高校学生成绩管理与智能跟踪预警系统的开发应用

本文探讨高校学生成绩管理与智能跟踪预警系统的开发与应用。该系统所包含的成绩查询功能、跟踪分析和预警功能、决策支持功能为教务管理工作和学生教育辅导工作提供了良好的支持,能够很好地应用到全校范围的学生管理工作中去。     

基于WAP技术的高校成绩查询系统的研究

针对目前高校学生人数的增多,教务管理的升级,设计并实现了利用WAP手机查询学生成绩的系统,使高校学生可以随时通过手机查询成绩。     

利用ASP技术实现成绩查询系统的开发

基于B/S结构,以ASP为关键技术的学生成绩查询系统的合理解决方案;该系统的设计与开发能够实现学生成绩查询的网络化,加快管理的现代化进程.     

浅谈学生成绩管理系统

近几年由于不断扩招,对众多学生的成绩管理方面变得越来越复杂化,为了管理好每个学生的成绩以方便管理人员进行管理、任课教师输入成绩和学生进行查询。针对目前学生成绩管理效率低下的现状。设计与实现了学生成绩管理系统．分别从系统的需求、数据库的应用,以及以学生信息管理功能为例对部分代码进行分析来阐述。     

浅谈数据库数据的复杂查询利用工具的进行

数据库应用系统中数据的复杂查询问题,长期困扰着使用者和开发者。本文通过具体问题,论述了实现复杂查询的方法,同时论证了几种方法的特点及适用范围。     

基于B/S模式的远程教育在线考试系统设计与实现

针对远程教育的特点,设计并实现了基于B/S模式的远程教育在线考试系统,讨论了系统的功能模块和采用的关键技术。该系统实现了在线考试、随机抽题组卷、系统自动评分、成绩查询统计等功能,极大提高了远程教育考试环节的工作效率,有较为广阔的应用前景。     

基于VFP6.0的中小学教学管理系统的研究与开发

本文详细论述了根据软件工程的原理,结合面向对象方法,应用VFP6.0语言开发完成中小学教学管理系统开发的基本流程,主要功能有基础数据管理、成绩管理、课程管理、查询统计,其核心部分是基础数据管理和成绩管理。     

平面中点对一般多边形的最近邻查询研究

平面中点对一般多边形的最近邻查询问题是要在一般多边形集合中找到查询点的最近邻以及顺序最近邻。针对查询对象的特殊性,以R树作为索引结构,采用一般多边形的凸包组织空间结构。通过判断可见边计算查询点到凸包的最小距离。采用优先队列的方法给出查询点到每个凸包的最小距离的排序,最终找到查询点的最近邻和顺序最近邻。     

基于多元特征K分布融合的数据库深度查询

数据库深度查询的难点在于如何以最优匹配度实现数据匹配。传统数据库查询方法采用关键字匹配方法,在单一条件下效果较佳,但是当查询条件较多时,查询效率低且匹配度差。提出了一种基于多元特征K分布融合的数据库深度查询方法,通过数据库分元方法设置多元,在多元分布基础上,提取多元分布的每个特征,成为多元特征集合,通过对多元特征集合的K分布特征融合,实现数据查询特征的深度融合,降低查询条件冗余度,提高查询效率。采用实际数据库进行查询测试实验,结果显示,基于K分布特征融合的数据库深度查询方法,查询效率提高了35%,具有应用价值。     

基于VB的地理信息校园电子地图的设计

介绍在一种在Visual Basic 6.0可视化编程环境下,将Map Objects组件对象链接嵌入到编程语言中,并且利用Map Objects对象的图形与GIS功能组件,结合模块化的设计思想,对地图数据进行显示、查询、分析,并根据系统的具体要求以及需要实现的功能,从而开发出一种具有特征查询、属性查询等基本功能及辅助工具,实现具有成本低、操作便捷等优点的电子地图应用系统。     

Examining the effectiveness of real-time query expansion

Interactive query expansion (IQE) (c.f. [Efthimiadis, E. N. (1996). Query expansion. Annual Review of Information Systems and Technology, 31, 121–187]) is a potentially useful technique to help searchers formulate improved query statements, and ultimately retrieve better search results. However, IQE is seldom used in operational settings. Two possible explanations for this are that IQE is generally not integrated into searchers’ established information-seeking behaviors (e.g., examining lists of documents), and it may not be offered at a time in the search when it is needed most (i.e., during the initial query formulation). These challenges can be addressed by coupling IQE more closely with familiar search activities, rather than as a separate functionality that searchers must learn. In this article we introduce and evaluate a variant of IQE known as Real-Time Query Expansion (RTQE). As a searcher enters their query in a text box at the interface, RTQE provides a list of suggested additional query terms, in effect offering query expansion options while the query is formulated. To investigate how the technique is used – and when it may be useful – we conducted a user study comparing three search interfaces: a baseline interface with no query expansion support; an interface that provides expansion options during query entry, and a third interface that provides options after queries have been submitted to a search system. The results show that offering RTQE leads to better quality initial queries, more engagement in the search, and an increase in the uptake of query expansion. However, the results also imply that care must be taken when implementing RTQE interactively. Our findings have broad implications for how IQE should be offered, and form part of our research on the development of techniques to support the increased use of query expansion.     

Reformulation of queries using similarity thesauri

One of the major problems in information retrieval is the formulation of queries on the part of the user. This entails specifying a set of words or terms that express their informational need. However, it is well-known that two people can assign different terms to refer to the same concepts. The techniques that attempt to reduce this problem as much as possible generally start from a first search, and then study how the initial query can be modified to obtain better results. In general, the construction of the new query involves expanding the terms of the initial query and recalculating the importance of each term in the expanded query. Depending on the technique used to formulate the new query several strategies are distinguished. These strategies are based on the idea that if two terms are similar (with respect to any criterion), the documents in which both terms appear frequently will also be related. The technique we used in this study is known as query expansion using similarity thesauri.     

Search result diversification on attributed networks via nonnegative matrix factorization

Search result diversification is an effective way to tackle query ambiguity and enhance result novelty. In the context of large information networks, diversifying search result is also critical for further design of applications such as link prediction and citation recommendation. In previous work, this problem has mainly been tackled in a way of implicit query intent. To further enhance the performance on attributed networks, we propose a novel search result diversification approach via nonnegative matrix factorization. Our approach encodes latent query intents as well as nodes as representation vectors by a novel nonnegative matrix factorization model, and the diversity of the results accounts for the query relevance and the novelty w.r.t. these vectors. To learn the representation vectors of nodes, we derive the multiplicative updating rules to train the nonnegative matrix factorization model. We perform a comprehensive evaluation on our approach with various baselines. The results show the effectiveness of our proposed solution, and verify that attributes do help improve diversification performance.     

New query suggestion framework and algorithms: A case study for an educational search engine

Query suggestion is generally an integrated part of web search engines. In this study, we first redefine and reduce the query suggestion problem as “comparison of queries”. We then propose a general modular framework for query suggestion algorithm development. We also develop new query suggestion algorithms which are used in our proposed framework, exploiting query, session and user features. As a case study, we use query logs of a real educational search engine that targets K-12 students in Turkey. We also exploit educational features (course, grade) in our query suggestion algorithms. We test our framework and algorithms over a set of queries by an experiment and demonstrate a 66–90% statistically significant increase in relevance of query suggestions compared to a baseline method.     

Practical and effective IR-style keyword search over semantic web

This paper presents a novel IR-style keyword search model for semantic web data retrieval, distinguished from current retrieval methods. In this model, an answer to a keyword query is a connected subgraph that contains all the query keywords. In addition, the answer is minimal because any proper subgraph can not be an answer to the query. We provide an approximation algorithm to retrieve these answers efficiently. A special ranking strategy is also proposed so that answers can be appropriately ordered. The experimental results over real datasets show that our model outperforms existing possible solutions with respect to effectiveness and efficiency.     

分布式数据库中数据查询优化策略研究

分布式数据库中,数据的分散存放和冗余给故障恢复带来了方便,同时也使得分布式查询处理变得更加复杂,因此如何快速准确地查询用户想要的数据成了分布式数据库系统中的一个重要问题。文章介绍了分布式数据库的特点,详细阐述了一般的查询优化策略,给出了常用的查询优化技术。     

A review of ontology based query expansion

This paper examines the meaning of context in relation to ontology based query expansion and contains a review of query expansion approaches. The various query expansion approaches include relevance feedback, corpus dependent knowledge models and corpus independent knowledge models. Case studies detailing query expansion using domain-specific and domain-independent ontologies are also included. The penultimate section attempts to synthesise the information obtained from the review and provide success factors in using an ontology for query expansion. Finally the area of further research in applying context from an ontology to query expansion within a newswire domain is described.     

Enhancing query translation with relevance feedback in translingual information retrieval

As an effective technique for improving retrieval effectiveness, relevance feedback (RF) has been widely studied in both monolingual and translingual information retrieval (TLIR). The studies of RF in TLIR have been focused on query expansion (QE), in which queries are reformulated before and/or after they are translated. However, RF in TLIR actually not only can help select better query terms, but also can enhance query translation by adjusting translation probabilities and even resolving some out-of-vocabulary terms. In this paper, we propose a novel relevance feedback method called translation enhancement (TE), which uses the extracted translation relationships from relevant documents to revise the translation probabilities of query terms and to identify extra available translation alternatives so that the translated queries are more tuned to the current search. We studied TE using pseudo-relevance feedback (PRF) and interactive relevance feedback (IRF). Our results show that TE can significantly improve TLIR with both types of relevance feedback methods, and that the improvement is comparable to that of query expansion. More importantly, the effects of translation enhancement and query expansion are complementary. Their integration can produce further improvement, and makes TLIR more robust for a variety of queries.     

Detecting verbose queries and improving information retrieval

Although most of the queries submitted to search engines are composed of a few keywords and have a length that ranges from three to six words, more than 15% of the total volume of the queries are verbose, introduce ambiguity and cause topic drifts. We consider verbosity a different property of queries from length since a verbose query is not necessarily long, it might be succinct and a short query might be verbose. This paper proposes a methodology to automatically detect verbose queries and conditionally modify queries. The methodology proposed in this paper exploits state-of-the-art classification algorithms, combines concepts from a large linguistic database and uses a topic gisting algorithm we designed for verbose query modification purposes. Our experimental results have been obtained using the TREC Robust track collection, thirty topics classified by difficulty degree, four queries per topic classified by verbosity and length, and human assessment of query verbosity. Our results suggest that the methodology for query modification conditioned to query verbosity detection and topic gisting is significantly effective and that query modification should be refined when topic difficulty and query verbosity are considered since these two properties interact and query verbosity is not straightforwardly related to query length.     

A microprocessor architecture for bibliographic retrieval system

In this paper the features of a microprocessor based architecture for bibliographic retrieval system are illustrated. The proposed system consists of the following three functional blocks: the “query processor”, the “simple query executers” and the “answer composer”. The query processor parses the queries and breaks the complex query into simple queries. Each simple query executer is able to perform the operations satisfying a simple query. Finally, the answer composer puts together the results of all simple query executers and produces the response to the query originally raised. This machine will allow the implementation of a very powerfull query language. The basic design goals are the system modularity and a whatever complex query's fulfilment. This is achieved through the proposed query language and by means of the system architecture allowing high parallelism in the performed operations.