(untitled) (International Journal of Engineering and Computer Science, 2018)
Data streams” is defined as class of data produced over “text, audio and video” channel in uninterrupted form. The streams are of unlimited length and may consist of ordered or unordered data. With these features, it is hard to store and process data streams with simple and static strategies. The processing of data stream poses four key challenges to researchers. These are countless length, concept-development, and concept-flow and feature development. Unlimited-length is because the amount of data has no limits. Concept- flow is due to sluggish changes in the theory of stream. Concept-development occurs due to existence of unfamiliar classes in data. Feature-development is due to development new features and deterioration of old features. To carry out any analytics data streams, the translation to knowledgeable form is important. The researcher in past have anticipated different strategies, most of the research is focused on difficulty of unlimited -length and concept-flow. The research work offered in the paper describes a well-organized string based methodology to process “data streams” and manage the challenges of unlimited length, concept-development and concept-flow. Subject areas Data mining, Machine learning.