Anomalous symmetry succession for seek out

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –

6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME

282

ANOMALOUS SYMMETRY SUCCESSION FOR SEEK OUT A

FLANKING KIN IN A SET OF LINKS

Rahul Jassal1, Chaman Singh

2

1Assistant Professor,

Department of Computer Science and Application

Punjab University Regional Centre Hoshiarpur, Punjab, India,

Email:[email protected]

2Assistant Professor and Head

Department of Computer Application,

Govt. P.G. College Chamba, H.P. University,

Chamba, Himachal Pradesh, India 176310,

Email:[email protected]

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

INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING

& TECHNOLOGY (IJCET) ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 3, Issue 3, October - December (2012), pp. 282-290 © IAEME: www.iaeme.com/ijcet.asp Journal Impact Factor (2012): 3.9580 (Calculated by GISI) www.jifactor.com

IJCET

© I A E M E

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976

6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October

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

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

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

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 mo

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 combine

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 in

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

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.

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 includ

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

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.

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976

6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME

283

some times user cannot connects with each other due to NAT


information and that’s why many of the companies are looking for analytic vendors to come



g and reporting whereas unstructured data analytics utilizes text



data available on net categorize in structured or unstructured way as

Figure 1:-Categorization of Data



Simply enabling independent searches of these does not produce the most value. Valuable



and the conclusions. When the searches are combined, they produce a plethora of

Security and workflows as like in dual networks [7][8]are important


like local and private networks . The initial efforts of management of data beyond the



ality would increase by sharing data, efforts were made to define



mation that is feeding the interpretation systems.



documents located on shared and personnel computer disks began to

hold larger and larger volumes of important information.


organization. There are good reasons to include the unstructured information in an enterprise



he unstructured data stores hold the intellectual capital and the



y pretext grids [10] on data for facilitating online education purpose.


December (2012), © IAEME

some times user cannot connects with each other due to NAT


are looking for analytic vendors to come



g and reporting whereas unstructured data analytics utilizes text





st value. Valuable



d, they produce a plethora of

Security and workflows as like in dual networks [7][8]are important


itial efforts of management of data beyond the



ality would increase by sharing data, efforts were made to define





documents located on shared and personnel computer disks began to


e the unstructured information in an enterprise



he unstructured data stores hold the intellectual capital and the



y pretext grids [10] on data for facilitating online education purpose.



284

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

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

Information

Retrieval

Transformin

g Text

Delivery Reporting

Analytics

Text

Analytics

Process

Collect and retrieve information from

internal and external sources

Content cleaning

removing duplicates

Selecting attributes discovering patterns



285

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.

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

From

A

Via

A

Via

B

Via

C

Via

D

To A

To B 3

To C 23

To D

From

C

Via

A

Via

B

Via

C

Via

D

To A 23

To B 2

To C

To D 5

From

B

Via

A

Via

B

Via

C

Via

D

To A 3

To B

To C 2

To D

From

D

Via

A

Via

B

Via

C

Via

D

To A

To B

To C 5

To D



286

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.



287

Figure 7:- Weight Short Algorithm

Figure 7:- Weight Short Algorithm

//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

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

}

}



288

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.

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

COLUMN

Start

Promenade starts at cells

(1,1),(2,1),(2,2).cell(mxn)

Is temp

reaches

mxn

llist(a,b)<-llist(a,b+1);//data

shifted to next higher node

on row check

llist(a+1,b)<-llist(a,b);//data shifted to next

higher node on column check

llist(a+1,b-1)<-

llist(a,b);//data shifted

to next higher node

Display the timing for visiting the

routes

ROW

NO

DIAGONAL

YES

NO

Stop



289

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.

[2]. 14th

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.



290

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

Documents

Anomalous symmetry succession for seek out