Call for Book Chapters: Data Mining in Time Series and Streaming Databases

Publisher: World Scientific, Singapore.

Editors
Prof. Mark Last, Ben-Gurion University of the Negev, Israel. Email: mlast@bgu.ac.il.
Prof. Abraham Kandel, Florida International University, FL, USA. Email: Akandel@cis.fiu.edu.
Prof. Horst Bunke, University of Bern, Switzerland. Email: bunke@inf.unibe.ch.

Important Dates
Proposal Submission: February 15, 2016
Notification of Proposal Acceptance: February 28, 2016
Full Chapter Submission: June 30, 2016
Notification for chapter acceptance: August 31, 2016
Submission of the camera-ready chapters: October 31, 2016
Anticipated book publication: First quarter of 2017

Scope and Purpose
Traditional data mining and time series analysis methods are designed to deal with “static” data, which is stored entirely in a database system and where the patterns of interest do not change significantly over time. Many data mining algorithms even ignore the arrival ordering of observations as irrelevant to the knowledge discovery process. With these assumptions being sufficiently accurate in some applications, an increasing amount of systems and sensors produce massive, high-speed streams of ever-changing data generated by dynamic processes. The high volume and velocity of such data streams require real time or near real time processing due to the volatility of the incoming observations, which can be stored for a limited, if any, time only. Dynamic data streams can be found in a variety of fields including weather monitoring, traffic control, stock trading, cyber security, and, more recently, Internet of Things (IoT). Mining real-world time series and streaming data creates a need for new technologies and algorithms, which are still being developed and tested by data scientists worldwide.

The purpose of this volume is to present the most recent advances in pre-processing, mining, and utilization of streaming data that is generated by modern information systems. Mining big time series and data streams introduces new aspects and challenges to the tasks of data mining and knowledge discovery. Examples of these new challenges include: finding the most efficient representation of streaming data, developing privacy-preserving methods for data stream mining, incremental pre-processing of continuous time series and data streams in parallel to the data mining process, handling delayed information, mining entity-related time series, and developing online monitoring systems.

Submissions are solicited on the following topics, but not limited to:
• Preprocessing streaming data for data mining
• Privacy-preserving data stream mining
• Time series representation, summarization, and indexing
• Feature extraction from temporal data
• Similarity measures and clustering of time series
• Induction of temporal patterns and rules
• Classification and forecasting from streaming data
• Distributed processing of streaming data
• Resource-aware methods for mining big time series and data streams
• Entity stream mining and event history analysis
• Handling incomplete, delayed and/or costly information
• Online segmentation methods
• Concept drift detection and change detection in evolving data streams
• Anomaly detection in univariate and multivariate time series
• Mining fuzzy time series
• Multi-criteria evaluation of data stream mining systems
• Detailed descriptions of real-world projects in mining streaming data
• Software tools for mining time series and data streams


Submission Deadlines

PROPOSAL SUBMISSION: Prospective authors should submit a chapter proposal by February 15, 2016 including the following information:
• Title of the contribution/chapter
• Name of author, co-authors, institution, email-address
• Preliminary abstract of the proposed article (150 – 250 words)

PROPOSAL ACCEPTANCE NOTIFICATION: Authors will be notified by February 28, 2016 about the status of their proposals.

FULL CHAPTER SUBMISSION: Chapters have to be 20-25 pages length and will be reviewed by two/three expert reviewers to ensure the quality of the volume. All contributions must be original work, which have not been published elsewhere nor are currently under review for any other publication. The deadline of submission is June 30, 2016.

CHAPTER ACCEPTANCE NOTIFICATION: Authors of submitted chapters will be notified by August 31, 2016 about their acceptance/rejection.

CAMERA-READY CHAPTER DUE: Camera-ready version of the accepted chapters incorporating revisions (if any) is expected to be submitted by October 31, 2016.

BOOK PUBLICATION: The book is anticipated to appear in print in the first quarter of 2017.

Inquiries and submissions can be forwarded to Prof. Mark Last (mlast@bgu.ac.il). Please use the subject "Data Streams Book".