scholarly journals Integrating Related XML Data into Multiple Data Warehouse Schemas

2012 ◽  
Author(s):  
Soumya Sen
Keyword(s):  
Data Warehouse ◽  
Xml Data ◽  
Multiple Data

Implementation an XML Multi-Signature Scheme for XML Data Authentication

2011 ◽  
Vol 65 ◽  
pp. 236-240
Author(s):  
Bao Long Liu ◽  
Hua Chen ◽  
Yi Guo
Keyword(s):  
Signature Scheme ◽  
Hierarchical Network ◽  
Xml Data ◽  
Data Authentication ◽  
Multiple Data ◽  
Xml Signature ◽  
Parallel Graph

XML signature is used to ensure XML data authentication. An XML multi-signature should satisfy multiple data authentication requirements for XML data which passes a hierarchical network of different responsibilities. Based on presented series-parallel XML multi-signature scheme, the implementation of a series-parallel XML multi-signature scheme is illustrated. The paper describes the algorithm for possible XPath in DTD, algorithm for series-parallel graph converting to sub-signing group, and the presentation for signed results.

scholarly journals Rancang Bangun Real-Time Business Intelligence Untuk Subjek Kegiatan Akademik pada Universitas Menggunakan Change Data Capture

2014 ◽  
Vol 5 (2) ◽  
Author(s):  
Stephanie Pamela Adithama
Keyword(s):  
Real Time ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Data Capture ◽  
Strategic Decision ◽  
Change Data Capture ◽  
Multiple Data ◽  
Academic Activities ◽  
Using Data ◽  
Unit Unit

Abstract. The running of academic activities in university continuously adds more data to the existing operational system. The data are not ready for the university strategic decision making, preparing reports for accreditation purposes and academic units. Real-time business intelligence application using data warehouse can become a solution for data analysis. The process of creating a data warehouse includes designing data warehouse, retrieving academic data from multiple data sources, extracting, transforming, loading (ETL) process, creating cube; and generating report. ETL processes are conducted by using a Pull Change Data Capture approach so that data changes during a certain period can be transferred in real-time. The higher the frequency of data change requests brings us closer to real-time and requires less time than loading all the data.Keywords: real-time, business intelligence, data warehouse, academic, change data capture Abstrak.  Kegiatan akademik di universitas berjalan terus menerus dan semakin menambah banyak data pada sistem operasional yang sudah ada. Data tersebut masih belum dapat dimanfaatkan oleh pihak universitas dalam pengambilan keputusan strategis, pembuatan laporan untuk keperluan akreditasi dan unit-unit akademik. Aplikasi real-time business intelligence menggunakan data warehouse menjadi solusi untuk analisa data. Proses pembuatan data warehouse meliputi perancangan data warehouse; pengambilan data akademik dari sumber data; proses extraction, transformation, loading (ETL); pembuatan cube; dan pembuatan laporan. Proses ETL dilakukan menggunakan pendekatan Change Data Capture Pull agar perubahan data selama periode tertentu dapat dipindahkan secara real-time. Semakin tinggi frekuensi permintaan perubahan data akan semakin mendekati real-time dan semakin membutuhkan waktu yang singkat dibandingkan dengan me-load semua data.Kata Kunci: real-time, business intelligence, data warehouse, akademik, change data capture

A relational-XML data warehouse for data aggregation with SQL and XQuery

2008 ◽  
Vol 38 (11) ◽  
pp. 1183-1213 ◽  
Author(s):  
Joseph Fong ◽  
Herbert Shiu ◽  
Davy Cheung
Keyword(s):  
Data Warehouse ◽  
Data Aggregation ◽  
Xml Data

XML Warehousing and OLAP

2011 ◽  
pp. 2109-2116 ◽  
Author(s):  
Hadj Mahboubi
Keyword(s):  
Data Warehouse ◽  
Data Warehousing ◽  
Markup Language ◽  
Future Trends ◽  
Xml Data ◽  
Easy Task ◽  
Extensible Markup ◽  
On Line ◽  
Analytical Processing ◽  
Business Data

With the eXtensible Markup Language (XML) becoming a standard for representing business data (Beyer et al., 2005), a new trend toward XML data warehousing has been emerging for a couple of years, as well as efforts for extending the XQuery language with near On-Line Analytical Processing (OLAP) capabilities (grouping, aggregation, etc.). Though this is not an easy task, these new approaches, techniques and architectures aim at taking specificities of XML into account (e.g., heterogeneous number and order of dimensions or complex measures in facts, ragged dimension hierarchies…) that would be intricate to handle in a relational environment. The aim of this article is to present an overview of the major XML warehousing approaches from the literature, as well as the existing approaches for performing OLAP analyses over XML data (which is termed XML-OLAP or XOLAP; Wang et al., 2005). We also discuss the issues and future trends in this area and illustrate this topic by presenting the design of a unified, XML data warehouse architecture and a set of XOLAP operators expressed in an XML algebra.

scholarly journals BioDWH2: an automated graph-based data warehouse and mapping tool

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Marcel Friedrichs
Keyword(s):  
Data Warehouse ◽  
Heterogeneous Data ◽  
Vital Role ◽  
Specific Data ◽  
Workflow Systems ◽  
Mapping Tool ◽  
Data Formats ◽  
Multiple Data ◽  
Data Source ◽  
Cross Reference

Abstract Data integration plays a vital role in scientific research. In biomedical research, the OMICS fields have shown the need for larger datasets, like proteomics, pharmacogenomics, and newer fields like foodomics. As research projects require multiple data sources, mapping between these sources becomes necessary. Utilized workflow systems and integration tools therefore need to process large amounts of heterogeneous data formats, check for data source updates, and find suitable mapping methods to cross-reference entities from different databases. This article presents BioDWH2, an open-source, graph-based data warehouse and mapping tool, capable of helping researchers with these issues. A workspace centered approach allows project-specific data source selections and Neo4j or GraphQL server tools enable quick access to the database for analysis. The BioDWH2 tools are available to the scientific community at https://github.com/BioDWH2.

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