Exploring Big Data Clustering Algorithms for Internet of Things Applications

With the rapid development of the Big Data and Internet of Things (IoT), Big Data technologies have emerged as a key data analytics tool in IoT, in which, data clustering algorithms are considered as an essential component for data analysis. However, there has been limited research that addresses the challenges across Big Data and IoT and thus proposing a research agenda is important to clarify the research challenges for clustering Big Data in the context of IoT. By tackling this specific aspect - clustering algorithm in Big Data, this paper examines on Big Data technologies, related data clustering algorithms and possible usages in IoT. Based on our review, this paper identifies a set of research challenges that can be used as a research agenda for the Big Data clustering research. This research agenda aims at identifying and bridging the research gaps between Big Data clustering algorithms and IoT.

[1]  Klaus Moessner,et al.  Context-aware stream processing for distributed IoT applications , 2015, 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT).

[2]  Anind K. Dey,et al.  Understanding and Using Context , 2001, Personal and Ubiquitous Computing.

[3]  Eleni Constantinou,et al.  Landmark selection for spectral clustering based on Weighted PageRank , 2017, Future Gener. Comput. Syst..

[4]  Pierpaolo D'Urso,et al.  Exponential distance-based fuzzy clustering for interval-valued data , 2017, Fuzzy Optim. Decis. Mak..

[5]  Rem W. Collier,et al.  A Survey of Clustering Techniques in WSNs and Consideration of the Challenges of Applying Such to 5G IoT Scenarios , 2017, IEEE Internet of Things Journal.

[6]  George Mastorakis,et al.  Internet of Things (IoT) in 5G Mobile Technologies , 2016 .

[7]  Shivani Goel,et al.  A comprehensive study on clustering approaches for big data mining , 2015, 2015 2nd International Conference on Electronics and Communication Systems (ICECS).

[8]  Jiawei Han,et al.  CLARANS: A Method for Clustering Objects for Spatial Data Mining , 2002, IEEE Trans. Knowl. Data Eng..

[9]  Felix Wortmann,et al.  Internet of Things , 2015, Business & Information Systems Engineering.

[10]  M. A. Dalal,et al.  A survey on clustering in data mining , 2011, ICWET.

[11]  Rob van Kranenburg,et al.  The Internet of Things : A Critique of Ambient Technology and the All-Seeing network of RFID , 2008 .

[12]  Jian Ma,et al.  A network-assisted co-clustering algorithm to discover cancer subtypes based on gene expression , 2014, BMC Bioinformatics.

[13]  Ludovic Noirie,et al.  A Scalable IoT Service Search Based on Clustering and Aggregation , 2013, 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing.

[14]  Marimuthu Palaniswami,et al.  Scalable single linkage hierarchical clustering for big data , 2013, 2013 IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information Processing.

[15]  Ying Wah Teh,et al.  Iterative big data clustering algorithms: a review , 2016, Softw. Pract. Exp..

[16]  Ying Wah Teh,et al.  On Density-Based Data Streams Clustering Algorithms: A Survey , 2014, Journal of Computer Science and Technology.

[17]  Jiong Yang,et al.  STING: A Statistical Information Grid Approach to Spatial Data Mining , 1997, VLDB.

[18]  Yang Lu,et al.  Big data analytics and big data science: a survey , 2016 .

[19]  Eero Vainikko,et al.  Adapting scientific computing problems to clouds using MapReduce , 2012, Future Gener. Comput. Syst..

[20]  Wu He,et al.  Internet of Things in Industries: A Survey , 2014, IEEE Transactions on Industrial Informatics.

[21]  Ying Wah Teh,et al.  Big Data Clustering: A Review , 2014, ICCSA.

[22]  Hong Liu,et al.  A grouping method based on grid density and relationship for crowd evacuation simulation , 2017 .

[23]  Ying Li,et al.  Impact of Next-Generation Mobile Technologies on IoT-Cloud Convergence , 2017, IEEE Commun. Mag..

[24]  Qishan Zhang,et al.  Community discovery by propagating local and global information based on the MapReduce model , 2015, Inf. Sci..