The influence of spatial position means how deep it affects the spatial objects and can be measured by the number of affected spatial objects. The evaluation of spatial position influence which widely used in architectural planning and facility location is a typical study in the spatial database. In previous studies, a spatial object was supposed to affect only one spatial position, the influence of the object calculated by the number of the space objects in the area. However, the spatial object can affect many spatial positions and the effects are multiple. In this study, we provide a new evaluation model based on RkNN. A new measurement method was proposed by calculating the weight of the contribution based on the distance between the space object and the space position. The new measurement method makes the model more suitable for the practical application. In addition, a location algorithm was proposed based on the RkNN influence evaluation model. The algorithm can solve the problem such as making the facilities to provide the best service to the customers and using each facility effectively. The influence of each facility is calculated in this algorithm and the rationality of the location scheme is evaluated by equilibrium coefficient, the smaller the equilibrium coefficient, the more reasonable the scheme. The location algorithm based on the new model shows a better performance in the practical application, it contributes to the more reasonable and effective facility location.

Along with the proliferation of online digital map and location-based service, very large POI (point of interest) databases have been constructed where a record corresponds to a POI with information including name, category, address, geographical location and other features. A basic spatial query in POI database is POI retrieval. In many scenarios, a user cannot provide enough information to pinpoint the POI except some clue. For example, a user wants to identify a caf é in a city visited many years ago. SHe cannot remember the name and address but she still recalls that "the caf é is about 200 meters away from a restaurant; and turning left at the restaurant there is a bakery 500 meters away, etc.". Intuitively, the clue, even partial and approximate, describes the spatio-textual context around the targeted POI. Motivated by this observation, this work investigates clue-based spatio-textual query which allows user providing clue, i.e., some nearby POIs and the spatial relationships between them, in POI retrieval. The objective is to retrieve k POIs from a POI database with the highest spatio-textual context similarities against the clue. This work has deliberately designed data-quality-tolerant spatio-textual context similarity metric to cope with various data quality problems in both the clue and the POI database. Through crossing valuation, the query accuracy is further enhanced by ensemble method. Also, this work has developed an index called roll-out-star R-tree (RSR-tree) to dramatically improve the query processing efficiency. The extensive tests on data sets from the real world have verified the superiority of our methods in all aspects.

This paper proposes and solves a novel type of spatial queries named Subject-oriented Top-k hot Region (STR) queries. Given a subject S defined by a feature set R and features importance denoted by weights, an STR query retrieves k non-overlapping regions that have the highest scores computed by the number of feature objects and their weights. As an example, the culture subject is defined by exhibition halls, libraries and museums. On the subject, an STR query finds cultural centers intensively distributed feature objects. In this paper, we propose two efficient algorithms, single-partition (SP) algorithm and dual-partition (DP) algorithm, to process STR queries. Extensive experiments evaluate the proposed solutions under a wide range of parameter settings.

Continuously identifying pre-defined patterns in a streaming time series has strong demand in various applications. While most existing works assume the patterns are in equal length and tolerance, this work focuses on the problem where the patterns have various lengths and tolerances, a common situation in the real world. The challenge of this problem roots on the strict space and time requirements of processing the arriving and expiring data in high-speed stream, combined with difficulty of coping with a large number of patterns with various lengths and tolerances. We introduce a novel concept of converging envelope which bounds the tolerance of a group of patterns in various tolerances and equal length and thus dramatically reduces the number of patterns for similarity computation. The basic idea of converging envelope has potential to more general index problems. To index patterns in various lengths and tolerances, we partition patterns into sub-patterns in equal length and an multi-tree index is developed in this paper.