- Email: [email protected]
Semantics, intelligent processing and services for big data
Future Generation Computer Systems (
)
–
Contents lists available at ScienceDirect
Future Generation Computer Systems journal homepage: www.elsevier.com/locate/fgcs
Editorial
Semantics, intelligent processing and services for big data✩ Fatos Xhafa a,∗ , Leonard Barolli b a
Campus Nord, Ed. Omega, Universitat Politècnica de Catalunya (UPC), C/Jordi Girona, 1-3, 08034 Barcelona, Spain
b
Fukuoka Institute of Technology (FIT), 3-30-1 Wajiro-higashi, Higashi-ku, Fukuoka, 811-0295, Japan
article
info
Article history: Available online xxxx Keywords: Big data Semantics Intelligent services Security Massive and distributed processing
abstract With the continuous increase of data, scaling up to unprecedented amounts, generated by Internet-based systems, Big Data has emerged as a new research field, coined as ‘‘Big Data Science’’. The core of Big Data Science is the extraction of knowledge from data as a basis for intelligent services and decision making systems, however, it encompasses many research topics and investigates a variety of techniques and theories from different fields, including data mining and machine learning, information retrieval, analytics, and indexing services, massive processing and high performance computing. Altogether the aim is the development of advanced data-aware knowledge based systems. This special issue presents advances in Semantics, Intelligent Processing and Services for Big Data and their applications to a variety of domains including mobile computing, smart cities, forensics and medicine. © 2014 Published by Elsevier B.V.
Big Data is a recent research trend from Data Science, with the particularity of data sets scaling up to large and very large amounts. The features that define Big Data such as volume, velocity, variety, etc., bring several challenges to data processing and analysis [1]. There is increasing interest on Big Data, which is promoted, on the one hand by the increasing capability of Networked, Distributed and Cloud Computing system [2] to store and manage unprecedented amounts of data, efficient scheduling and resource allocation at large scale [3,4] and, by the significance and relevance to businesses, which see data as a new asset for their business activity. This special issue follows The 15th International Conference on Network-Based Information Systems (NBiS-2012), Melbourne, Australia September 26th–28th, 2012 as well as from an open call. It comprises seven papers selected from twenty five submissions. The selected papers present research findings on semantics, information retrieval, inference, intelligent processing and services, scalability and performance evaluation for Big Data. The content of the special issue is organized as follows. Castiglione et al. [5] in the first paper present a novel modeling approach for performance evaluation of big data systems based on mean field analysis and a set of methods for approximate inference of probabilistic models, derived from statistical physics.
✩ Guest Editorial Preface of Special Issue ‘‘Big Data Science’’.
∗
Corresponding editor. Tel.: +34 93 413 7880; fax: +34 93 413 7833. E-mail addresses: [email protected] (F. Xhafa), [email protected] (L. Barolli).
http://dx.doi.org/10.1016/j.future.2014.02.004 0167-739X/© 2014 Published by Elsevier B.V.
In the second paper, Alghamdi et al. [6], address semantic approaches for structural and content indexing as a basis for efficient retrieval of queries over large XML data repositories. The authors of the third paper, Alamri et al. [7], investigate techniques for indexing structures of moving objects arising in many spatial information applications such as in mobile computing applications. Such techniques are then used for moving object database indexing and querying. Farruggia et al. [8] in the fourth paper, present a text-based indexing system for mammographic image retrieval and classification. The proposed approach addresses challenges in modern medical systems storing huge amount of text, words, images and videos in ad hoc databases as well as the needs to extract precise information from that large amount of data. The fifth paper by Chen et al. [9] and sixth paper by Li et al. [10] deal with security and confidentiality on sensitive data in epayment and forensics in Web and Cloud computing systems. Issues of lower computation and communication complexity are addressed in their approaches. Finally, in the seventh paper by Dobre et al. [11], the authors analyze current approaches for massive data processing and present solutions designed to support next-generation Big Data applications. The paper brings the case study of smart cities, where big data is having a real impact. The study covers a full data cycle, starting from data gathering, data integration, processing to intelligent services for consumption from city planners and administration bodies.
2
F. Xhafa, L. Barolli / Future Generation Computer Systems (
Acknowledgments The guest editors of the special issue would like to thank all authors for their contributions and cooperation in the preparation of this special issue. We appreciate the efforts of reviewers to provide timely and constructive feedback to authors. Finally, we are very grateful to Prof. Sloot, the Editor-in-Chief of FGCS Journal for his support and encouragement, as well as to the editorial and managerial journal team for their assistance during the edition of this special issue. The first guest editor acknowledges the support from FORMALISM research project. References [1] Rizwan Mian, Patrick Martin, Jose Luis Vazquez-Poletti, Provisioning data analytic workloads in a cloud, Future Gener. Comput. Syst. 29 (6) (2013) 1452–1458. [2] Buyya, et al., Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility, Future Gener. Comput. Syst. 25 (6) (2009) 599–616. [3] E. Dodonov, R. de Mello, A novel approach for distributed application scheduling based on prediction of communication events, Future Gener. Comput. Syst. 26 (5) (2010) 740–752.
)
–
[4] Anton Beloglazov, Jemal Abawajy, Rajkumar Buyya, Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing, Future Gener. Comput. Syst. 28 (2012) 755–768. http://dx.doi.org/10.1016/j.future.2011.04.017. [5] Aniello Castiglione, Marco Gribaudo, Mauro Iacono, Francesco Palmieri, Exploiting mean field analysis to model performances of big data architectures, Future Gener. Comput. Syst. (2014), http://dx.doi.org/10.1016/j.future.2013. 07.016, in this issue. [6] Norah S. Alghamdi, Wenny Rahayu, Eric Pardede, Semantic-based structural and content indexing for the efficient retrieval of queries over large XML data repositories, Future Gener. Comput. Syst. (2014) http://dx.doi.org/10.1016/j.future.2014.02.010, in this issue. [7] Sultan Alamri, David Taniar, Maytham Safar, A taxonomy for moving object queries in spatial databases, Future Gener. Comput. Syst. (2014) http://dx.doi.org/10.1016/j.future.2014.02.007, in this issue. [8] Alfonso Farruggia, Rosario Magro, Salvatore Vitabile, A text based indexing system for mammographic image retrieval and classification, Future Gener. Comput. Syst. (2014) http://dx.doi.org/10.1016/j.future.2014.02.008, in this issue. [9] Xiaofeng Chen, Jin Li, Jianfeng Ma, Wenjing Lou, Duncan S. Wong, New efficient conditional e-payment systems with transferability, Future Gener. Comput. Syst. (2014) http://dx.doi.org/10.1016/j.future.2013.07.015 in this issue. [10] Jin Li, Xiaofeng Chen, Qiong Huang, Duncan S. Wong, Digital provenance: enabling secure data forensics in cloud computing, Future Gener. Comput. Syst. (2014) http://dx.doi.org/10.1016/j.future.2013.10.006, in this issue. [11] Ciprian Dobre, Fatos Xhafa, Intelligent services for big data science, Future Gener. Comput. Syst. (2014) http://dx.doi.org/10.1016/j.future.2013.07.014, in this issue.
)
–
Contents lists available at ScienceDirect
Future Generation Computer Systems journal homepage: www.elsevier.com/locate/fgcs
Editorial
Semantics, intelligent processing and services for big data✩ Fatos Xhafa a,∗ , Leonard Barolli b a
Campus Nord, Ed. Omega, Universitat Politècnica de Catalunya (UPC), C/Jordi Girona, 1-3, 08034 Barcelona, Spain
b
Fukuoka Institute of Technology (FIT), 3-30-1 Wajiro-higashi, Higashi-ku, Fukuoka, 811-0295, Japan
article
info
Article history: Available online xxxx Keywords: Big data Semantics Intelligent services Security Massive and distributed processing
abstract With the continuous increase of data, scaling up to unprecedented amounts, generated by Internet-based systems, Big Data has emerged as a new research field, coined as ‘‘Big Data Science’’. The core of Big Data Science is the extraction of knowledge from data as a basis for intelligent services and decision making systems, however, it encompasses many research topics and investigates a variety of techniques and theories from different fields, including data mining and machine learning, information retrieval, analytics, and indexing services, massive processing and high performance computing. Altogether the aim is the development of advanced data-aware knowledge based systems. This special issue presents advances in Semantics, Intelligent Processing and Services for Big Data and their applications to a variety of domains including mobile computing, smart cities, forensics and medicine. © 2014 Published by Elsevier B.V.
Big Data is a recent research trend from Data Science, with the particularity of data sets scaling up to large and very large amounts. The features that define Big Data such as volume, velocity, variety, etc., bring several challenges to data processing and analysis [1]. There is increasing interest on Big Data, which is promoted, on the one hand by the increasing capability of Networked, Distributed and Cloud Computing system [2] to store and manage unprecedented amounts of data, efficient scheduling and resource allocation at large scale [3,4] and, by the significance and relevance to businesses, which see data as a new asset for their business activity. This special issue follows The 15th International Conference on Network-Based Information Systems (NBiS-2012), Melbourne, Australia September 26th–28th, 2012 as well as from an open call. It comprises seven papers selected from twenty five submissions. The selected papers present research findings on semantics, information retrieval, inference, intelligent processing and services, scalability and performance evaluation for Big Data. The content of the special issue is organized as follows. Castiglione et al. [5] in the first paper present a novel modeling approach for performance evaluation of big data systems based on mean field analysis and a set of methods for approximate inference of probabilistic models, derived from statistical physics.
✩ Guest Editorial Preface of Special Issue ‘‘Big Data Science’’.
∗
Corresponding editor. Tel.: +34 93 413 7880; fax: +34 93 413 7833. E-mail addresses: [email protected] (F. Xhafa), [email protected] (L. Barolli).
http://dx.doi.org/10.1016/j.future.2014.02.004 0167-739X/© 2014 Published by Elsevier B.V.
In the second paper, Alghamdi et al. [6], address semantic approaches for structural and content indexing as a basis for efficient retrieval of queries over large XML data repositories. The authors of the third paper, Alamri et al. [7], investigate techniques for indexing structures of moving objects arising in many spatial information applications such as in mobile computing applications. Such techniques are then used for moving object database indexing and querying. Farruggia et al. [8] in the fourth paper, present a text-based indexing system for mammographic image retrieval and classification. The proposed approach addresses challenges in modern medical systems storing huge amount of text, words, images and videos in ad hoc databases as well as the needs to extract precise information from that large amount of data. The fifth paper by Chen et al. [9] and sixth paper by Li et al. [10] deal with security and confidentiality on sensitive data in epayment and forensics in Web and Cloud computing systems. Issues of lower computation and communication complexity are addressed in their approaches. Finally, in the seventh paper by Dobre et al. [11], the authors analyze current approaches for massive data processing and present solutions designed to support next-generation Big Data applications. The paper brings the case study of smart cities, where big data is having a real impact. The study covers a full data cycle, starting from data gathering, data integration, processing to intelligent services for consumption from city planners and administration bodies.
2
F. Xhafa, L. Barolli / Future Generation Computer Systems (
Acknowledgments The guest editors of the special issue would like to thank all authors for their contributions and cooperation in the preparation of this special issue. We appreciate the efforts of reviewers to provide timely and constructive feedback to authors. Finally, we are very grateful to Prof. Sloot, the Editor-in-Chief of FGCS Journal for his support and encouragement, as well as to the editorial and managerial journal team for their assistance during the edition of this special issue. The first guest editor acknowledges the support from FORMALISM research project. References [1] Rizwan Mian, Patrick Martin, Jose Luis Vazquez-Poletti, Provisioning data analytic workloads in a cloud, Future Gener. Comput. Syst. 29 (6) (2013) 1452–1458. [2] Buyya, et al., Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility, Future Gener. Comput. Syst. 25 (6) (2009) 599–616. [3] E. Dodonov, R. de Mello, A novel approach for distributed application scheduling based on prediction of communication events, Future Gener. Comput. Syst. 26 (5) (2010) 740–752.
)
–
[4] Anton Beloglazov, Jemal Abawajy, Rajkumar Buyya, Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing, Future Gener. Comput. Syst. 28 (2012) 755–768. http://dx.doi.org/10.1016/j.future.2011.04.017. [5] Aniello Castiglione, Marco Gribaudo, Mauro Iacono, Francesco Palmieri, Exploiting mean field analysis to model performances of big data architectures, Future Gener. Comput. Syst. (2014), http://dx.doi.org/10.1016/j.future.2013. 07.016, in this issue. [6] Norah S. Alghamdi, Wenny Rahayu, Eric Pardede, Semantic-based structural and content indexing for the efficient retrieval of queries over large XML data repositories, Future Gener. Comput. Syst. (2014) http://dx.doi.org/10.1016/j.future.2014.02.010, in this issue. [7] Sultan Alamri, David Taniar, Maytham Safar, A taxonomy for moving object queries in spatial databases, Future Gener. Comput. Syst. (2014) http://dx.doi.org/10.1016/j.future.2014.02.007, in this issue. [8] Alfonso Farruggia, Rosario Magro, Salvatore Vitabile, A text based indexing system for mammographic image retrieval and classification, Future Gener. Comput. Syst. (2014) http://dx.doi.org/10.1016/j.future.2014.02.008, in this issue. [9] Xiaofeng Chen, Jin Li, Jianfeng Ma, Wenjing Lou, Duncan S. Wong, New efficient conditional e-payment systems with transferability, Future Gener. Comput. Syst. (2014) http://dx.doi.org/10.1016/j.future.2013.07.015 in this issue. [10] Jin Li, Xiaofeng Chen, Qiong Huang, Duncan S. Wong, Digital provenance: enabling secure data forensics in cloud computing, Future Gener. Comput. Syst. (2014) http://dx.doi.org/10.1016/j.future.2013.10.006, in this issue. [11] Ciprian Dobre, Fatos Xhafa, Intelligent services for big data science, Future Gener. Comput. Syst. (2014) http://dx.doi.org/10.1016/j.future.2013.07.014, in this issue.