Special Issue Editorial: Big Data for Mobile Services

Excerpt

We are in era of “Big Data”. As data and knowledge volume keep increasing while global means for information dissemination continue to diversify, new methods, modeling paradigms and structures are needed to efficiently mount scalability requirements [1]. In the last few years, we have seen the proliferation of the use of heterogeneous distributed systems, ranging from simple Networks of Workstations, to highly complex grid computing environments. Such computational paradigms have been preferred due to their reduced costs and inherent scalability, which pose many challenges to scalable systems and applications in terms of information access, storage and retrieval. Cluster computing [2], cloud computing technology [3], data and knowledge bases, distributed information retrieval technology [4] and networking technology [5] should all converge to address the scalability concern. Furthermore, with the advent of emerging computing architectures (e.g., SMTs, GPUs, and multicores), the importance of designing techniques explicitly targeting these systems is becoming more and more important. …