AN ARCHITECTURE FOR DATA WAREHOUSING SUPPORTING DATA INDEPENDENCE AND INTEROPERABILITY

We report on the design of a novel architecture for data warehousing based on the introduction of an explicit "logical" layer to the traditional data warehousing framework. This layer serves to guarantee a complete independence of OLAP applications from the physical storage structure of the data warehouse and thus allows users and applications to manipulate multidimensional data ignoring implementation details. For example, it makes possible the modification of the data warehouse organization (e.g. MOLAP or ROLAP implementation, star scheme or snowflake scheme structure) without influencing the high level description of multidimensional data and programs that use the data. Also, it supports the integration of multidimensional data stored in heterogeneous OLAP servers. We propose [Formula: see text], a simple data model for multidimensional databases, as the reference for the logical layer. [Formula: see text] provides an abstract formalism to describe the basic concepts that can be found in any OLAP system (fact, dimension, level of aggregation, and measure). We show that [Formula: see text] databases can be implemented in both relational and multidimensional storage systems. We also show that [Formula: see text] can be profitably used in OLAP applications as front-end. We finally describe the design of a practical system that supports the above logical architecture; this system is used to show in practice how the architecture we propose can hide implementation details and provides a support for interoperability between different and possibly heterogeneous data warehouse applications.

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Time in Multidimensional Databases

Multidimensional Databases ◽

10.4018/978-1-59140-053-0.ch006 ◽

2003 ◽

pp. 166-199 ◽

Cited By ~ 7

Author(s):

Alberto O. Mendelzon ◽

Alejandro A. Vaisman

Keyword(s):

Data Warehouse ◽

Query Language ◽

Data Warehousing ◽

Experimental Results ◽

Temporal Databases ◽

Multidimensional Model ◽

Level Of Abstraction ◽

Multidimensional Databases ◽

History Of ◽

High Level

In spite of the obvious importance of time in data warehousing and OLAP, current commercial systems do not support tracking the history of a data warehouse, either at the schema or instance level. In this chapter we address this issue, introducing the Temporal Multidimensional Model and a query language, denoted TOLAP, allowing expressing temporal OLAP queries at a high level of abstraction. Further, we show that previous work in temporal databases needs to be extended in order to handle evolution and versioning in OLAP. Finally, we present an implementation, along with preliminary experimental results.

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A Survey of Data Warehouse Model Evolution

Handbook of Research on Innovations in Database Technologies and Applications ◽

10.4018/978-1-60566-242-8.ch015 ◽

2009 ◽

pp. 129-136

Author(s):

Cécile Favre ◽

Fadila Bentayeb ◽

Omar Boussaid

Keyword(s):

Data Warehouse ◽

Data Warehousing ◽

Business Environment ◽

Heterogeneous Data ◽

Data Sources ◽

Future Trends ◽

Model Evolution ◽

Heterogeneous Data Sources ◽

Key Points ◽

Existing Data

A data warehouse allows the integration of heterogeneous data sources for analysis purposes. One of the key points for the success of the data warehousing process is the design of the model according to the available data sources and the analysis needs (Nabli, Soussi, Feki, Ben-Abdallah & Gargouri, 2005). However, as the business environment evolves, several changes in the content and structure of the underlying data sources may occur. In addition to these changes, analysis needs may also evolve, requiring an adaptation to the existing data warehouse’s model. In this chapter, we provide an overall view of the state of the art in data warehouse model evolution. We present a set of comparison criteria and compare the various works. Moreover, we discuss the future trends in data warehouse model evolution.

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Data Warehouse Schemas using Multidimensional Data Model for Retail

International Journal of Engineering and Emerging Technology ◽

10.24843/ijeet.2017.v02.i01.p17 ◽

2017 ◽

Vol 2 (1) ◽

pp. 84

Author(s):

Kheri Arionadi Shobirin ◽

Adi Panca Saputra Iskandar ◽

Ida Bagus Alit Swamardika

Keyword(s):

Decision Making ◽

Real Time ◽

Data Warehouse ◽

Data Model ◽

Transaction Processing ◽

Multidimensional Data ◽

Complex Query ◽

On Line ◽

Analytical Processing ◽

Operational Data

A data warehouse are central repositories of integrated data from one or more disparate sources from operational data in On-Line Transaction Processing (OLTP) system to use in decision making strategy and business intelligent using On-Line Analytical Processing (OLAP) techniques. Data warehouses support OLAP applications by storing and maintaining data in multidimensional format. Multidimensional data models as an integral part of OLAP designed to solve complex query analysis in real time.

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Domain/Mapping Model: A Novel Data Warehouse Data Mode

International Journal of Computers Communications & Control ◽

10.15837/ijccc.2017.2.2876 ◽

2017 ◽

Vol 12 (2) ◽

pp. 166 ◽

Cited By ~ 2

Author(s):

Ivan Bojicic ◽

Zoran Marjanovic ◽

Nina Turajlic ◽

Marko Petrovic ◽

Milica Vuckovic ◽

...

Keyword(s):

Data Warehouse ◽

Data Model ◽

Heterogeneous Data ◽

Physical Structure ◽

Data Sources ◽

Mapping Model ◽

Heterogeneous Data Sources ◽

Domain Mapping ◽

Source Models ◽

Data Source

In order for a data warehouse to be able to adequately fulfill its integrative and historical purpose, its data model must enable the appropriate and consistent representation of the different states of a system. In effect, a DW data model, representing the physical structure of the DW, must be general enough, to be able to consume data from heterogeneous data sources and reconcile the semantic differences of the data source models, and, at the same time, be resilient to the constant changes in the structure of the data sources. One of the main problems related to DW development is the absence of a standardized DW data model. In this paper a comparative analysis of the four most prominent DW data models (namely the relational/normalized model, data vault model, anchor model and dimensional model) will be given. On the basis of the results of [1]a, the new DW data model (the Domain/Mapping model- DMM) which would more adequately fulfill the posed requirements is presented.

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Spatial Data Warehouse Modelling

Progressive Methods in Data Warehousing and Business Intelligence ◽

10.4018/978-1-60566-232-9.ch014 ◽

2011 ◽

pp. 282-300

Author(s):

Maria Luisa Damiani ◽

Stefano Spaccapietra

Keyword(s):

Data Warehouse ◽

Spatial Data ◽

Data Model ◽

Spatial Dimension ◽

Research Area ◽

Multidimensional Data ◽

Spatial Data Warehouse ◽

Definition Of ◽

Spatial Data Warehouses ◽

Current Standards

This chapter is concerned with multidimensional data models for spatial data warehouses. Over the last few years different approaches have been proposed in the literature for modelling multidimensional data with geometric extent. Nevertheless, the definition of a comprehensive and formal data model is still a major research issue. The main contributions of the chapter are twofold: First, it draws a picture of the research area; second it introduces a novel spatial multidimensional data model for spatial objects with geometry (MuSD – multigranular spatial data warehouse). MuSD complies with current standards for spatial data modelling, augmented by data warehousing concepts such as spatial fact, spatial dimension and spatial measure. The novelty of the model is the representation of spatial measures at multiple levels of geometric granularity. Besides the representation concepts, the model includes a set of OLAP operators supporting the navigation across dimension and measure levels.

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A Distributed Multidimensional Data Model of Data Warehouse

Lecture Notes in Computer Science - Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing ◽

10.1007/3-540-39205-x_112 ◽

2007 ◽

pp. 664-667

Author(s):

Youfang Lin ◽

Houkuan Huang ◽

Hongsong Li

Keyword(s):

Data Warehouse ◽

Data Model ◽

Multidimensional Data ◽

Multidimensional Data Model

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Temporal Semistructured Data Models and Data Warehouses

Data Warehouses and OLAP ◽

10.4018/987-1-59904-364-7.ch012 ◽

2011 ◽

pp. 277-297 ◽

Cited By ~ 2

Author(s):

Carlo Combi ◽

Barbara Oliboni

Keyword(s):

Data Warehouse ◽

Data Model ◽

Heterogeneous Data ◽

Data Models ◽

Semistructured Data ◽

Data Sources ◽

Time Varying ◽

Data Warehouses ◽

Time Dimension ◽

Heterogeneous Data Sources

This chapter describes a graph-based approach to represent information stored in a data warehouse, by means of a temporal semistructured data model. We consider issues related to the representation of semistructured data warehouses, and discuss the set of constraints needed to manage in a correct way the warehouse time, i.e. the time dimension considered storing data in the data warehouse itself. We use a temporal semistructured data model because a data warehouse can contain data coming from different and heterogeneous data sources. This means that data stored in a data warehouse are semistructured in nature, i.e. in different documents the same information can be represented in different ways, and moreover, the document schemata can be available or not. Moreover, information stored into a data warehouse is often time varying, thus as for semistructured data, also in the data warehouse context, it could be useful to consider time.

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Spatial Data Warehouse Modelling

Processing and Managing Complex Data for Decision Support ◽

10.4018/978-1-59140-655-6.ch001 ◽

2011 ◽

pp. 1-27 ◽

Cited By ~ 12

Author(s):

Maria Luisa Damiani ◽

Stefano Spaccapietra

Keyword(s):

Data Warehouse ◽

Spatial Data ◽

Data Model ◽

Spatial Dimension ◽

Research Area ◽

Multidimensional Data ◽

Spatial Data Warehouse ◽

Definition Of ◽

Spatial Data Warehouses ◽

Current Standards

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The Design and Implementation of Agricultural Production Data Warehouse

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.52-54.978 ◽

2011 ◽

Vol 52-54 ◽

pp. 978-983

Author(s):

Li Juan Zhou ◽

Xiao Xu He ◽

Kang Li

Keyword(s):

Data Warehouse ◽

Agricultural Production ◽

High Capacity ◽

Heterogeneous Data ◽

Multidimensional Data ◽

Production Data ◽

Effective Management ◽

View Selection ◽

Materialized View ◽

Materialized View Selection

This paper designed a data warehouse model of agricultural production. And it built an effective and viable agricultural production data warehouse, by using some key technologies: multidimensional data analysis, cube, materialized view selection, materialized view maintenance. Finally, it provided a solution for the effective management and maintenance problems about high-capacity heterogeneous data.

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Application of Multidimensional Data Model in the Traffic Accident Data Warehouse

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.548-549.1857 ◽

2014 ◽

Vol 548-549 ◽

pp. 1857-1861

Author(s):

Mou Zhong Liu ◽

Min Sun

Keyword(s):

Data Mining ◽

Statistical Analysis ◽

Data Warehouse ◽

Data Model ◽

Traffic Accident ◽

Multidimensional Data ◽

Processing Theory ◽

Accident Data ◽

Real Time Information ◽

Time Information

The traffic administrative department would record real-time information of accidents and update the corresponding database when dealing with daily traffic routines. It is of great significance to study and analyze these data. In this paper, we propose a Multi-dimensional Data Warehouse Model (M-DWM) combined with the concept of Data Warehouse and multi-dimensional data processing theory. The model can greatly improve the efficiency for statistical analysis and data mining.

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