Grade And Exact In Order Of Textual Substance

Ranking and returning the most relevant results for a question is probably the most popular form of XML query processing. To resolve this issue, we first suggest an elegant framework for query relaxation processes to support difficult XML queries. The solutions on which this framework is based are not required, however, to satisfy the precisely defined query syntax, as they can be based on the qualities that can be deduced in the initial query. It does not have the power to elegantly combine structures and content to answer comfortable questions. In our solution, we classify nodes into two groups: categorical nodes and statistical nodes and pattern-based approaches in assessing the similarity relationship of categorical nodes and statistical nodes. We continue to use a comprehensive set of experiences to demonstrate the effectiveness of our proposed approach to the accuracy and recovery of values. Querying XML data often becomes difficult in practical applications because the hierarchical structure of XML documents can be heterogeneous, so any slight misunderstanding of the document structure can certainly increase the risk of unsatisfactory queries. This is very difficult, especially given that such queries produce empty solutions, even if there are no translation errors. In addition, we design a non-periodic evidence-based vector diagram to create and adjust the weakening of the structure and develop an inefficient evaluation parameter to evaluate the similarity relationship on structures. So, we design a new approach to take the highest k that can intelligently create the most promising solutions in a linked order using the ranking scale

The Structural Multiple and Information Satisfied Mixture of XML

Perhaps the order of the most relevant results for the question and return to the most common form of XML query processing. To solve this problem, we first propose an elegant query release framework that supports approximate XML data queries. The solutions that underpin this framework are not forced to strictly conform to the specified query format, but may be based on attributes that cannot be inferred in the original query. However, the current proposals do not take sufficient account of structures, nor do they have the power to combine structures and content neatly to answer relaxation questions. Within our solution we divide nodes into two groups: categorization attribute contracts and statistical attribute nodes. We continue to use a comprehensive set of experience to demonstrate the effectiveness of our proposed approach in terms of accuracy and the restoration of benchmarks. In practical applications, it is often impossible to query XML data because the hierarchical structure of XML documents can be heterogeneous, so any misunderstanding of the document structure can certainly increase the risk of formulating unsatisfactory queries. This is really difficult, especially given the fact that such queries lead to empty solutions, although there are no translation errors. In addition, we propose an evidence-based acyclic graph that generates and regulates the relaxation of the structure and develops an inefficient assessment coefficient to evaluate the relationship of structure similarity. We are therefore developing a new top-to-search approach that can intelligently create promising solutions in a ranking-related order

ARRANGE AND EXTRACT ACCURATE INFORMATION ABOUT XML CONTENT

Order and Return The most relevant results may be the most common form of XML query processing. To work around this problem, we first suggest an elegant query framework to support rough queries across XML data. The solutions based on this framework do not have to accurately fulfill the wording of the query but may be based on attributes that can be inferred in the original query. However, the current proposals do not take the structures into account adequately, in addition they do not have the power to combine structures and contents neatly to answer relaxation queries. Within our solution, we classify the contract into two groups: class attribute points, statistical attribute points, and pattern of related methods in relation to similarity ratings for holding the class attribute and statistical attribute points. We continue to benefit from a comprehensive set of experiments to demonstrate the effectiveness of our proposed approach when it comes to accuracy and recall metrics. XML data cannot be queried in practical applications, because the hierarchical structure of XML documents may be heterogeneous, or any slight misunderstanding of the structure of the document can certainly increase the risk of unsatisfactory query formulation. This is really difficult, especially given the fact that such inquiries give empty solutions, although they are not aggregative errors. In addition, we design a polygonal diagram based on an idea to create and regulate the relaxation of the structure and develop an inefficient evaluation coefficient to assess the relative relationship to structures. We therefore create a new retrieval approach from top k that can intelligently create promising solutions in a contextual arrangement using the order scale

An Effective Method to Measure Disease Similarity Using Gene and Phenotype Associations

Motivation: In order to create controlled vocabularies for shared use in different biomedical domains, a large number of biomedical ontologies such as Disease Ontology (DO) and Human Phenotype Ontology (HPO), etc., are created in the bioinformatics community. Quantitative measures of the associations among diseases could help researchers gain a deep insight of human diseases, since similar diseases are usually caused by similar molecular origins or have similar phenotypes, which is beneficial to reveal the common attributes of diseases and improve the corresponding diagnoses and treatment plans. Some previous are proposed to measure the disease similarity using a particular biomedical ontology during the past few years, but for a newly discovered disease or a disease with few related genetic information in Disease Ontology (i.e., a disease with less disease-gene associations), these previous approaches usually ignores the joint computation of disease similarity by integrating gene and phenotype associations.Results: In this paper we propose a novel method called GPSim to effectively deduce the semantic similarity of diseases. In particular, GPSim calculates the similarity by jointly utilizing gene, disease and phenotype associations extracted from multiple biomedical ontologies and databases. We also explore the phenotypic factors such as the depth of HPO terms and the number of phenotypic associations that affect the evaluation performance. A final experimental evaluation is carried out to evaluate the performance of GPSim and shows its advantages over previous approaches

Compressing Labels of Dynamic XML Data using Base-9 Scheme and Fibonacci Encoding

The flexibility and self-describing nature of XML has made it the most common mark-up language used for data representation over the Web. XML data is naturally modelled as a tree, where the structural tree information can be encoded into labels via XML labelling scheme in order to permit answers to queries without the need to access original XML files. As the transmission of XML data over the Internet has become vibrant, it has also become necessary to have an XML labelling scheme that supports dynamic XML data. For a large-scale and frequently updated XML document, existing dynamic XML labelling schemes still suffer from high growth rates in terms of their label size, which can result in overflow problems and/or ambiguous data/query retrievals. This thesis considers the compression of XML labels. A novel XML labelling scheme, named “Base-9”, has been developed to generate labels that are as compact as possible and yet provide efficient support for queries to both static and dynamic XML data. A Fibonacci prefix-encoding method has been used for the first time to store Base-9’s XML labels in a compressed format, with the intention of minimising the storage space without degrading XML querying performance. The thesis also investigates the compression of XML labels using various existing prefix-encoding methods. This investigation has resulted in the proposal of a novel prefix-encoding method named “Elias-Fibonacci of order 3”, which has achieved the fastest encoding time of all prefix-encoding methods studied in this thesis, whereas Fibonacci encoding was found to require the minimum storage. Unlike current XML labelling schemes, the new Base-9 labelling scheme ensures the generation of short labels even after large, frequent, skewed insertions. The advantages of such short labels as those generated by the combination of applying the Base-9 scheme and the use of Fibonacci encoding in terms of storing, updating, retrieving and querying XML data are supported by the experimental results reported herein