Big data technologies are used majorly for computational biology research nowadays due to the emerge of massive amounts of biological data that have been generated and collected at an unprecedented speed and scale and with different structures. As an example, the processing of billions of DNA sequence data per day represents the new generation of sequencing technologies. It is expected that the cost of acquiring and analyzing biomedical data will decrease with the assistance of technology upgrades in the computational biology field. The application of big data in health care is a fast-growing field, with many new discoveries and methodologies. In this study, an overview of recent developments and technologies in big data in the computational biology field. Moreover, in this study, we summarize and highlight the challenges, gaps and opportunities to improve and advance big data applications in computational biology. Also, this review presents a useful starting point for the application of Big Data in future Bioinformatics research. Finally, some of the Big Data sources in computational biology that help in applying these new emerging technologies are mentioned.

Goal modeling techniques plays a crucial role in requirements engineering since they facilitate the reach of requirements to stakeholders in an understandable easy manner and also in a professional well defined pattern to developers. Modeling of goals are encouraged and proposed during requirements elicitation in order to detail, understand and describe problems associated with current organizational structures and behavior. However, a current challenge appears which is how to manage modeling of goals if either these goals were elicited in early or late phases? Eventually if that occurred the rest of the challenge comes in how to model these goals in the presence of uncertainty? This paper presents a systematic literature review of the current goal modeling techniques dealing with both early and late requirements such as: i* framework, tropos, GRL and UML. The results for research in this study show that although there are models for both early and late requirements, most techniques to a great extent are used for modeling early requirements. The findings lead us to identify two future work elicited in the study that might help a lot in modeling goals.