Future Directions in Semantic Technology, Data Analytics, and Sustainability

The future of scientific development can probably be predicted through the treatment given or due to the pressure of human interests, both local, national, and global. Treatment is learning and something that provides output in the form of documents published by qualified publishers, perhaps even guaranteed by indexers. In scientific development with evidence of document growth, learning describes the implications – internal growth – of a science from the past to the present, with teaching to handle it better, with research to strengthen that growth, or perhaps with community service so that there is a shift for the better for the future of the scientific field with predictions that have the potential to be conclusions. In relation to human interests, data science as a current sexy study cannot be separated from the scientific field in theory about semantic technology, data analytics, and sustainability. Without theory, a prediction is impossible, where the role of semantic technology is to interpret different situations that are the conditions for meaningful data, with the support of data analytics to ensure that the interpretation becomes facts-information-knowledge, or the existence of sustainability in the development of each scientific field. This paper proposes a way to determine the future directions of semantic technology, data analytics and sustainability by involving an approach.