Call for Book Chapters (Information Granularity, Big Data, and Computational Intelligence)(Springer)

Posted on 2013/10/08. Filed under: 綜合 | 標籤: |

CALL FOR CHAPTERS

Information Granularity, Big Data, and Computational Intelligence

To be published by Springer Verlag

Witold Pedrycz and Shyi-Ming Chen (editors)

The recent pursuits emerging in big data processing, interpretation, collection and organization have emerged in numerous sectors including business, industry, and not-for-profit organizations. Data sets such as customer transactions for a mega-retailer, weather monitoring, intelligence gathering, can quickly outpace the capacity of traditional techniques and tools of data analysis. We have been witnessing emergence of new techniques and tools including NoSQL databases, MapReduce, Natural Language Processing, Machine Learning, visualization, acquisition, and serialization.

It becomes imperative to fully become aware what happens when big data grows up: how it is being applied and where it is playing a vital role. One has to become cognizant of implications and requirements imposed on the existing techniques and those under current development.

Computational Intelligence being regarded as a plethora of technologies of fuzzy sets (or Granular Computing), neurocomputing and evolutionary optimization brings forward a number of unique features that might be instrumental to the development of concepts and algorithms to deal with big data. In particular, setting up a suitable level of abstraction by forming semantically meaningful information granules is essential. In light of their sheer volume, big data may call for distributed processing, intensive data mining realized locally whose results are afterwards reconciled leading to information granules of higher type. Neurocomputing operating at information granules leads to more tractable learning tasks. Evolutionary computing comes as an essential framework supporting global optimization. In light of the challenges encountered in big data, the synergy of the key technologies of Computational Intelligence becomes crucial when addressing sheer amount of data, their complexity, and variability (3V challenge).

Given the theme of this project, this book is aimed at a broad audience of researchers and practitioners. It is addressed to well-established communities including those active in various disciplines in which big data, their analysis and optimization. Those involved in data mining, data analysis, management, various branches of engineering, and economics will also benefit from the exposure to the subject matter.

Topics of interest include, but are not limited to, the following: (1) Notions, main quests and key directions of big data analysis, (2)Big data analytics, (3)Information granules, processing information granules and their optimization mechanisms, (4)Existing data mining methodologies and their scalability, (5)New scalable Computational Intelligence algorithms for big data mining, (6)Studies in learning, visualization and interpretation schemes for big data, (7)Collaboration schemes in big data analytics and their dynamics, (8)Case studies – coverage of a suite of representative areas of applications, (9)
Other closely related topics

Submission Procedure

Potential authors are invited to submit a brief one-page summary of the proposed chapter clearly identifying the main objectives of their research. Authors of the accepted proposals will be notified and provided with detailed guidelines. Brief Proposals of Chapters are to be submitted by November 15, 2013. All manuscripts will be thoroughly reviewed. The lead authors will be provided with a complimentary copy of the volume.

The proposals and manuscripts are to be submitted electronically to both editors (wpedrycz@ualberta.ca and smchen@mail.ntust.edu.tw).

Important Dates

November 15, 2013 Brief Proposal Submission

November 25, 2013 Notification of Acceptance

January 15, 2014 Full Chapter Submission

February 15, 2014 Review Results Returned

March 15, 2014 Final Chapter Submission

發佈日期:2013/10/05
聯絡人:國立台灣科技大學陳錫明教授
聯絡電話:(02) 27376417
相關網址:http://fuzzylab.et.ntust.edu.tw
E-Mail:smchen(at)mail.ntust.edu.tw

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