Skyline查询处理数据立方体代数

维空间的Skyline查询处理技术是近年来数据库技术领域的一个研究重点和热点.目前所有的研究工作都是直接在原始数据表上执行关系查询代数操作来获得最终的结果集,然而,随着原始数据表的数据量和维目标个数的增大,这些研究工作将不再适用.基于此,首次研究Skyline集合上的查询代数操作,使得Skyline查询处理的输入数据来自于小规模的Skyline结果集,而非海量的原始数据表.并且,首次给出一个集成多维对象集合和该对象集合上的Skyline结果集的形式化模型,该模型适合目前Skyline查询计算的应用,并在该模型的实例上研究Skyline集合的查询代数操作.同时,给出查询代数体系的代价评估模型.实验表明,给出的数据模型和查询代数体系具有有效性和实用性.     

基于位置敏感哈希的网络视频重复检测

针对当前网络上存在着大量的重复或近似重复的视频问题,提出了一种基于镜头层比较和位置敏感哈希的快速准确的网络视频重复检测方法。通过视频间匹配的镜头数占查询视频总镜头数的比例来判断视频的相似性。除此之外,还利用著名的近似最近邻查找技术——LSH在镜头层来快速查找相似镜头,从而提高检测速度。通过将镜头作为检索单元,把数据库中所有视频的镜头放到一起构建一个新的数据集,将种子(查询)视频的每一个镜头作为一个查询请求,应用基于LSH的近似近邻检索方法,检索出与查询镜头相匹配的所有镜头,最后融合这些返回的结果,得到查询视频的重复或者近似重复的视频集。通过在包含12 790个视频的CC_WEB_VIDEO数据集上的实验结果表明,该方法取得了相比已有方法更好的检测性能。     

基于B*树聚簇索引的加密字符串查询方法

为了提高在数据库中查询加密字符串数据的性能, 提出一种在索引特征值上创建B^*树聚簇索引的查询方法. 每一个待加密字符串数据对应一个索引特征值, 索引特征值以数值的形式保存在索引字段中. 查询时使用两阶段查询策略, 首先利用索引字段对加密数据进行一次粗糙查询过滤掉不相干的记录, 然后在返回的粗糙集合解密的基础上进行明文查询, 得到最终结果. 实验表明该方法较现有查询方法在查询性能有较大的提升.     

Web页面动态表格数据导入Excel的JavaScript实现

<正>Web应用系统经常需要通过客户端浏览器实现对服务器数据库的查询访问,并根据客户端要求格式显示查询结果。若客户端需要进一步报表处理数据时,如果能够导入Excel电子表格进行处理就可极大地提高灵活性。本文从实际应用的角度,阐述利用JavaScript实现Web页面动态表格数据的Excel导入的方法。1动态表格数据基于B／S模式应用系统的特点在于客户端通过浏览器实现对服务器数据的访问。如果客户端浏览器中的web表格数据由客户端浏览器中的输入来决定,浏览器将根据客户的输入在服务器数据库中进行相应的查询,并将查询结果动态反映在浏览器中。在此以SQLServer2000数据库中自带的WorthWind数据库为例说明。查询该数据库中Customers表中所有的记录,并以客户联系表的形式在客户浏览器中显示。代码1利用内嵌ASP脚本产生的表格数据：     

数据库加密字符串快速查询方法研究

为了提高在数据库中查询加密字符串数据的性能,提出一种新的基于对偶特征码的快速查询方法。特征码与加密字符串数据一一对应,作为索引保存在索引字段中。查询时使用两阶段查询策略,首先利用索引字段对加密数据进行一次粗糙查询以过滤掉与查询不相关的记录,然后在解密的数据上再进行一次精确查询,对粗糙查询结果进行二次过滤,得到符合查询条件的记录。实验表明,该方法的性能比现有查询方法有较大提高。     

基于区块链的工业互联网数据溯源技术实现

针对工业互联网数据量大、数据关系复杂的问题,提出一种基于区块链和PROV-DM模型的数据溯源方法,该方法采用PROV-DM模型进行溯源信息记录,可描述多种工业互联网数据关系;储存结构为区块链-区块链,可减少溯源查询过程的数据计算量,提高溯源查询效率;在每个区块链都设置有智能合约,可有效保证加密解密速率。经试验,该数据溯源方法可完成数据溯源记录和溯源查询等多项功能、对数据量较大的溯源信息加密解密速率高、溯源查询效率高、吞吐量大,基本满足工业互联网数据溯源需求。因此,基于区块链和PROV-DM模型的数据溯源方法在工业互联网的实际应用中具有一定的价值。     

用于重复充电运营记录的基于块采样的高效聚集查询算法

现有查询分析方法通常将实体识别作为线下预处理过程清洗整个数据集,然而,随着数据规模的不断增大,这种高计算复杂性的线下清洗模式已经很难满足实时性分析应用的需求。针对重复充电运营记录上的聚集查询问题,提出一种将近似聚集查询处理与实体识别相结合的方法。首先,通过基于块的采样策略采集样本;然后,在采集到的样本上利用实体识别方法识别出重复的实体;最后,根据实体识别的结果重构得到聚集结果的无偏估计。所提方法避免了识别全部实体的时间代价,通过识别少量样本数据即可返回满足用户需求的查询结果。真实数据集和合成数据集上的实验结果验证了所提方法的高效性和可靠性。     

高强度隐私保护下的数据优化查询仿真

高强度数据隐私保护下,存在高强度的数据加密,导致传统的数据查询方法,需要频繁的进行数据加密和解密运算才能完成查询过程,极大降低了数据查询效率.为提高快捷数据查询的精度,提出了一种应用前缀编码验证机制的数据优化查询方法,高强度隐私约束规则的数据属性分解,需要实现最小加密数据属性分解,依据该规范构建数据查询模型,对采集到的数据进行加密和编码处理,融入前缀编码验证机制,在感知端传递密文数据的同时,上传用于感知数据线性关系对比的编码数据,外包服务器利用线性关系对比,明确包含查询结果的最小加密数据属性集,再上传到用户端,由用户端进行解密,运算出数据查询结果,实现高强度隐私保护下数据优化查询.实验结果说明,所提方法可在高强度隐私保护下,对数据进行快速查询,并且具有较高的查询精度.     

电子病历检索中基于词权调整的查询重构

电子病历EMR(Electronic Medical Records)检索是信息检索研究中的一个新领域。医学术语在电子病历检索中占有重要地位,通常用来限定检索条件、表达用户的检索意图。针对这种情况,提出一种基于医学术语权重调整的查询重构方法,以提高电子病历检索的性能。该方法首先从原始查询语句中筛选出医学术语,然后使用自信息来度量每个医学术语的权重,最后将加权的医学术语与原始查询语句按照一定的权重比例结合,构造出新的查询语句。将该方法在TREC数据集上进行实验,结果表明与原始查询结果相比,重构后的查询结果在MAP、bpref和P10这三项指标上,分别提高了14.2%、10.1%和9.6%,验证了该方法的有效性。     

Secure multidimensional range queries over outsourced data

In this paper, we study the problem of supporting multidimensional range queries on encrypted data. The problem is motivated by secure data outsourcing applications where a client may store his/her data on a remote server in encrypted form and want to execute queries using server??s computational capabilities. The solution approach is to compute a secure indexing tag of the data by applying bucketization (a generic form of data partitioning) which prevents the server from learning exact values but still allows it to check if a record satisfies the query predicate. Queries are evaluated in an approximate manner where the returned set of records may contain some false positives. These records then need to be weeded out by the client which comprises the computational overhead of our scheme. We develop a bucketization procedure for answering multidimensional range queries on multidimensional data. For a given bucketization scheme, we derive cost and disclosure-risk metrics that estimate client??s computational overhead and disclosure risk respectively. Given a multidimensional dataset, its bucketization is posed as an optimization problem where the goal is to minimize the risk of disclosure while keeping query cost (client??s computational overhead) below a certain user-specified threshold value. We provide a tunable data bucketization algorithm that allows the data owner to control the trade-off between disclosure risk and cost. We also study the trade-off characteristics through an extensive set of experiments on real and synthetic data.     

理性与可验证的联邦学习框架

联邦学习作为解决数据孤岛问题的有效方法,在服务器计算全部梯度的过程中,由于服务器的惰性和自利性会存在全局梯度不正确计算问题,因此需要验证全局梯度的完整性.现有的基于密码算法的方案验证开销过大.针对这些问题,提出一种理性与可验证的联邦学习框架.首先,结合博弈论,设计囚徒合约与背叛合约迫使服务器诚实.其次,所提方案使用基于复制的验证方案实现全局梯度的完整性验证,且支持客户端离线.最后,经分析证明所提方案的正确性,并经实验表明,该方案与已有的验证算法相比,客户端的计算开销降为0,一次迭代的通信轮数由原来的3轮优化到2轮,且训练开销与客户端的离线率成反比.     

Verifiable Outsourcing of High-Degree Polynomials and Tts Application in Keyword Search

In big data era, people cannot afford more and more complex computation work due to the constrained computation resources. The high reliability, strong processing capacity, large storage space of cloud computing makes the resource-constrained clients remotely operate the heavy computation task with the help of cloud server. In this paper, a new algorithm for secure outsourcing of high degree polynomials is proposed. We introduce a camouflage technique, which the real polynomial will be disguised to the untrusted cloud server. In addition, the input and output will not be revealed in the computation process and the clients can easily verify the returned result. The application of the secure outsourcing algorithm in keyword search system is also studied. A verification technique for keyword search is generated based on the outsourcing algorithm. The client can easily verify whether the server faithfully implement the search work in the whole ciphertext space. If the server does not implement the search work and returns the client “null” to indicate there is no files with the query keyword, the client can easily verify whether there are some related files in the ciphertext database.     

A scalable low-latency cache invalidation strategy for mobile environments

Caching frequently accessed data items on the client side is an effective technique for improving performance in a mobile environment. Classical cache invalidation strategies are not suitable for mobile environments due to frequent disconnections and mobility of the clients. One attractive cache invalidation technique is based on invalidation reports (IRs). However, the IR-based cache invalidation solution has two major drawbacks, which have not been addressed in previous research. First, there is a long query latency associated with this solution since a client cannot answer the query until the next IR interval. Second, when the server updates a hot data item, all clients have to query the server and get the data from the server separately, which wastes a large amount of bandwidth. In this paper, we propose an IR-based cache invalidation algorithm, which can significantly reduce the query latency and efficiently utilize the broadcast bandwidth. Detailed analytical analysis and simulation experiments are carried out to evaluate the proposed methodology. Compared to previous IR-based schemes, our scheme can significantly improve the throughput and reduce the query latency, the number of uplink request, and the broadcast bandwidth requirements.     

基于Voronoi-R*的隐私保护路网k近邻查询方法

针对已有的保护位置隐私路网k近邻查询依赖可信匿名服务器造成的安全隐患,以及服务器端全局路网索引利用效率低的缺陷,提出基于路网局部索引机制的保护位置隐私路网近邻查询方法.查询客户端通过与LBS服务器的一轮通信获取局部路网信息,生成查询位置所在路段满足l-路段多样性的匿名查询序列,并将匿名查询序列提交LBS服务器,从而避免保护位置隐私查询对可信第三方服务器的依赖.在LBS服务器端,提出基于路网基本单元划分的分段式近邻查询处理策略,对频繁查询请求路网基本单元,构建基于路网泰森多边形和R^*树的局部Vor-R^*索引结构,实现基于索引的快速查找.对非频繁请求路网基本单元,采用常规路网扩张查询处理.有效降低索引存储规模和基于全局索引进行无差异近邻查询的访问代价,在保证查询结果正确的同时,提高了LBS服务器端k近邻查询处理效率.理论分析和实验结果表明,所提方法在兼顾查询准确性的同时,有效地提高了查询处理效率.     

Materialized Sample Views for Database Approximation

We consider the problem of creating a sample view of a database table. A sample view is an indexed materialized view that permits efficient sampling from an arbitrary range query over the view. Such "sample views" are very useful in applications that require random samples from a database: approximate query processing, online aggregation, data mining, and randomized algorithms are a few examples. Our core technical contribution is a new file organization called the appendability, combinability, and exponentiality (ACE) tree that is suitable for organizing and indexing a sample view. One of the most important aspects of the ACE tree is that it supports online random sampling from the view. That is, at all times, the set of records returned by the ACE tree constitutes a statistically random sample of the database records satisfying the relational selection predicate over the view. Our paper presents experimental results that demonstrate the utility of the ACE tree.     

分布式 LDAP 系统的引用研究

LDAP是轻量级目录访问协议的缩写。LDAP是X．500标准在TCP／IP上的实现,它采用树状层次存储结构,树的各层节点就是条目。分布式LDAP系统中,多个LDAP服务器构建不同的域,任何服务器上都可以查询到整个系统的数据。分布式LDAP系统使用引用机制在LDAP服务器间建立连接关系。客户端访问本地服务器查询条目,如果本地服务器发现查询条目不属于本域,它就会根据引用地址访问其他服务器,直至返回被查询条目的数据。但如果客户端查询的条目不在任何域内,则会导致引用死锁,客户端得不到响应,一直处于等待状态。针对上述问题,论文提出增加LDAPMessage字段的解决方案,并测试验证方案可行性。     

ANATAGONOMY: a personalized newspaper on the World Wide Web

This paper describes a personalized newspaper on the World Wide Web (WWW), called ANATAGONOMY. The main feature of this system is that the newspaper is personalized without asking the users to specify their preferences explicitly. The system monitors user operations on the articles and reflects them in the user profiles. Differently from conventional newspapers on the WWW, our system sends an interaction agent implemented as a Java applet to the client side, and the agent monitors the user operations and creates each user's newspaper pages automatically. The server side manages user profiles and anticipates how interesting an article would be for each user. The interaction agent on the client side manages all the user interactions, including the automatic layout of pages. Our system has page multiple layout algorithms and the user can switch from one view to another anytime, according to the preference or machine environment. On one of the views, the user can even see all the articles sequentially without performing any operations. We evaluated a scheme in which the user scores each article explicitly, and a scheme in which all the personalization is done automatically. The results show that automatic personalization works well when some parameters are set properly.     

A cache invalidation scheme for continuous partial match queries in mobile computing environments

The continuous partial match query is a partial match query whose result remains consistently in the client’s memory. Conventional cache invalidation methods for mobile clients are record ID-based. However, since the partial match query uses content-based retrieval, the conventional ID-based approaches cannot efficiently manage the cache consistency of mobile clients. In this paper, we propose a predicate-based cache invalidation scheme for continuous partial match queries in mobile computing environments. We represent the cache state of a mobile client as a predicate, and also construct a cache invalidation report (CIR), which the server broadcasts to clients for cache management, with predicates. In order to reduce the amount of information that is needed for cache management, we propose a set of methods for CIR construction (in the server) and identification of invalidated data (in the client). Through experiments, we show that the predicate-based approach is very effective for the cache management of mobile clients.     

Efficiently instantiating view-objects from remote relational databases

View-objects are complex objects that are instantiated by delivering a query to a database and converting the query result into a nested structure. In relational databases, query results are conventionally retrieved as a single flat relation, which contains duplicate subtuples in its composite tuples. These duplicate subtuples increase the amount of data to be handled and thus degrade performance. In this article, we describe two new methods that retrieve a query result in structures other than a single flat relation. One method retrieves a set of relation fragments, and the other retrieves a single-nested relation. We first describe their algorithms and cost models, and then present the cost comparison results in a client-server architecture with a relational main memory database residing on a server.