DWMS: Data Warehouse Management System.
Narendra Mohan:
DWMS: Data Warehouse Management System.
VLDB 1996: 588@inproceedings{DBLP:conf/vldb/Mohan96,
author = {Narendra Mohan},
editor = {T. M. Vijayaraman and
Alejandro P. Buchmann and
C. Mohan and
Nandlal L. Sarda},
title = {DWMS: Data Warehouse Management System},
booktitle = {VLDB'96, Proceedings of 22th International Conference on Very
Large Data Bases, September 3-6, 1996, Mumbai (Bombay), India},
publisher = {Morgan Kaufmann},
year = {1996},
isbn = {1-55860-382-4},
pages = {588},
ee = {db/conf/vldb/Mohan96.html},
crossref = {DBLP:conf/vldb/96},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
Abstract
After a thorough investigation
of end user requirement differences from a Database and a Data
Warehouse, a strong case is made for specific Data Warehouse Management
Systems (DWMS), in contrast to the trend of utilizing DBMS for
Data Warehouse construction. The three type (Temporal, Nested
Relational, Multidimensional) DWMS hypothesis, an original concept
of Yoshioki Ishii, President, Software AG of Far East, Inc. is
explained in detail. Customer solutions and related products
are introduced.
From DBMS to DWMS
Data contained in
a Database can be broadly classified into master type (fact) data,
transaction type (event) data and summary type (aggregation) data.
Attributes such as the number of fields, volume of updates, amount
of data appends, and count of multiple occurrence fields differ
vastly for each of the above. A Data Warehouse is basically constructed
by systematic accumulation of the data that is originally stored
in Database(s). For optimally utilizing the data warehousing
concept, however; fundamental differences between Database and
Data Warehouse such as current vs. historical data, large volume
vs. very large volume data, mission critical vs. decision support
application, etc. must be reviewed. Rather than adopting RDBMS
for transaction based as well as information based applications,
a clear distinction, DBMS for the former and DWMS for the latter,
is recommended.
In view of the distinct data types, an innovative DWMS design,
deemed essential for achieving the intuitive comprehensibility
and performance levels that end users aspire, is proposed.
Nested Relation over Normalization
Normalization methodology
was introduced for shrinking the overall database size, easing
updates, and effecting program/data independence. Ironically,
a Data Warehouse assumes expansive size, does not necessitate
dynamic updates, and is used primarily for analytical rather than
repetitive processes. Hence, normalization is not only futile
but is in fact counterproductive towards factors like performance
and ease of use. The numerous join processes generated between
normally distributed tables causes slow and cryptic query systems
(SQL based) with non-instinctive application logic for Data Warehouse
users. The Nested Relational Model is presented as an ideal solution.
Types of DWMS
Figure missing
While a DBMS is optimal when
centralized, DWMS must be differentiated according to requirements.
Corresponding to the database file types, three DWMS varieties,
Temporal DWMS, Nested Relational DWMS and Multidimensional DWMS
composed respectively of the Time Cube, Nested Relation, and Multi-Dimensional
Cube as the fundamental units of data are introduced. For Data
Warehouse applications, master type data (time variable inclusive)
is managed in the Temporal DWMS, transaction type data (after
undergoing normalization by merging with relevant master type
data) in the Nested RDWMS, and summary type data in the Multidimensional
DWMS.
Tools and Product Offerings
Customer solution packages
with ADABAS (a high performance Nested Relational Model based
database) as the Nested RDWMS, Essbase as the Multidimensional
DWMS, SOAR as the Nested RDBMS+ Servers respectively, and DB-FRONT
as an end user tool have been marketed with remarkable success
in Japan. An original Temporal DWMS product is currently under
development.
Copyright © 1996 by the VLDB Endowment.
Permission to copy without fee all or part of this material is granted provided that the copies are not made or
distributed for direct commercial advantage, the VLDB
copyright notice and the title of the publication and
its date appear, and notice is given that copying
is by the permission of the Very Large Data Base
Endowment. To copy otherwise, or to republish, requires
a fee and/or special permission from the Endowment.
Online Paper
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Printed Edition
T. M. Vijayaraman, Alejandro P. Buchmann, C. Mohan, Nandlal L. Sarda (Eds.):
VLDB'96, Proceedings of 22th International Conference on Very Large Data Bases, September 3-6, 1996, Mumbai (Bombay), India.
Morgan Kaufmann 1996, ISBN 1-55860-382-4
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