HCH for Checking Containment of XPath Fragment

XPath is ubiquitous in XML applications for navigating XML trees and selecting a set of element nodes. In XPath query processing, one of the most important issues is how to efficiently check containment relationship between two XPath expressions. To get out of the intricacy and complexity caused by numerous XPath features, we investigate this issue on a frequently used fragment of XPath expressions that consists of node tests, the child axis （/）, the descendant axis （//）, branches （[]） and label wildcards （＊）. Prior work has shown that homomorphism technology can be used for containment checking. However, homomorphism is the sufficient but not necessary condition for containment. For special classes of this fragment, the homomorphism algorithm returns false negatives. To address this problem, this paper proposes two containment techniques, conditioned homomorphism and hidden conditioned homomorphism, and then presents sound algorithms for checking containment. Experimental results confirm the practicability and efficiency of the proposed algorithms.

Finding and sharing GIS methods based on the questions they answer

Geographic information has become central for data scientists of many disciplines to put their analyzes into a spatio-temporal perspective.However,just as the volume and variety of data sources on the Web grow,it becomes increasingly harder for analysts to be familiar with all the available geospatial tools,including toolboxes in Geographic Information Systems(GIS),R packages,and Python modules.Even though the semantics of the questions answered by these tools can be broadly shared,tools and data sources are still divided by syntax and platform-specific technicalities.It would,therefore,be hugely beneficial for information science if analysts could simply ask questions in generic and familiar terms to obtain the tools and data necessary to answer them.In this article,we systematically investigate the analytic questions that lie behind a range of common GIS tools,and we propose a semantic framework to match analytic questions and tools that are capable of answering them.To support the matching process,we define a tractable subset of SPARQL,the query language of the Semantic Web,and we propose and test an algorithm for computing query containment.We illustrate the identification of tools to answer user questions on a set of common user requests.

Use of the Spatial kD-Tree in Computational Physics Applications

The need to perform spatial queries and searches is commonly encountered within the field of computational physics.The development of applications ranging from scientific visualization to finite element analysis requires efficient methods of locating domain objects relative to general locations in space.Much of the time,it is possible to form and maintain spatial relationships between objects either explicitly or by using relative motion constraints as the application evolves in time.Occasionally,either due to unpredictable relative motion or the lack of state information,an application must perform a general search(or ordering)of geometric objects without any explicit spatial relationship information as a basis.If previous state information involving domain geometric objects is not available,it is typically an involved and time consuming process to create object adjacency information or to order the objects in space.Further,as the number of objects and the spatial dimension of the problem domain is increased,the time required to search increases greatly.This paper proposes an implementation of a spatial k-d tree(skD-tree)for use by various applications when a general domain search is required.The skD-tree proposed in this paper is a spatial access method where successive tree levels are split along different dimensions.Objects are indexed by their centroid,and the minimum bounding box of objects in a node are stored in the tree node.The paper focuses on a discussion of efficient and practical algorithms for multidimensional spatial data structures for fast spatial query processing.These functions include the construction of a skD-tree of geometric objects,intersection query,containment query,and nearest neighbor query operations.