从文科学生的信息素养与计算思维能力培养的角度,探索相关课程的设计.以"网页设计"课程为例,通过分析该课程的培养目标和终极使命,在培养目标指导下,结合课程内容与学生知识结构进行具体的课程设计和建设,分享了课程建设的成果和经验,并对未来教学工作进行展望.

在中国人民大学计算机教学的新一轮教学改革中,提高学生的动手实践能力是核心和关键[2],因此课程设计和实验设计始终围绕这一中心问题开展.文章探讨教改中《网页设计》课程的综合实验设计思路,以典型案例的方式,分析综合实验设计的几个重要因素,并分享实验实施以来的初步效果和进一步工作打算.

针对文科数据库课程,分析目前面临的问题和挑战,提出改革的目标、原则和方向,结合数据库课程改革实例,具体介绍文科数据库课程的教学设计要点,并以调研数据说明实施评价结果,最后对未来的工作提出建议.

随着油库自动化建设的全面推进和深化应用,形成了油库生产运行各方面大量的数据资产,数字化油库综合信息管理系统成为整合、分析和应用这些数据的平台.本文介绍了数字化油库综合信息管理系统的关键技术,对系统功能设计做了详细说明,并对系统的应用效果进行了总结.

现有文本数据集上的实体搜索和自然语言查询方法无法处理需要将分散在不同文档中的信息碎片链接起来以满足有复杂实体关系的查询,而知识库上的查询虽然可以表示实体间的复杂关系,但由于知识库的异构性和不完全性,通常查全率较低.针对这些问题,提出使用文本数据集对知识库进行扩展,并设计相应的含文本短语的三元组模式查询以支持对知识库和文本数据的统一查询.在此基础上,设计并实现了查询放松机制和对结果元组的评分模型,并给出了高效的查询处理方法.使用YAGO、ClueWeb09和其上的FACC1数据集,在三个不同的查询测试集(实体检索、实体关系检索和复杂的实体关系查询)上与两个典型相关工作作了比较.实验结果显示,扩展知识图谱上使用查询放松规则的实体关系检索系统的检索效果大大超出了其他系统,具体地在三个查询测试集上,其平均正确率均值(MAP)比其他系统分别提升了27％、37％和64％以上.

混合数据分布是指数据分布的不同区域具有不同的特殊分布.例如销售额和地区两个属性之间,在销售额比较低的数值区间中,两者呈现近似相互独立的数据分布;而在销售额比较高的数值区间,二者呈现近似函数依赖的数据分布.现有检测数据相关性的研究专注于给出一个总体的二维相关性的度量,而无法检测出子区域的特殊相关性.在统计分析时,这类具有特殊相关性的子区域有更丰富的统计意义,值得引起重视.研究并提出了存在这类混合数据分布的情况下,检测数据相关性的新方法HY-COCA.该方法在熵相关系数的基础上,缩小了子区域的搜索空间,与Naive方法相比,降低了复杂度;同时HY-COCA还讨论了子区域的相关性差异判别与结果展示等问题.在生成的数据和测试基准数据上进行了实验,结果验证了方法的有效性.

联邦搜索是从大规模深层网上获取信息的一种重要技术。给定一个用户查询,联邦搜索系统需要解决的一个主要问题是数据源选择问题,即从海量数据源中选出一组最有可能返回相关结果的数据源。现有的数据源选择算法大多基于数据源的样本文档集和查询之间的关键词匹配,通常无法很好地解决少量样本文档的信息缺失问题。针对这一问题,提出了基于隐含狄利克雷分布（LDA）主题模型进行数据源选择的方法。首先,使用LDA主题模型获得数据源和查询的主题概率分布;然后,通过比较两者主题概率分布的相近性来对所有数据源进行排序。通过将数据源和查询映射到低维的主题空间来解决高维词条空间稀疏性所带来的信息缺失问题。在TREC Fed Web 2013和2014 Track的测试集上分别进行了实验,并和其他参赛方法的结果进行了比较。在Fed Web 2013测试集上的实验结果显示比其他参赛方法的最好结果提高了24%;在Fed Web 2014测试集上的实验结果显示比传统的基于小文档和大文档的关键词匹配方法分别提高了22%和43%。另外,使用文档片段来代替文档还可以大幅提升系统的效率,更增加了此方法的实用性和可行性。

With the development of wireless network access techniques, such as 3G, 4G and WiFi, andthe popularity of smart phones, the mobile Internet is changing the way of our work and life. As a result of a variety of security vulnerabilitiesexist in the mobile Internet during design and implementation, the mobile Internet brings not only convenience and efficiency but also provides new attack ways andcrime methods for the illegal criminal offender. First, the development situation of mobile Internet was summarized, and then typical network attacks and crimesin recent years of the mobile Internet were analyzed, including pseudo base stations, operating system vulnerabilitiesof mobile phones, malicious code and information disclosure. Finally, prediction was carried out to the future network attacks and crime situation of the mobile Internet.

Imagine that your database has all the right indexes. Its buffer manager has been tuned to give a high hit ratio, the buffer fits in RAM, and the data is well distributed on disk. You're done, right? Well, no, because the application code might be poorly written. It might include delinquent design patterns. The demoed tuning tool AppSleuth will find those delinquent design patterns but it is the demo visitor's job to fix them.

Excellent work ([1]-[6]) has shown that memory management and transaction concurrency levels can often be tuned automatically by the database management systems. Other excellent work ([7]]-[14]) has shown how to use the optimizer to do automatic physical design or to make the optimizer itself more self-adaptive ([15]-[17]). Our performance tuning experience across various industries (finance, gaming, data warehouses, and travel) has shown that enormous additional tuning benefits (sometimes amounting to orders of magnitude) can come from reengineering application code and table design. The question is: can a tool help in this effort? We believe so. We present a tool called AppSleuth that parses application code and the tracing log for two popular database management systems in order to lead a competent tuner to the hot spots in an application. This paper discusses (i) representative application "delinquent design patterns", (ii) an application code parser to find them, (iii) a log parser to identify the patterns that are critical, and (iv) a display to give a global view of the issue. We present an extended sanitized case study from a real travel application to show the results of the tool at different stages of a tuning engagement, yielding a 300 fold improvement. This is the first tool of its kind that we know of.

Excellent work ([1]-[6]) has shown that memory management and transaction concurrency levels can often be tuned automatically by the database management systems. Other excellent work ([7]]-[14]) has shown how to use the optimizer to do automatic physical design or to make the optimizer itself more self-adaptive ([15]-[17]). Our performance tuning experience across various industries (finance, gaming, data warehouses, and travel) has shown that enormous additional tuning benefits (sometimes amounting to orders of magnitude) can come from reengineering application code and table design. The question is: can a tool help in this effort? We believe so. We present a tool called AppSleuth that parses application code and the tracing log for two popular database management systems in order to lead a competent tuner to the hot spots in an application. This paper discusses (i) representative application "delinquent design patterns", (ii) an application code parser to find them, (iii) a log parser to identify the patterns that are critical, and (iv) a display to give a global view of the issue. We present an extended sanitized case study from a real travel application to show the results of the tool at different stages of a tuning engagement, yielding a 300 fold improvement. This is the first tool of its kind that we know of.

Automatic physical database design is an important part in self-tuning and self-managing databases.To reflect upon the main research work on this topic,this paper gave a brief survey on the classic technology in physical database design tools in terms of background,problem definition,techniques,and potential future research directions.Specifically,this paper analyzed the common techniques from four dimensions,namely,cost optimality,scalability,high usability and benchmarking.And by comparing different strategies,it also spoted and evaluated their technical differences in detail.The paper discussed new research endeavors in recent years which went beyond the limits of the classic physical design problem.In the end,it gave the conclusion and the vision of the future research directions.

Nowadays, due to users’ increasing requirements of fast and reliable data management for mobile applications, major electronic device vendors use embedded DBMS on their mobile devices such as MP3 players, mobile phones, digital cameras and PDAs. However, in embedded database, data logging is the bottleneck against fast response time. There has been a lot of work on minimizing logging overhead to provide the best online performance to database workloads. However, to the best of our knowledge, there is still no recovery method taken into consideration. In this paper, we propose a novel logging method called LB-logging to support high efficiency in recovery of crashed databases. LB-logging is based on list structures instead of sequential structures in traditional databases. In addition, by making use of the history data versions which are naturally located in flash memory due to the out-of-place update, we take the full advantage of high I/O performance of flash memory to accelerate our recovery algorithm. Experimental results on Oracle Berkeley DB show that our LB-Logging method significantly outperforms the traditional recovery by 2X-15X, and other logging methods for SSD by 1.5X-6X.

Key-value in-memory databases have the characteristics of efficiency,usability and scalability.Because of the limits of the capacity of main memory,the applications dealing with large amount of data have to swap data between main memory and disks.While solid state disks(SSDs) have the high performance of random reads as a new storage medium,they can speed up random reads on virtual memory.To remedy the lower performance of random writes on SSDs,this paper proposes an optimization method of write buffer of SSD,which transforms several random writes to a sequential write,and designs a garbage collection policy of SSD,which transforms several random writes to a sequential read and a sequential write,to improve the spatial utilization of key-value in-memory database.Finally,an SSD-based virtual memory implementation is proposed to realize high performance of key-value main memory databases,and the improvement which is at most 40%,is confirmed by changing the source code of Redis in experiment.

SIGMOD has offered, since 2008, to verify the experiments published in the papers accepted at the conference. This year, we have been in charge of reproducing the experiments provided by the authors (repeatability), and exploring changes to experiment parameters (workability). In this paper, we assess the SIGMOD repeatability process in terms of participation, review process and results. While the participation is stable in terms of number of submissions, we find this year a sharp contrast between the high participation from Asian authors and the low participation from American authors. We also find that most experiments are distributed as Linux packages accompanied by instructions on how to setup and run the experiments. We are still far from the vision of executable papers.

The paper describes the details of using J-SIM in main memory database parallel recovery simulation. In update intensive main memory database systems, I/O is still the dominant performance bottleneck. A proposal of parallel recovery scheme for large-scale update intensive main memory database systems is presented. Simulation provides a faster way of evaluating the new idea compared to actual system implementation. J-SIM is an open source discrete time simulation software package. The simulation implementation using J-SIM is elaborated in terms of resource modeling, transaction processing system modeling and workload modeling. Finally, with simulation results analyzed, the effectiveness of the parallel recovery scheme is verified and the feasibility of J-SIM's application in main memory database system simulation is demonstrated.

面向更新密集型应用的内存数据库系统,其检查点技术应符合几个关键的要求,包括检查点操作对正常事务处理的干扰尽可能小、能够处理存取倾斜状况、支持数据库系统的快速恢复、提供恢复过程中的系统可用性等.该文提出一种事务一致的分区检查点技术,采用基于元组的动态多版本并发控制机制,避免了读写事务的加锁冲突,提高系统吞吐能力;检查点操作以只读事务形式实现,在多版本并发控制下,避免检查点操作对正常事务处理的堵塞;由于检查点文件是事务一致的,只需要记录事务的Redo日志信息,在系统恢复过程中,只需要对日志文件进行一遍扫描处理,加快恢复过程;基于优先级的数据分区装载和恢复,使得恢复过程中新事务的数据存取请求迅速得到满足,保证了恢复过程中的系统可用性.由于采用两级版本管理机制以及动态版本共享技术,多版本管理的空间开销降低到可以接受的水平.实验结果表明,文中提出的检查点技术方案获得比模糊检查点技术高27%的系统吞吐量,同时版本管理的空间开销在可接受的范围之内,满足高性能应用的要求.

基于代价的RDBMS优化器需要对含有范围查询的合取谓词的结果集基数进行准确的估计,多维直方图对多维数据分布进行模拟,避免在估计结果集基数时采用数据独立性假设,造成估计误差过大,进而导致选择非优化的查询执行计划.在不同的数据分布情况下,传统的多维直方图（如MHist-2）效果有很大不同.数据相关系数和值域密度、值域参数是准确刻画多维数据分布的有效指标,文中提出了根据不同的指标采用不同的动态优化的多维直方图算法COCA-Hist,可以大大改善传统多维直方图在平均情况下的准确性.通过分析传统的多维直方图的最坏情况,COCA-Hist的改进算法可以改进传统的多维直方图在最坏情况下的准确性.实验比较了COCA-Hist和传统的多维直方图MHist-2以及GENHist和STHoles的准确性和时间效率.实验显示无论在平均情况下还是在最坏情况下COCA-Hist的改进算法均优于传统的MHist-2直方图,并且COCA-Hist的准确性和创建时间均比GENHist有极大的改善,在准确性方面COCA-Hist较优于STHoles,而在空间预算有限时STHoles的创建时间比COCA-Hist高两个数量级.

In reality,it is often the case that multi-dimensional data distributions do not exhibit one single type of data distribution as a whole,but rather,in different regions of the data space,different types of data distributions are obviously shown.The authors proposed a new kind of hybrid multi-dimensional histograms-COCA-Hist-based hybrid data distributions to tackle the problem.The method built up COCA-Hist,which was composed of different kinds of buckets according to different regions in the data space with different data distribution characteristics,under the given space budget.The aim was to enhance the estimation accuracy of the multi-dimensional histograms in general.Because COCA-Hist had to scan the tree structure twice of the histogram being built to discern the different data regions and allocated the space budget among them,COCA-Hist was a little inferior in efficiency.But the improvement in both universality and estimation accuracy made the cost in time worthwhile.