Anomalous symmetry succession for seek out

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Anomalous symmetry succession for seek out

  1. 1. 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.aspJournal Impact Factor (2012): 3.9580 (Calculated by GISI) ©IAEMEwww.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 282
  2. 2. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME October Decemberand uniformed decisions, some times user cannot connects with each other due to NATdevices and Firewalls [4]. Analytic study helps in converging the bulk into meaningfulinformation and that’s why many of the companies are looking for analytic vendors to comeup with products that blend unstructured analytics. And the conversion into actionableintelligence is called “unstructured data analytics”. Structured data analytics [1] uses businessintelligence tools for querying and reporting whereas unstructured data analytics utilizes text gprocessing and keyword searches (to locate documents in servers). Unstructured analytics hasevolved 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 DataAll organizations are aware that a considerable amount of technical and business informationand 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 mostconclusions are represented in reports that were developed from investigating structured dataand these are lost from view when searching only databases. Together, they provide the factsand 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 importantdesign consideration to achieve clear, quick access to multiple disparate information sourceslike local and private networks . The initial efforts of management of data beyond the initialworkgroups 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. Recognizingthat project speed and quality would increase by sharing data, efforts were made to define alitymassive multidisciplinary data models. These persist today and stand alongside the activeproject data stores. Most of these large scale repositories are used to store the raw incominginformation that is feeding the interpretation systems. mationAt the same time, document management systems were growing they started on the businessside and migrated toward the scientific and technical by storing reports of interpretation. Overtime, emails and the documents located on shared and personnel computer disks began tohold larger and larger volumes of important information.These repositories are becoming the new targets for mining valuable information in anorganization. There are good reasons to include the unstructured information in an enterprise includedata management solution. These files contain interpretations, descriptions and decisions.The structured data stores contain mostly raw primary data such as well logs and seismictraces. One could say that the unstructured data stores hold the intellectual capital and the hestructured data stores hold the valuable basic factual data. So integrating these twoinformation sources into the Enterprises data management strategy makes a lot of sense. Wecan also apply pretext grids [10] on data for facilitating online education purpose. y 283
  3. 3. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME1.1 Extracting information from unstructured data stores Increasingly capable technical solutions are enabling access to these attractiveknowledge stores. Systems can access the email and documents. Systems can extractpatterned 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 areenabling delivery to the desktop hundreds if not thousands of documents. Just indexing thedocuments and files can be a daunting task and is frequently a road back to success in theseprojects1.1.1 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 ProcessWhere Reporting deals with different mechanisms foe notifying results like dashboards, alertsetc 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 periodicallysends routing updates to all kin by broadcasting their entire route tables. Some sort of timegaps called periodic updates is maintained before next transmission. And this time gapsvaries from varied companies as ranges 10 seconds for AppleTalk’s RTMP and 90 secondsfor Cisco IGRP. So frequent data updating and their broad cast leads to congestion and CPUoverload. So how this all communication is possible, the simplest is to air out the updates tothe broadcast address on an IP like 255.255.255.255, the neighboring kin with same mask orrouting protocol will hear the broadcast.The Figure 3 is a pictorial representation of points on a LAN which is either wired or wirelessand the second one carries processed data depicting a of the axes and the position of nodes onthe matrix, Consider four routers joined in a network like In this network we have 4 routersA, B, C, and D 284
  4. 4. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME 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 RepresentationWe shall mark the current time (or iteration) in the algorithm with T, and shall begin (at time0, or T=0) by creating distance matrices for each router to its immediate neighbors. As webuild 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 ViaB A B C D C A B C D D A B C DTo A 3 To A 23 To ATo B To B 2 To BTo C 2 To C To C 5To D To D 5 To D Figure 4:- Distance MatricesRather as describes above sending the information from one node to another in distancevector routing, the following diagram shows the presence of routers in a matrix anddetermining the presence of next kin with the help of weightshort algorithm and dynamicallylocating the router with an walk and as soon as we visit the router closest to former aprocedure call sends the information to next level and reaches a stage of calculated stratumcarrying the router details in a matrix.Pictorial Representation of neighboring element in 3-D Figure 5:- Density of Data Figure 6:- Representation of DataThe paper presents a data structure that helps us in locating a next neighboring element whileadopting a technique that either we can find the next kin if we have a walk towards column side orrow side or diagonally but while traversing, the rules we must kept in our mind is that no repetitions 285
  5. 5. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME 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. 286
  6. 6. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME //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 temp->nextval(neighbor) 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 temp->nextval (neighbor) } //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 temp->nextval (neighbor) // Checking existence of the next element in //same column. //Column Check:- temp= (a, b) nextval= (a+1, b)//if element exists temp->nextval(neighbor) //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) 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 temp->nextval(neighbor) llist(a+1,b+1)<-llist(a,b);//data shifted to next higher node on diagonal right check } } Figure 7:- Weight Short Algorithm 287
  7. 7. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME 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 AlgorithmIII. 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
  8. 8. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEMEpoints 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 ontraversed data in knowing the closest kin without sending the load of number of hopes orjoined kin to next neighboring element as it is with case of distance vector routing. This all isdone under umbrella of constraint that the existence of next node can be determined on samecolumns/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 shouldoccur.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
  9. 9. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME 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 290

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