those approaches as new data types are integrated. We also devise the processing methods for the top-k SKR query. Finally, through extensive experiments on the top-k SKR query using real and synthetic data sets, we compare efficiency of the methods in terms of the query performance and storage.     

Using genetic algorithms to work out index configurations for the class-hierarchy indexing in object databases

Efficient indexing on a class hierarchy is essential for the achievement of high performance in query evaluation for object databases. In this paper, we present a practical indexing scheme, Partition Index Configuration Scheme (PINS), which provides good index configurations for any real database environment. PINS considers the distribution of key values, as well as query patterns such as query frequency on each class. PINS can easily be applied to any database system, since it uses the B⁺-tree structure. We develop a cost model and, through experiments, demonstrate the performance of the proposed policy over various class hierarchies.     

A query refinement framework for xml keyword search

Existing work of XML keyword search focus on how to find relevant and meaningful data fragments for a query, assuming each keyword is intended as part of it. However, in XML keyword search, user queries usually contain irrelevant or mismatched terms, typos etc, which may easily lead to empty or meaningless results. In this paper, we introduce the problem of content-aware XML keyword query refinement, where the search engine should judiciously decide whether a user query Q needs to be refined during the processing of Q, and find a list of promising refined query candidates which guarantee to have meaningful matching results over the XML data, without any user interaction or a second try. To achieve this goal, we build a novel content-aware XML keyword query refinement framework consisting of two core parts: (1) we build a query ranking model to evaluate the quality of a refined query RQ, which captures the morphological/semantical similarity between Q and RQ and the dependency of keywords of RQ over the XML data; (2) we integrate the exploration of RQ candidates and the generation of their matching results as a single problem, which is fulfilled within a one-time scan of the related keyword inverted lists optimally. Finally, an extensive empirical study verifies the efficiency and effectiveness of our framework.     

A study on the structure of linear recursion

We study a general class of single linear recursions and the properties of their expansions by analyzing the structures of the recursions. We show that the expansions of a linear recursion of this class are very regular in that the variable connections are heavily shared and change periodically with respect to the expansions. The variable connections can be precisely characterized as static bindings and chain connections. We conclude that a single linear recursion under our assumptions either is bounded or can be expressed as chain recursions. This study contributes to query processing, because it provides the basis for rule compilation as a general and powerful technique for query processing. Combined with query information, the expansion properties of the recursion provide optimized query-processing plans     

超级节点网络中的并行查询和排序机制

超级节点网络中的超级节点可能成为网络性能的瓶颈并影响检索结果的统一排序,针对该问题提出一种并行查询和排序机制。给出类特征等索引建立方法和查询节点选择算法,减少超级节点的存储和计算负担,使其在负载能力范围内,尽可能多地连接普通节点。在获取全局参数的前提下,提出查询节点的查询和排序方法,以提高检索质量。实验结果验证了该机制的有效性。