Report on the ACM Fourth International Workshop on Data
Warehousing and OLAP (DOLAP 2001)
Joachim Hammer
Department of Computer & Information Science & Engineering
University of Florida
Gainesville, FL 32611-6120, U.S.A.
jhammer@cise.ufl.edu
Introduction
The Fourth Annual ACM International
Workshop on Data Warehousing and Online
Analytical Processing (DOLAP 2001) was heldin Atlanta, GA, USA, in November 2001, inconjunction with the Tenth International
Conference on Information and Knowledge
Management (CIKM 2001). Although this was
only the fourth annual meeting, DOLAP has
already become an important and broadly
accepted forum for researchers and practitioners
to share their findings in theoretical foundations,current methodologies, practical experiences,and new research directions in the areas of data
warehousing and online analytical processing(OLAP). Despite the fact that conference
attendance has been down since the horrific
events of September 11, DOLAP 2001 attracted
researchers from Europe, Asia and theAmericas.
The DOLAP 2001 program, which occupied afull day immediately following CIKM 2001,
included a keynote talk, technical presentations,and a final discussion involving all workshop
participants. As in previous years, the quality ofthe submitted papers was high and the programcommittee had a difficult time deciding which of
the 31 submissions from 16 different countries
should be accepted for presentation. The
submitted research papers covered the state-ofthe-art in data warehousing and related fields
including data warehouse architecture, designand evolution, multi-dimensional modeling,
queryoptimization,indexing,viewmaterialization
andmaintenance,data
warehouse quality, XML- and object-based
warehouses, and data warehousing and the Web.
In addition, the committee received a number ofindustrial submissions describing ongoing datawarehousing projects and novel applications forwarehouses. After careful review, 12 researchand industrial papers were selected forpresentation at DOLAP 2001. The proceedings
are published by ACM Press, and are alsoavailable
onlineatwww.informatik.unitrier.de/~ley/db/conf/dolap/dolap2001.html.

In addition, the authors of the three highestranked papers have been invited to submit anupdated, more detailed version to the Special
Issue on Advances in Data Warehousing of theData and Knowledge Engineering Journal to bepublished in early 2003.

Keynote Address
An important goal of DOLAP is to bring
together researchers and practitioners of data
warehouse technology from academia as well asindustry. This is particularly important since thework in industrial laboratories has often goneunnoticed by the academic research community.
In order to provide workshop participants withan industrial perspective, the DOLAP programcommittee invited Rick Cole, Senior TechnicalStaff Member in the Business Intelligence
Development group at IBM’s Silicon Valley
Laboratory to talk about his R&D experienceand how Red Brick’s products have impacted
the field of data warehousing.
Rick Cole is a 7-year veteran of Red BrickSystems, Informix Software and recently IBM’s
Silicon Valley Laboratory, where he has heldprinciple engineering, architecture, and senior
management positions. Rick Cole has
responsibility for DB2 and Red Brick productintegration and technology transfer.

During the hour-long presentation titled “TheRed Brick Road,” Cole took the workshop
attendants on a tour of his various career stops,
outlining his early experiences in data
warehousing applications, star schema data
modeling, and star schema query processing.
The tour started at the IBM Advanced Business
Systems Division in Rochester, Minnesota
where Cole played a lead role in the design anddevelopment of SQL query processing in DB2
for the IBM AS/400. For example, he described
how query-processing problems in the document
and control database of IBM’s System/38 weredue to its use of a conceptual schema
reminiscent of the now familiar star schema.
Subsequent work on overcoming the query
problem lead to the first implementation of thestar join algorithm.
At IBM’s Santa Teresa lab, Cole gained firsthand experience of the benefits of close
collaboration among various database productgroups and one of the research divisions at IBM.
Thiscollaboration
resultedin
betterunderstanding of the strengths and weaknessesof several products, faster technology transfersfrom research to production, and eventually insignificant product improvements such as fastersorting for SQL/MVS, hash joins for AS/400
and the use of partial indexes for SQL/400.
Next stop on the tour was the Volcano project at
the University of Colorado at Boulder, whose
breakthroughs in query optimization laid someof the foundations for modern query processing
in database systems including dynamic query
processing in the Red Brick data warehouse.
Rick Cole became directly involved with datawarehousing when he joined Red Brick Systems.
Although the company has undergone several
changes and transitions over the past years, its
data warehousing product consistently remainedat the forefront of the technology curve. Among
Red Brick’s contributions to the field of data
warehousing are several star schema join
algorithms, analytic extensions to SQL (RISQL),
dynamic query plans, parallel loading, various
intelligent scanning mechanisms, integrated data
mining, and integrated materialized views. The
talk concluded with Cole’s observation thatIBM’s acquisition of Red Brick has allowed him

to come back (home) to the place where hiscareer started many years ago (hence the title ofthe talk).
During the subsequent discussion immediately
after the talk, Cole also offered his opinion onwhat he thinks the important and worthwhile
research problems in data warehousing are. His
list included the need for researchers to consider
warehouse schemas containing multiple fact
tables having a subset of common dimensions aswell as fact tables which are connected via
hierarchicalrelationships;
paralleldatawarehousing (cube) algorithms; efficienthandling of changing dimensions, which
includes (1) deleting old data from dimension
tables while ensuring referential integrity
between the fact and dimensions, and (2)updating of dimensional attributes while
maintaining an accurate history of changes; theneed for technologies to help ease system
configuration and tuning; and more generally,
data warehouse support for new applications
including customer relationship management, ecommerce, etc.

Paper Presentations
Originally, data warehousing and online
analytical processing have emerged as key
technologies for enterprises wishing tostreamline the management of their operationaland business data and to improve decision
support. However, judging by the number anddiversity of this year’s submissions, it isapparent that data warehousing continues to takeon broader roles, for example, in the context ofintegrating
semistructured
datasources,
metadata management, and internet-based ecommerce. This broadening was also reflected in
the DOLAP 2001 program.
In order to provide a framework for the different
presentation topics, the accepted papers weredivided into three topic areas: Multi-dimensional
Modeling, Query Processing and Optimization,
and Data Warehouse Operation.
Multi-dimensional Modeling
The usability of a decision support systemdepends on the implementation of the multi-2dimensional data cube and the ease with which
the cube can be designed to fit the analysts’
needs. Important inputs to the design process are
the expected decision-support queries and a
description of contents and schema of thesources or the integrated data warehouse if one
exists. The four papers in this first session
describe various approaches to simplifying thedesign of OLAP systems, using different
assumptions about the inputs and desired output.
The paper by Niemi, Nummenmaa and Thanisch(University of Tampere, University ofEdinburgh) presented a new technique toautomate cube design given the data warehouse,functional dependency information, and sample
OLAP queries. Their method constructs
complete but minimal cubes with low risks
related to sparsity and incorrect aggregations.
The design process is iterative and improves
over time, as more information about theexpected queries is known.
Abelló, Samos, and Saltor (U. Politècnica deCatalunya, U. de Granada) provided atheoretical foundation for understanding multidimensional data and how it should be modeled.
By providing a sound and complete algebra formanipulating cubes (using an object-orientedapproach), the authors argue that designing andhence querying cubes can become moreintuitive, and storing cubes can be made more
efficient.
The papers by Pokorný (Charles University) andby Golfarelli, Rizzi and Vrdoljak (University ofBologna, University of Zagreb) recognized thefact that increasing amounts of interesting data isstored in the XML data format; bothpresentations described tools to help integrate
XML data in data warehousing environments.
Pokorný showed how the existing star schema
approach to multi-dimensional modeling could
be adapted to an XML-based data warehouse,
where the underlying sources are also XMLbased. Golfarelli, Rizzi and Vrdoljak described
how the design of a data mart could be carriedout starting directly from an XML source (rather
than converting it into an equivalent relational
representation first). To do so, the authors
proposed a semi-automatic approach forbuilding the conceptual schema, leaving thechoice of how to implement the data mart to theuser.
Query Processing and Optimization
The four papers in this topic area considered
new approaches to improving the performanceof OLAP queries against a multi-dimensional
data warehouse. Although ultimately concernedwith improving the execution speed, two of thepapers were equally concerned with improving
the users’ ability to formulate meaningful anduseful queries.
The paper by Theodoratos and Tsois (NewJersey Institute of Technology, National
Technical University of Athens) presented anarchitecture called CBS star that uses onedimensional
hierarchicalclusteringandencoding techniques to organize the dimension
tables and multi-dimensional access methods toorganize the fact table. User queries against a
traditional star schema are rewritten by the query
processor to run over the corresponding CBS
star schema instead. The authors showed that
this re-writing in conjunction with the optimized
CBS star improves the performance of a large
class of OLAP queries containing expensive
star-join operations.
Like Thedoratos and Tsois, Choong, Laurentand Marcel (Université F. Rabelais), havedeveloped a new design for multi-dimensional
data sets; however, their main objective is toimprove navigation rather than processing
performance. To this end, the authors havedefined a measurement of the quality of arepresentation of multi-dimensional data andpresented a framework for computing the best
possible representation for a given usage
scenario. An important contribution of the work
was their analysis of the difficulties ofcomputing such representations.
Deschler and Rundensteiner (Worcester
Polytechnic Institute) examined the problem of
how to balance query performance against
warehouse update performance to meet themaintenance window in large data warehouses.
Specifically, the authors have introduced theRB+ (Red-Black+) tree as a practicalreplacement for the popular B+ tree. The RB+3tree uses persistent red-black binary trees insteadof sorted records for leaf pages. The paper
shows that this organization improves memory
performance up to 3,000% for updates andprovides query performance comparable to a B+tree, making it practical for large, frequently
updated warehouses.
The paper by Espil and Vaisman (Pontificia
Universidad Católica Argentina, Universidad de
Buenos Aires) presented a language called
IRAH (Intensional Redefinition for Aggregation
Hierarchies) for specifying the maintenance ofdimension hierarchies in a multi-dimensional
database. The authors showed how IRAH
supports the definitions of exceptions (e.g.,causedbyimprecise
knowledge
orunpredictability of the underlying data) that
override the extensions of rollup functions
implied in the hierarchies. Moreover, IRAH
supports materialization of exception paths,
which extends the usefulness of the multidimensional model and enhances query
processing.
Data Warehouse Operation
The final session included three papers, whichwere concerned with issues relating to data
warehouse operations including data warehouse
replication, metadata management and OLAP
query support in commercial database servers.
Unfortunately, due to the events of September
11, 2001, the authors of the fourth paper, Drs.
Bębel and Wrembel (Poznań University ofTechnology), were unable to attend theworkshop.
The paper by Schlesinger, Bauer, Lehner,Ediberidze and Gutzmann (University ofErlangen-Nürnberg) focused on efficientsynchronization of multi-dimensional data in aclient-server environment, which allows users tostore and maintain parts of the warehouse on
personal computers closer to where the data isneeded. The main contribution of the paper is analgorithm to detect changes to the schema data,
and to efficiently synchronize between clientand server while exploiting the special needs andrequirements of data warehousing.

Vaduva, Kietz and Zücker (University of Zürich,Swiss Life) reported on a new metamodel called
M4 (MetaModel of Mining Mart), to support
metadata management in a variety ofenvironments including data preprocessing fordata mining and data warehousing. To illustrate
the merits of M4, the authors discussed thebenefits of metedata management in integration
systems such as increased support forconsistency, uniform and easy access to all
information, and data lineage tracing.
The final paper of the workshop by Andreas
Weininger (IBM) outlined specific features thata database server should have to efficiently
process queries on a database with a star schemamodel. The basis for his investigation was ananalysis of the features provided by the IBM
Extended Parallel Server (XPS). His results
concluded that special star join methods like thePush-Down Hash Semi Join, new accessmethods like Generalized Key (GK) indices, andspecific index usages like multi-index scans are
essential for the efficient processing of queries
that arise frequently in data warehousing
contexts.

Discussion and Closing Remarks
The workshop ended with a lively discussion ofthe next-generation data warehouse problems
offered by the keynote speaker earlier in the day.
The workshop participants concluded that data
warehousing and OLAP remain interesting andfruitful research areas with many unsolvedchallenges.

Acknowledgements
DOLAP 2001 was sponsored by ACM’s Special
Interest Groups on Information Retrieval(SIGIR) and Management Information Systems(SIGMIS). We also express our thanks toProfessor Il-Yeol Song, the CIKM 2001Workshop Chair, to Professor E.K. Park,
treasurer and registration chair, and to theextremely competent members of the DOLAP
Program Committee for helping us put together
a strong workshop program.

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