Academic literature on the topic 'Flexible XML matching'

Background Displeasure with the functionality of clinical decision support systems (CDSSs) is considered the primary challenge in CDSS development. A major difficulty in CDSS design is matching the functionality to the desired and actual clinical workflow. Computer-interpretable guidelines (CIGs) are used to formalize medical knowledge in clinical practice guidelines (CPGs) in a computable language. However, existing CIG frameworks require a specific interpreter for each CIG language, hindering the ease of implementation and interoperability. Objective This paper aims to describe a different approach to the representation of clinical knowledge and data. We intended to change the clinician’s perception of a CDSS with sufficient expressivity of the representation while maintaining a small communication and software footprint for both a web application and a mobile app. This approach was originally intended to create a readable and minimal syntax for a web CDSS and future mobile app for antenatal care guidelines with improved human-computer interaction and enhanced usability by aligning the system behavior with clinical workflow. Methods We designed and implemented an architecture design for our CDSS, which uses the model-view-controller (MVC) architecture and a knowledge engine in the MVC architecture based on XML. The knowledge engine design also integrated the requirement of matching clinical care workflow that was desired in the CDSS. For this component of the design task, we used a work ontology analysis of the CPGs for antenatal care in our particular target clinical settings. Results In comparison to other common CIGs used for CDSSs, our XML approach can be used to take advantage of the flexible format of XML to facilitate the electronic sharing of structured data. More importantly, we can take advantage of its flexibility to standardize CIG structure design in a low-level specification language that is ubiquitous, universal, computationally efficient, integrable with web technologies, and human readable. Conclusions Our knowledge representation framework incorporates fundamental elements of other CIGs used in CDSSs in medicine and proved adequate to encode a number of antenatal health care CPGs and their associated clinical workflows. The framework appears general enough to be used with other CPGs in medicine. XML proved to be a language expressive enough to describe planning problems in a computable form and restrictive and expressive enough to implement in a clinical system. It can also be effective for mobile apps, where intermittent communication requires a small footprint and an autonomous app. This approach can be used to incorporate overlapping capabilities of more specialized CIGs in medicine.

<p>The Mango Suite is a set of three freely downloadable cross-platform authoring programs for flexible network-based CALL exercises. They are Adobe Air applications, so they can be used on Windows, Macintosh, or Linux computers, provided the freely-available Adobe Air has been installed on the computer. The exercises which the programs generate are all Adobe Flash based. The three programs are: (1) Mango-multi, which constructs multiple-choice exercises with an optional sound and/or image; (2) Mango-match, which is for word/phrase matching exercises, and has an added feature intended to promote memorization, whereby an item must be matched correctly not once but an optional consecutive number of times; (3) Mango-gap, which produces seamless gap filling exercises, where the gaps can be as small as desired, down to the level of individual letters, and correction feedback is similarly detailed. Sounds may also be inserted at any desired points within the text, so that it is suitable for listening or dictation exercises. Each exercise generated by any of the programs is produced in the form of a folder containing all of the necessary files for immediate upload and deployment (except that if sound files are used in a Mango-gap exercise, they must be copied to the folder manually). The html file in which the flash exercise is embedded may be edited in any way to suit the user, and an xml file controlling the appearance of the exercise itself may be edited through a wysiwyg interface in the authoring program. The programs aim to combine ease of use with features not available in other authoring programs, toprovide a useful teaching and research tool.</p>

Generating accurate elevation data from satellite images is a prerequisite step for applications that involve disaster forecasting and management using GIS platforms. In this respect, the high resolution satellite optical sensors may be regarded as one of the prime and valuable sources for generating accurate and updated elevation information. However, one of the main drawbacks of conventional approaches for automatic elevation generation from these satellite optical data using image matching techniques is the lack of flexibility in the image matching functional models to take dynamically into account the geometric and radiometric dissimilarities between the homologue stereo image points. The classical least squares image matching (LSM) method, on the other hand, is quite flexible in incorporating the geometric and radiometric variations of image pairs into its functional model. The main objective of this paper is to evaluate and compare the potential of the LSM technique for generating disparity maps from high resolution satellite images to achieve sub pixel precision. To evaluate the rate of success of the LSM, the size of the y-disparities between the homologous points is taken as the precision criteria. The evaluation is performed on the Cartosat-1 stereo along track images over a highly mountainous terrain. The precision improvement is judged based on the standard deviation and the scatter pattern of the y-disparity data. The analysis of the results indicate that, the LSM has achieved the matching precision of about 0.18 pixels which is clearly superior to the manual pointing that yielded the precision of 0.37 pixels.

Accurate 3D surface reconstruction of our environment has become essential for an unlimited number of emerging applications. In the past few years, Unmanned Aerial Systems (UAS) are evolving as low-cost and flexible platforms for geospatial data collection that could meet the needs of aforementioned application and overcome limitations of traditional airborne and terrestrial mobile mapping systems. Due to their payload restrictions, these systems usually include consumer-grade imaging and positioning sensor which will negatively impact the quality of the collected geospatial data and reconstructed surfaces. Therefore, new surface reconstruction surfaces are needed to mitigate the impact of using low-cost sensors on the final products. To date, different approaches have been proposed to for 3D surface construction using overlapping images collected by imaging sensor mounted on moving platforms. In these approaches, 3D surfaces are mainly reconstructed based on dense matching techniques. However, generated 3D point clouds might not accurately represent the scanned surfaces due to point density variations and edge preservation problems. In order to resolve these problems, a new region-based 3D surface renostruction trchnique is introduced in this paper. This approach aims to generate a 3D photo-realistic model of individually scanned surfaces within the captured images. This approach is initiated by a Semi-Global dense Matching procedure is carried out to generate a 3D point cloud from the scanned area within the collected images. The generated point cloud is then segmented to extract individual planar surfaces. Finally, a novel region-based texturing technique is implemented for photorealistic reconstruction of the extracted planar surfaces. Experimental results using images collected by a camera mounted on a low-cost UAS demonstrate the feasibility of the proposed approach for photorealistic 3D surface reconstruction.

Remote sensing based on unmanned airborne vehicles (UAVs) is a rapidly developing field of technology. UAVs enable accurate, flexible, low-cost and multiangular measurements of 3D geometric, radiometric, and temporal properties of land and vegetation using various sensors. In this paper we present a geometric processing chain for multiangular measurement system that is designed for measuring object directional reflectance characteristics in a wavelength range of 400&ndash;900 nm. The technique is based on a novel, lightweight spectral camera designed for UAV use. The multiangular measurement is conducted by collecting vertical and oblique area-format spectral images. End products of the geometric processing are image exterior orientations, 3D point clouds and digital surface models (DSM). This data is needed for the radiometric processing chain that produces reflectance image mosaics and multiangular bidirectional reflectance factor (BRF) observations. The geometric processing workflow consists of the following three steps: (1) determining approximate image orientations using Visual Structure from Motion (VisualSFM) software, (2) calculating improved orientations and sensor calibration using a method based on self-calibrating bundle block adjustment (standard photogrammetric software) (this step is optional), and finally (3) creating dense 3D point clouds and DSMs using Photogrammetric Surface Reconstruction from Imagery (SURE) software that is based on semi-global-matching algorithm and it is capable of providing a point density corresponding to the pixel size of the image. We have tested the geometric processing workflow over various targets, including test fields, agricultural fields, lakes and complex 3D structures like forests.

Since its standardization by the World Wide Web Consortium (W3C) in 1998, the XML (acronym for eXtensible Markup Language) has been acknowledged as the de-facto standard format for data, besides being a data format employed by a wide and increasing number of application domains. XML allows data and textual contents to be structured; the structural elements are specified in plain text using strings of characters that can be easily read by computer programs, while maintaining human-readability. XPath and XQuery represent the two main standard languages that have been defined to inquire XML data; the two languages allow to select a subset of elements from an XML document, and to further manipulate its contents and to restructure the document tree form. Both XPath and XQuery are based on a Database perspective of XML documents, where the evaluation of the query clauses is performed like in the database query language SQL, from which both the XML languages took inspiration. The data-centric perspective adopted by the XQuery and XPath languages has been recently extended by an Information Retrieval oriented approach, where a new set of content-based constraints have been defined that allow a full-text search in an IR-style, with an element relevance scoring computation. This extension is called XQuery/XPath Full-Text and has been standardized by the W3C. In the Information Retrieval community other approaches have appeared that take into account the document structure and propose a set of approximate structural matching techniques, where the standard XQuery and XPath structural constraints are evaluated by path relaxation algorithms. Such approaches, however, do not offer the user the possibility to express vague structural constraints the approximate evaluation of which produces a set of weighted fragments, where the weight express the relevance of the fragment with respect to the structural constraints. This thesis describes the definition and the implementation of a formal XQuery Full-Text extension named FleXy, aimed at taking into account the user perspective in the formulation of structure-based constraints, where vagueness can be associated to the specification of such constraints. FleXy has been designed as an extension of the XQuery Full-Text language to inherit both the full-text search features from the Full-Text extension, and the standard element selection provided by XQuery. The evaluation of two new vague structural constraints defined in the FleXy language, named Below and Near, produces a set of weighted elements, where a structural-score is computed by taking into account the distance from the user required target element and the actually retrieved one. Thresholds variants of the Below and Near constraints have also been defined which allow to specify the extent of the application of the vague structural constraints. The formal definition of the FleXy language is here provided through its syntax, its semantics, and the algorithms that define the Below and the Nnear axes. The language implementation has been performed on top of an Open Source XQuery engine named BaseX, a fully featured XQuery and XPath engine with a complete adherence to the Full-Text language specification. Performance evaluations have been subsequently provided to compare the FleXy constraints with the standard XQuery counterparts, when available. Finally, a patent search application has been developed by leveraging the FleXy implementation provided on top of the BaseX engine: the XML structure of the US Patent Collection (USPTO) has been exploited in conjunction with the textual contents of the patents to help non-expert users to effectively retrieve relevant patents by also offering a result categorization strategy.

With the growing importance of XML in data exchange, much research has been done in providing flexible query mechanisms to extract data from XML documents. A core operation for XML query processing is to find all occurrences of a twig pattern Q (or small tree) in a document T. Prior work has typically decomposed Q into binary structural relationships, such as parent-child and ancestor-descendant relations, or root-to-leaf paths. The twig matching is achieved by: (a) matching the binary relationships or paths against XML databases, and (b) using the join algorithms to stitch together all the matching binary relationships or paths. In the worst case, the time for doing joins can be exponential (in the number of query nodes or decomposed paths). In this chapter, we discuss a new algorithm for this task with no path joins involved. The time and space complexities of the algorithm are bounded by O (|T|·Qleaf) and O (Tleaf·Qleaf), respectively, where Tleaf stands for the number of the leaf nodes in T and Qleaf for the number of the leaf nodes in Q. Our experiments show that our method is efficient in supporting twig pattern queries.