Research And Implementation For IBD-tree Index Of Boolean Range Continuous Query

Massive amount of data that are geo-tagged and associated with text information are being generated at an unprecedented scale. Users may want to be notified of interesting geo-textual objects during a period of time. For example, users may want to be notisfied when some publish a message at 5 miles around them that says "There is sales in the supermarket!".In this paper, we implement a demo system to satisfy this need of users. The functions of system are publishing message, submiting subscribing boolean range continous queries and notisfying users arrivaling matching geo-textual objects. A subscribing model is a boolean range continous query. A message model is geo-textual object. Based on the above model, the system combine the boolean matching, range matching and time matching to match the geo-textual objects and boolean range continous queries. The matched geo-textual objects for boolean range continous query is regarded as the interested objects of the user. It will be pushed to the user’s app. The system use index to accelerate the matching procedure.In this paper, the key point is to develop an efficient index for matching the geo-textual objects and the boolean range continous query. We study the capacity of different kinds of index for boolean range continous query, such as quad tree inverted hybrid index. Based on previous research, we design a buddy tree based hybrid index which improves the boolean range continous query processing capacity. On the experiment study, we compare the capac-ity of buddy tree based hybrid index and that of quad tree based hybrid index(IQ-tree). We can infer from the experiment that the buddy tree based hybrid index improves the system’s capacity.