Research On The Generic Location-aware Rank Query Based On Temporal

With the development of mobile communication technology,location aware data set is not only more and more created and used,but also the information it carries is more and more complex.Because the Generic Location-aware Rank Query(GLRQ)allows for the use of different searchable attributes,such as space,text and numeric values,it is very important to deal with the Generic Location-aware Rank Query effectively and establish efficient indexing mechanism and query algorithm for different user requirements.In view of the query predicates contain the time attributes in real life,presenting a hybrid index structure wich called IR-Temporal Synopses tree,and giving the query algorithm based on the mixed index structure.The main contents are as follows.Firstly,aiming at how to deal with General Location Aware Rank Queries with time attributes effectively,a IR-Temporal Synopses tree hybrid index structure is proposed.The overall structure design and implementation steps of the IR-Temporal Synopses tree are given,and the update maintenance algorithm based on the mixed index structure is introduced,including the concrete implementation of the insert algorithm,the delete algorithm and the modified algorithm.Secondly,a synopses pruning query algorithm based on IR-Temporal Synopses tree is presented on the basis of IR-Temporal Synopses tree hybrid index structure.Aiming at the requirement of spatial scope when querying in some practical applications,a synopses and range pruning query algorithm based on IR-Temporal Synopses tree is proposed.The two kinds of query algorithms are introduced in detail,and the implementation steps of the algorithm are described and analyzed.Finally,the experimental system is constructed based on IR-Temporal Synopses tree,and the design idea of the system is given.The functions and operation examples of each part of the system are introduced respectively.In the same experimental environment,comparing and analyzing the performance of IR-Temporal Synopses tree and IR-Synopses tree.Experiments show that the IR-Temporal Synopses tree has good performance both in building and querying.