GPU-Accelerated Quantification Filters for Analytical Queries in Multidimensional Databases

2015 ◽  
pp. 229-242 ◽  
Author(s):  
Peter Tim Strohm ◽  
Steffen Wittmer ◽  
Alexander Haberstroh ◽  
Tobias Lauer
Keyword(s):  
Multidimensional Databases

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.

On Modeling and Analysis of Multidimensional Geographic Databases

2013 ◽  
pp. 91-107 ◽  
Author(s):  
Sandro Bimonte
Keyword(s):  
Data Warehouse ◽  
Complex Nature ◽  
Web Based ◽  
Modeling And Analysis ◽  
Multidimensional Databases ◽  
Geographic Data ◽  
Analytical Review ◽  
Flexible Framework ◽  
Web Based System ◽  
Support Decision Making

Data warehouse and OLAP systems are tools to support decision-making. Geographic information systems (GISs) allow memorizing, analyzing and visualizing geographic data. In order to exploit the complex nature of geographic data, a new kind of decision support system has been developed: spatial OLAP (SOLAP). Spatial OLAP redefines main OLAP concepts: dimension, measure and multidimensional operators. SOLAP systems integrate OLAP and GIS functionalities into a unique interactive and flexible framework. Several research tools have been proposed to explore and the analyze spatio-multidimensional databases. This chapter presents a panorama of SOLAP models and an analytical review of research SOLAP tools. Moreover, the authors describe their Web-based system: GeWOlap. GeWOlap is an OLAP-GIS integrated solution implementing drill and cut spatio-multidimensional operators, and it supports some new spatio-multidimensional operators which change dynamically the structure of the spatial hypercube thanks to spatial analysis operators.

Incomplete Information in Multidimensional Databases

2003 ◽  
pp. 282-309 ◽  
Author(s):  
Cirtis E. Dyreson ◽  
Torben Bach Pedersen ◽  
Christian S. Jensen
Keyword(s):  
Incomplete Information ◽  
Complete Information ◽  
Multidimensional Data ◽  
Uncertain Information ◽  
Real World Data ◽  
Multidimensional Database ◽  
Simple Strategy ◽  
Multidimensional Databases ◽  
Derived Data ◽  
Base Data

While incomplete information is endemic to real-world data, current multidimensional data models are not engineered to manage incomplete information in base data, derived data, and dimensions. This chapter presents several strategies for managing incomplete information in multidimensional databases. Which strategy to use is dependent on the kind of incomplete information present, and also on where it occurs in the multidimensional database. A relatively simple strategy is to replace incomplete information with appropriate, complete information. The advantage of this strategy is that all multidimensional databases can manage complete information. Other strategies require more substantial changes to the multidimensional database. One strategy is to reflect the incompleteness in computed aggregates, which is possible only if the multidimensional database allows incomplete values in its hierarchies. Another strategy is to measure the amount of incompleteness in aggregated values by tallying how much uncertain information went into their production.

Basic Notions

2003 ◽  
pp. 1-45
Author(s):  
Maurizio Rafanelli
Keyword(s):  
Graph Model ◽  
Aggregate Data ◽  
Multidimensional Data ◽  
Formal Definition ◽  
Aggregation Process ◽  
Data Cubes ◽  
Multidimensional Databases ◽  
On Line ◽  
Analytic Processing ◽  
Definition Of

This chapter presents the basic notions regarding multidimensional (aggregate) databases by referring to different definitions given for them in the literature. It illustrates the important concepts of micro, macro, and metadata; presents a formal definition of the aggregation process, discussing the concepts of dimension and dimension hierarchies; describes the multidimensional aggregate data structure, distinguishing between simple, complex, and composite structure; illustrates the different types of null values; and discusses differences and similarities which exist between multidimensional aggregate data (generally called statistical data because they are used mainly by statisticians) and the On-Line-Analytic Processing (OLAP) of multidimensional data represented by different data cubes, also discussing the different (symmetric and non-symmetric) treatment of dimensions and measures required by OLAP and aggregate multidimensional databases. Finally it discusses a graph model and a tabular model for this kind of data, and gives a set of definitions regarding the OLAP terminology.

Mining Association Rules in Data Warehouses

2008 ◽  
pp. 303-335
Author(s):  
Haorianto Cokrowijoyo Tjioe ◽  
David Taniar
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Strategic Decision ◽  
Multidimensional Data ◽  
Data Warehouses ◽  
Multidimensional Databases ◽  
Efficient Data ◽  
Decision Making Processes ◽  
Mining Association Rules ◽  
Transactional Data

Data mining applications have enormously altered the strategic decision-making processes of organizations. The application of association rules algorithms is one of the well-known data mining techniques that have been developed to cope with multidimensional databases. However, most of these algorithms focus on multidimensional data models for transactional data. As data warehouses can be presented using a multidimensional model, in this paper we provide another perspective to mine association rules in data warehouses by focusing on a measurement of summarized data. We propose four algorithms — VAvg, HAvg, WMAvg, and ModusFilter — to provide efficient data initialization for mining association rules in data warehouses by concentrating on the measurement of aggregate data. Then we apply those algorithms both on a non-repeatable predicate, which is known as mining normal association rules, using GenNLI, and a repeatable predicate using ComDims and GenHLI, which is known as mining hybrid association rules.

A Multidimensional Methodology with Support for Spatio-Temporal Multigranularity in the Conceptual and Logical Phases

2011 ◽  
pp. 194-230 ◽  
Author(s):  
Concepción M. Gascueña ◽  
Rafael Guadalupe
Keyword(s):  
Spatial Data ◽  
Hierarchical Structures ◽  
The Other ◽  
Fe Model ◽  
Multidimensional Databases ◽  
Object Relational ◽  
Spatio Temporal ◽  
The One ◽  
Spatial Semantics ◽  
Thematic Data

The Multidimensional Databases (MDB) are used in the Decision Support Systems (DSS) and in Geographic Information Systems (GIS); the latter locates spatial data on the Earth’s surface and studies its evolution through time. This work presents part of a methodology to design MDB, where it considers the Conceptual and Logical phases, and with related support for multiple spatio-temporal granularities. This will allow us to have multiple representations of the same spatial data, interacting with other, spatial and thematic data. In the Conceptual phase, the conceptual multidimensional model—FactEntity (FE)—is used. In the Logical phase, the rules of transformations are defined, from the FE model, to the Relational and Object Relational logical models, maintaining multidimensional semantics, and under the perspective of multiple spatial, temporal, and thematic granularities. The FE model shows constructors and hierarchical structures to deal with the multidimensional semantics on the one hand, carrying out a study on how to structure “a fact and its associated dimensions.” Thus making up the Basic factEnty, and in addition, showing rules to generate all the possible Virtual factEntities. On the other hand, with the spatial semantics, highlighting the Semantic and Geometric spatial granularities.

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