Accelerated PSO Swarm Search Feature Selection for Data Stream Mining Big Data using Genetic Algorithm

Abstract

In the modern world there is brisk development in the field of networking technology which handles huge
data at a time. This data can be structured, semi structured or unstructured. To perform efficient minning of valuable
information from such type of data the big data technology is gaining importance nowadays. Data minning application is
used mostly in all fields right from science and engineering domains to social networking and biomedical science. It is
been used in public and private sectors of industry because of its advantage over conventional networking technology to
analyze large real time data. Data minning mainly relies on 3 V’s namely, Volume, Varity and Velocity of processing
data. Volume refers to the huge amount of data it collects, Velocity refers to the speed at which it process the data and
Variety defines that multi dimensional data does which can be numbers, dates, strings, geospatial data, 3D data, audio
files, video files, social files, etc. These data which is stored in big data will be from different source at different rate and
of different type; hence it will not be synchronized. This is one of the biggest challenge in working with big data. Second
challenge is related to minning the valuable and relevant information from such data adhering to 3rd V i.e velocity.
Speed is highly important as it is associated with cost of processing. This paper focus on analyzing the big data
technology and provide detail study of accelerated PSO Swarm search feature selection.