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.

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.

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 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.

scholarly journals Knowledge Graph OLAP

Semantic Web ◽  
2020 ◽  
pp. 1-35
Author(s):  
Christoph G. Schuetz ◽  
Loris Bozzato ◽  
Bernd Neumayr ◽  
Michael Schrefl ◽  
Luciano Serafini
Keyword(s):  
Data Analysis ◽  
Real World ◽  
Multidimensional Data ◽  
Knowledge Graph ◽  
Analytical Processing ◽  
Hierarchical Nature ◽  
High Level ◽  
Multidimensional Models ◽  
Query Operations ◽  
Selection Of

A knowledge graph (KG) represents real-world entities and their relationships. The represented knowledge is often context-dependent, leading to the construction of contextualized KGs. The multidimensional and hierarchical nature of context invites comparison with the OLAP cube model from multidimensional data analysis. Traditional systems for online analytical processing (OLAP) employ multidimensional models to represent numeric values for further analysis using dedicated query operations. In this paper, along with an adaptation of the OLAP cube model for KGs, we introduce an adaptation of the traditional OLAP query operations for the purposes of performing analysis over KGs. In particular, we decompose the roll-up operation from traditional OLAP into a merge and an abstraction operation. The merge operation corresponds to the selection of knowledge from different contexts whereas abstraction replaces entities with more general entities. The result of such a query is a more abstract, high-level view – a management summary – of the knowledge.

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.

GENERAL ASSESSMENT OF THE PROSPECTS FOR THE APPLICATION OF THE BIG DATA PARADIGM IN SOCIO-ECONOMIC SYSTEMS

2021 ◽  
pp. 47-52
Author(s):  
V. A. Konovalov
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Multidimensional Data ◽  
Economic Systems ◽  
Mathematical Methods ◽  
Data Synthesis ◽  
Analytical Processing ◽  
Economic Nature ◽  
The Impact ◽  
Scientific Ideas

The paper assesses the prospects for the application of the big data paradigm in socio-economic systems through the analysis of factors that distinguish it from the well-known scientific ideas of data synthesis and decomposition. The idea of extracting knowledge directly from big data is analyzed. The article compares approaches to extracting knowledge from big data: algebraic and multidimensional data analysis used in OLAP-systems (OnLine Analytical Processing). An intermediate conclusion is made about the advisability of dividing systems for working with big data into two main classes: automatic and non-automatic. To assess the result of extracting knowledge from big data, it is proposed to use well-known scientific criteria: reliability and efficiency. It is proposed to consider two components of reliability: methodical and instrumental. The main goals of knowledge extraction in socio-economic systems are highlighted: forecasting and support for making management decisions. The factors that distinguish big data are analyzed: volume, variety, velocity, as applied to the study of socio-economic systems. The expediency of introducing a universe into systems for processing big data, which provides a description of the variety of big data and source protocols, is analyzed. The impact of the properties of sample populations from big data: incompleteness, heterogeneity, and non-representativeness, the choice of mathematical methods for processing big data is analyzed. The conclusion is made about the need for a systemic, comprehensive, cautious approach to the development of fundamental decisions of a socio-economic nature when using the big data paradigm in the study of individual socio-economic subsystems.

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).

Evolution of Data Cube Computational Approaches

2011 ◽  
pp. 469-476
Author(s):  
Rebecca Boon-Noi Tan
Keyword(s):  
Decision Support ◽  
Data Analysis ◽  
Support System ◽  
Database Systems ◽  
Data Cube ◽  
Multidimensional Data ◽  
Multidimensional Data Analysis ◽  
Computational Approaches ◽  
Simultaneous Group ◽  
Analytical Processing

Aggregation is a commonly used operation in decision support database systems. Users of decision support queries are interested in identifying trends rather than looking at individual records in isolation. Decision support system (DSS) queries consequently make heavy use of aggregations, and the ability to simultaneously aggregate across many sets of dimensions (in SQL terms, this translates to many simultaneous group-bys) is crucial for Online Analytical Processing (OLAP) or multidimensional data analysis applications (Datta, VanderMeer, & Ramamritham, 2002; Dehne, Eavis, Hambrusch, & Rau-Chaplin, 2002; Elmasri & Navathe, 2004; Silberschatz, Korth & Sudarshan, 2002).

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