XML Data Storage and Query Optimization in Relational Database by XPath Processing Model

The Next-Generation Attenuation for subduction zone regions project (NGA-Sub) has developed data resources and ground motion models for global subduction zone regions. Here we describe the NGA-Sub database. To optimize the efficiency of data storage, access, and updating, data resources for the NGA-Sub project are organized into a relational database consisting of 20 tables containing data, metadata, and computed quantities (e.g. intensity measures, distances). A database schema relates fields in tables to each other through a series of primary and foreign keys. Model developers and other users mostly interact with the data through a flatfile generated as a time-stamped output of the database. We describe the structure of the relational database, the ground motions compiled for the project, and the means by which the data can be accessed. The database contains 71,340 three-component records from 1880 earthquakes from seven global subduction zone regions: Alaska, Central America and Mexico, Cascadia, Japan, New Zealand, South America, and Taiwan. These data were processed on a component-specific basis to minimize noise effects in the data and remove baseline drifts. Provided ground motion intensity measures include peak acceleration, peak velocity, and 5%-damped pseudo-spectral accelerations for a range of oscillator periods.

Download Full-text

Research on Store XML Data in Relational Database Based on XML Schema

2007 IFIP International Conference on Network and Parallel Computing Workshops (NPC 2007) ◽

10.1109/npc.2007.126 ◽

2007 ◽

Cited By ~ 1

Author(s):

XIE Yi-wu ◽

WANG Chen-yang ◽

CAO Zhi-ying ◽

CHEN Yan

Keyword(s):

Relational Database ◽

Xml Schema ◽

Xml Data

Download Full-text

Database Considerations for an ICU Patient Data Management System

ASME 1991 Computers in Engineering Conference: Volume 2 — Finite Elements/Computational Geometry; Computers in Education; Robotics and Controls ◽

10.1115/cie1991-0174 ◽

1991 ◽

Author(s):

N. Fumai ◽

C. Collet ◽

M. Petroni ◽

K. Roger ◽

E. Saab ◽

...

Keyword(s):

Data Management ◽

Real Time ◽

Data Storage ◽

Relational Database ◽

Management System ◽

Patient Data ◽

Patient Data Management System ◽

Data Management System ◽

Data Types ◽

Management Structure

Abstract A Patient Data Management System (PDMS) is being developed for use in the Intensive Care Unit (ICU) of the Montreal Children’s Hospital. The PDMS acquires real-time patient data from a network of physiological bedside monitors and facilitates the review and interpretation of this data by presenting it as graphical trends, charts and plots on a color video display. Due to the large amounts of data involved, the data storage and data management processes are an important task of the PDMS. The data management structure must integrate varied data types and provide database support for different applications, while preserving the real-time acquisition of network data. This paper outlines a new data management structure which is based primarily on OS/2’s Extended Edition relational database. The relational database design is expected to solve the query shortcomings of the previous data management structure, as well as offer support for security and concurrency. The discussion will also highlight future advantages available from a network implementation.

Download Full-text

Efficient Storage and Parallel Query of Massive XML Data in Hadoop

Advances in Data Mining and Database Management - Emerging Technologies and Applications in Data Processing and Management ◽

10.4018/978-1-5225-8446-9.ch012 ◽

2019 ◽

pp. 242-262

Author(s):

Wei Yan

Keyword(s):

Query Processing ◽

Query Optimization ◽

Data Transfer ◽

Programming Model ◽

Xml Data ◽

Efficient Storage ◽

Parallel Query ◽

Parallel Query Processing

In order to solve the problem of storage and query for massive XML data, a method of efficient storage and parallel query for a massive volume of XML data with Hadoop is proposed. This method can store massive XML data in Hadoop and the massive XML data is divided into many XML data blocks and loaded on HDFS. The parallel query method of massive XML data is proposed, which uses parallel XPath queries based on multiple predicate selection, and the results of parallel query can satisfy the requirement of query given by the user. In this chapter, the map logic algorithm and the reduce logic algorithm based on parallel XPath queries based using MapReduce programming model are proposed, and the parallel query processing of massive XML data is realized. In addition, the method of MapReduce query optimization based on multiple predicate selection is proposed to reduce the data transfer volume of the system and improve the performance of the system. Finally, the effectiveness of the proposed method is verified by experiment.

Download Full-text

Research Background

Advances in Data Mining and Database Management - Design, Performance, and Analysis of Innovative Information Retrieval ◽

10.4018/978-1-4666-1975-3.ch014 ◽

2013 ◽

pp. 187-200

Author(s):

Ibrahim Dweib ◽

Joan Lu

Keyword(s):

Data Storage ◽

Relational Database ◽

Query Languages ◽

Xml Schema ◽

Application Program Interface ◽

Database Model ◽

Xml Documents ◽

Similarities And Differences ◽

Schema Languages ◽

Program Interface

In this chapter, the research background is discussed. This includes XML model, XML query languages, XML schema languages, XML Application Program Interface, XML documents types, XML data storage approaches, relational database model, and the similarities and differences between XML model and relational database model. Finally the chapter summary is given.

Download Full-text

Data Mining on XML Data

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch079 ◽

2011 ◽

pp. 506-510

Author(s):

Qin Ding

Keyword(s):

Data Mining ◽

Data Storage ◽

Efficient Algorithms ◽

Relational Data ◽

Xml Data ◽

Structure Information ◽

Data Mining Algorithms ◽

Mining Algorithms ◽

Naive Approach

With the growing usage of XML data for data storage and exchange, there is an imminent need to develop efficient algorithms to perform data mining on semistructured XML data. Mining on XML data is much more difficult than mining on relational data because of the complexity of structure in XML data. A naïve approach to mining on XML data is to first convert XML data into relational format. However the structure information may be lost during the conversion. It is desired to develop efficient and effective data mining algorithms that can be directly applied on XML data.

Download Full-text

A novel method for providing relational databases with rich semantics and natural language processing

Journal of Enterprise Information Management ◽

10.1108/jeim-01-2015-0005 ◽

2017 ◽

Vol 30 (3) ◽

pp. 503-525

Author(s):

Kamal Hamaz ◽

Fouzia Benchikha

Keyword(s):

Natural Language ◽

Data Storage ◽

Language Processing ◽

Relational Database ◽

Relational Databases ◽

Data Access ◽

New Techniques ◽

Content Type ◽

Semantic Tree ◽

Novel Method

Purpose With the development of systems and applications, the number of users interacting with databases has increased considerably. The relational database model is still considered as the most used model for data storage and manipulation. However, it does not offer any semantic support for the stored data which can facilitate data access for the users. Indeed, a large number of users are intimidated when retrieving data because they are non-technical or have little technical knowledge. To overcome this problem, researchers are continuously developing new techniques for Natural Language Interfaces to Databases (NLIDB). Nowadays, the usage of existing NLIDBs is not widespread due to their deficiencies in understanding natural language (NL) queries. In this sense, the purpose of this paper is to propose a novel method for an intelligent understanding of NL queries using semantically enriched database sources. Design/methodology/approach First a reverse engineering process is applied to extract relational database hidden semantics. In the second step, the extracted semantics are enriched further using a domain ontology. After this, all semantics are stored in the same relational database. The phase of processing NL queries uses the stored semantics to generate a semantic tree. Findings The evaluation part of the work shows the advantages of using a semantically enriched database source to understand NL queries. Additionally, enriching a relational database has given more flexibility to understand contextual and synonymous words that may be used in a NL query. Originality/value Existing NLIDBs are not yet a standard option for interfacing a relational database due to their lack for understanding NL queries. Indeed, the techniques used in the literature have their limits. This paper handles those limits by identifying the NL elements by their semantic nature in order to generate a semantic tree. This last is a key solution towards an intelligent understanding of NL queries to relational databases.

Download Full-text