The term big data is the buzzing word in present days

ADVANCED RESEARCH TOPIC: Next-Generation Big Data Analytics:
State of the Art
RESEARCH QUESTION: What is the common challenge in infrastructure-support
applications in terms of efficiency in security for next-generation big data analytics?
ABSTRACT:
The term big data is the buzzing word in present days. The greater number of subjects we use
everyday life to basic research fields, which involves big data problems. In this research
study we review the most popular data types, data security, privacy and storage models
related to network big data. The data types we use for our study are online network data,
mobile and IoT data, geography data, spatial temporal data and streaming and real-time
data. Various kinds of domain give particular challenges which are properly estimated, and
important impact is created by next-generation of big data systems. The increased
performance in the building designs of big data are improves by the effect of functionality
and special interest in big data systems of next generation. The safety and protection of big
data is a very vital problem. The analysis big data can be done by the MapReduce-related
method. Also, in this study we have explained the applications of big data as transient power
prediction, content recommendation, smart city and user behaviour prediction. The common
challenge is that the infrastructure is vital to support machine learning algorithms to
implement new ones.
Key words: Big data, Machine learning algorithm, Data types, and challenge.
Literature Review
According to Fan, Han and Liu (2014), Online Network Data has been one of the
major data set that is to be used for understanding the challenges that are to be considered is
Online Networking data. This section ensures that data that are generated via the platform of
Facebook and Second Life are considered in this data set. Adware and Spam are the major
threats that are present in the online network data. Due to occurrence of these attacks data
might not be protected and hence wise collection of data and hence wise analysis of the same
gets affected in a negative manner. Another major trend that is seen is data that are collected
from the mobile and IoT data [5]. These data are also considered as a major data set that is
used by the Next generation Bug Data in the analysis process [9]. There is a major threat that
has been affecting the data analysis section. Integrated mobile webs have been using
unlicensed spectrum. This leads to the fact that not all the fata that are used by the users can
be gathered. Usage of cognitive radio technology has been affecting the entire data collection
and analysis process. Power consumption attacks has been another threat that has been
affecting the analysis process. Structuring of the data has also been a major problem in the
functional process. Geography data is also considered as a major data set that helps in
performing proper data analysis via next generation data analysis [2]. Despite the fact that
this data sets are in process of getting collaborated with the OSN, the data itself has
importance in the data analytics section.
It has been seen that researchers have been using unprocessed data sets. This kind of
unprocessed data set includes the fact that the subsets can be created and hence wise
replication of the data can be performed. Another major issue that can be stated is that

collection of this type of data will be requiring proper knowledge in geography as well as in
topological section. Hence a proper blend of topological as well IT informatics acts as a
major challenge in the analysis process. This might lead to inaccurate data analytics.
Manipulation of geographic data has been another issue as well. Spatial temporal data type is
another data type that acts important in the data analytics section. In this case data collection
acts as a main challenge [7]. The collaterals that are present in the path of spatial temporal
data acts as a major threat to the data collection process. Data are collected as per the sensors
and the security process might be fragile in this kind of data collection process. This leads to
the fact that data collection and data analysis might be getting affected. Another data type that
is considered is Streaming and Real time data. In this case the major issuer that is present is
that implementation of firewall cannot be performed [1]. This leads to the fact that with the
presence of firewall, detecting of anomalies was easier and without the presence of the
firewall the entire process gets affected in a negative manner. These are the main threats that
might be affecting the functional process of the data management. However real time data
analytics itself acts helpful in detection of the threat. As with the help of the real time data
analytics, a holistic view of the network can be established. This leads to the fact that in case
of any anomaly in the network, detection of the anomaly gets easier. Real time visual data is
also acts as an important data type that has been providing enough data to the next generation
big data for the data analytics section [6]. There are a lot of challenges that are faced in the
collection and analysis of the data that are generated via the visual data. Analysis and
retrieval of data has been a major challenge in the process of data collection and assessment.
The main aspect that is to be considered is the retrieval of the data. Analysis of the data has
been one of the major issue that is to be faced. Presence of weak semi supervised deep
learning is also another issue that is to be considered. Image annotation process is negatively
affected due to this terminology [3]. Weak labelling of images might be acting as a major
issue. Presence of triplet similarity loss has also been acting as a major challenge and threat
in case of the visual data collection process. It can be stated that the data that are weakly
weighted pairwise ranking loss is yet another issue that is seen in the faced by the next
generation big data.
There are a certain challenges irrespective of the domain that effects the analytics of
data with the help of next generation Big Data [10]. The major challenges includes the likes
of presence of noise accumulation and hence wise spurious correlation as well as the
incidental homogeneity also occurs due to the presence of noise accumulation. Another
challenge that is faced includes the likes of having high dimension along with the combined
large sample size. This leads to creation of challenges such as instability in the algorithm of
the entire program. Another issue that was faced includes the likes of high computational
cost. Heterogeneity is also another challenge that is present in performing data analytics via
the platform of new generation big data [4]. These heterogeneity causes the statistical biases.
This statistical biases leads to proper assessment of the adaptive and robust processing. It is
seen that the adaptive nature of the entire process has been very low. Despite the issues
present there are many recommendations that can eliminate the issues and enhance the data
analytics process. Performing of statistical accuracy has been performing proper assessment
of statistical analysis and hence wise higher accuracy can be established [8]. This process
tackles the functioning of the noise that are accumulated in data and hence wise elimination
of noise accumulation can be performed. This will increase the accuracy in the data analytics
section.