In recent years, management of moving objects has emerged as an active topic of spatial access methods. Various data structures （indexes） have been proposed to handle queries of moving points, for example, the well-known B^x-tree uses a novel mapping mechanism to reduce the index update costs. However, almost all the existing indexes for predictive queries are not applicable in certain circumstances when the update frequencies of moving objects become highly variable and when the system needs to balance the performance of updates and queries. In this paper, we introduce two kinds of novel indexes, named B^y-tree and αB^y-tree. By associating a prediction life period with every moving object, the proposed indexes are applicable in the environments with highly variable update frequencies. In addition, the αB^y-tree can balance the performance of updates and queries depending on a balance parameter. Experimental results show that the B^y-tree and αB^y-tree outperform the B^x-tree in various conditions.
Querying XML data is a computationally expensive process due to the complex nature of both the XML data and the XML queries. In this paper we propose an approach to expedite XML query processing by caching the results of frequent queries. We discover frequent query patterns from user-issued queries using an efficient bottom-up mining approach called VBUXMiner. VBUXMiner consists of two main steps. First, all queries are merged into a summary structure named "compressed global tree guide" (CGTG). Second, a bottom-up traversal scheme based on the CGTG is employed to generate frequent query patterns. We use the frequent query patterns in a cache mechanism to improve the XML query performance. Experimental results show that our proposed mining approach outperforms the previous mining algorithms for XML queries, such as XQPMinerTID and FastXMiner, and that by caching the results of frequent query patterns, XML query performance can be dramatically improved.
Storing and querying XML (eXtensible Markup Language) data in relational form can exploit various services offered by modern relational database management systems (RDBMSs). Due to structural complexity of XML, there are many equivalent relational mapping schemes for the same XML data and queries. In this paper, we propose the adaptive XML to relational mapping (AX2RM) system, which considers finding optimal XML to relational (X2R) mapping as four separate but correlated procedures: logical database design, data scale estimation, workload transformation, and physical database design. We view the whole process as an autonomic computing problem and formalize the adaptive X2R mapping problem. Search spaces for each procedure are investigated individually, and five approaches for finding the optimal mapping are studied. We propose an integrated approach with greedy pruning (IT-GP), which views the mapping procedures as a whole and exploits heuristic rules in each procedure to prune impossible mappings as early as possible. Evaluation of these approaches shows the validity and high efficiency of IT-GP.
