Anomalous symmetry succession for seek out

INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –
6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME
& TECHNOLOGY (IJCET)
ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 3, Issue 3, October - December (2012), pp. 282-290
IJCET
© IAEME: www.iaeme.com/ijcet.asp
Journal Impact Factor (2012): 3.9580 (Calculated by GISI) ©IAEME
www.jifactor.com

ANOMALOUS SYMMETRY SUCCESSION FOR SEEK OUT A
FLANKING KIN IN A SET OF LINKS
Rahul Jassal1, Chaman Singh2
1
Assistant Professor,
Department of Computer Science and Application
Punjab University Regional Centre Hoshiarpur, Punjab, India,
Email:-rahulatssgpurchsp@gmail.com

2
Assistant Professor and Head
Department of Computer Application,
Govt. P.G. College Chamba, H.P. University,
Chamba, Himachal Pradesh, India 176310,
Email:-chaman83mca@gmail.co.in

ABSTRACT

The paper illustrates a post calculated stratum which works on a representation of 2-D points
to determine the next neighboring element with the lowest traverse value. Weight Short
Algorithm traverses the whole matrix in an odd promenade and works out on traversed data
in knowing the closest kin without sending the load of number of hopes or joined kin to next
neighboring element as it is with case of distance vector routing. This all is done under
umbrella of constraint that the existence of next node can be determined on same
columns/rows in a matrix or may be locate the element at sloping position, if still not found,
some row or column shifts is performed as per algorithm such that no repetition of the same
pair should occur.

Keywords: Distance Vector Routing, Dual Networks, Mobility, Pretext Knowledge, Weight
Short Algorithm,

I. INTRODUCTION

Analytic data available on internet either reveals eighty percent of data [1] is in the
form of unstructured and bogging its presence in the form of blogs, surveys, Wikipedia’s.
Data on internet can be shared using wired or wireless or Dual Networks [3]. Many of times
we come across situations of forwarding the received data to next user in dual networks [5]
i.e. forcing to increase one more imitate of the document and it keep on adding and definitely
a big risk to normalized databases. Proper analysis of data helps anyone from ignored risks
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October December

and uniformed decisions, some times user cannot connects with each other due to NAT
devices and Firewalls [4]. Analytic study helps in converging the bulk into meaningful
information and that’s why many of the companies are looking for analytic vendors to come
up with products that blend unstructured analytics. And the conversion into actionable
intelligence is called “unstructured data analytics”. Structured data analytics [1] uses business
intelligence tools for querying and reporting whereas unstructured data analytics utilizes text
g
processing and keyword searches (to locate documents in servers). Unstructured analytics has
evolved over time, moving towards next generation techniques like video and audio analytics.
The data available on net categorize in structured or unstructured way as

Figure 1:-Categorization of Data

All organizations are aware that a considerable amount of technical and business information
and knowledge resides in both the structured data bases and in unstructured repositories [2].
Simply enabling independent searches of these does not produce the most value. Valuable
most
conclusions are represented in reports that were developed from investigating structured data
and these are lost from view when searching only databases. Together, they provide the facts
and the conclusions. When the searches are combined, they produce a plethora of
combined,
information, Usability, Security and workflows as like in dual networks [7][8]are important
design consideration to achieve clear, quick access to multiple disparate information sources
like local and private networks . The initial efforts of management of data beyond the
initial
workgroups concentrated on the shared structures data, such as well logs and seismic data.
Interpretation results, [6] such as horizons, picks and faults were shared next. Recognizing
that project speed and quality would increase by sharing data, efforts were made to define
ality
massive multidisciplinary data models. These persist today and stand alongside the active
project data stores. Most of these large scale repositories are used to store the raw incoming
information that is feeding the interpretation systems.
mation
At the same time, document management systems were growing they started on the business
side and migrated toward the scientific and technical by storing reports of interpretation. Over
time, emails and the documents located on shared and personnel computer disks began to
hold larger and larger volumes of important information.
These repositories are becoming the new targets for mining valuable information in an
organization. There are good reasons to include the unstructured information in an enterprise
include
data management solution. These files contain interpretations, descriptions and decisions.
The structured data stores contain mostly raw primary data such as well logs and seismic
traces. One could say that the unstructured data stores hold the intellectual capital and the
he
structured data stores hold the valuable basic factual data. So integrating these two
information sources into the Enterprises data management strategy makes a lot of sense. We
can also apply pretext grids [10] on data for facilitating online education purpose.
y
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1.1 Extracting information from unstructured data stores
Increasingly capable technical solutions are enabling access to these attractive
knowledge stores. Systems can access the email and documents. Systems can extract
patterned information out of them, such as zip codes, addresses and telephone numbers.
Search engines that operate in a Google-Like manner have been implemented and are
enabling delivery to the desktop hundreds if not thousands of documents. Just indexing the
documents and files can be a daunting task and is frequently a road back to success in these
projects
1.1.1 Text Analytics Process

Information
Retrieval Collect and retrieve information from
internal and external sources

Transformin
Analytics
g Text
Text Content cleaning
Analytics
Process
removing duplicates

Selecting attributes discovering patterns
Reporting Delivery

Figure 2:- Text Analytical Process

Where Reporting deals with different mechanisms foe notifying results like dashboards, alerts
etc and delivery lies in steps to augment existing data & store enriched information.
What goes routers side?
Distance vector routing algorithm uses a router algorithm [9] in which the router periodically
sends routing updates to all kin by broadcasting their entire route tables. Some sort of time
gaps called periodic updates is maintained before next transmission. And this time gaps
varies from varied companies as ranges 10 seconds for AppleTalk’s RTMP and 90 seconds
for Cisco IGRP. So frequent data updating and their broad cast leads to congestion and CPU
overload. So how this all communication is possible, the simplest is to air out the updates to
the broadcast address on an IP like 255.255.255.255, the neighboring kin with same mask or
routing protocol will hear the broadcast.

The Figure 3 is a pictorial representation of points on a LAN which is either wired or wireless
and the second one carries processed data depicting a of the axes and the position of nodes on
the matrix, Consider four routers joined in a network like In this network we have 4 routers
A, B, C, and D

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From Via Via Via Via
A A B C D
To A
To B 3
To C 23
To D

Figure 3:- Local Area Networks Representation
We shall mark the current time (or iteration) in the algorithm with T, and shall begin (at time
0, or T=0) by creating distance matrices for each router to its immediate neighbors. As we
build the routing tables below, the shortest path is highlighted in the boxes.

From Via Via Via Via From Via Via Via Via From Via Via Via Via
B A B C D C A B C D D A B C D
To A 3 To A 23 To A
To B To B 2 To B
To C 2 To C To C 5
To D To D 5 To D

Figure 4:- Distance Matrices
Rather as describes above sending the information from one node to another in distance
vector routing, the following diagram shows the presence of routers in a matrix and
determining the presence of next kin with the help of weightshort algorithm and dynamically
locating the router with an walk and as soon as we visit the router closest to former a
procedure call sends the information to next level and reaches a stage of calculated stratum
carrying the router details in a matrix.
Pictorial Representation of neighboring element in 3-D

Figure 5:- Density of Data Figure 6:- Representation of Data
The paper presents a data structure that helps us in locating a next neighboring element while
adopting a technique that either we can find the next kin if we have a walk towards column side or
row side or diagonally but while traversing, the rules we must kept in our mind is that no repetitions

285


should occur in forming pairs. Figure: -5 describe density of the data in three of axis, the first pie
describes the region with probability of existence of data is most and decreasing respectively for axis
(x, z) and (y, z).Figure 6 is pictorial representation of data in all axis, the operations that can be applied on one
segment can be applied on last 2 segments.
Actually, what we are doing is traversing each cell of matrix in symmetry where first walk visits the cell (1, 1),
next walk carries cell like (2,1), (2,2),(1,2) and the next contains (3,1),(3,2),(3,3),(2,3),(1,3) and so-on. And the
walks increasing its path elements in odd pattern like (1, 3, 5, 7, 9….) and it goes high as we increase the size of
the matrix or data grows. Walks help us in visiting each of the cells in the matrix but determining the relation
between former and the new element is only done through checks like:-
1).Checking existence of the next element in the same row.
2). Checking existence of the next element in same column.
3). Checking existence diagonally left or diagonally right.
By going through checks these things must be kept in mind that while creating a pair no repetition of the same
pair should occur. In Continuation to search for neighbor element we have to walk on data structure which
should have moves like listed below for figure 2.
*(1, 1)
(2, 1), (2, 2), (1, 2)
(3, 1), (3, 2), (3, 3), (2, 3), (1, 3)
(4, 1), (4, 2), (4, 3), (4, 4), (3, 4), (2, 4), (1, 4) and So-on. For a data structure with moves as above mentioned
the following techniques works. Suppose ‘n’ is the number of cells in a cube,Then the calculation x= √݊ ,y=
2(x)-1 ,y1=y-2, y2=y1-2, y3=y2-2 and so-on
So the data structure inside this cube is growing its cells size in odd symmetry like {y1, y2, y3,yn} for instance,
suppose we are with total no of cells=49
x=√49, x=7,y=2(7)-1, y=13,y1=y-2, y1=13-2, y1=11
y2=y1-2, y2=11-2, y2=9 and so-on.
And we are with results like {13, 11, 9, 7, 5, 3, 1} in decreasing order or counter clock wise {1, 3, 5, 7, 9, 11,
13} whose first value 1 represents first cell with data in (1,1) position next element 3 defines 3cells with
corresponding data in three positions like (2, 1), (2, 2), (1, 2) and we are with symmetry defined above*.The
following algorithm describes the search for the next neighbor position in x-y axis and can be implemented for
left y-z, z-x axis.

II. WIGHT SHORT ALGORITHM
NeighbourPosition (row col, new, temp, nextval, llist)
{
Consider any axis and move the pointer from left to right in row direction and step down to column
when value of the both the x, y becomes same i.e. (2, 2), (3, 3) so-on and with tautology .
“row=column+1”
//row, col is used for row and column pointers
//j is a set containing values {1, 3, 5, 7, 9, 11, 13} as discussed above.
//new, temp and nextval are temporary location just used for storing and determine relationships.
//llist is the structure storing the data available on first router.
//First Half
Struct llist
{
int hop;// path determination
int destination;//flag variable
struct llist *ptr;
}
count=0;
for (row=1,j=1; row<=x; row++,j+2)
{
for (col=1;col<=row; col++)
{
++count;
temp=cube [row, col];
//Checking existence of the next element in //the same row.

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//Row Check
//temp= (a, b)
//nextval= (a, b+1) if element exists
temp->nextval(neighbor)
llist(a,b)<-llist(a,b+1);//data shifted to next higher node on row check
// Checking existence of the next element in //same column.
//Column Check:-
temp= (a, b)
nextval= (a+1, b)//if element exists
llist(a+1,b)<-llist(a,b);//data shifted to next higher node on column check
//Checking existence diagonally left or //diagonally right.
//Diagonal Check
//Left Diagonal Check
temp= (a, b)
nextval may be at (a+1, b-1).//if element exists
temp->nextval (neighbor)
//Right diagonal Check
temp= (a, b)
nextval may be at (a+1, b+1)//if element exists
}
//Last Half
If (count<j)
{new=j-count)}
for (left=1, left<=new; left++)
cube [row-1, row]
//Checking existence of the next element in //the same row.
//Row Check
//temp= (a, b)
//nextval= (a, b+1) if element exists
// Checking existence of the next element in //same column.
//Column Check:-
temp= (a, b)
nextval= (a+1, b)//if element exists
//Checking existence diagonally left or //diagonally right.
//Diagonal Check
//Left Diagonal Check
temp= (a, b)
nextval may be at (a+1,b-1).//if element exists
llist(a+1,b-1)<-llist(a,b);//data shifted to next higher node on diagonal left check
//Right diagonal Check
Figure 7:- Weight Short Algorithm
temp= (a, b)
nextval may be at (a+1, b+1)//if element exists
llist(a+1,b+1)<-llist(a,b);//data shifted to next higher node on diagonal right check
}
}

Figure 7:- Weight Short Algorithm

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The algorithm is divided in two halves, the first half describing the cases like
For traversing up to (3, 3) the first half bring values like
(1, 1)
(2, 1), (2, 2)
(3, 1), (3, 2), (3, 3)
Rather we are in need of
(1, 1)
(2, 1), (2, 2), (1, 2)*
(3, 1), (3, 2), (3, 3), (2, 3)*, (1, 3)*
So second half of the algorithm helps us in determine left marked columns.
Note: - this might be the case if at positions we don’t find any element either on the row,
column or towards its left diagonal or right diagonal then in that case the particular position is
just simply added to penultimate element.

Start

Promenade starts at cells
(1,1),(2,1),(2,2).cell(mxn)

llist(a,b)<-llist(a,b+1);//data Is temp
shifted to next higher node ROW reaches
YES
on row check mxn
NO

NO

llist(a+1,b)<-llist(a,b);//data shifted to next COLUMN
higher node on column check

llist(a+1,b-1)<- Display the timing for visiting the
DIAGONAL routes
llist(a,b);//data shifted
to next higher node

Stop
Figure 8:- Flow Chart for Weight Short Algorithm

III. CONCLUSION
The theory or computational logic helps one in designing games, or help user in
determining next neighboring element in finite passes and one can work on logic and with
better time complexity and one can work upon the new ways for keeping records in user
defined structures illustrates a post calculated stratum which works on a representation of 2-D

288


points to. Paper determines the next neighboring element with the lowest traverse value.
Weight Short Algorithm traverses the whole matrix in an odd promenade and works out on
traversed data in knowing the closest kin without sending the load of number of hopes or
joined kin to next neighboring element as it is with case of distance vector routing. This all is
done under umbrella of constraint that the existence of next node can be determined on same
columns/rows in a matrix or may be locate the element at sloping position, if still not found,
Some row or column shifts as per algorithm such that no repetition of the same pair should
occur.

REFERENCES
[1]. Structure, Models and Meaning: Is "unstructured" data merely unmodeled? Intelligent
Enterprise, March 1, 2005.
th
[2]. 14 PNEC Conference “The challenges of Structured and Unstructured Data” 2010.
[3]. K.L.Bansal, Chaman Singh, “Dual Stack Implementation of Mobile IPv6 Software
Architecture”, Foundation of Computer Science IJCA- Volume 25, No 9, July 2011.
[4]. K.L.Bansal, Chaman Singh, “NAT Traversal and Detection on Dual Stack Implementation
of Mobile IPv6”, Foundation of Computer Science IJCA- Volume 29, No 7, September
2011.
[5]. Chaman Singh, S Kumar, S Kumar, K.L.Bansal,” Design and Implementation of Mobile
IPv6 Data Communication in Dual Networks”, IJCSI -Volume 1, Issue 9, Page N0. 182-
190, January 2012.
[6]. Ledion Bitincka, Archana Ganapathi, Stephen Sorkin and Steve Zhang. “Optimizing Data
Analysis with a Semi-structured Time Series Database”,
[7]. Chaman Singh, K.L.Bansal,”NAT Traversal Capability and Keep-Alive Functionality with
IPSec in IKEv2 Implementation”, IJCSCN -Volume 2, Issue 1, Page N0. 99-110, February
2012.
[8]. Chaman Singh, K.L.Bansal,” Linux Based Implementation and Performance Measurements
of Dual Stack Mobile IPv6”, IJCSCN -Volume 2, Issue 2, Page N0. 240-255, April 2012.
[9]. “A more Efficient Distance Vector Routing”, Zhengyu Xu,Sa Dai Computer Engineering
Department School of Engineering University of California.
[10]. Rahul Jassal, Chaman Singh “Pretext Knowledge Grids on Unstructured Data for
Facilitating Online Education” IOSR Journal of Computer Engineering ISBN: 2278-
8727Volume 5, Issue 5 (Sep-Oct. 2012), Page No. 22-27.

AUTHORS PROFILE

Rahul Jassal is working as Assistant Professor in Department of
Computer Science & Application, Panjab University Regional
Centre, Hoshiarpur, India. He received Master of Computer
Application in year 2007 and clear the UGC-NET examination for
subject “Computer Science & Application in the same year. He is
with the post from last 5 years.

289


Chaman Singh (B.Sc., MCA, NET, Ph.D.) Has received the Master
of Computer Application Degree in 2007 and completed Ph.D. in
Computer Science 2012 from Department of Computer Science
H.P.University Shimla, India. He also qualified UGC NET 2006.
Have more than 4 years of Working Experience in Teaching,
Guidance for PGDCA, MCA, BCA students, Software Development
(Programming) and Networks. Published number of National and
International Papers in like IJCA, IJCSI, IJCSCN, IJCSE, IOSR etc

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Anomalous symmetry succession for seek out

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