We are now in the era of the Big Data revolution where nearly every aspect of computing engineering is driven by increasingly large, complex, and diverse datasets. Big Data presents not only a world of new opportunities but also new challenges. With threats multiplying exponentially, the ability to gather and analyze massive information will be a decisive factor in the battle against malicious software and adversaries. It is natural to consider a cloud-computing environment to address the computational requirements for big data analytic applications. However, it is equally important to address the security concerns in terms of policies, technologies, and controls deployed to protect data, applications, and the associated infrastructure of cloud computing. In this special issue of JCSM on Big Data, the article by Miller et al. focuses on technical and policy infrastructure for digital forensic analysis in the cloud as cyber-crime is a growing trend around the world. Further, the massive collections of imagery on the Internet have inspired a stream of interesting work on image processing related big data topics. The article by Shen et al. describes their novel structure-based image completion algorithm for object removal while maintaining visually plausible content with consistent structure and scene texture in photos. Such novel technique can benefit a diverse range of applications, from image restoration, to privacy protection, to photo localization. A surge of graph-computing frameworks has appeared in both academia and industry to address the needs of processing complex and large graph-structured datasets, where each has its respective benefits and drawbacks. Leveraging the right platform for the right task is daunting for users of these frameworks. A review by Zhao et al. provides the context for selecting the right graph-parallel processing framework given the tasks in hand. They have studied several popular distributed graph-computing systems aiming to reveal the characteristics of those systems in performing common graph algorithms with real-world datasets. Their findings are extremely informative.