In the current decade, doing the search on massive data to find “hidden” and valuable
information within it is growing. This search can result in heavy processing on considerable …

One of the most successful techniques in large-scale data-intensive computations is
MapReduce programming. MapReduce is based on a divide and conquer approach that …

Multi-label classification is one of the most dynamically growing fields of machine learning,
due to its numerous real-life applications in solving problems that can be described by …

Cloud and fog computing are two emerging Internet-based collaborative technologies for big
data analytics. An interesting question arising is whether the two technologies can resonate …

HPC Shelf is a proposal of a cloud computing platform for development, deployment and
execution of component-based HPC applications with large-scale parallel processing …

Cloud computing and big data technologies have gained great popularity in recent years.
MapReduce is still one of the most efficient and well-adopted computing paradigms for …

This survey article reviews the challenges associated with deploying and optimizing big data
applications and machine learning algorithms in cloud data centers and networks. The …

Introduction Many data analytics algorithms are originally designed for in-memory data.
Parallel and distributed computing is a natural first remedy to scale these algorithms to “Big …

Achieving an optimal solution for NP-complete problems is a big challenge nowadays. The
paper deals with the Traveling Salesman Problem (TSP) one of the most important …

随着存储设备的容量不断变大, 数据恢复速度慢日益成为该领域当前突出的问题.
快速数据恢复成为当前数据安全, 数字取证领域的一个研究热点和难点问题. 以SQLite …