Advanced Techniques for Scientific Data Warehouses
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Abstract:
Data warehouses using a multidimensional view of data have become very popular in both business and science in recent years. Data warehouses for scientific purposes such as medicine and bio-chemistry pose several great challenges to existing data warehouse technology. Data warehouses usually use pre-aggregated data to ensure fast query response. However, pre-aggregation cannot be used in practice if the dimension structures or the relationships between facts and dimensions are irregular. A technique for overcoming this limitation and some experimental results are presented. Queries over scientific data warehouses often need to reference data that is external to the data warehouse, e.g., data that is too complex to be handled by current data warehouse technology, data that is "owned" by other organizations, or data that is updated frequently. This paper presents a federation architecture that allows the integration of multidimensional warehouse data with complex external data.Keywords:
Dimensional modeling
Online analytical processing
Multidimensional data
Business Intelligence
Data Warehousing and On-Line Analytical Processing (OLAP) are technologies for providing executives and managers with timely, integrated access to critical information from multiple, distributed, heterogeneous databases and other information sources for analysis and decision making activities.The ACM International Workshop on Data Warehousing and Online Analytical Processing (DOLAP) is an annual event that provides an international forum where both researchers and practitioners can share their findings in theoretical foundations, current methodologies, practical experiences, and new research directions in the areas of data warehousing and online analytical processing. The fifth DOLAP workshop was held this year in McLean, VA, USA.These proceedings contain the papers selected for presentation at the workshop. We received 22 submissions form 16 different countries. The submitted papers covered various aspects of Data Warehousing and OLAP including multidimensional modeling, materialized view selection, extract-transform-merge processes, view maintenance, temporal and object-oriented aspects of data warehouses, query languages for OLAP, query processing and optimization, indexing, and XML data warehouses. After careful review the program committee selected 11 papers for presentation at the workshop. The accepted papers were presented in three sessions: data warehouse conceptual modeling, data warehouse logical design, and data warehouse operation and query optimization.
Online analytical processing
Dimensional modeling
Multidimensional data
Presentation (obstetrics)
Materialized view
Merge (version control)
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Data warehouses are dedicated to collecting heterogeneous and distributed data in order to perform decision analysis. Based on multidimensional model, OLAP commercial environments such as they are currently designed in traditional applications are used to provide means for the analysis of facts that are depicted by numeric data (e.g., sales depicted by amount or quantity sold). However, in numerous fields, like in medical or bioinformatics, multimedia data are used as valuable information in the decisional process. One of the problems when integrating multimedia data as facts in a multidimensional model is to deal with dimensions built on descriptors that can be obtained by various computation modes on raw multimedia data. Taking into account these computation modes makes possible the characterization of the data by various points of view depending on the user's profile, his best-practices, his level of expertise, and so on. We propose a new multidimensional model that integrates functional dimension versions allowing the descriptors of the multidimensional data to be computed by different functions. With this approach, the user is able to obtain and choose multiple points of view on the data he analyses. This model is used to develop an OLAP application for navigation into a hypercube integrating various functional dimension versions for the calculus of descriptors in a medical use case.
Online analytical processing
Multidimensional data
Dimensional modeling
Multidimensional analysis
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Online analytical processing
Multidimensional data
Schema (genetic algorithms)
Star schema
Dimensional modeling
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Multidimensional in data warehouse is a compulsion and become the most important for information delivery, without multidimensional data warehouse is incomplete. Multidimensional give the able to analyze business measurement in many different ways. Multidimensional is also synonymous with online analytical processing (OLAP). Keywords: multidimensional, data warehouse, OLAP
Online analytical processing
Multidimensional data
Business Intelligence
Dimensional modeling
Multidimensional analysis
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Online analytical processing
Data cube
Dimensional modeling
Multidimensional data
Cube (algebra)
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Citations (30)
Online analytical processing
Dimensional modeling
Star schema
Multidimensional data
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Citations (8)
This paper introduces an academic base for building multidimensional analytical model of data warehouse. Using OLAP technology,a designing method of multidimensional analytical mode of sale data warehouse is presented and illustrated with examples,thus the necessary and feasible practice is prepared for analytical environment of data warehouse.
Online analytical processing
Mode (computer interface)
Dimensional modeling
Base (topology)
Multidimensional data
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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.
Online analytical processing
Multidimensional data
Dimensional modeling
Business Intelligence
Multidimensional analysis
Data cube
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Aiming at the multi-purpose analysis of Internet public opinion,a method of organizing opinion data with data warehouse is proposed.The architecture of this data warehouse is designed,and its conceptual model is built up by the method of DWER modeling,including the definition of fact,dimensions and their relationships,the establishment of fact and aggregation models.Then,the multidimensional model of the data warehouse is built up logically.Based on the multidimensional model,the OLAP operations on opinion data cube are introduced,and the OLAP methods to analyze the Internet public opinion are defined.
Online analytical processing
Dimensional modeling
Multidimensional data
Conceptual model
Cube (algebra)
Data cube
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Multidimensional Data modeling is one of the key technologies for developing enterprise data warehouse.The data structures and the semantics of data warehouse can't be indicated efficiently by the traditional data models(such as entity models and relational models),and also they are difficult to support OLAP applications.In this paper,an optimized multidimensional modeling method-various granularity modeling is put forward, and how to establish a multidimensional model developed by Essbase is shown.Test shows this optimized method can efficiently improve OLAP's response time and data warehouse performance.
Online analytical processing
Granularity
Dimensional modeling
Multidimensional data
Data model (GIS)
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