2009 IEEE International Advance Computing Conference (IACC 2009)Patiala, India, 6-7 March 2009

Concurrency Control in CAD With KBMSUsing Counter Propagation Neural Network

P.Raviram Dr.R.S.D.Wahidabanu Dr.Purushothaman SrinivasanResearch Scholar/Department of Professor and Head, Department Principal, Sun College of

Computer Science and ofECE Engineering and Technology,Engineering Government College of Erachakulum-629902,INDIA,

Vinayaka Missions University, Engineering, dr.s.purushothaman(gmail.comTamilnadu- 636 308, INDIA, Salem - 636 011, INDIA,

ravirampedu.gmail. com rsdwb@yahoo.com

Abstract- Concurrency control plays important role in Incorrect updates are prevented by using locksadvanced database management system (ADMS) allotted during transactions. A transaction must claimespecially in computer aided design (CAD) with a shared (read) or exclusive (write) lock on a dataknowledge database management system (KBMS). item before read or write. Lock prevents anotherADMS related databases involve longer transaction transaction from modifying item or even reading it, intime and unsure when the transaction is committed. In the case of a write lock. Rules of locking are, ifsuch situations, how the data edited by more than one transaction has shared lock on item, can read but notuser is preserved. It is by using version control or using update item, and if transaction has exclusive lock onlocking methods or both. Longer transactions can be item, can both read and update item. More than onebetter controlled by intelligent method. In this work, transaction can hold shared locks simultaneously onCPN has been implemented for transaction control ofCAD database. same item however exclusive lock gives transaction

exclusive access to that item.Keywords-Concurrency Control; Counter II. PROBLEM DEFINITION

propagation Network;, Transaction Locks; KnowledgeManagement One of the shortcomings of traditional

general purpose database management systemI. INTRODUCTION (DBMS) is the inability to provide consistency in the

Knowledge management in advanced database has database when long transactions are involved. Thebeen considered as an interesting research area in the transaction with undefined time limit will not be ablerecent past. Researchers concentrate on the integration to identify if there is any violation of databaseof active and real-time database systems. New consistency during the time of commitment, then itproblems are evolved in concurrency control (CC) [1- indicates wastage of huge amount of time and4] of real-time database systems. Conventional CC resources. The activities of many users working onprotocols are more concerned about the serializability. shared objects are not serializable. Existing two phase

locking and optimistic transactions will result inTransaction is series of actions, which accesses deadlock in case of long transaction (LT). Two phase

and changes contents of database. It is a basic unit of locking forces to lock resources for long time evenwork on the database. Transaction transforms after they have finished using them. Otherdatabase from one consistent state to another. During transactions that need to access the same resources arethis process, consistency may be violated [5-6]. The blocked. The problem in optimistic mechanism withprocess of managing simultaneous operations on the time stamping is that it causes repeated rollback ofdatabase without having them interfere with one transactions when the rate of conflicts increasesanother is called concurrency. It prevents interference significantly. We are using a counter propagationwhen two or more users are accessing database network (CPN) to manage the locks allotted to objectssimultaneously. Even though two transactions may be and locks are claimed appropriately to be allotted forcorrect in themselves, interleaving of operations may other objects during subsequent transactions.produce an incorrect result. Important problemscaused by concurrency are lost update, inconsistentanalysis and uncommitted dependency.

978-1-4244-2928-8/09/$25.OO ( 2009 IEEE 1521

files 2 and vice versa. However, when the part files 1f_3Se ;J-j. and 2 are combined into a single assembly file, then

/It;SEtlv>_f1I , /1ll'1gp~ti;I. rinconsistency in the shape and dimension of the set I11SL~t~lMSdrl1iiiand set 2, during matching should not occur. Hence,

_ gllillolt1ell ,>2/ provisions can be made in controlling the dimensionsand shapes with upper and lower limits confirming tostandards. At any part of time when a subsequent user

4 ,94,-v / t | is trying access locked features, he can modify the_,ti , features on his system and store as an additional

_ -slielt,! @ ittl |modified copy of the features with time stamping and1_L4l4^lafhluleIl3ai Th~version names (allotted by the user / allotted by the

system).Figure 1. Bracket joint

III. COUNTERPROPAGATIONNETWORK(CPN)

Inbuilt library functions for the bracket An artificial neural network (ANN) is an abstract(Figure 1) are available in standard CAD [7-11] We simulation of a real nervous system that contains aassume initially the drawing of the bracket is available collection of neuron units, communicating with eachin the central database. Subsequently due to customer other via axon connections. Such a model bears arequirements at different locations, the designer edits strong resemblance to axons and dendrites in athe bracket in the central database and modifies nervous system. Due to this self-organizing anddifferent features of the bracket. During the process of adaptive nature, the model offers potentially a newmodifications of different features, consistency of the parallel processing paradigm. This model could bedata has to be maintained. In such case the following more robust and user-friendly than the traditionalsequences of locking objects have to be done approaches. ANN can be viewed as computingwhenever a particular user access a specific feature of elements, simulating the structure and function of thethe bracket. Each feature is treated as an object. The biological neural network. These networks arefeatures are identified with numbers and expected to solve the problems, in a manner which iscorresponding feature names. In this explanation, 01 different from conventional mapping. Neuralrefers to object / feature marked as 1. networks are used to mimic the operational details of

In general, the following sequences are the human brain in a computer. Neural networks areformed when creating bracket. Even though library made of artificial 'neurons', which are actuallyfiles are available for bracket drawing; customized simplified versions of the natural neurons that occurdrawing bracket file is discussed. The major in the human brain. It is hoped, that it would beparameters involved in creating the bracket are possible to replicate some of the desirable features ofwedge, thickness, hole and pin. The various the human brain by constructing networks that consistconstraints that have to be imposed during of a large number of neurons. A neural architecturemodifications of features by many users on this comprises massively parallel adaptive elements withbracketiare interconnection networks, which are structured

hierarchically.* During wedge formation both angle and slant Counter-propagation neural network was

edge have to be associated.developed as a means to combine an unsupervised

* During diameter modification, chamfering has Kohonen layer with a supervised output layer. Theto be locked middle layer acts as an adaptive look-up table.

* During length or depth modification, diameter The Figure 3-6 gives the architecture of the CPN.has to be locked. Counter-propagation network is composed of three

This bracetaswomjoret. layers: an input layer that reads input patterns fromTibrcehatwmaoenis the training set and forwards them to the network, a

1) Features 1,2,3,6,16,18,4,5,7,15 (set 1) hidden layer that works in a competitive fashion andassociates each input pattern with one of the hidden

2) 8, 17, 9, 1 1, 10,13,14,12 (set 2) units, and the output layer which is trained via a

Set 1 and set 2 can be made into individual teaching algorithm that tries to minimize the meandrawing part files (part file 1 and part file 2) and square error (MSE) between the actual network outputcombined into one assembly file (containing the part and the desired output associated with the currentfiles 1 and 2 are intact). When the users are accessing input vector. The training and testing flow chart forindividual part files , then transactions in part file 1 training CPN is given in Figure 2.need not worry about the type of transactions in part

11522 2009 IEEE Internactionalz Advance Computing Conference (IACC 2009)

Readlput lshared (S) mode. Data item can only be read..Readl inpult

intention-shared (IS): indicates explicit locking atInitialize weights a lower level of the tree but only with shared locks.

InputW

o intention-exclusive (IX): indicates explicit lockingIntioti to Distance, Sdl= patterns - weighlt w9 matrixHiddin at a lower level with exclusive or shared locks

S.= l:sd shared and intention-exclusive (SIX): the subtreerooted by that node is locked explicitly in shared

Find highest value, P=sort(su) mode and explicit locking is being done at a lowerlevel with exclusive-mode locks.

Wq(n+1 )=w(n)+q`sdAn intention locks allow a higher level node to be

Distance, Sd= patterns - welght v matrix locked in S or X mode without having to check alldescendent nodes.

Hiddn Su= t' In Table 2, column 1 represents the lock type.layerto ~~~~~~~~~~~column2 represents the value to be used in the input

layeripliTI Find highest value P=sort(su) layer of the CPN. Column 3 gives binaryl_ I lrepresentation of Lock type to be used in the output

vij(n+1)=vi1(n)+q*sd layer of CPN. The values are used as target outputs inthe module during lock release on a data item.

Output obtained,r=o(po)*x

Figure 2. Training and Testing of Counterpropagation network TABLE II. BINARY REPRESENTATION OF LOCK TYPE

IV. PROPOSED INTELLIGENT LOCKING Lock type (input layer BinarySTRATEGY representation representation

numerical value). in target layer

Let us assume that there are two users of the CPNediting the bracket. Userl edits 01 and hence 07 will Object Not 0 000be locked sequentially (Table 1). Immediately user2 lockedwants to edit 07, however he will not get transaction x 2 010as already 07 iS locked. However, user2 or any other is 3 011user can try to access 06, 011 and 013- IX 4 100

TABLE I. SHAPE AND DIMENSION CONSISTENCY This work uses four modules of algorithms whichMANAGEMENT work using CPN given in Figure 2. The module given

Group First feature Remaining featurein Table 3 gives their usage for learning and findingRemainingfeature the lock states. OML(Object, Mode, Lock) and OL

GI1_te (Object, Lock)G2 7 16G3 6 3,17,13G4 11 10, 12 TABLE III. MODULES USED FOR LEARNING THE LOCKG5 13 14 STATUS OF AN OBJECT

V. IMPLEMENTATION Module Name Training / CPN TopologyTesting

The variables used for training the ANN about 1 OML Training 2tTransactionlocks assigned to different objects are transaction id, (Figure 3) number andobject id, lock mode (Table 2). object id} x {no.

of nodes in

Transaction id represents the client or any other hidden layer} xintermediate transactions 3 tLock value}

2 OML Testing 2t TransactionObject id represents the entire feature or an entity (Figure 4) number and

in the file object id } x (no.ofnodes in

Mode represents type of lock assigned to an hidden layer) xobject. 3(Lock value)

3 OL Training 1 {object id} x 2exclusive (X) mode. Data item can be both read as (Figure 5) {no. of nodes in

well as written,._ hidden layer} x

2009 IEEE Inxternational Advanxce Computing Conference (IACC 2009) 1523

3{lockvalue} STEP 1: Find distance between input patterns and4 OL Testing I{object id} x 2 the centres.

(Figure 6) {no. of nodes inhidden layer} x3{lock value}

In the fourth column of this table, 3 values are given in the order, no. of nodes in Hiddenthe input layer, no. of nodes in the hidden layer which can be anything and no. of

nodes in the output layer which is 3 (fixed)

Inputs= (ObjectHidden name)

Inputs= (Object //

| Hidden | _ c t0~~~~~~~~~~~Hide

Figure 3.OMLtraig. 0t

Hidden ~ ~ ~~ ~ ~ ~ ~ ~~~~~~~~Hde

Figre3L OM rann

lnputs~(Object= (ObjectL name, maode} b b } * n

,.r ~~~~~~~~~~~~~~~~Figure6. OL testing

}\g/ ~~~~~~~Tiedits 01 with write mode. Table 4 shows pattern/~~~~~~~~~~~formed for the OML training.

TABLE IV. FIRST TIME PATTERN USED FOR TRAININGOMLCPN

Figure 4. OML testing __________________________Object number Input Target output

pattern pattern01 [1 1] [010]

VI. RESULTSANDDISCUSSIONS Similarly the following pattern is developed forthe OL training (Table 5).

Initially, user 1 and user 2 have opened the samebracket file from the common database. The following TABLE V. FIRST PATTERN USED FOR TRAINING OL CPNsteps shows sequence of execution and results

Object number Input Target output

11524 2009 IEEE Internacztionalz Advance Computing Conference (IACC 2009)

pattern pattern VII. CONCLUSION

01 [1 ] [ 0 1 0] An approach has been attempted toOML and OL are trained separately. implement CPN in concurrency control to maintain

consistency in the CAD database. A bracket has beenSTEP 2: The transaction manager locks objects considered that contains 17 objects. The 17 objectsmentioned in the third column ofTable 2. Repeat step have categorized into 5 groups. The transaction1 with the patterns given in Table 6. behavior and concurrency control by the two users on

the 17 objects have been controlled using CPNnetwork. We have found less memory required for

TABLE VI. ADDITIONAL PATTERNS USED FOR TRAINING storing lock information about the objects. TheOML CPN computational complexity is very minimal.

Object number Input Target outputpattern pattern VIII. REFERENCES

°1 [1 1] [ 0 1 0] [1] Pei-Jyun Leu AND Bharat Bhargava, Clarification of Two7 1] 0 1 0] Phase Locking in Concurrent Transaction Processing, IEEE

07 [ 1] [0 1 0] Transactions on Software Engineering. Vol. 13. No 1

Similarly train OL module with the patterns given January 1988Table [2] A. A. Akintola, G. A. Aderounmu, A.U. Osakwe and M.O.

in TaDle7. Adigun, 2005. Performance Modeling of an EnhancedOptimistic Locking Architecture for Concurrency Control ina Distributed Database System. In: Journal of Research and

TABLE VII. ADDITIONAL PATTERNS USED FOR TRAINING Practice in Information Technology, 37 (4): 365-380.OL CPN

[3] K. Vidyasankar, A Non-Two-Phase Locking Protocol forObject number Input Target output Global Concurrency Control in Distributed Heterogeneous

pattern pattern Database Systems, IEEE Transactions on Knowledge and°1[ 1 ] [ 0 1 0] Data Engineering, Vol. 3, No, 2, June 1991

07 [7 ] [0 1 0] [4] Abraham Silberschatz, A Case for Non-Two-Phase LockingProtocols that Ensure Atomicity, IEEE Transactions OnSTEP 3: A new transaction T2 access 07. A pattern Software Engineering, Vol. SE-9, No. 4, July 1983

is formed to verify if lock has been assigned to 05 and [5] C. Mohan, Donald Fussell, Zvi M. Kedem AND Abrahamits associated objects 012. Only when the locks are not Silberschatz, Lock Conversion in Non-Two-Phase Lockingassigned to 07 and 016 then T2 is allowed. Protocols, IEEE Transactions On Software Engineering,

VOL. SE-lI, No. 1, January 1985The following input patterns are presented to the [6] Bharat Bhargava, Concurrency Control in Database Systems,

OL testing module to find if the output [ 0 0 0] is Ieee Transactions On Knowledge And Data Engineering,obtained in the output layer. During OL testing, the Vol. 11, NO. 1, January / February 1999, Proceedings offinal weights obtained during OL training will be DETC'00:used. Otherwise it means that lock has been assigned [7] Raymond C. W. Sung, Jonathan R. Comey and Doug E. R.to either 07. In such case, transaction is denied for T2 Clark, Octree Based Recognition Of Assembly Features

Else the following Table .is presented in stepI ,Proceedings of DETC'00: September 10-13, 2000,Else the following Table 8 iS presented in step 1 Baltimore, Maryland[8] M. L. Brodie, B. Blanstein, U. Dayal, F. Manola and A.

TABLE VIII. ADDITIONAL PATTERNS USED FOR TRAINING Rosenthal, 1984. CAD/CAM Database Management. IEEEOML CPN Database Engineering, 7 (2): 12-20.

[9] Alexandtos Biliris and Huibin Zhao, 1989.Design Versions inObject number Input pattern Target output a Distributed CAD Environment. IEEE, pp:354-359.

pattern [10] M. A. Ketabchi and V. Berzins, 1987. Modeling and01 [11] [0 1 0] Managing CAD Databases. IEEE Computer, pp: 93-10202 [2 1] [0 1 0] [11] Hamideh Afsarmanesh and David Knapp, An Extensible06 [6 1] [0 1 0] Object-Oriented Approach to Databases for VLSI / CAD'

03 [3 1] [0 1 0 ] Proceedings ofVLDB 85, Stockholm, pp 13-24

017 [17 1] [0 1 0]013 [13 1] [0 1 0]

STEP 4: To know the type of lock value assignedto an object and for a transaction, OML testing isused. OML testing uses the final weights created byOML training

The proposed CPN for lock state learning andlock state finding have been implemented usingMatlab 7. Module 1 and Module 3 are trained usingdistance measure.

2009 IEEE Inxternational Advanxce Computing Conference (IACC 2009) 1525