Network computing and applications for Big Data analytics
Big Data presents myriad possibilities for transforming science, while applying different values to the system param engineering, medicine, healthcare, finance, business and ultimately society itself. It offers significant opportunities for organizations to obtain critical intelligence to drive decisions and obtain insights as never before (Bhattacharya et al. 2015) However, the volume and variety of data mandates development of new approaches and applications grounded in emerging analytics techniques and emerging processing technologies within the Big Data paradigm (Abawajy, 2015) This special issue presents original contributions in the area of network computing and applications for Big Data. With the exponential increase in an online information, how to find information of interest has become a serious challenge. This problem necessitated the development of personalized online information services. Xu et al. (2015) proposed a novel two stage approach for R&D project opportunity recommendation to interested researchers and practitioners. At the top level, a filtering approach is used to identity appropriate R&D projects as a candidate set for researchers and practitioners. The second level deploys an information aggregation method with various constraints to recommend suitable R&D projects to interested researchers and practitioners. Amini et al. (2015) addressed the problem of clustering data streams. They propose a method called the MuDi Stream which combines an online phase and an offline phase with four main components. In the online phase, summary information about the evolving multi density data stream is maintained in the form of core mini clusters. In the offline phase, the final clusters are generated using an adapted density based clustering algorithm. MuDiStream employs the grid based method as an outlier buffer to deal with both noises and multi density data as well as to reduce the time complexity of combining the clusters. Empirical evaluation of MuDi Stream on various synthetic and real world datasets using different quality metrics shows that MuDi Stream improves clustering quality in multi density environments. Villaca et al. (2015) discuss a system called HCube that can be used for routing and similarity search in Data Centers. In contrast to Data Centres (DCs) based data storage/retrieval, HCube data storage/retrieval is based on the similarity search where similar content is collected on servers physically close within the HCube. A three dimensional structure is used to arrange the HCube network. HCube uses the Gray Space Filling Curve (SFC) in combinationwith the RandomHyperplane Hashing (RHH) function and the XOR based flat routing mechanism for the similarity search. Mershad et al. (2015) conducted analysis and performance evaluation of the Reconfigurable Active SSD (RASSD) Middleware Server (MWS) which combines active solid state drives and reconfigurable FPGAs into a storage compute node that can be used by a cloud data center to achieve accelerated computations while running data intensive applications. They studied the utilization of three important elements of the MWS: CPU, memory, and network bandwidth. For each, we derive the parameters that govern and affect its operations, and propose formulas for its utilization factor. We use the analysis results,
J. Netw. Comput. Appl.