The cyber physical world has computation and communication power which grows in vast manner. Thus as because of this it produces large volume of data to handle this processes. There are four main challenges related to big data and they are volume, variety, velocity, veracity
By Ashish Jadhao and Swapnaja Hiray
Abstract:
The cyber
physical world has computation and communication power which grows in vast
manner. Thus as because of this it produces large volume of data to handle this
processes. There are four main challenges related to big data and they are
volume, variety, velocity, veracity. Volume and variety are managed by some
store data processing system like Hadoop. But the velocity and veracity of such
large amount of data is too much complex process. In this paper we are going to
implement such system which can handle high speed and different pattern of data
with its large volume. We are going to implement correlation analytics and
mining on the data stream to extract meaningful information. The system should
provide real time data processing so that it used Event processing engine as
Esper which has different language queries to generate different events. To
catch real time data and for simple filtering of that data stream Storm is used
which used topology. Correlation and mining takes place by two different
algorithm Apriori and FP-Growth algorithm.
Image Attribute: Cyber Physical System Framework / Source: University of Notre Dame
Keywords:
Analytics, Apriori, Big data, CEP, Cyber Physical System, Esper, FP-growth,
Mining, Sliding window, Storm
Introduction
Big data sizes
are a continuously achieving its peak, as at present it is changing its range
from gigabytes to number of tera-bytes in a single data storage. Much of this
data explosion is the result of a dramatic increase in devices located at the
periphery of the network including embedded sensors, smart phones, and tablet
computers. So now a day's cyber physical system has face big data handling
property and obtain the core data from that is also a main challenge with fast
growing resources. There are large numbers of challenges with big data its
veracity, size, accuracy, hardness, and security. This challenges rise right
away during data collection, where this data tsunami force us to take such
decision, which are not practically meaningful about which data is stored and
which is discarded, and how that data is going to store according to pattern of
that data. The format of data today is not properly structured; for example, in
which tweets and blogs are loosely structured parts of text, while images and
video arrangement provides storage and display, and not used in search and
semantic content: and converting such content into structured is the biggest
challenge.
Cyber-Physical
System is integrations of computation and physical processes. In this system
the design of communication infrastructure is of key importance since it
conveys information from sensors to controllers. As the use of cyber physical
system increases in vast manner the data related to that system also gathered
in very big amount. The offline operation on cyber physical data is not too
much difficult because we already know the pattern of data stream at each time.
But the biggest problem rise when there is manipulation of real time data.
Related Work
Big data
subject to where number of amount of data transaction of large amount of high
speed data with different variety of it takes place or compute capacity for
accurate and timely decision making. In this system the design of communication
infrastructure is of key importance since it conveys information from sensors
to controllers. The row data coming from various sources are stored in storage
where already has terabyte of data is stored so to handle such data and get
useful data from that is main problem for this it has to implement data stream
analytics and mining for cyber physical system. There are many important cyber
physical systems in practice such as smart grid and unmanned aerial vehicle
networks.
Data and
Information or Knowledge has a significant role on human activities. Data
mining is the process where of evolution of knowledge by studying the large
volumes of data from various perspectives and summarizing it into useful
information. And Analytics are the methods of decomposing concepts or substances
into smaller pieces, to understand their workings. In past the data arrive from
various sources are stored in big data storage. Here all the data stream
analytics and mining process takes place. As the big data contains tera-bytes of
data already taking analytics and mining in storage is hectic and also prone to
various challenges. This challenges are not simple to eliminate because it
already knows that the data comes from large sources is in large volume with
very high velocity. To make analytics and mining of such data lead to complex
process. And after storing data in storage and then implement all this
operation on that data increases cost very high. In initial days, data mining
algorithms work best for numerical data collected from a single storage of
data, and this techniques of data mining have developed for continuous files,
and also where data is stored in table format. In past days of data mining most
of the algorithms employed only statistical techniques.
There are two
challenges the process has to face they are designing fast mining methods for
data streams and need to detect promptly changing concepts and data
distribution as real time because of highly dynamic nature of data streams.
Memory management is a main challenge in stream processing because many real
data streams have irregular arrival rate and variation of data arrival rate
over time. In many applications like sensor networks, stream mining algorithms
with high memory cost is not applicable. Therefore, it is necessary to develop
summarizing techniques for collecting valuable information from data streams.
By considering the size of memory and the huge amount of data stream that
continuously arrive to the system, it is essential to have a perfect data
structure for storage, frequent improvement and to access the stored
information [2].If such structure is not present, quality of using of mining
algorithm will sharply decrease. Some traditional used for mining are slow and
not so efficient for online data processing. The algorithm which are used for
analytics and mining have to consider all the factors which are effecting the
value of data and importance of that system. Also number of data sources and
fluctuations of data properties processing the data online is also a problem.
Sometime
streaming data coming from different sources lead to various errors in which
one is missing the tuple, out of ordering of it during sending the data to
storage. Sometime wrong values are send to the operations which will gives
wrong result. Also to avoid unwanted data for saving further CPU, energy cost
is important. In all of this the big challenge is to implement stream
correlation and rule mining on the same system so that at the same time it will
work on the online data stream with rule mining and emerge new data mining rule
which store the same system so that they can be useful for future.
Main Method
1. System
Architecture
System
architecture of data stream analytics goes through three parts they are data
stream management system, complex event processing engine and at last business
process management and visualization.
Fig 1. Data stream
analytics and mining architecture
The row
material or row data is first subject to the data stream management. Thus data
stream management system is a pipeline structure where basic main working is to
eliminate unwanted data from the row data so that in future it will not affect
the stream analytics and mining process and also it will avoid CPU utilization
and storage and memory cost. Data stream management system also provide
scheduling and proper maintaining of data stream so that there will not be
traffic of collision of data tuples with each other. Thus data stream
management system provides all pre-processing that will require before the
actual complex processing. Also it reduces the data size to row wise and column
wise. The next part include in it is complex event processing engine where all
core filtering takes place and which is used offline or online according the
system need. Here it also contains one important part that is data base
management system with No SQL queries which are used for the statistical
analysis. In complex event processing engine the basic filter data is
correlated with the standard data which already stored in the DBMS system by
attacking various No SQL queries so that the knowledgeable data that required
for some specific application are drawn out. No SQL queries are nearly similar
like structured query language but it will provide some extra opportunities. By
combining this two structures DSMS and complex event processing tool it lead to
main filtering tool and it will handle as below.
2. Stream
Analytics
Stream
analytics is the process of making correlation of raw data with the standard
data that values are already store in the database system. Thus system used
Pearson product moment correlation over the row data stream. Stream analytics
analyze big data to find patterns and relationships, make informed predictions,
deliver actionable intelligence, and gain business insight from this steady
influx of information. Organizations in every industry are trying to make sense
of the massive influx of big data, as well as to develop analytic platforms
that can synthesize traditional structured data with semistructured and
unstructured sources of information. If big data is properly handle and manage
it can provide priceless information related to market related problem, damage
of equipment, buying patterns, maintenance cycles and many other business
issues, decrement in costs, and able to make more unique business decisions. To
obtain value from big data, you need a cohesive set of solutions for capturing,
processing, and analyzing the data, from acquiring the data and discovering new
insights to making repeatable decisions and scaling the associated information
systems.
Actually
correlation is the covariance of two variables divided by their standard
deviation. Thus it means obtain the input of the data as variable of
considering example or application and taking covariance means current values
compare with each other and then divided by standard value which is stored
already by studying all the patterns and conditions related to the stream of
data. By taking correlation it get the result of statistical analytics which
will be in range of (-1 to1).
Lets consider if there is high positive
correlation then it will provide +1 value, if there is no relation then its
value is 0 and for highly negative correlation its result is -1.
The
application aim is to study the daily routing of bus transportation. This
application provides the actual position of bus on daily path and compares its
parameters with itself and other vehicle. For statistical correlation of bus
application standard data about the bus transport has to be already store in
database management system so that correlation should be maintain with proper
parameter. Thus for example it finds the correlation of two buses running on
different following queries is used to find their analytics. This analytics can
be carried out by the use of sliding window. If there is small window size then
there are less result are available to compare and it will raise alarm for very
short fault or errors. So large window will provide large result to compare so
that small fault are neglected and this is useful for bus application as bus
will catch its fault in some time further. To reduce the amount of processing
and output produced, we could use tumbling windows which only publish results
at the end of a time or count period. For tumbling windows the delay increases
with window size, because all the data that is collected until the end of a
time interval is processed at once.
3. Rule
Mining
The structure
of Data streams are frequently changing, arranged for some specific time, vast
and potentially with no limit in real time system. Because of high volume and
speed of input data, it is needed to use semi-automatic interactional
techniques to extract embedded knowledge from data. The main algorithm is
association rule mining under which two different algorithms implemented and
they are Apriori and FPGrowth algorithms which are used for rule mining in
various applications. In an association rule denoted by XY (S,C), X and Y refer
to the frequent item sets, while S is support which is the percentage of record
that contain item set either X or Y or both. C is the confidence which is is
the percentage of record which contains both X and Y.
Figure 2: General Process of Data Stream Mining
As it is
already knows that there is very large amount of data stored in data set before
as there is continuous, unlimited, and very high speed fluctuated data streams
in both offline and online condition and because of that scanning the data
again and again is not efficient by using traditional data mining algorithms.
So it best to use algorithm like Apriori which counts frequent item sets,
generates candidate item sets using the minimum support value, prunes the
infrequent ones, calculates confidence on all permutations of the frequent item
sets and selects those above the given Confidence threshold. Next is FP-Growth
algorithm. FP-Growth algorithm does a first pass over the transactions creating
a frequency sorted database of items, omits the in frequent items, and finally
creates an FP-tree Compared with Apriori based algorithms; it achieves higher
performance by avoiding iterative candidate generations.
Thus total
process goes through Data Stream, which are the simple tuples with fixed number
of field comes from various sources is taken into Apache Kafka as messaging
structure which send it to storm which has elements like spouts and bolts. Data
Streams are first taken into SPOUTS (data emitters), which retrieve the streams
and put them into the storm clusters. This data inserted into BOLTS (data
processor) which will perform some primary processing task and then emits those
data into one or more streams. The data streams comes from storm is taken into
Esper where Complex event processing is done using CEP engine, where complex
event are process by continuously firing the NoSQL queries over the data to
filter it and then applying the Apriori & FP-Growth for the stream mining,
for that some kind of threshold data is with the system for association.
Conclusion:
The basic aim
is to achieve or to develop particular system that will provide proper data
stream analytics and mining on the real time data stream. It provides the past
technique for analytics and mining. On the basis of past technique it compares
its working and show that how this system overcome nearly all the challenges of
past technique. The main thing is that it provides the analytics and mining
structure on the same system so that it possible to perform this operation
online as it provides application of sliding type window. The main conclusion
of this system is that it can handle the data stream by using various tools
like Esper, Data stream management system tool like Storm. There are number of
algorithm studied under this implementation they are association rule mining,
Apriori and FPGrowth.
About The
Authors:
Ashish Jadhao,
Department of Computer Engineering, Sinhgad College of Engineering, University
of Pune, India.
Swapnaja Hiray,
Associate Professor, Department of Computer Engineering, Sinhgad College of
Engineering, University of Pune, India
Publication
Details:
This article was originally published under the title "Big Data Analytics in Cyber Physical Systems" at International
Journal of Engineering Research & Technology (IJERT) Vol. 2 Issue 10, IJERT ISSN: 2278-0181 / Creative Commons
License 3.0
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