Linking and Mining Heterogeneous and Multi-view Data (2018)

You have reached the homepage of Linking and Mining Heterogeneous and Multi-view Data, a book that is planned to be published in late 2018. This book focuses on linking and mining heterogeneous and multi-view data, and would comprise chapters that cover recent advancements in this emerging area in data-intensive systems and methods. This book will be published by Springer International Publishing. This book is currently inviting chapters for publication, and if you have interests in the area, you are welcome to submit a chapter for possible inclusion in the book. This book will be published within Springer's book series on Unsupervised and Semi-Supervised Learning.

Scope of the Book

This book will highlight the recent research advances in linking and mining data coming from across varied data sources. Modern data takes many complex forms (views) such as varying types of text, images, videos or time series. Furthermore, the same entity may be described through different representations of the same data type (e.g. text), such as the same research publication being referred to differently in author homepages, indexing services, and citations. To ensure that myriad data sources are exploited well in exploratory data analysis, it is necessary to identify object-level linkages between data sources, to enable cross-source reasoning in downstream applications and tasks. The cross-source linking and mining is often complex due to the varying nature of similarity measures that might be considered appropriate for a specific data type, as well as the plurality of methods that could be used to fuse information from across data sources. This book will focus on recent advances in this flourishing field of multi-source data fusion, with an emphasis on unsupervised and semi-supervised settings.

Call for Chapters

This book solicits contributions from researchers and practitioners with interests in the area of linking and mining heterogeneous and multi-view data. Chapters are expected to be self-contained and may be one of:

  • Review/Survey: Articles that offer a review of recent work in an emerging direction of interest
  • Original Work: Describe original work in an area of interest within the scope of the book
  • Experiences: Descriptions of experiences (comprising reusable value) of addressing analytics scenarios within the book's scope

Expanded versions of work published in premier data analytics avenues are also welcome, but should have at least 30% new content that is clearly identified. Submissions are invited on topics from the following non-comprehensive list:

  • Record Linkage
  • Clustering
  • Dimensionality Reduction
  • Deep Learning
  • Ensemble Learning
  • Common Representation Learning
  • Anomaly Detection
  • Outlier Identification
  • Graph Partitioning
  • Data Completion
  • Label Propagation
  • Uncertainty Modelling
  • Social Network Analysis
  • Data Fusion
  • Active Learning
  • Matrix Factorization
  • Natural Language Processing
  • Multi-modal Learning
  • Image Processing

Whilst most of the above topics fall within the general scope of data analytics, submissions to this volume are expected to focus on analytics methods and applications to heterogeneous and multi-view data. We specifically invite and also forsee that the vast majority of submissions would comprise methods for particular types of multi-view data; exemplary focus topics could include analytics over graph and text data sources for social media analytics, or linking analyst reports with items in a company database.

Key Dates

This book employs a two stage submission process, first soliciting extended abstracts, followed by submissions of full papers for accepted abstracts. The dates are as follows:

  • Extended abstracts (1 page limit): March 1, 2018
  • Initial editorial decisions: March 10, 2018
  • Full chapters due: April 30, 2018
  • Final editorial decisions based on peer-review: June 15, 2018
  • Revisions due (where revisions are sought): July 15, 2018
  • Final versions due: July 31, 2018
  • Expected publication of book: End of 2018


The submissions are to be made through the EasyChair Submission System here. Submissions should use the Springer templates for preparing their submission. Extended abstracts should describe what would go into the chapter submissions, and should also clearly identify the type of submission (review, original work, experiences, or expanded versions). Full chapters are generally expected to be between 20 and 40 pages; these page limits are not strict.


The main point of contact for any queries are the editors of this volume:

Please write to us for any queries or if you are unsure whether your work is likely to be relevant to this book.