Empowering the OLAP Technology to Support Complex Dimension Hierarchies

2008 ◽  
pp. 2164-2184
Author(s):  
Svetlana Mansmann ◽  
Marc H. Scholl
Keyword(s):  
Real World ◽  
Multidimensional Data ◽  
Complex Data ◽  
Two Phase ◽  
Academic Administration ◽  
On Demand ◽  
Complex Dimension ◽  
On Line ◽  
Analytical Processing ◽  
Uniform Manner

Comprehensive data analysis has become indispensable in a variety of domains. OLAP (On-Line Analytical Processing) systems tend to perform poorly or even fail when applied to complex data scenarios. The restriction of the underlying multidimensional data model to admit only homogeneous and balanced dimension hierarchies is too rigid for many real-world applications and, therefore, has to be overcome in order to provide adequate OLAP support. We present a framework for classifying and modeling complex multidimensional data, with the major effort at the conceptual level as to transform irregular hierarchies to make them navigable in a uniform manner. The properties of various hierarchy types are formalized and a two-phase normalization approach is proposed: heterogeneous dimensions are reshaped into a set of wellbehaved homogeneous subdimensions, followed by the enforcement of summarizability in each dimension’s data hierarchy. Mapping the data to a visual data browser relies solely on metadata, which captures the properties of facts, dimensions, and relationships within the dimensions. The navigation is schema-based, that is, users interact with dimensional levels with on-demand data display. The power of our approach is exemplified using a real-world study from the domain of academic administration.

Empowering the OLAP Technology to Support Complex Dimension Hierarchies

2011 ◽  
pp. 450-470
Author(s):  
Svetlana Mansmann
Keyword(s):  
Data Analysis ◽  
Real World ◽  
Multidimensional Data ◽  
Complex Data ◽  
Two Phase ◽  
Academic Administration ◽  
Complex Dimension ◽  
On Line ◽  
Analytical Processing ◽  
Uniform Manner

Comprehensive data analysis has become indispensable in a variety of domains. OLAP (On-Line Analytical Processing) systems tend to perform poorly or even fail when applied to complex data scenarios. The restriction of the underlying multidimensional data model to admit only homogeneous and balanced dimension hierarchies is too rigid for many real-world applications and, therefore, has to be overcome in order to provide adequate OLAP support. We present a framework for classifying and modeling complex multidimensional data, with the major effort at the conceptual level as to transform irregular hierarchies to make them navigable in a uniform manner. The properties of various hierarchy types are formalized and a two-phase normalization approach is proposed: heterogeneous dimensions are reshaped into a set of well-behaved homogeneous subdimensions, followed by the enforcement of summarizability in each dimension’s data hierarchy. Mapping the data to a visual data browser relies solely on metadata, which captures the properties of facts, dimensions, and relationships within the dimensions. The navigation is schema-based, that is, users interact with dimensional levels with ondemand data display. The power of our approach is exemplified using a real-world study from the domain of academic administration.

Schema Evolution Models and Languages for Multidimensional Data Warehouses

2009 ◽  
pp. 119-128
Author(s):  
Edgard Benítez-Guerrero ◽  
Ericka-Janet Rechy-Ramírez
Keyword(s):  
Data Analysis ◽  
Data Warehouse ◽  
Historical Data ◽  
Multidimensional Systems ◽  
Multidimensional Data ◽  
Schema Evolution ◽  
Time Dimension ◽  
On Line ◽  
Analytical Processing ◽  
Factual Data

A Data Warehouse (DW) is a collection of historical data, built by gathering and integrating data from several sources, which supports decisionmaking processes (Inmon, 1992). On-Line Analytical Processing (OLAP) applications provide users with a multidimensional view of the DW and the tools to manipulate it (Codd, 1993). In this view, a DW is seen as a set of dimensions and cubes (Torlone, 2003). A dimension represents a business perspective under which data analysis is performed and organized in a hierarchy of levels that correspond to different ways to group its elements (e.g., the Time dimension is organized as a hierarchy involving days at the lower level and months and years at higher levels). A cube represents factual data on which the analysis is focused and associates measures (e.g., in a store chain, a measure is the quantity of products sold) with coordinates defined over a set of dimension levels (e.g., product, store, and day of sale). Interrogation is then aimed at aggregating measures at various levels. DWs are often implemented using multidimensional or relational DBMSs. Multidimensional systems directly support the multidimensional data model, while a relational implementation typically employs star schemas(or variations thereof), where a fact table containing the measures references a set of dimension tables.

scholarly journals Data Warehouse Schemas using Multidimensional Data Model for Retail

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.

scholarly journals Navigation Rules for Exploring Large Multidimensional Data Cubes

2008 ◽  
pp. 1334-1354
Author(s):  
Navin Kumar ◽  
Aryya Gangopadhyay ◽  
George Karabatis ◽  
Sanjay Bapna ◽  
Zhiyuan Chen
Keyword(s):  
Knowledge Discovery ◽  
Multidimensional Data ◽  
Discovery Process ◽  
High Recall ◽  
Data Cubes ◽  
Driven System ◽  
On Line ◽  
Analytical Processing ◽  
Multidimensional Data Cubes ◽  
Drill Down

Navigating through multidimensional data cubes is a nontrivial task. Although On-Line Analytical Processing (OLAP) provides the capability to view multidimensional data through rollup, drill-down, and slicing-dicing, it offers minimal guidance to end users in the actual knowledge discovery process. In this article, we address this knowledge discovery problem by identifying novel and useful patterns concealed in multidimensional data that are used for effective exploration of data cubes. We present an algorithm for the DIscovery of Sk-NAvigation Rules (DISNAR), which discovers the hidden interesting patterns in the form of Sk-navigation rules using a test of skewness on the pairs of the current and its candidate drill-down lattice nodes. The rules then are used to enhance navigational capabilities, as illustrated by our rule-driven system. Extensive experimental analysis shows that the DISNAR algorithm discovers the interesting patterns with a high recall and precision with small execution time and low space overhead.

On Handling the Evolution of External Data Sources in a Data Warehouse Architecture

2011 ◽  
pp. 106-147 ◽  
Author(s):  
Robert Wrembel
Keyword(s):  
Data Warehouse ◽  
Real World ◽  
Practical Importance ◽  
Data Sources ◽  
External Data ◽  
External Data Source ◽  
On Line ◽  
Advanced Analysis ◽  
Data Source ◽  
Analytical Processing

A data warehouse architecture (DWA) has been developed for the purpose of integrating data from multiple heterogeneous, distributed, and autonomous external data sources (EDSs) as well as for providing means for advanced analysis of integrated data. The major components of this architecture include: an external data source (EDS) layer, and extraction-transformation-loading (ETL) layer, a data warehouse (DW) layer, and an on-line analytical processing (OLAP) layer. Methods of designing a DWA, research developments, and most of the commercially available DW technologies tacitly assumed that a DWA is static. In practice, however, a DWA requires changes among others as the result of the evolution of EDSs, changes of the real world represented in a DW, and new user requirements. Changes in the structures of EDSs impact the ETL, DW, and OLAP layers. Since such changes are frequent, developing a technology for handling them automatically or semi-automatically in a DWA is of high practical importance. This chapter discusses challenges in designing, building, and managing a DWA that supports the evolution of structures of EDSs, evolution of an ETL layer, and evolution of a DW. The challenges and their solutions presented here are based on an experience of building a prototype Evolving-ETL and a prototype Multiversion Data Warehouse (MVDW). In details, this chapter presents the following issues: the concept of the MVDW, an approach to querying the MVDW, an approach to handling the evolution of an ETL layer, a technique for sharing data between multiple DW versions, and two index structures for the MVDW.

Some Issues in Design of Data Warehousing Systems

2011 ◽  
pp. 22-76
Author(s):  
Ladjel Bellatreche ◽  
Kamalakar Karlapalem ◽  
Mukesh Mohania
Keyword(s):  
Decision Making ◽  
Data Warehouse ◽  
Data Warehousing ◽  
Complex Data ◽  
Time Varying ◽  
Intelligent Decision Making ◽  
Intelligent Decision ◽  
On Line ◽  
Analytical Processing ◽  
Warehousing Systems

Information is one of the most valuable assets of an organization, and when used properly can assist intelligent decision-making that can significantly improve the functioning of an organization. Data warehousing is a recent technology that allows information to be easily and efficiently accessed for decision-making activities. On-line analytical processing (OLAP) tools are well studied for complex data analysis. A data warehouse is a set of subject-oriented, integrated, time varying and non-volatile databases used to support the decision-making activities (Inmon, 1992).

scholarly journals Research and implementation of HTAP for distributed database

2021 ◽  
Vol 39 (2) ◽  
pp. 430-438
Author(s):  
Changhong Jing ◽  
Wenjie Liu ◽  
Jintao Gao ◽  
Ouya Pei
Keyword(s):  
Data Analysis ◽  
Relational Databases ◽  
Distributed Databases ◽  
Distributed Database ◽  
Transaction Processing ◽  
Complex Data ◽  
Main Application ◽  
On Line ◽  
Analytical Processing ◽  
Implementation Method

Data processing can be roughly divided into two categories, online transaction processing OLTP(on-line transaction processing) and online analytical processing OLAP(on-line analytical processing). OLTP is the main application of traditional relational databases, and it is some basic daily transaction processing, such as bank pipeline transactions and so on. OLAP is the main application of the data warehouse system, it supports some more complex data analysis operations, focuses on decision support, and provides popular and intuitive analysis results. As the amount of data processed by enterprises continues to increase, distributed databases have gradually replaced stand-alone databases and become the mainstream of applications. However, the current business supported by distributed databases is mainly based on OLTP applications, lacking OLAP implementation. This paper proposes an implementation method of HTAP for distributed database CBase, which provides an implementation method of OLAP analysis for CBase, and can easily deal with data analysis of large amounts of data.

Toward Integrating Data Warehousing with Data Mining Techniques

2011 ◽  
pp. 253-276 ◽  
Author(s):  
Rokia Missaoui ◽  
Ganaël Jatteau ◽  
Ameur Boujenoui ◽  
Sami Naouali
Keyword(s):  
Data Mining ◽  
Data Warehousing ◽  
Multidimensional Data ◽  
Concept Lattices ◽  
Data Mining Techniques ◽  
Multidimensional Database ◽  
On Demand ◽  
Closed Itemsets ◽  
Analytical Processing ◽  
Ultimate Objective

In this paper, we present alternatives for coupling data warehousing and data mining techniques so that they can benefit from each other’s advances for the ultimate objective of efficiently providing a flexible answer to data mining queries addressed either to a bidimensional (relational) or a multidimensional database. In particular, we investigate two techniques: (i) the first one exploits concept lattices for generating frequent closed itemsets, clusters and association rules from multidimensional data, and (ii) the second one defines new operators similar in spirit to online analytical processing (OLAP) techniques to allow “data mining on demand” (i.e., data mining according to user’s needs and perspectives). The implementation of OLAP-like techniques relies on three operations on lattices, namely selection, projection and assembly. A detailed running example serves to illustrate the scope and benefits of the proposed techniques.

Toward Integrating Data Warehousing with Data Mining Techniques

2008 ◽  
pp. 3346-3363
Author(s):  
Rokia Missaoui ◽  
Ganaël Jatteau ◽  
Ameur Boujenoui ◽  
Sami Naouali
Keyword(s):  
Data Mining ◽  
Data Warehousing ◽  
Multidimensional Data ◽  
Concept Lattices ◽  
Data Mining Techniques ◽  
Multidimensional Database ◽  
On Demand ◽  
Closed Itemsets ◽  
Analytical Processing ◽  
Ultimate Objective

In this paper, we present alternatives for coupling data warehousing and data mining techniques so that they can benefit from each other’s advances for the ultimate objective of efficiently providing a flexible answer to data mining queries addressed either to a bidimensional (relational) or a multidimensional database. In particular, we investigate two techniques: (i) the first one exploits concept lattices for generating frequent closed itemsets, clusters and association rules from multidimensional data, and (ii) the second one defines new operators similar in spirit to online analytical processing (OLAP) techniques to allow “data mining on demand” (i.e., data mining according to user’s needs and perspectives). The implementation of OLAP-like techniques relies on three operations on lattices, namely selection, projection and assembly. A detailed running example serves to illustrate the scope and benefits of the proposed techniques.

scholarly journals DATA WAREHOUSING APPLICATIONS: AN ANALYTICAL TOOL FOR DECISION SUPPORT SYSTEM

2014 ◽  
pp. 294-299
Author(s):  
MOHAMMED SHAFEEQ AHMED
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Data Storage ◽  
Data Warehousing ◽  
Transaction Processing ◽  
Multidimensional Data ◽  
Data Warehouses ◽  
On Line ◽  
Analytical Processing ◽  
On Line Analytical Processing

Data-driven decision support systems, such as data warehouses can serve the requirement of extraction of information from more than one subject area. Data warehouses standardize the data across the organization so as to have a single view of information. Data warehouses (DW) can provide the information required by the decision makers. The data warehouse supports an on-line analytical processing (OLAP), the functional and performance requirements of which are quite different from those of the on-line transaction processing (OLTP) applications traditionally supported by the operational databases. Data warehouses provide on-line analytical processing (OLAP) tools for the interactive analysis of multidimensional data of varied granularities, which facilitates effective data mining. Data warehousing and OLAP have emerged as leading technologies that facilitate data storage, organization and then, significant retrieval. Both are essential elements of decision support, which has increasingly become a focus of the database industry. This paper provides a detailed picture of Data warehousing (DW), exploring the features of it, applications and the architecture of DW over Data Mining, Online Analytical Processing (OLAP), On-line Transaction Processing (OLTP) technologies.

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