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A. Rasoolzadegan R. Alesheykh A. Abdollahzadeh
A New Approach for Event Triggering Probability Estimation in Active Database Systems to Rule Scheduling Improvement 2nd International Conference on Information & Communication Technologies: from Theory to Applications (ICTTA) AUT CE & IT A. Rasoolzadegan R. Alesheykh A. Abdollahzadeh Department of Computer Engineering and Information Technology - Amirkabir University of Technology - Intelligent Systems Laboratory
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Passive versus Active Databases
Passive databases only execute explicit queries and transactions. Semantics of the domain are expressed in code associated with application programs. Constraints and semantics distributed across many application programs. Database may be updated without “going through” an application program, thereby making the database inconsistent with the rules governing the data. Users and application programs need to periodically poll the database to verify if anticipated conditions are valid. Could become a performance bottleneck The applications and knowledge-based systems interfacing with the database may be out of sync with one another. Department of Computer Engineering and Information Technology - Amirkabir University of Technology - Intelligent Systems Laboratory
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Active Databases An active database is capable of monitoring the database state to Alert applications programs, expert systems, and more general knowledge-based systems, Update the database through rule-generated updates. Shared application semantics may be embedded within the database itself. Research has focused on integrating AI production systems with the database. Issues related to expressing the rules, their execution models, and rule optimization. Department of Computer Engineering and Information Technology - Amirkabir University of Technology - Intelligent Systems Laboratory
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Rules in Active Databases
Rules provide a uniform and powerful mechanism to express: Integrity constraints and their enforcement Assertions about the database Triggers and alerts Corporate Policy Specification Configuration Management Policies Update semantics for shared objects with alerts to notify of changes to relevant objects Performance monitoring for 1) load balancing, 2) query optimization, and database reorganization Usage monitoring for knowledge discovery regarding user profiles and preferences. Department of Computer Engineering and Information Technology - Amirkabir University of Technology - Intelligent Systems Laboratory
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Rule Processing Unit 5 1 2 4 3 3 External Events Temporal Events
Application ………………. raise-event(e1) ……………….. External Events Event Detection Internal Events Rules Execution 5 Temporal Events 1 Rule Processing Unit 2 4 Condition Evaluation 3 3 Rule selection Active Rules Set Data Flow Control Flow Department of Computer Engineering and Information Technology - Amirkabir University of Technology - Intelligent Systems Laboratory
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coupling modes in Active Database
Immediate Deferred Independent Child Transaction Parent Transaction T t0 t1 t2 t3 t4 t5 t6 t7 t8 t9 t10 S(T) F(T) Department of Computer Engineering and Information Technology - Amirkabir University of Technology - Intelligent Systems Laboratory
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Ex-SJF Rule Scheduling Method
Based on SJF Algorithm SJF algorithm is not useful for rule scheduling in ADBS due to active work load nature of it there is defined preprocess for preparing rule base to use the SJF algorithm for rule scheduling in Ex-SJF Rule Execution Tree Virtual Rule Exact Rule Execution Time Calculation Two version of Ex-SJF: Ex-SJFEXA Ex-SJFPRO Department of Computer Engineering and Information Technology - Amirkabir University of Technology - Intelligent Systems Laboratory
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Preparing of Rules Base in Ex-SJF
Rules Definition Rules Execution Tree construction Virtual Rules Construction Rule Execution Time Calculation in Ex-SJFEXA or Ex-SJFPRO Rules Base Department of Computer Engineering and Information Technology - Amirkabir University of Technology - Intelligent Systems Laboratory
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Rule Execution Tree in Run Time
s(T) t1 t0 t20 t19 t21 t23 t25 t28 t26 t30 c(T) f(T) t6 t7 t9 t10 t14 t17 t18 t22 t24 t27 t29 T a(T) T Department of Computer Engineering and Information Technology - Amirkabir University of Technology - Intelligent Systems Laboratory
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Rule Execution Tree Construction before Running
imm def R15 R12 R16 def imm R13 R10 R9 R5 R14 imm def Virtual Rules Extraction Rule Execution Tree Construction R8 R4 imm R13 R9 R14 def imm R1 R13 R10 R8 R9 R7 R6 R5 R4 R11 R12 R3 R2 R14 imm ind def R15 R16 R15 R1 R13 R10 R8 R9 R7 R6 R5 R4 R11 R12 R3 R2 R14 R16 imm def Omitting of Independent Rule Ledges Department of Computer Engineering and Information Technology - Amirkabir University of Technology - Intelligent Systems Laboratory
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Ex-SJFEXA: Extended Shortest Job FirstEXACTLY
R1,X1 R8,X8 R9,X9 R7,X7 R6,X6 R5.X5 R4,X4 R3,X3 R2,X2 R13,X13 R14,X14 R12,X12 R11,X11 R10,X10 R19,X19 R20,X20 R18,X18 R17,X17 R16,X16 R25,X25 R26,X26 R24,X24 R23,X23 R22,X22 R21,X21 R15,X15 … 1 ……………………… 2 ………………... I ………………... I+1 ……... n …………… Department of Computer Engineering and Information Technology - Amirkabir University of Technology - Intelligent Systems Laboratory
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The post order traversal of Rule Execution Tree in Ex-SJFEXA
def imm imm imm R3,3 R4,1 R4,3 R5,16 R5,2 R6,5 R6,2 R7,11 R7,5 def imm def imm def R8,2 R9,4 R9,7 R10,7 R11,3 R12,1 R12,6 def def imm imm R13,2 R14,1 R15,3 R16,2 Department of Computer Engineering and Information Technology - Amirkabir University of Technology - Intelligent Systems Laboratory
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Ex-SJFPRO: Extended Shortest Job FirstPROBABILISTIC
R1,X1,P1 R8,X8,P8 R9,X9,P9 R7,X7,P7 R6,X6,P6 R5.X5,P5 R4,X4,P4 R3,X3,P3 R2,X2,P2 … 1 ……………………… 2 ………………... I ………………... I+1 ……... n …………… R13,X13,P13 R14,X14,P14 R12,X12,P12 R11,X11,P11 R10.X10,P10 R18,X18,P18 R19,X19,P19 R17,X17,P17 R16,X16,P16 R15.X15,P15 R20,X20,P20 R24,X24,P24 R25,X25,P25 R23,X23,P23 R22,X22,P22 R21.X21,P21 (4-2) Department of Computer Engineering and Information Technology - Amirkabir University of Technology - Intelligent Systems Laboratory
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The post order traversal of Rule Execution Tree in Ex-SJFPRO
def imm imm imm R3,3,0.5 R4,1,0.6 R4,1.2,0.6 R5,2,0.8 R5,7.6,0.8 R6,3.2,0.1 R6,2,0.1 R7,5,0.9 R7,6.6,0.9 def imm imm def def R8,2,0.1 R9,4,0.2 R9,5.3,0.2 R10,7,0.6 R11,3,0.4 R12,1,0.5 R12,3.2,0.5 def def imm imm R13,2,0.4 R14,1,0.5 R15,3,0.6 R16,2,0.2 Department of Computer Engineering and Information Technology - Amirkabir University of Technology - Intelligent Systems Laboratory
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Evaluation Parameters
N = Number of Executed Rules ART= Average Response Time RTSV= Response Time Standard Variance UCPU = CPU Utilization TOPT = Time Overhead Per Transaction Department of Computer Engineering and Information Technology - Amirkabir University of Technology - Intelligent Systems Laboratory
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Benchmarking Deferred mode Immediate mode Composite mode
Department of Computer Engineering and Information Technology - Amirkabir University of Technology - Intelligent Systems Laboratory
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Time Overhead per Transaction
Results of simulation of available rule scheduling approaches in deferred mode CPU Utilization Time Overhead per Transaction Throughput Response Time Variance Average Response Time Evaluation Parameters Approaches 3 1 2 Random Static Priority FCFS 5 EDFPD EDFDIV 4 EDFSL Ex-SJFEXA Ex-SJFPRO Department of Computer Engineering and Information Technology - Amirkabir University of Technology - Intelligent Systems Laboratory
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Results of simulation of available rule scheduling approaches in immediate mode
CPU Utilization Time Overhead per Transaction Throughput Response Time Variance Average Response Time Evaluation Parameters Approaches 3 2 Random Static Priority 1 FCFS EDFPD EDFDIV EDFSL Ex-SJFEXA 4 Ex-SJFPRO Department of Computer Engineering and Information Technology - Amirkabir University of Technology - Intelligent Systems Laboratory
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Results of simulation of available rule scheduling approaches in composite mode
CPU Utilization Time Overhead per Transaction Throughput Response Time Variance Average Response Time Evaluation Parameters Approaches 1 3 4 Random 2 Static Priority FCFS EDFPD EDFDIV 5 EDFSL Ex-SJFEXA Ex-SJFPRO Department of Computer Engineering and Information Technology - Amirkabir University of Technology - Intelligent Systems Laboratory
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Ex-SJFPRO-V.1.8 Scheduling Approach
In this Method at first, execution time of each rule is calculated in the Ex-SJFPRO approach Then in every time which an activated rule such as R1 is selected according to scheduling approach from activated rules list for condition evaluation, correctness probability of each logical statement of condition part of R1, i.e. P(R,LSi) is repeatedly calculated and stored based on : This process is repeated for each logical statement of condition part of the corresponding rule until the changes rate of correctness probability of that logical statement, achieves to a desired value (e.g., and in general mode ) At this time, new value is replaced with primary default value (i.e. ½) Department of Computer Engineering and Information Technology - Amirkabir University of Technology - Intelligent Systems Laboratory
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Percentage of rule scheduling optimization in Ex-SJFPRO-V. 1
Percentage of rule scheduling optimization in Ex-SJFPRO-V.1.8 in compare with Ex-SJFPRO Throughput Response Time Variance Average Evaluation parameters Mode 9% 12.6% 8% Immediate 14.3% 33.6% 18.8% Deferred 12% 21.4% 14.2% Composite Department of Computer Engineering and Information Technology - Amirkabir University of Technology - Intelligent Systems Laboratory
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References C. J. Date, “An Introduction to Database Systems”, 8th Edition, Addison-Wesley, 2003. E. Bertino, B. Catania and G. P. Zarri, "Intelligent Database Systems", Addison-Wesley, 2001. S. Potaminsto and M. Stonebraker, "The POSTGRES Rule System", in Active Database Systems: Triggers and Rules for Advanced Systems, Morgann Kaufmann Publishers, Sanfrancisco, CA, 1996. H. Fritshi and Z. Flaach, “A Component Framework to Construct Active Database Management Systems”, PhD Thesis, CS Department, University of Zurich, 2002. Vadua, "Rule Development for active database", PhD Thesis, CS Department, University of Zurich, 1999. Vaduva, S. Gatziu and K. R. Dittrich, "Investigating Termination in Active Database Systems with Expressive Rule Languages". In Proceedings of the 3rd International Workshop on Rules In Database Systems, pp , Skovde(Sweden), 1997. R. M. Sivasankaran, J. A. Stankovic, D. Towsley, B. Purimetla and K. Ramamritham, "Priority Assignment in Real-Time Active Databases", The International Journal on Very Large Data Bases, Vol. 5, No. 1, January 1996. S. Ceri, C. Gennaro, S. Paraboschi and G. Serazzi, “Effective Scheduling of Detached Rules in Active Database”, IEEE Transaction on Knowledge and Data Engineering, Vol. 15, No.1, January/February 2003. Department of Computer Engineering and Information Technology - Amirkabir University of Technology - Intelligent Systems Laboratory
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References R. Alesheykh, A. Abdollahzadeh, "Evaluation of Shortest Job First Approach for Rule Scheduling in Active Database Systems", in the Proceedings of the 9th IASTED International Conference on Artificial Intelligence & Soft Computing (ASC'05), Benidorm, Spain, September 2005. S. Chakravarthy, "Architectures and monitoring techniques for active databases: An evaluation". In Technical Report TR , University of Florida, Gainesville, 1992. J. Stankovic, M. Spuri, M.D. Natale and G.C. Buttazo, ”Implications of Classical Scheduling Results for Real-Time Systems”, IEEE Computer Society Press, Los Alamitos, CA, 1995. Geppert, S. Gatziu, K. R. Dittrich, H. Fritschi, and A. Vaduva, "Architecture and implementation of the active object-oriented database management system SAMOS", Technical Report 95.29, CS Department, University of Zurich, 1995. H. Theodore, "A survey of Active Database Systems", available in active.ps, April 1997. R. Alesheykh, A. Abdollahzadeh, "Evaluation and Comparison of Rule Scheduling Approaches in Active Database Systems", in the Proceedings of the 2nd IASTED international Multi-Conference on Automation, Control, and Information Technology (ACIT’05), Russia, June 2005. Department of Computer Engineering and Information Technology - Amirkabir University of Technology - Intelligent Systems Laboratory
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