Oceanographic ontology-based spatial knowledge query

The construction of oceanographic ontologies is fundamental to the ＂digital ocean＂. Therefore, on the basis of introduction of new concept of oceanographic ontology, an oceanographic ontology-based spatial knowledge query （OOBSKQ） method was proposed and developed. Because the method uses a natural language to describe query conditions and the query result is highly integrated knowledge, it can provide users with direct answers while hiding the complicated computation and reasoning processes, and achieves intelligent, automatic oceanographic spatial information query on the level of knowledge and semantics. A case study of resource and environmental application in bay has shown the implementation process of the method and its feasibility and usefulness.

Fast Web - Based Data Transmission

Since web based GIS processes large size spatial geographic information on internet, we should try to improve the efficiency of spatial data query processing and transmission. This paper presents two efficient methods for this purpose: division transmission and progressive transmission methods. In division transmission method, a map can be divided into several parts, called “tiles”, and only tiles can be transmitted at the request of a client. In progressive transmission method, a map can be split into several phase views based on the significance of vertices, and a server produces a target object and then transmits it progressively when this spatial object is requested from a client. In order to achieve these methods, the algorithms, “tile division”, “priority order estimation” and the strategies for data transmission are proposed in this paper, respectively. Compared with such traditional methods as “map total transmission” and “layer transmission”, the web based GIS data transmission, proposed in this paper, is advantageous in the increase of the data transmission efficiency by a great margin.

Bitmap lattice index in road networks

A novel technique called the bitmap lattice index(BLI) is proposed, which combines the advantages of a wireless broadcasting environment with a road network. Existing road networks are based on the on-demand method: a server's workload increases as the query request increases when a server sends a client information. To solve this problem, we propose the BLI. The BLI denotes an object and a node as 0 and 1 in the Hilbert curve(HC) map. The BLI can identify the position of a node and an object through bit information; it can also reduce the broadcasting frequency of a server by reducing the size of the index, thereby decreasing the access latency and query processing times. Moreover, the BLI is highly effective for data filtering, as it can identify the positions of both an object and a node. In a road network, if filtering is done via the Euclidean distance, it may result in an error. To prevent this, we add another validation procedure. The experiment is conducted by applying the BLI to kNN query, and the technique is assessed by a performance evaluation experiment.

An accurate selectivity estimation method for window queries and an implementation thereof

Spatial selectivity estimation is crucial to choose the cheapest execution plan for a given query in a query optimizer.This article proposes an accurate spatial selectivity estimation method based on the cumulative density(CD)histograms,which can deal with any arbitrary spatial query window.In this method,the selectivity can be estimated in original logic of the CD histogram,after the four corner values of a query window have been accurately interpolated on the continuous surface of the elevation histogram.For the interpolation of any corner points,we first identify the cells that can affect the value of point(x,y)in the CD histogram.These cells can be categorized into two classes:ones within the range from(0,0)to(x,y)and the other overlapping the range from(0,0)to(x,y).The values of the former class can be used directly,whereas we revise the values of any cells falling in the latter class by the number of vertices in the corresponding cell and the area ratio covered by the range from(0,0)to(x,y).This revision makes the estimation method more accurate.The CD histograms and estimation method have been implemented in INGRES.Experiment results show that the method can accurately estimate the selectivity of arbitrary query windows and can help the optimizer choose a cheaper query plan.