New Issue of the International Journal of Data Warehousing and Mining (IJDWM)

)

The contents of the latest issue of:
 
 International Journal of Data Warehousing and Mining (IJDWM)
 Official Publication of the Information Resources Management Association
 Volume 3, Issue 4, October-December 2007
 Published: Quarterly in Print and Electronically
 ISSN: 1548-3924 EISSN: 1548-3932
 Published by IGI Publishing, Hershey, PA, USA
 www.igi-global.com/ijdwm
 
 Editor-in-Chief: David Taniar, Monash University, Australia
 
 GUEST EDITORIAL PREFACE:
 "Advanced Techniques in Data Warehousing and Knowledge Discovery
Applications: The Special Issues of Data Warehouse and Knowledge
Discovery"
 
 Tho Manh Nguyen, Vienna University of Technology, Austria
 Juan Trujillo, University of Alicante, Spain
 A Min Tjoa, Vienna University of Technology, Austria
 
 Data warehousing and knowledge discovery has been widely accepted as
a key technology for enterprises and organizations to improve their
abilities in data analysis, decision support, and the automatic
extraction of knowledge from data. With the exponential growing amount
of information to be included in the decision making process, the data
to be considered becomes more and more complex in both structure and
semantics. Consequently, the process of retrieval and knowledge
discovery from this huge amount of heterogeneous complex data builds
the litmus test for the research in the area.
 
 To read the preface, please consult this issue of IJDWM in your library.
 
 PAPER ONE:
 
 "Semantics-Aware Advanced OLAP Visualization of Multidimensional Data Cubes"
 
 Alfredo Cuzzocrea, University of Calabria, Italy
 Domenico Saccŕ, University of Calabria, Italy
 Paolo Serafino, University of Calabria, Italy
 
 Efficiently supporting advanced OLAP visualization of
multidimensional data cubes is a novel and challenging research topic,
which results to be of interest for a large family of data warehouse
applications relying on the management of spatio-temporal (e.g.,
mobile) data, scientific and statistical data, sensor network data,
biological data, etc. On the other hand, the issue of visualizing
multidimensional data domains has been quite neglected from the
research community, since it does not belong to the well-founded
conceptual-logical-physical design hierarchy inherited from relational
database methodologies. Inspired from these considerations, in this
article we propose an innovative advanced OLAP visualization technique
that meaningfully combines (i) the so-called OLAP dimension flattening
process, which allows us to extract two-dimensional OLAP views from
multidimensional data cubes, and (ii) very efficient data compression
techniques for such views, which allow us to generate
"semantics-aware" compressed representations where data are grouped
along OLAP hierarchies.
 
 To obtain a copy of the entire article, click on the link below.
 http://www.igi-global.com/articles/details.asp?id=7450
 
 PAPER TWO:
 
 "Empowering the OLAP Technology to Support Complex Dimension Hierarchies"
 
 Svetlana Mansmann, University of Konstanz, Germany
 Marc H. Scholl, University of Konstanz, Germany
 
 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 application 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 on-demand data display. The power of our
approach is exemplified using a real-world study from the domain of
academic administration
 
 To obtain a copy of the entire article, click on the link below.
 http://www.igi-global.com/articles/details.asp?id=7451
 
 PAPER THREE:
 
 "Managing Late Measurements In Data Warehouses"
 
 Matteo Golfarelli, University of Bologna, Italy
 Stefano Rizzi, University of Bologna, Italy
 
 Though in most data warehousing applications no relevance is given to
the time when events are recorded, some domains call for a different
behavior. In particular, whenever late measurements of events take
place, and particularly when the events registered are subject to
further updates, the traditional design solutions fail in preserving
accountability and query consistency. In this article, we discuss the
alternative design solutions that can be adopted, in presence of late
measurements, to support different types of queries that enable
meaningful historical analysis. These solutions are based on the
enforcement of the distinction between transaction time and valid time
within the schema that represents the fact of interest. Besides, we
provide a qualitative and quantitative comparison of the solutions
proposed, aimed at enabling well-informed design decisions.
 
 To obtain a copy of the entire article, click on the link below.
 http://www.igi-global.com/articles/details.asp?id=7452
 
 PAPER FOUR:
 
 "Evolutionary Induction of Mixed Decision Trees"
 
 Marek Kretowski, Bialystok Technical University, Poland
 Marek Grzes, Bialystok Technical University, Poland
 
 This article presents a new evolutionary algorithm (EA) for induction
of mixed decision trees. In non-terminal nodes of a mixed tree,
different types of tests can be placed, ranging from a typical
inequality test up to an oblique test based on a splitting
hyper-plane. In contrast to classical top-down methods, the proposed
system searches for an optimal tree in a global manner, that is it
learns a tree structure and finds tests in one run of the EA.
Specialized genetic operators are developed, which allow the system to
exchange parts of trees, generating new sub-trees, pruning existing
ones as well as changing the node type and the tests. An informed
mutation application scheme is introduced and the number of
unprofitable modifications is reduced. The proposed approach is
experimentally verified on both artificial and real-life data and the
results are promising. Scaling of system performance with increasing
training data size was also investigated.
 
 To obtain a copy of the entire article, click on the link below.
 http://www.igi-global.com/articles/details.asp?id=7453
 
 PAPER FIVE:
 
 "Acquiring Semantic Sibling Associations from Web Documents"
 
 Marko Brunzel, Otto-von-Guericke-Universität Magdeburg, Germany
 Myra Spiliopoulou, Otto-von-Guericke-Universität Magdeburg, Germany
 
 The automated discovery of relationships among terms contributes to
the automation of the ontology engineering process and allows for
sophisticated query expansion in information retrieval. While there
are many findings on the identification of direct hierarchical
relations among concepts, less attention has been paid on the
discovery sibling terms. These are terms that share a common, a priori
unknown parent such as co-hyponyms and co-meronyms. In this study, we
present our results on the discovery of pairs or groups of sibling
terms with XTREEM-SA (Xhtml TREE mining for sibling associations), an
algorithm that extracts semantics from Web documents. While
conventional methods process an appropriately prepared corpus,
XTREEM-SA takes as input an arbitrary collection of Web documents on a
given topic and finds sibling relations between terms in this corpus.
It is thus independent of domain and language, does not require
linguistic preprocessing, and does not rely on syntactic or other
rules on text formation. We describe XTREEM-SA and evaluate it toward
two reference ontologies. In this context, we also elaborate on the
challenges of evaluating semantics extracted from the Web against
handcrafted ontologies of high quality but possibly low coverage.
 
 To obtain a copy of the entire article, click on the link below.
 http://www.igi-global.com/articles/details.asp?id=7454
 
 PAPER SIX:
 
 "An Information-Theoretic Framework for Process Structure and Data Mining"
 
 Gianluigi Greco, University of Calabria, Italy
 Antonella Guzzo, University of Calabria, Italy
 Luigi Pontieri, University of Calabria, Italy
 
 Mining process logs has been increasingly attracting the data mining
community, due to the chances the development of process mining
techniques can offer to the analysis and design of complex processes.
Currently, these techniques focus on "structural" aspects by only
considering which activities were executed and in which order, and
disregard any other kind of data usually kept by real systems (e.g.,
activity executors, parameter values, and time-stamps). In this
article, we aim at discovering different process variants by
clustering process logs. To this purpose, an information-theoretic
framework is used to simultaneously cluster the logged process traces,
encoding structural information, as well as a number of performance
metrics associated with them. Each cluster is equipped with a specific
model, so providing the analyst with a compact and handy description
of major execution scenarios for the process.
 
 To obtain a copy of the entire article, click on the link below.
 http://www.igi-global.com/articles/details.asp?id=7455
 
 *****************************************************
 For full copies of the above articles, check for this issue of the
International Journal of Data Warehousing and Mining (IJDWM) in your
institution's library. If your library is not currently subscribed to
this journal, please recommend an IJDWM subscription to your
librarian.
 *****************************************************
 CALL FOR PAPERS
 
 Mission of IJDWM:
 The International Journal of Data Warehousing and Mining (IJDWM)
publishes and disseminates knowledge on an international basis in the
areas of data warehousing and data mining. It is published multiple
times a year, with the purpose of providing a forum for
state-of-the-art developments and research, as well as current
innovative activities in data warehousing and mining. In contrast to
other journals, this journal focuses on the integration between the
fields of data warehousing and data mining, with emphasis on the
applicability to real world problems. The journal is targeted at both
academic researchers and practicing IT professionals.
 
 Coverage of IJDWM:
 Data mart and practical issues
 Data mining methods
 Data models
 Data structures
 Design data warehousing process
 Online analytical process
 Tools and languages
 
 The journal is devoted to the publications of high quality papers on
theoretical developments and practical applications in data
warehousing and data mining. Original research papers,
state-of-the-art reviews, and technical notes are invited for
publications.
 
 Interested authors should consult the journal's manuscript submission
guidelines at www.igi-global.com/ijdwm .
 
 All inquiries and submissions should be sent to:
 Editor-in-Chief: Dr. David Taniar at david.taniar@infotech.monash.edu.au
 
Received on Fri Sep 14 11:58:22 2007