scholarly journals Effective XQuery keyword using XML query processing

2018 ◽  
Vol 14 (1) ◽  
pp. 450
Author(s):  
E. Seshatheri ◽  
T. Bhuvaneswari
Keyword(s):  
Structural Data ◽  
Data Representation ◽  
Structured Data ◽  
Frequent Pattern ◽  
Simple Task ◽  
Data Format ◽  
Xml Document ◽  
Data Files ◽  
Hierarchical Data Representation ◽  
Inaccurate Result

<span>The data has structured is determined using the standard is known as XML whereaslarge amount of data has consumed through internet consist of the both structural data format as well as semi structural data format which gets stored and processed whereas XML allow the data of semi-structured and hierarchical data representation not only consist of concept with individual items from various kind of database but also have relationship among data items. The utilized knowledge bed is provided with concise ideas for both structured and semi structured data files, XML document contents and rapid with exact solutions for the queries required at any time. The user can search their resources with the help of queries. Searching the resources with the help of queries is not a simple task, where inaccurate result and complexity would occur. Hence it is not a better way for searching the resources. This paper proposes the query answering system of Linear search usingwild card searchfor extracting the frequent pattern to maximize your search results in library database on XML document to extract the most relevant feeds from the large file directly. It will help the user to find his resources completely.</span>

scholarly journals Data Reduction in the String Space for Efficient kNN Classification Through Space Partitioning

2020 ◽  
Vol 10 (10) ◽  
pp. 3356 ◽  
Author(s):  
Jose J. Valero-Mas ◽  
Francisco J. Castellanos
Keyword(s):  
Data Reduction ◽  
Structural Data ◽  
Data Representation ◽  
Space Partitioning ◽  
Classification Rate ◽  
Symbolic Data ◽  
Knn Classifier ◽  
Initial Dataset ◽  
Low Efficiency ◽  
High Level

Within the Pattern Recognition field, two representations are generally considered for encoding the data: statistical codifications, which describe elements as feature vectors, and structural representations, which encode elements as high-level symbolic data structures such as strings, trees or graphs. While the vast majority of classifiers are capable of addressing statistical spaces, only some particular methods are suitable for structural representations. The kNN classifier constitutes one of the scarce examples of algorithms capable of tackling both statistical and structural spaces. This method is based on the computation of the dissimilarity between all the samples of the set, which is the main reason for its high versatility, but in turn, for its low efficiency as well. Prototype Generation is one of the possibilities for palliating this issue. These mechanisms generate a reduced version of the initial dataset by performing data transformation and aggregation processes on the initial collection. Nevertheless, these generation processes are quite dependent on the data representation considered, being not generally well defined for structural data. In this work we present the adaptation of the generation-based reduction algorithm Reduction through Homogeneous Clusters to the case of string data. This algorithm performs the reduction by partitioning the space into class-homogeneous clusters for then generating a representative prototype as the median value of each group. Thus, the main issue to tackle is the retrieval of the median element of a set of strings. Our comprehensive experimentation comparatively assesses the performance of this algorithm in both the statistical and the string-based spaces. Results prove the relevance of our approach by showing a competitive compromise between classification rate and data reduction.

scholarly journals A comprehensive, FAIR file format for neuroanatomical structure modeling

2020 ◽  
Author(s):  
A. E. Sullivan ◽  
S. J. Tappan ◽  
P. J. Angstman ◽  
A. Rodriguez ◽  
G. C. Thomas ◽  
...  
Keyword(s):  
Biological Research ◽  
File Format ◽  
Data Types ◽  
Alternative Analysis ◽  
Data Format ◽  
Digital Reconstruction ◽  
Data Files ◽  
Rich Data ◽  
Scientific Questions ◽  
Neuroscience Community

AbstractWith advances in microscopy and computer science, the technique of digitally reconstructing, modeling, and quantifying microscopic anatomies has become central to many fields of biological research. MBF Bioscience has chosen to openly document their digital reconstruction file format, Neuromorphological File Specification (4.0), available at www.mbfbioscience.com/filespecification (Angstman et al. 2020). One of such technologies, the format created and maintained by MBF Bioscience is broadly utilized by the neuroscience community. The data format’s structure and capabilities have evolved since its inception, with modifications made to keep pace with advancements in microscopy and the scientific questions raised by worldwide experts in the field. More recent modifications to the neuromorphological data format ensure it abides by the Findable, Accessible, Interoperable, and Reusable (FAIR) data standards promoted by the International Neuroinformatics Coordinating Facility (INCF; Wilkinson et al. 2016). The incorporated metadata make it easy to identify and repurpose these data types for downstream application and investigation. This publication describes key elements of the file format and details their relevant structural advantages in an effort to encourage the reuse of these rich data files for alternative analysis or reproduction of derived conclusions.

Development of an XML-Based Specification for Traffic Model Data Exchange

2002 ◽  
Vol 1804 (1) ◽  
pp. 144-150
Author(s):  
Kenneth G. Courage ◽  
Scott S. Washburn ◽  
Jin-Tae Kim
Keyword(s):  
Traffic Control ◽  
Data Exchange ◽  
Transportation Network ◽  
Data Entry ◽  
Data Representation ◽  
Traffic Model ◽  
Model Data ◽  
Traffic Demand ◽  
Data Formats ◽  
Data Files

The proliferation of traffic software programs on the market has resulted in many very specialized programs, intended to analyze one or two specific items within a transportation network. Consequently, traffic engineers use multiple programs on a single project, which ironically has resulted in new inefficiency for the traffic engineer. Most of these programs deal with the same core set of data, for example, physical roadway characteristics, traffic demand levels, and traffic control variables. However, most of these programs have their own formats for saving data files. Therefore, these programs cannot share information directly or communicate with each other because of incompatible data formats. Thus, the traffic engineer is faced with manually reentering common data from one program into another. In addition to inefficiency, this also creates additional opportunities for data entry errors. XML is catching on rapidly as a means for exchanging data between two systems or users who deal with the same data but in different formats. Specific vocabularies have been developed for statistics, mathematics, chemistry, and many other disciplines. The traffic model markup language (TMML) is introduced as a resource for traffic model data representation, storage, rendering, and exchange. TMML structure and vocabulary are described, and examples of their use are presented.

Querying GML

2010 ◽  
pp. 11-19 ◽  
Author(s):  
Jose E. Córcoles ◽  
Pascual González
Keyword(s):  
Spatial Data ◽  
Query Language ◽  
Query Languages ◽  
Structured Data ◽  
Large Set ◽  
Topological Relationship ◽  
Xml Document ◽  
Spatial Operators

As a database format, XML (GML by extension) can be queried. In order to do this, we need a query language (of general use) to retrieve information from an XML document. Nevertheless, it is necessary to enrich the query language over XML features with spatial operators if we wish to apply it over spatial data encoded with GML. Otherwise, these query languages could only be used to query alphanumeric features of an XML document and not, for example, the topological relationship between two spatial regions. Today, there is a large set of query languages over XML. These query languages are different with respect to syntax, available operators and environment of applicability. However, they share the same features, that is, features of query languages over semi-structured data. With respect to GML, from the literature, it is known that four GML query languages have been proposed. The following chapter briefly describes these query languages over GML.

Electronic Charts for Oceangoing Vessels

1993 ◽  
Vol 46 (3) ◽  
pp. 406-411 ◽  
Author(s):  
Colin G. Weeks
Keyword(s):  
Information System ◽  
New Orleans ◽  
Software Package ◽  
Current Data ◽  
Differential Gps ◽  
Data Format ◽  
Blind Navigation ◽  
The Us ◽  
Electronic Chart ◽  
Data Files

It is probable that Congress will require oceangoing ships in US ports to use Differential GPS (DGPS) and electronic charts once DGPS is available on all coasts of the US, which is currently scheduled for completion by the end of 1996. The type of electronic chart currently envisaged, the Electronic Chart Display and Information System (ECDIS) under development by the IMO & IHO, is unsuitable for those ports which are entered through long dredged channels – a category that includes Houston, Gramercy and New Orleans, the three ports which handled the most foreign tonnage in 1991. A different type of electronic chart has been in daily use in such channels, with microwave positioning, since 1984; the combination of DGPS with such a software package has been called an Electronic Navigation System (ENS) and differs from ECDIS in that the ENS is designed to supplement the published chart, not replace it. The paper suggests that the utility of ECDIS would be enhanced if its specifications were modified to allow the entry of the data files used by the ENS, thus giving ECDIS a similar capability for blind navigation in confined waters. Standardization of the current data format would also permit alternative versions of ENS to be developed.

Study of Data Integration Model Based on Network Technology

2011 ◽  
Vol 268-270 ◽  
pp. 1868-1873
Author(s):  
Li Jun Yang
Keyword(s):  
Data Model ◽  
Data Representation ◽  
Heterogeneous Data ◽  
Structured Data ◽  
Common Data Model ◽  
Network Technology ◽  
Interaction Technique ◽  
Integration Model ◽  
Heterogeneous Data Sources ◽  
Meaningful Research

The existence of heterogeneous data sources brings great inconvenience to realize the exchange visits to data between different information systems. Therefore, it becomes a meaningful research topic to solve the problem of realizing convenient and flexible exchange visits. This paper combines the data representation format of XML generally used in current network with an interaction technique of WebService, and constructs a UDM data model, which can implement structured data of relational type as well as describe unstructured data and self-describing semi-structured data. So UDM data model can be used as a common data model integrated by heterogeneous data to integrate these heterogeneous data.

Efficient Identification of Structural Relationships for XML Queries using Secure Labeling Schemes

2016 ◽  
Vol 12 (4) ◽  
pp. 63-80 ◽  
Author(s):  
S. Sankari ◽  
S. Bose
Keyword(s):  
Information Exchange ◽  
Data Representation ◽  
Sibling Relationship ◽  
Object Model ◽  
Labeling Schemes ◽  
Structural Relationships ◽  
Xml Document ◽  
The World ◽  
Labeling Scheme ◽  
Better Than

XML emerged as a de-facto standard for data representation and information exchange over the World Wide Web. By utilizing document object model (DOM), XML document can be viewed as XML DOM tree. Nodes of an XML tree are labeled to uniquely identify every node by following a labeling scheme. This paper proposes a method to efficiently identify the two structural relationships namely document order (DO) and sibling relationship that exist between the XML nodes using two secure labeling schemes specifically enhanced Dewey coding (EDC) and secure Dewey coding (SDC). These structural relationships influence the performance of XML queries so they need to be identified in efficient time. This paper implements the method to identify DO and sibling relationship using EDC and SDC labels for various real-time XML documents. Experiment results show the identification of DO and sibling relationship using SDC labels performs better than EDC labels for processing XML queries.

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