Friday, March 12, 2010

CFP - XML Data Mining: Models, Methods, and Applications



Proposal Submission Deadline: April 15, 2010


XML Data Mining: Models, Methods, and Applications


Andrea Tagarelli, University of Calabria, Rende, Italy

To be published by IGI Global (



The widespread use of XML across the Web and in business as well as scientific databases has prompted the development of methodologies,techniques and systems for effectively and efficiently managing and analyzing XML data. This has increasingly attracted the attention of

different research communities, including database, information retrieval, pattern recognition, and machine learning, from which several proposals have been offered to address problems in XML data management and knowledge discovery. In this respect, the following

main categories of problems can be recognized:

* XML Structure Mining: Mining XML data has its roots in problems which originally arose from several applications in semi structured data management, such as integration of data sources and query processing. Such applications were initially focused on solutions for structurally comparing semi structured data. Important research contributions have especially regarded pattern matching, change

detection, similarity search and detection, and summarization, for XML schema as well as document collections.

* XML Structure and Content Mining: The need for discovering knowledge from XML data according to both structure and content features has become challenging, due to the increase in application contexts for which handling both structure and content information in XML data is

essential. XML Structure and Content Mining also represent a point of convergence for research works in semi structured data and text mining.

* Semantics-aware XML Mining: The increase in volume and heterogeneity of XML-based application scenarios makes data sources exhibit not only different structures and contents but also different ways to semantically annotate the data. The inherent difficulty of devising

suitable notions of semantic features and semantic relatedness among XML data leads to one of the hardest challenges in contexts of data management and knowledge discovery.


The book aims to collect and distil the knowledge from experts of database, information retrieval, machine learning, and knowledge management communities in developing models, methods and systems for

XML data mining. The book represents the first editorial opportunity to gather research works in the field of XML data mining, therefore it will aim to fill the lack of a single, valuable reference specifically concerning the realms of XML and data mining as a whole. For this

purpose, the book will address key issues and challenges in XML data mining, offering insights into the various existing solutions and best practices for modelling, processing, analyzing XML data, and for evaluating performance of XML data mining algorithms and systems.

Moreover, the book will be concerned with the investigation of real-world applications in a variety of domains, such as, e.g., business intelligence, bioinformatics, multimedia technology, and networking communications.


The book will address academia as well as industry. In the academic community, it will aim to provide a useful guide to, and through, advanced topics in information retrieval, machine learning, knowledge discovery and management for XML data. Therefore, the book will be a companion of any scholar or beginner-to-knowledgeable researcher in

data/text mining, Web mining, Web intelligence, and related research fields. Moreover, the book could likely be used as a supplement of basic courses on information retrieval, machine learning, knowledge management, and data mining, or as a major reference for upper-level

courses on advances in the aforementioned disciplines.

From an industry perspective, the book will be a reference for professionals in XML, database systems, knowledge management, information systems and technology for e-business and e-commerce. In this respect, the book will provide insights into the advantages and

challenges of using various data mining solutions for developing XML-based intelligent management and analysis systems.


Recommended topics include, but are not limited to, the following:

* Models and structures of XML information in data mining

* Similarity search and detection in XML data

* Synopsis models for XML understanding and its applications in XML data mining

* Clustering of XML data

* Classification of XML data

* Frequent pattern discovery and association rule mining of XML data

* Machine learning aspects in XML data processing

* Ontology-based XML mining

* Validity criteria and measures for the evaluation of XML mining results

* Integration of semi structured data models into traditional mining


* Schema matching and change detection for XML data mining frameworks

* Optimization of data mining applications that use XML databases

* XML mining for domain-specific interoperability (biological

databases, medical databases, spatio-temporal databases)

* Mining in probabilistic and uncertain XML data

* Efficient mining of XML data streams

* XML mining for Semantic Web, Grid and P2P infrastructure


Researchers and practitioners are invited to submit on or before April

15, 2010, a 2-3 page chapter proposal clearly explaining the mission

and concerns of the proposed chapter, as PDF or DOC file attachment,

to the Editor (xml.datamining[AT] The proposal should

contain the following information (please also visit the book website

to get a template of proposal submission):

* Name of chapter,

* Name of author(s), e-mail address and affiliation,

* Technical area covered in the chapter,

* Main contributions which will be made by author(s),

* Technical novelty in the contribution,

* Table of contents of the chapter.

Authors of accepted proposals will be notified by May 10, 2010 about

the status of their proposals and sent chapter guidelines. Full

chapters are expected to be submitted by September 1, 2010. All

submitted chapters will be double-blind reviewed by at least 3

reviewers. Contributors may also be requested to serve as reviewers

for this project.

Further details about this book project are available at:


This book is scheduled to be published by IGI Global (formerly Idea Group Inc.), publisher of the "Information Science Reference" (formerly Idea Group Reference), "Medical Information Science Reference", "Business Science Reference", and "Engineering Science Reference" imprints. For additional information regarding the publisher, please visit This publication is

anticipated to be released in 2011.


April 15, 2010: Proposal Submission Deadline

May 10, 2010: Notification of Acceptance

September 1, 2010: Full Chapter Submission

October 31, 2010: Review Results Returned

January 15, 2011: Final Chapter Submission

February 15, 2011: Final Deadline

Inquiries and submissions can be forwarded electronically to Andrea Tagarelli at xml.datamining[AT]


Andrea Tagarelli, PhD

Assistant Professor of Computer Science

Dept. Electronics, Computer and Systems Sciences

University of Calabria, Italy

ph: +390984494751