Pattern discovery and change detection of online music query streams

Mining of music data is one of the most important problems in multimedia data mining. In this paper, two research issues of mining music data, i.e., online mining of music query streams and change detection of music query streams, are discussed. First, we proposed an efficient online algorithm, FTP-stream (Frequent Temporal Pattern mining of streams), to mine all frequent melody structures over sliding windows of music melody sequence streams. An effective bit-sequence representation is used in the proposed algorithm to reduce the time and memory needed to slide the windows. An effective list structure is developed in the FTP-stream algorithm to overcome the performance bottleneck of 2-candidate generation. Experiments show that the proposed algorithm FTP-stream only needs a half of memory requirement of original melody sequence data, and just scans the music query stream once. After mining frequent melody structures, we developed a simple online algorithm, MQS-change (changes of Music Query Streams), to detect the changes of frequent melody structures in current user-centered music query streams. Two music melody structures (set of chord-sets and string of chord-sets) are maintained and four melody structure changes (positive burst, negative burst, increasing change and decreasing change) are monitored in a new summary data structure, MSC-list (a list of Music Structure Changes). Experiments show that the MQS-change algorithm is an effective online method to detect the changes of music melody structures over continuous music query streams.

A hybrid clustering and graph based algorithm for tagSNP selection

TagSNP selection, which aims to select a small subset of informative single nucleotide polymorphisms (SNPs) to represent the whole large SNP set, has played an important role in current genomic research. Not only can this cut down the cost of genotyping by filtering a large number of redundant SNPs, but also it can accelerate the study of genome-wide disease association. In this paper, we propose a new hybrid method called CMDStagger that combines the ideas of the clustering and the graph algorithm, to find the minimum set of tagSNPs. The proposed algorithm uses the information of the linkage disequilibrium association and the haplotype diversity to reduce the information loss in tagSNP selection, and has no limit of block partition. The approach is tested on eight benchmark datasets from Hapmap and chromosome 5q31. Experimental results show that the algorithm in this paper can reduce the selection time and obtain less tagSNPs with high prediction accuracy. It indicates that this method has better performance than previous ones.     

Semplore: A scalable IR approach to search the Web of Data

The Web of Data keeps growing rapidly. However, the full exploitation of this large amount of structured data faces numerous challenges like usability, scalability, imprecise information needs and data change. We present Semplore, an IR-based system that aims at addressing these issues. Semplore supports intuitive faceted search and complex queries both on text and structured data. It combines imprecise keyword search and precise structured query in a unified ranking scheme. Scalable query processing is supported by leveraging inverted indexes traditionally used in IR systems. This is combined with a novel block-based index structure to support efficient index update when data changes. The experimental results show that Semplore is an efficient and effective system for searching the Web of Data and can be used as a basic infrastructure for Web-scale Semantic Web search engines.     

Deep Web数据集成中查询处理的研究与进展

随着Web上在线数据库的大量涌现,Deep Web数据集成成为当前信息领域的一个研究热点,而查询处理是其中的一个重要的组成部分。由于Web数据库具有规模大、自治性、异构性以及动态性等特点,使得Deep Web数据集成中的查询处理比传统的分布环境下的查询处理更具挑战性。围绕Deep Web数据集成中查询处理的三个关键研究点:模式匹配、Web数据库的选择以及查询转换,综述了近年来国际上相关的、具代表性的研究成果,分析了这些方法的优缺点,总结并展望了未来的发展方向。     

基于XML的完全频繁查询模式挖掘算法

使用树结构建模对XML查询进行研究,提出了一种基于树同构的查询包含检测方法。采用最右分枝扩展方法,系统地枚举查询模式树的同根子树。在枚举过程中,采用Diffset结构记录包含同根子树的事务集的查询事务标识,并给出挖掘算法DiffFRSTMiner。实验结果证实了该算法合理、高效,并可以减少一定的内存开销。     

传感器网络中基于域的聚集算法

聚集运算是传感器网络查询处理中最重要的一个运算。提出了一种基于域聚簇的网内聚集算法PIA。在PIA中,首先结合传感器网络的节点特性和位置信息,提出了一种基于域的分布式数据汇聚模型,把传感器网络按域划分来构建连通核,查询只需在连通核中寻径,因而能明显降低寻径时间复杂度并且具有更好的分布性。在PIA中,核心节点把当前路径中的Max和Min值传送到节点上,如果节点的值不符合要求就放弃本次传送,因而能够明显减少数据的传送次数,从而达到节省能量的目的。理论分析和实验表明该算法较传统算法在节省能量上有较好的表现。     

多维数据的复杂查询聚集算法研究

对空间多维数据的复杂查询是多维数据研究的重点和难点,目前研究的结论相对较少。在传统算法的基础上,进行了几个方面的改进：按分组属性值进行数据分块;对分组数据进行有效的排序;在聚集函数的应用上进行优化。模拟数据的试验表明：改进算法较大地提高了查询效率。     

基于小世界模型的传感器网络节能查询策略

查询处理是传感器网络中最重要的技术之一。以降低查询能耗为目的,提出了一种基于小世界模型的查询策略（CardSN）。该策略以关联作为长程连接,降低了网络平均路径距离;将各节点的邻居相对定位,使查询处理获得了方向性。仿真实验结果表明：CardSN具有良好的可扩展性,且能耗明显低于ZRP和CAPTURE。     

基于数字水印的外包数据库查询验证

外包数据库的查询验证,是指客户端对外包数据库的数据来源真实性、查询运算正确性及查询结果完整性的验证。提出一种基于脆弱型数字水印对外包数据库查询的真实性进行验证的解决方案。在密钥的控制下,以混沌方程产生随机二值序偶作为水印信号并确定其嵌入方式,通过修改数值型属性低位数字的奇偶性嵌入水印,通过对数据库查询结果运行水印检测算法进行查询验证。该方法具有冗余存储量与网络附加负载小、隐蔽性好、能同时实现篡改定位等优点。     

XML文档压缩技术比较研究

XML已经成为互联网上信息交换和信息表示的事实标准。然而XML文档中包含大量重复出现的标签和结构等冗余信息,导致XML文档在查询处理和数据交换时付出更高的代价,特别在带宽和资源受限的设备上显得更为突出。压缩技术是解决这一问题的重要途径。搜集了近几年提出的各种XML压缩方法,从压缩率、压缩与解压时间、内存消耗、查询性能等方面比较分析了六个具有代表性的XML压缩技术,最后简要归纳了各自的优点和存在的不足,并探讨未来努力的方向。