TDM: A Tensor Data Model for Logical Data Independence in Polystore Systems

2019 ◽  
pp. 39-56 ◽  
Author(s):  
Eric Leclercq ◽  
Marinette Savonnet
Keyword(s):  
Data Model ◽  
Tensor Data ◽  
Data Independence ◽  
Logical Data

AN ARCHITECTURE FOR DATA WAREHOUSING SUPPORTING DATA INDEPENDENCE AND INTEROPERABILITY

2001 ◽  
Vol 10 (03) ◽  
pp. 377-397 ◽  
Author(s):  
LUCA CABIBBO ◽  
RICCARDO TORLONE
Keyword(s):  
Data Warehouse ◽  
Data Model ◽  
Data Warehousing ◽  
Heterogeneous Data ◽  
Multidimensional Data ◽  
Multidimensional Databases ◽  
Level Of Aggregation ◽  
High Level ◽  
Data Independence ◽  
Logical Architecture

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.

Toward logical data independence

1982 ◽  
Author(s):  
D. Maier ◽  
D. Rozenshtein ◽  
S. Salveter ◽  
J. Stein ◽  
D. S. Warren
Keyword(s):  
Data Independence ◽  
Logical Data

scholarly journals On the complexity of queries in the logical data model

1993 ◽  
Vol 116 (1) ◽  
pp. 33-57 ◽  
Author(s):  
Gabriel M. Kuper ◽  
Moshe Y. Vardi
Keyword(s):  
Data Model ◽  
Logical Data

Querying Multidimensional Data

2005 ◽  
pp. 2392-2396
Author(s):  
Leonardo Tininini
Keyword(s):  
Data Model ◽  
Data Cube ◽  
Level Of Detail ◽  
Multidimensional Data ◽  
Model Integration ◽  
Integration Policy ◽  
Multidimensional Database ◽  
The Core ◽  
Core Concepts ◽  
Logical Data

A powerful, easy-to-use querying environment is without doubt one of the most important components in a multidimensional database. Its effectiveness is influenced by many aspects, both logical (data model, integration, policy of view materialization, etc.) and physical (multidimensional or relational storage, indexes, etc.). Multidimensional querying is often based on the core concepts of multidimensional data modeling, namely the metaphor of the data cube and the concepts of facts, measures and dimensions (Agrawal, Gupta, & Sarawagi, 1997; Gyssens & Lakshmanan, 1997). In contrast to conventional transactional environments, multidimensional querying is often an exploratory process, performed by navigating along dimensions and measures, increasing/decreasing the level of detail and focusing on specific subparts of the cube that appear “promising” for the required information.

Logical Data Model (LDM): The Relational and Codasyl Cases

1991 ◽  
pp. 90-120
Author(s):  
Pham Thu Quang ◽  
Cyrille Chartier-Kastler
Keyword(s):  
Data Model ◽  
Logical Data
Sign in / Sign up

Export Citation Format

Share Document