Distributed Query Processing Plans Generation using

Teacher Learner based Optimization

Vikash Mishraa, Vikram Singha

aDepartment of Computer Engineering, National Institute of Technology, Kurukshetra, Haryana136119, India, E-mail: mishravikash03@gmail.com, viks@nitkkr.ac.in

With the growing popularity, the number of data sources and the amount of data has been growing veryfast in recent years. The distribution of operational data on disperse data sources impose a challenge onprocessing user queries. In such database systems, the database relations required by a query to answermay be stored at multiple sites. This leads to an exponential increase in the number of possible equivalentor alternatives of a user query. Though it is not computationally reasonable to explore exhaustively allpossible query plans in a large search space, thus a strategy is requisite to produce optimal query plansin distributed database systems. The query plan with most cost-effective option for query processingis measured necessary and must be generated for a given query. This paper attempts to generate suchoptimal query plans using a parameter less optimization technique Teaching- Learner Based Optimization(TLBO). The TLBO algorithm was experiential to go one better than the other optimization algorithmsfor the multi-objective unconstrained and constrained benchmark problems. Experimental comparisons ofTLBO based optimal plan generation with the multi-objective genetic algorithm based distributed queryplan generation algorithm shows that for higher number of relations, the TLBO based algorithm is able togenerate comparatively better quality Top-K query plans.

Keywords : Aggregation based Genetic Algorithm, Distributed Query Processing, Query Plans, QueryOptimization, Teacher Learner Based Optimization, Top-K, Vector Evaluated Genetic Algorithm

1. INTRODUCTION

Database systems of different type use vari-ous techniques to identify optimal query plans.In many application domains, end-users aremore interested in the most important (top-k)query answers in potentially huge answer space[1]. A distributed database encompasses co-herent data, spread across various operationalautonomous sites of a computer network [2].With the increase in the number of users orexpanding organization requirement, the sizeof database networks also expands. In recentyears, it is observed that, in any distributeddatabase the number of data sources and theamount of data has been growing very fast tomeet the challenge of growing popularity orbusiness expansion.

A Distributed Database Management Sys-tem (DDBMS) deals with managing such dis-

tributed databases. DDBMS provides access touser via a simple and unified interface over dis-perse databases through different transparencymechanism, due to which a user feel as if theywere not distributed [3]. The query process-ing is a prime activity in database and it iscontrolled by DBMS. The performance of aDBMS is determined by its ability to processqueries in an effective and efficient manner [4].The query processing in distributed databasesis more intricate, as there various parametersor constraints involve and they are affectingperformance [1].

Query processing connects to many databaseresearch areas, including query optimization,indexing methods, and query languages [5], [6].As a consequence, the impact of efficient pro-cessing of query is becoming apparent in an in-creasing number of applications. One commonway to identify the top-k objects is scoring all

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International Journal of Information Processing, 9(4), 46-60, 2015ISSN : 0973-8215IK International Publishing House Pvt. Ltd., New Delhi, India

58 Vikash Mishra and Vikram Singh

Figure 4. Generation of Optimal Top-K query plans with QueryCost (K=5 and QC=0.6, K=10and QC=0.4, K=15 and 0.2) for different Number of Sites (Ns) and relations sizes (Nr) in num-ber of iterations or generations of Algorithm (TLBO, Multi-Objective VEGA and Aggregationbased GA with, crossover probability (Pc)= 0.8, mutation probability (Pm)=0.2, WeightQAC=0.2,WeightQLC=0.5, WeightLPC=0.3)

various criteria are used to illustrate the per-formance for optimal query plans generation.

5. CONCLUSIONS

In distributed database system data is dis-persed over the multiple sites, this distribu-tion of data is based on partition or replica-tion based due to which a given relation canbe found in more than one sites. Query pro-cessing in such environment is difficult task forquery processor, as multiple equivalent alterna-tives. Query processing in such environment,major objective design are CPU, I/O and thesite-to-site communication cost, among these,the site-to-site communication cost is the domi-nant cost. This requires an optimization mech-anism to generate optimal set of query plans toretrieve results for user query. Multi-objectiveoptimization is a very important research areain engineering studies, because real-world de-sign problems require the optimization of agroup of objectives.

Multiple, often conflicting, objectives arise nat-urally in most real-world optimization scenar-

ios. Adding more than one objective to an op-timization problem adds complexity. In thispaper, TLBO algorithm is employed to gener-ate optimal top-k query plans for proposed de-sign objectives and in unconstrained functionand its performance is compared with nature-inspired optimization technique, genetic algo-rithm (GA). The experimental results showthat the TLBO performs competitively bet-ter on the generation top-k query plans withother optimization methods. Therefore, theTLBO algorithm is effective and robust andhas a great potential for solving similar multi-objective problems. The optimality on genera-tion of query plans by other swarm based opti-mization techniques is part of our future workbased on the similar design objectives.

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Vikash Mishra has completedmasters degree in ComputerEngineering with specializationof Database Systems and appli-cations from National Instituteof Technology Kurukshetra,Haryana, (NIT) in, June 2015.He is currently working as Jun

-ior Associate Software Developer in Nagarro Soft-ware Pvt. Ltd., Gurgaon, Haryana, India. Hisareas of interest are mostly on Database Systems,Big Data Analytics, and Optimization Techniques.

Vikram Singh is AssistantProfessor in the Computer En-gineering Department, NationalInstitute of Technology, Ku-rukshetra, Haryana, India sinceyear 2012. His research interestsare in Advanced DatabaseSystems, Big Data Analytics,

Query Processing and Optimization, DataSapceSystems, Personal Information Management Sys-tesm. He is a Member of the IEEE and ACMSociety.