As online spatial datasets grow both in number and sophistication, it becomes increasingly difficult for users to decide whether a dataset is suitable for their tasks, especially when they do not have prior knowledge of the dataset. In this paper, we propose browsing as an effective and efficient way to explore the content of a spatial dataset. Browsing allows users to view the size of a result set before evaluating the query at the database, thereby avoiding zero-hit/mega-hit queries and saving time and resources. Although the underlying technique supporting browsing is similar to range query aggregation and selectivity estimation, spatial dataset browsing poses some unique challenges. In this paper, we identify a set of spatial relations that need to be supported in browsing applications, namely, the contains, contained and the overlap relations. We prove a lower bound on the storage required to answer queries about the contains relation accurately at a given resolution. We then present three storage-efficient approximation algorithms which we believe to be the first to estimate query results about these spatial relations. We evaluate these algorithms with both synthetic and real world datasets and show that they provide highly accurate estimates for datasets with various characteristics.

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Spatial database operations are typically performed in two steps. In the filtering step, indexes and the minimum bounding rectangles (MBRs) of the objects are used to quickly determine a set of candidate objects, and in the refinement step, the actual geometries of the objects are retrieved and compared to the query geometry or each other. Because of the complexity of the computational geometry algorithms involved, the CPU cost of the refinement step is usually the dominant cost of the operation for complex geometries such as polygons. In this paper, we propose a novel approach to address this problem using efficient rendering and searching capabilities of modern graphics hardware. This approach does not require expensive pre-processing of the data or changes to existing storage and index structures, and it applies to both intersection and distance predicates. Our experiments with real world datasets show that by combining hardware and software methods, the overall computational cost can be reduced substantially for both spatial selections and joins.

An essential part of a text generation task is to extract critical information from the text. People usually obtain critical information in the text via manual extraction; however, the asymmetry between the ability to process information manually and the speed of information growth makes it impossible. This problem can be solved by automatic keyphrase extraction. In this paper, the mainstream unsupervised methods to extract keyphrases are summarized, and we analyze in detail the reasons for the differences in the performance of methods then provided some solutions.