Indexing in Big Data

Nowadays communication is through social media for almost all activities like business, knowledge, personal updates, etc. This leads to the generation of large amount of data related to different activities. Hence, social media have become a vital content of our life. But going through this huge data for analysis is a tedious and complex task. There are many solutions to overcome this problem. Data reduction, indexing, and sorting can be the solutions. Further, which will be used for visualization, recommendation, etc. Indexing techniques for highly repetitive data group have become a relevant discussion. These techniques are used to accelerate queries with value and dimension subsetting conditions. There are different types of indexing with the suitability of data type, data size, dimension, representation, storage, etc. Indexing is of vital need as whatever electronic text collection is available, it is mostly large scale and heterogeneous. Hence, the motto is to find an improved approach for text search as it is used right from the help services built into operating systems to locate file on computers. Tree-based indexing, multidimensional indexing, hashing, etc., are few indexing approaches used depending on the data structures and big data analysis (BDA). Indexing’s need is to address the speed of search. So, size of index shall be a fraction of original data and to be built at the speed of data generation to avoid delay in result. Here, few indexing techniques/search structures are discussed based on data structure, frame work, space need, simplified implementations, and applications.