C4BD2014
Workshop on
Complexity for Big Data

Schedule

8:00 Cliff Joslyn and Guozhu Dong, Intro and welcome
8:10 Erin-Elizabeth Durham, Andrew Rosen, and Robert Harrison, "A Model Architecture for Big Data applications using Relational Databases"
8:35 Ruoqian Liu, Ankit Agrawal, Wei-Keng Liao, and Alok Choudhary, "Search Space Preprocessing in Solving Complex Optimization Problems"
9:00 Walid Shalaby, Wlodek Zadrozny, and Sean Gallagher, "Knowledge Based Dimensionality Reduction for Technical Text Mining"
9:25 Xiaoguang Wang, Xuan Liu, and Stan Matwin, "A Distributed Instance-weighted SVM Algorithm on Large-scale Imbalanced Datasets"
9:50 Coffee Break
10:20 Philippe Calvez and Eddie Soulier, "Sustainable Assemblage for Energy (SAE) inside Intelligent Urban Areas How massive heterogeneous data could help to reduce energy footprints and promote sustainable practices and an ecological transition"
10:45 Xuan Liu, Xiaoguang Wang, Bo Liu, and Stan Matwin, "Vessel Route Anomaly Detection with Hadoop MapReduce"
11:10 Jiazhen Nian, Shan Jiang, and Yan Zhang, "HBGSim: A Structural Similarity Measurement over Heterogeneous Big Graph"
11:35 Eric L. Goodman, Edward Jimenez, Cliff Joslyn, David Haglin, Sinan al-Saffar, and Dirk Grunwald, "Optimizing Graph Queries with Graph Joins and Sprinkle SPARQL"
12:00 End workshop

Call for Papers

Complexity will lead to both big challenges and opportunities in big data research. Complexity in big data can be caused by many factors, including:

• large number of features, which are related to each other through a rich variety of relationships ranging from simple to complicated;
• a large number of heterogeneous dimensions, which offer a variety of kinds of insights and require different treatments, in addition to a large number of data records;
• a large variety of heterogeneous types, such as vectors, sequences, (labeled) graphs, images, and multimedia, in addition to a large number of instances;
• a rich set of logical, semantic, and ontological relationships.

Complexity has often been used to successfully characterize various kinds of complicated subjects, for example in computational complexity, descriptive complexity, Kolmogorov complexity. Since big data have many complicated parts with intricate relationships to each other, the study of complexity for big data has potential to be highly successful.

Research on complexity of big data needs to consider the following facts, among others:

• Big data can be used for different purposes such as data integration, cross domain fertilization, and data mining;
• Structures among various parts of big data can be explicit or hidden, and can be described using a rich variety of patterns and models.

The Complexity for Big Data workshop is intended to create an atmosphere of exchange of ideas, providing a forum for the presentation and discussion of the key topics related to Big Data Complexity, including (but not limited to):

• new concepts for complexity for Big Data
• new techniques for handling complexity for Big Data
• analysis of complex relationships
• type logic acquisition
• scalability of type logic reasoning
• dealing with semantic heterogeneity
• schema learning
• dimensionality-reduction techniques
• query and search over multiple data silos
• long-term data preservation
• capture and use of provenance information
• analysis of similarity and differences among heterogeneous parts

Submission

Researchers and practitioners are invited to submit original papers that have not been submitted for review or published elsewhere. Submitted papers must be written in English and should not exceed 9 pages (IEEE BigData Conference foramt) in the case of research and experience papers, or 5 pages in the case of position papers (including figures, bibliography and appendices). All submitted papers will be judged based on their relevance, originality, significance, technical quality and organisation. Papers must be submitted through easychair at https://www.easychair.org/conferences/?conf=c4bd.

Presentation

Selected papers will be presented during the workshop. The workshop will include extra time for discussion of the presentation allowing the group to have a better understanding of the issues, challenges, and ideas being presented.

Important Dates

Deadline for paper submission: September 14, 2014 EXTENDED
Notification of paper acceptance: September 27, 2014
Camera-ready paper due: October 3, 2014
Workshop date: 27th, 28th, 29th or 30th of October, 2014
IEEE BigData 2014 main conference: October 27-30, 2014

Workshop Organisers

Guozhu Dong (Wright State University, Dayton, OH, U.S.A.)
Pascal Hitzler (Wright State University, Dayton, OH, U.S.A.)
Cliff Joslyn (Pacific Northwest National Laboratory, Seattle, Washington, U.S.A.)

Programme Committee (incomplete)

James Bailey, University of Melbourne
Longbing Cao, University of Technology Sydney
Michel Dumontier, Stanford University
Prateek Jain, IBM
Krzysztof Janowicz, University of California, Santa Barbara
Craig Knoblock, University of Southern California
Steffen Lamparter, Siemens
Huan Liu, Arizona State University
Axel Polleres, Vienna University of Economics and Business
Erik Wilde, EMC Corporation

Additional Information

General questions concerning the workshop should be addressed to the Workshop Organizers at c4bd@easychair.org.