sql standard provides sql/XML publishing functions to publish the result of an sql query as XML values but it does not provide any sql/XML publishing function that can publish the result of a recursive sql query as re...
详细信息
sql standard provides sql/XML publishing functions to publish the result of an sql query as XML values but it does not provide any sql/XML publishing function that can publish the result of a recursive sql query as recursively structured XML values. Therefore, to publish transitively connected relational tuples as recursively structured XML values with the use of appropriate sql/XML publishing functions, we have to write a nested sql query. Writing that query, however, is not easy provided that the depth of the connections is deep even if we know the depth of them and is not possible once the depth of the connections is not known in advance. To solve the problem, we propose a new sqlfunction XMLNEST that can publish the result of a recursive sql query as recursively structured XML values. Both the recursive structure and the order of sibling XML elements of the XML values can be specified in the invocation of XMLNEST. Our experiments show that the proposed scheme will be the unique reasonable solution to the problem.
Keyword search in RDBs has been extensively studied in recent years. The existing studies focused on finding all or top-k interconnected tuple-structures that contain keywords. In reality, the number of such interconn...
详细信息
ISBN:
(纸本)9781424489589
Keyword search in RDBs has been extensively studied in recent years. The existing studies focused on finding all or top-k interconnected tuple-structures that contain keywords. In reality, the number of such interconnected tuple-structures for a keyword query can be large. It becomes very difficult for users to obtain any valuable information more than individual interconnected tuple-structures. Also, it becomes challenging to provide a similar mechanism like group-&-aggregate for those interconnected tuple-structures. In this paper, we study computing structural statistics keyword queries by extending the group-&-aggregate framework. We consider an RDB as a large directed graph where nodes represent tuples, and edges represent the links among tuples. Instead of using tuples as a member in a group to be grouped, we consider rooted subgraphs. Such a rooted subgraph represents an interconnected tuple-structure among tuples and some of the tuples contain keywords. The dimensions of the rooted subgraphs are determined by dimensional-keywords in a data driven fashion. Two rooted subgraphs are grouped into the same group if they are isomorphic based on the dimensions or in other words the dimensional-keywords. The scores of the rooted subgraphs are computed by a user-given score function if the rooted subgraphs contain some of general keywords. Here, the general keywords are used to compute scores rather than determining dimensions. The aggregates are computed using an sql aggregate function for every group based on the scores computed. We give our motivation using a real dataset. We propose new approaches to compute structural statistics keyword queries, perform extensive performance studies using two large real datasets and a large synthetic dataset, and confirm the effectiveness and efficiency of our approach.
暂无评论