IJRDET - Proceedings

1.

A Review on Automotive Car Composite Leaf Spring Design and Optimization

Authors: Prasad P.Kunzarkar, Prof. Tushar V.Gujrathi

Abstract- Nowadays growth in competition and innovation in automotive sector tends to modify the existing products or replace old and outdated products by newly innovated and advanced material products.A suspension system of any vehicle is also an area where these types of innovations are carried out consistantly.To increasing the comfort of user there are lots of efforts are taken.Basically leaf springs are mostly used as a suspension system in automotive vehicles.The leaf spring design optimization will be performed periodically by changing from conventional steel to composite material.generally the composite materials are mostly used in various areas such as marine,automobile,aerospace structure etc. Due to high strength they are widely used in the low weight applications and also as an alternate for metals to reduce the material cost.This paper presents literature review on, compatibility of composite material for leaf spring in automobile.Comparison with the conventional leaf spring. Also the design and analysis of composite leafspring.

Keywords— Ansys, Carbon fiber material, composite material, F.E.A, Static analysis.

References-

[1] Senthilkumar and Vijayarangan, “Analytical and Experimental studies on Fatigue life Prediction of steel leaf spring and composite leaf multi leaf spring for Light passanger vehicles using life data analysis” ISSN: 1392-1320, material science ,Vol. 13 No.2 (2007).

[2] Roselita Fragoudakis, Georgios Savaidis , NikolaosMichailidis, “Optimizing the development and manufacturing of 6SiCr7 leaf springs”,International Journal of Fatigue 103 (2017),pp. 168–175.

[3] Hiroyuki Sugiyama , Ahmed A. Shabana , Mohamed A. Omar , WeiYi Loh , “Development of nonlinear elastic leaf spring model for multibody vehicle systems”, sciencedirect,Comput. Methods Appl. Mech. Engg. 195 (2006),pp. 6925–6941.

[4] Vinkelarora, “A Comparative Study of CAE and Experimental Results of Leaf Springs in Automotive Vehicles”, International Journal of Engineering Science and Technology (IJEST), ISSN : 0975-5462, Vol. 3,September (2011).

[5] J.J. Fuentes , H.J. Aguilar , J.A. Rodriguez, E.J. Herrera, “Premature fracture in automobile leaf springs”, sciencedirect Engineering Failure Analysis 16 (2009),pp. 648–655.

[6] Krishan Kumar, M.L.Aggarwal, “Optimization of Various Design Parameters for EN45A Flat Leaf Spring”,ScienceDirect, Materials Today: Proceedings 4 (2017),pp. 1829–1836.

[7] Keshavamurthy Y. C, Chetan H. S, Dhanush C.&NithishPrabhu T, “Design and finite element analysis of hybrid composites mono leaf spring, International Journal of Mechanical and Production Engineering Research and Development”, (IJMPERD), ISSN 2249- 6890 Vol. 3, Issue 3, August(2013), pp. 77-82

[8] K. Ganesan , C. Kailasanathan , Y. Kumarasamy, “Analysis of Composite Leaf Spring Enhanced With Nanoparticles”, Carbon – Science and Technology, ISSN 0974 – 0546,December 2015.

[9] Z. Triveni, B. Amara Babu, “Finite element analysis on leaf spring made of composite material”, IJARSE,Vol-5,Issue No-4, April 2016.

[10] Ms. Surekha S. Sangale, Dr. KishorB. Kale, Dighe Y S, “ Design Analysis of Carbon/Epoxy Composite Leaf Spring”, International Journal of Research in Advent Technology (E-ISSN: 2321- 9637),March 2015.

[11] AL-Qureshi H.A, “Automobile Leaf springs from composite materials”, Journal of Material Processing Technology, 118, (2001),pp. 58-61.

[12] Jadhavk.k. and Dr.Dalu R.S, “Design and analysis of composite leaf spring”,IJRMET,Vol.4,October (2014).

[13] Shaikh N.S and Rajmane S.M, “Modelling and analysis of suspension system of TATA SUMO by using composite material under the static load condition by using FEA”,IJETT,ISSN:2231- 5381,Vol.2,June (2014).

1-4

2.

Prediction of Permeability Characteristics of Fine Grained Soils and its Validation

Authors: Dr. R Chitra, Dr. Manish Gupta, Harendra Prakash, Shahid Noor

Abstract— Permeability of soils forms one of the important soil parameters required in the design of water retaining structures like Dams. Grain size distribution and density are known to influence the permeability of soils. It is one of the most important physical properties of soil used in geotechnical engineering. Though many correlations have been developed by many researchers in the past, the applicability of these correlations to the real practice have been always doubtful. Also these studies have concentrated on mostly the coarse grained soils only. Fine grained soils are complex in itself with the presence of finer particles. The behavior of the fine grained soil depends mostly on the clay and silt contents. The present study focuses on the applicability of the existing empirical relationship to the permeability of fine grained soils. The data obtained from the investigations carried out by CSMRS for various river valley projects is used in the present study. An attempt is also made to develop correlation/empirical relations between k and grain sizes, index properties, and compaction characteristics. Attempts are also made to validate the equations thus obtained.

Keywords—Coefficient of Permeability, Grain Sizes, Void Ratio, Maximum Dry Density, Atterberg Limits, Specific Gravity.

References-

[1] Alam Singh, G.R.Chowdhary. (1994), Soil Engineering – In Theory and Practice, Volume-I, Fundamentals and General Principles. CBS Publishers & Distributors, Delhi.

[2] Chitra, R., Manish Gupta and A. K. Dhawan (2005), Assessing Permeability of Soils using ANN, Proceedings of the All India Seminar on Advances in Geotechnical Engineering, Rourkela, 22 - 23 January, 2005.

[3] Carmen, P.C., 1956, Flow of gases through porous media: Academic press, New York. 182 p.

[4] Das, B.M., 2008, Advanced soil mechanics: Taylor & Francis, New York, NY, 567 p.

[5] Holtz, R.D., Kovacks, W. D. and Sheahan, T. C., 2011, An introduction to Geotechnical Engineering: Prentice-Hall, Upper Saddle River, NJ, 853 p.

[6] Kozeny, J., 1927, Uber kapillare leitung des wassers im boden: Sitzungsber. Acad. Wiss. Wien, Vol. 136, pp. 271-306. 80

[7] Kozeny, J., 1927, Uber kapillare leitung des wassers im boden: Sitzungsber. Acad. Wiss. Wien, Vol. 136, pp. 271-306.

[8] Lambe T.W. and Robert V.Whitman (1984), Soil Mechanics. Wiley Eastern Limited, Delhi

[9] Manish Gupta and Chitra, R., (2015), Artificial Neural Networks for Assessing Permeability Characteristics of Soils, International Journal of Engineering Sciences & Research Technology, Volume 4 Issue 3, March 2015, pp. 338-346.

[10] Mitchell, J.K., and Soga, K., 2005, Fundamentals of Soil Behavior: John Wiley & Sons Inc., Hoboken, NJ, 592 p.

[11] Shashi K Gulhati. (1981), Engineering Properties of Soils. Tata McGraw-Hill Publishing Company Limited, Delhi.

[12] Shenbaga R Kaniraj. (1994), Design Aids in Soil Mechanics and Foundation Engineering. Tata McGraw-Hill Publishing Company Limited, Delhi.

[13] Terzaghi, K. and Peck, R. B., 1964, Soil Mechanics in Engineering Practice: John Wiley and Son, New York

5-12

3.

Sensor Integration in BS VI Exhaust after Treatment System for Automobiles

Authors: Mylaudy Dr. S. Rajadurai, S. Shibu Anand, P. Matcharaja

Abstract - The demanding regulatory requirement mandates the need for new engine-management system and its integration with exhaust-treatment technologies strategy. Different types of sensors play vital role on the engine out and the efficient performance of the exhaust after treatment system. Location, geometry and the orientation of the sensor installation is discussed in detail. The sensitivity of the sensor with the HEGO index guidelines provide the required signal without the noise interference The impact of various particles such as conductive water particles and engine particulates are implemented during the design of exhaust after treatment system. This improves the life expectancy, accuracy level of sensing of the sensors. Advancement of gas sensor technology over the past few decades has led to significant progress in pollution control and thereby environmental protection.

Keywords - Exhaust after treatment, Oxygen/Lambda, Temperature, pressure, Nox, PM, Emission Norms, HEGO Index, DOC, DPF, SCR, ASC, LNT, Hydro Carbon, Oxides of carbon and Oxides of Nitrogen.

References-

[1] Jonathan Zhang “Catalytic Converter – Part I of Automotive Aftertreatment System”

[2] C. Scott Nelson, David Chen, Joseph Ralph and Eric D’Herde “The Development of a RTD Temperature Sensor for Exhaust Applications”

[3] Potential and pitfalls in the use of dual exhaust gas oxygen sensors for three-way catalyst monitoring and control J C Peyton Jones1* and R A Jackson2

13-22

4.

Improving the Performance of Hive by Parallel Processing of Massive Data

Authors: Ankul Barman, Deepak Paranjpe

Abstract- Data refers to technologies and initiatives that involve data that is too diverse, fast-changing or massive for conventional technologies, skills and infra- structure to address efficiently is called Big data. Hive is a data warehouse infrastructure tool, well suited for query processing and data analysis. Hive is gaining popularity for its SQL like query language HiveQL and for supporting majority of the SQL operations in relational database management systems (RDBMS). Being the expensive operation in RDBMS, join has been the focus of many query optimization techniques to improve performance of database systems. This work proposes the use of Numerous Query Optimization (NQO) techniques to improve the overall performance of Hive. During parallel execution of numerous queries, many opportunities can arise for conjoint scan and/or computation tasks. Executing frequent jobs only once can reduce the total execution time of all queries remarkably. Our framework, transforms a set of interrelated HiveQL queries into new global queries that can produce the same results in remarkably smaller total execution times. It is experimentally shown that ConjointHive outperforms the conventional Hive by 30-60% reduction, depending on the number of queries and percentage of conjoint tasks, in the total execution time of correlated TPC-H queries.

Keywords- Big Data, Hive, HiveQL, Numerous Query Optimization, ConjointHive

References-

[1] Hadoop project. http://hadoop.apache.org/. [2] A. Thusoo, J. S. Sarma, N. Jain, Z. Shao, P. Chakka, S. Anthony, H. Liu, P. Wycko_, and R. Murthy. Hive A Warehousing Solution Over a MapReduce Framework. VLDB, 2009.

[3] A. Thusoo, J. S. Sarma, N. Jain, Z. Shao, P. Chakka, N. Jain, S. Anthony, H. Liu, and R. Murthy. Hive A Petabyte Scale Data Warehouse Using Hadoop. IEEE, 2010.

[4] The Hive Project. Hive website, 2009. http: //hadoop.apache.org/hive/.

[5] R. Stewart. Performance and Programmability of High Level Data Parallel Processing Languages: Pig, Hive, JAQL & Java-MapReduce, 2010. Heriot-Watt University.

[6] J. Dean and S. Ghemawat. MapReduce: Simplified data processing on large clusters. In OSDI '04, pages 137{150, 2004.

[7] IBM Research. Jacl website. http://www.jaql.org.

[8] C. Olston, B. Reed, A. Silberstein, and U. Srivastava. Automatic optimization of parallel dataow programs. In USENIX 2008 Annual Technical Conference, pages 267{273, 2008.

[9] C. Olston, B. Reed, U. Srivastava, R. Kumar, and A. Tomkins. Pig latin: a not-so-foreign language for data processing. In SIGMOD '08, pages 1099{1110, 2008.

[10] Sellis, T.K. (1988). Multiple-query optimization. ACM Transactions on Database Systems (TODS), 13(1), 23-52.

[11] Bayir, M. A., Toroslu, I. H., and Cosar, A. (2007). Genetic algorithm for the multiplequery optimization problem. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 37(1), 147-153.

[12] Y. Jia and Z. Shao. A Benchmark for Hive, PIG and Hadoop,2009 https://issues.apache.org/jira/browse/HIVE.

[13] Mehta, M. and DeWitt, D.J. (1995). Managing intraoperator parallelism in parallel database systems. VLDB (382-394).

[14] Beynon, M., et al. (2002). Processing large-scale multidimensional data in parallel and distributed environments. Parallel Computing, 28(5), 827-859.

[15] Jarke, M. (1985). Common subexpression isolation in multiple query optimization. In Query Processing in Database Systems (pp. 191-205). Springer Berlin Heidelberg.

[16] Chen, G., et al. (2011). Optimization of sub-query processing in distributed data integration systems. Journal of Network and Computer Applications, 34(4), 1035-1042.

[17] S. Cluet and G. Moerkotte. On the Complexity of Generating Optimal Left-Deep Processing Trees with Cross Products. International Conference on DatabaseTheory, 1995.

[18] J. Dean and S. Ghemawat. MapReduce: Simpli_ed Data Processing on Large Clusters. Operating Systems Design and Implementation, 2004.

[19] A. Ganapathi, Y. Chen, A. Fox, R. Katz, and D. Patterson. Statistics-Driven Workload Modeling for the Cloud. SMDB 2010, 2010.

[20] G. Graefe. Query Evaluation Techniques for Large Databases. ACM Computing Surveys 25:2, p. 73-170, 1993. [21] M. Jarke and J. Koch. Query Optimization in Database Systems. ACM Computing Surveys, 1984.

[22] Y. Jia and Z. Shao. A Benchmark for Hive, PIG and Hadoop, 2009. https://issues.apache.org/jira/browse/HIVE-396.

[23] B. J. Oommen and M. Thiyagarajah. Rectangular Attribute Cardinality Map: A New Histogram-like Technique for Query Optimization. Proceedings of the International Database Engineering and Applications Symposium , IDEAS'99, Montreal, Canada, 1999.

[24] V. Poosala, Y. E. Ioannidis, P. J. Haas, and E. J. Shekita. Improved Histograms for Selectivity Estimation of Range Predicates. Proceedings of the ACM SIGMOD International Conference on Management of Data, 1996.

23-29

5.

Optimizing the Performance of HADOOP Using Index-Based Join Operation

Authors: Anshul Shrivas, Deepak Paranjpe

Abstract - Data refers to technologies and initiatives that involve data that is too diverse, fast-changing or massive for conventional technologies, skills and infra- structure to address efficiently is called Big Data. Hive is a data warehouse infrastructure tool, well suited for query processing and data analysis. Hive is gaining popularity for its SQL like query language HiveQL and for supporting majority of the SQL operations in relational database management systems (RDBMS). Being the expensive operation in RDBMS, join has been the focus of many query optimization techniques to improve performance of database systems. We investigate such techniques for join operations in Hive and develop a two-way join algorithm for queries in HiveQL. When a query requires only a small subset of data selected by a predicate in the WHERE clause, the brute-force method which scans the entire tables results in poor performance for redundant disk I/Os, and irrelevant maps initiation in case the query is issued using the MapReduce. In this work, we implement the proposed index-based join technique and integrate it in Hive. To add our extension, we obtain Hive architecture details by reverse engineering the code and map our design to the conceptual optimization flow. To evaluate the performance, after setting up the environment, we run relevant test queries on datasets generated using the industry standard benchmark, TPC-H. Our results indicate significant performance gain over relatively large data or highly selective queries.

Keywords : Big Data, Hive, Hadoop, Indexing, Join.

References-

[1] Antony, S., Chakka, P., Jain, N., J., Liu, Murthy, R., Sarma, J. S., Thusoo, A., Zhang, N “Hive – A Petabyte Scale Data Warehouse Using Hadoop,” IEEE 26th Intl. Conf. Data Engineering (ICDE), Long Beach, CA, 2010, pp. 996 – 1005.

[2] Apache Hadoop [Online]. Available: http://hadoop.apache.org/

[3] Dean, J., Ghemawat, S. “MapReduce: Simplified Data Processing on Large Clusters,” Mag. Commun. ACM 50thanniversary, vol. 51, issue 1, 2008, pp.107-113

[4] http://www.hadooptpoint.com/introduction-hive/ [5] Yue Liu1,6,7 , Songlin Hu1 “DGFIndex for Smart Grid: Enhancing Hive with a Cost-Effective multidimensional Range Index” 40th International Conference on Very Large Data Bases, September 1st - 5th 2014, Hangzhou, China.

[6] ANTLR [Online]. Available: http://www.antlr.org/

[7] An, M., Wang, W., Wang, Y., “Using Index in the MapReduce Framework, ”, 12th Intl. Asia Pacific Web Conf. (APWEB),Beijing, China, 2010, pp. 52-58

[8] Dean, J., Ghemawat, S. “MapReduce: Simplified DataProcessing on Large Clusters,” Mag. Commun. ACM 50th anniversary, vol. 51, issue 1, 2008, pp.107-113

[9]Capriolo, E., Rutherglen, J., Wampler, D. Programming Hive: Data Warehouse and Query Language for Hadoop, 1st ed, O'Reilly Media, 2012

[10] TPC-H[Online]. http://www.tpc.org/tpch/ [11]HIVE 1694[Online]. Available: https://issues.apache.org/jira/browse/HIVE-1694

[12] Hive index design doc [Online]. Available: https://cwiki.apache.org/confluence/display/Hive/IndexDe

[13] Hive JIRA [Online]. Available: https://issues.apache.org/jira/browse/HIVE

[14] HIVE-1644 [Online]. Available: https://issues.apache.org/jira/browse/HIVE-1644

[15] N. Jain, L. Tang, “Join strategies in Hive”, Facebook, Rep. Hadoop summit 2011, 2011 [Online].

[16] Li, Z., Ross, K. A. “Fast joins using join indices”, in The International Journal on Very Large Data Bases, vol. 8, issue 1, 1999, pp.1–24

[17] Lou, W., Ren, K., Wang, C., Wang, Q. Privacy-Preserving Public Auditing for Storage Security in Cloud Computing, Proc. 30th IEEE Int'l Conf. Computer Communications (INFOCOM 10), IEEE Press, San Diego, CA, 2010, pp. 525– 533.

[18] S. Madden: “From Databases to Big Data,” IEEE Internet Computer., vol.16, issue 3, pp. 4-6, May-June, 2012

[19] MapReduce Tutorial [Online]. Available (date): http://hadoop.apache.org/docs/mapreduce/r0.22.0/mapred_tut orial.html

[20]MongoDB[Online]. Available: http://www.mongodb.org/

[21]Neo4j[Online]. Available(write date): http://www.neo4j.org/

[22] Gruenheid, A., Mark, L., Omnecinski, E. “Query Optimization using column statistics in Hive,” in Proc. 15th Symp. Intl. Database Engineering & Applications (IDEAS), Lisbon, Portugal, 2011, pp. 97-105, 2011

30-35

6.

Load Balancing Techniques in Cloud Computing

Authors: Aakash Dhar Badgaiyan, Rajendra Arakh

30-35

S.No.	Volume 7, Issue 4, April 2018	Page No.
1.	A Review on Automotive Car Composite Leaf Spring Design and Optimization Authors: Prasad P.Kunzarkar, Prof. Tushar V.Gujrathi Abstract- Nowadays growth in competition and innovation in automotive sector tends to modify the existing products or replace old and outdated products by newly innovated and advanced material products.A suspension system of any vehicle is also an area where these types of innovations are carried out consistantly.To increasing the comfort of user there are lots of efforts are taken.Basically leaf springs are mostly used as a suspension system in automotive vehicles.The leaf spring design optimization will be performed periodically by changing from conventional steel to composite material.generally the composite materials are mostly used in various areas such as marine,automobile,aerospace structure etc. Due to high strength they are widely used in the low weight applications and also as an alternate for metals to reduce the material cost.This paper presents literature review on, compatibility of composite material for leaf spring in automobile.Comparison with the conventional leaf spring. Also the design and analysis of composite leafspring. Keywords— Ansys, Carbon fiber material, composite material, F.E.A, Static analysis. References- [1] Senthilkumar and Vijayarangan, “Analytical and Experimental studies on Fatigue life Prediction of steel leaf spring and composite leaf multi leaf spring for Light passanger vehicles using life data analysis” ISSN: 1392-1320, material science ,Vol. 13 No.2 (2007). [2] Roselita Fragoudakis, Georgios Savaidis , NikolaosMichailidis, “Optimizing the development and manufacturing of 6SiCr7 leaf springs”,International Journal of Fatigue 103 (2017),pp. 168–175. [3] Hiroyuki Sugiyama , Ahmed A. Shabana , Mohamed A. Omar , WeiYi Loh , “Development of nonlinear elastic leaf spring model for multibody vehicle systems”, sciencedirect,Comput. Methods Appl. Mech. Engg. 195 (2006),pp. 6925–6941. [4] Vinkelarora, “A Comparative Study of CAE and Experimental Results of Leaf Springs in Automotive Vehicles”, International Journal of Engineering Science and Technology (IJEST), ISSN : 0975-5462, Vol. 3,September (2011). [5] J.J. Fuentes , H.J. Aguilar , J.A. Rodriguez, E.J. Herrera, “Premature fracture in automobile leaf springs”, sciencedirect Engineering Failure Analysis 16 (2009),pp. 648–655. [6] Krishan Kumar, M.L.Aggarwal, “Optimization of Various Design Parameters for EN45A Flat Leaf Spring”,ScienceDirect, Materials Today: Proceedings 4 (2017),pp. 1829–1836. [7] Keshavamurthy Y. C, Chetan H. S, Dhanush C.&NithishPrabhu T, “Design and finite element analysis of hybrid composites mono leaf spring, International Journal of Mechanical and Production Engineering Research and Development”, (IJMPERD), ISSN 2249- 6890 Vol. 3, Issue 3, August(2013), pp. 77-82 [8] K. Ganesan , C. Kailasanathan , Y. Kumarasamy, “Analysis of Composite Leaf Spring Enhanced With Nanoparticles”, Carbon – Science and Technology, ISSN 0974 – 0546,December 2015. [9] Z. Triveni, B. Amara Babu, “Finite element analysis on leaf spring made of composite material”, IJARSE,Vol-5,Issue No-4, April 2016. [10] Ms. Surekha S. Sangale, Dr. KishorB. Kale, Dighe Y S, “ Design Analysis of Carbon/Epoxy Composite Leaf Spring”, International Journal of Research in Advent Technology (E-ISSN: 2321- 9637),March 2015. [11] AL-Qureshi H.A, “Automobile Leaf springs from composite materials”, Journal of Material Processing Technology, 118, (2001),pp. 58-61. [12] Jadhavk.k. and Dr.Dalu R.S, “Design and analysis of composite leaf spring”,IJRMET,Vol.4,October (2014). [13] Shaikh N.S and Rajmane S.M, “Modelling and analysis of suspension system of TATA SUMO by using composite material under the static load condition by using FEA”,IJETT,ISSN:2231- 5381,Vol.2,June (2014).	1-4
2.	Prediction of Permeability Characteristics of Fine Grained Soils and its Validation Authors: Dr. R Chitra, Dr. Manish Gupta, Harendra Prakash, Shahid Noor Abstract— Permeability of soils forms one of the important soil parameters required in the design of water retaining structures like Dams. Grain size distribution and density are known to influence the permeability of soils. It is one of the most important physical properties of soil used in geotechnical engineering. Though many correlations have been developed by many researchers in the past, the applicability of these correlations to the real practice have been always doubtful. Also these studies have concentrated on mostly the coarse grained soils only. Fine grained soils are complex in itself with the presence of finer particles. The behavior of the fine grained soil depends mostly on the clay and silt contents. The present study focuses on the applicability of the existing empirical relationship to the permeability of fine grained soils. The data obtained from the investigations carried out by CSMRS for various river valley projects is used in the present study. An attempt is also made to develop correlation/empirical relations between k and grain sizes, index properties, and compaction characteristics. Attempts are also made to validate the equations thus obtained. Keywords—Coefficient of Permeability, Grain Sizes, Void Ratio, Maximum Dry Density, Atterberg Limits, Specific Gravity. References- [1] Alam Singh, G.R.Chowdhary. (1994), Soil Engineering – In Theory and Practice, Volume-I, Fundamentals and General Principles. CBS Publishers & Distributors, Delhi. [2] Chitra, R., Manish Gupta and A. K. Dhawan (2005), Assessing Permeability of Soils using ANN, Proceedings of the All India Seminar on Advances in Geotechnical Engineering, Rourkela, 22 - 23 January, 2005. [3] Carmen, P.C., 1956, Flow of gases through porous media: Academic press, New York. 182 p. [4] Das, B.M., 2008, Advanced soil mechanics: Taylor & Francis, New York, NY, 567 p. [5] Holtz, R.D., Kovacks, W. D. and Sheahan, T. C., 2011, An introduction to Geotechnical Engineering: Prentice-Hall, Upper Saddle River, NJ, 853 p. [6] Kozeny, J., 1927, Uber kapillare leitung des wassers im boden: Sitzungsber. Acad. Wiss. Wien, Vol. 136, pp. 271-306. 80 [7] Kozeny, J., 1927, Uber kapillare leitung des wassers im boden: Sitzungsber. Acad. Wiss. Wien, Vol. 136, pp. 271-306. [8] Lambe T.W. and Robert V.Whitman (1984), Soil Mechanics. Wiley Eastern Limited, Delhi [9] Manish Gupta and Chitra, R., (2015), Artificial Neural Networks for Assessing Permeability Characteristics of Soils, International Journal of Engineering Sciences & Research Technology, Volume 4 Issue 3, March 2015, pp. 338-346. [10] Mitchell, J.K., and Soga, K., 2005, Fundamentals of Soil Behavior: John Wiley & Sons Inc., Hoboken, NJ, 592 p. [11] Shashi K Gulhati. (1981), Engineering Properties of Soils. Tata McGraw-Hill Publishing Company Limited, Delhi. [12] Shenbaga R Kaniraj. (1994), Design Aids in Soil Mechanics and Foundation Engineering. Tata McGraw-Hill Publishing Company Limited, Delhi. [13] Terzaghi, K. and Peck, R. B., 1964, Soil Mechanics in Engineering Practice: John Wiley and Son, New York	5-12
3.	Sensor Integration in BS VI Exhaust after Treatment System for Automobiles Authors: Mylaudy Dr. S. Rajadurai, S. Shibu Anand, P. Matcharaja Abstract - The demanding regulatory requirement mandates the need for new engine-management system and its integration with exhaust-treatment technologies strategy. Different types of sensors play vital role on the engine out and the efficient performance of the exhaust after treatment system. Location, geometry and the orientation of the sensor installation is discussed in detail. The sensitivity of the sensor with the HEGO index guidelines provide the required signal without the noise interference The impact of various particles such as conductive water particles and engine particulates are implemented during the design of exhaust after treatment system. This improves the life expectancy, accuracy level of sensing of the sensors. Advancement of gas sensor technology over the past few decades has led to significant progress in pollution control and thereby environmental protection. Keywords - Exhaust after treatment, Oxygen/Lambda, Temperature, pressure, Nox, PM, Emission Norms, HEGO Index, DOC, DPF, SCR, ASC, LNT, Hydro Carbon, Oxides of carbon and Oxides of Nitrogen. References- [1] Jonathan Zhang “Catalytic Converter – Part I of Automotive Aftertreatment System” [2] C. Scott Nelson, David Chen, Joseph Ralph and Eric D’Herde “The Development of a RTD Temperature Sensor for Exhaust Applications” [3] Potential and pitfalls in the use of dual exhaust gas oxygen sensors for three-way catalyst monitoring and control J C Peyton Jones1* and R A Jackson2	13-22
4.	Improving the Performance of Hive by Parallel Processing of Massive Data Authors: Ankul Barman, Deepak Paranjpe Abstract- Data refers to technologies and initiatives that involve data that is too diverse, fast-changing or massive for conventional technologies, skills and infra- structure to address efficiently is called Big data. Hive is a data warehouse infrastructure tool, well suited for query processing and data analysis. Hive is gaining popularity for its SQL like query language HiveQL and for supporting majority of the SQL operations in relational database management systems (RDBMS). Being the expensive operation in RDBMS, join has been the focus of many query optimization techniques to improve performance of database systems. This work proposes the use of Numerous Query Optimization (NQO) techniques to improve the overall performance of Hive. During parallel execution of numerous queries, many opportunities can arise for conjoint scan and/or computation tasks. Executing frequent jobs only once can reduce the total execution time of all queries remarkably. Our framework, transforms a set of interrelated HiveQL queries into new global queries that can produce the same results in remarkably smaller total execution times. It is experimentally shown that ConjointHive outperforms the conventional Hive by 30-60% reduction, depending on the number of queries and percentage of conjoint tasks, in the total execution time of correlated TPC-H queries. Keywords- Big Data, Hive, HiveQL, Numerous Query Optimization, ConjointHive References- [1] Hadoop project. http://hadoop.apache.org/. [2] A. Thusoo, J. S. Sarma, N. Jain, Z. Shao, P. Chakka, S. Anthony, H. Liu, P. Wycko_, and R. Murthy. Hive A Warehousing Solution Over a MapReduce Framework. VLDB, 2009. [3] A. Thusoo, J. S. Sarma, N. Jain, Z. Shao, P. Chakka, N. Jain, S. Anthony, H. Liu, and R. Murthy. Hive A Petabyte Scale Data Warehouse Using Hadoop. IEEE, 2010. [4] The Hive Project. Hive website, 2009. http: //hadoop.apache.org/hive/. [5] R. Stewart. Performance and Programmability of High Level Data Parallel Processing Languages: Pig, Hive, JAQL & Java-MapReduce, 2010. Heriot-Watt University. [6] J. Dean and S. Ghemawat. MapReduce: Simplified data processing on large clusters. In OSDI '04, pages 137{150, 2004. [7] IBM Research. Jacl website. http://www.jaql.org. [8] C. Olston, B. Reed, A. Silberstein, and U. Srivastava. Automatic optimization of parallel dataow programs. In USENIX 2008 Annual Technical Conference, pages 267{273, 2008. [9] C. Olston, B. Reed, U. Srivastava, R. Kumar, and A. Tomkins. Pig latin: a not-so-foreign language for data processing. In SIGMOD '08, pages 1099{1110, 2008. [10] Sellis, T.K. (1988). Multiple-query optimization. ACM Transactions on Database Systems (TODS), 13(1), 23-52. [11] Bayir, M. A., Toroslu, I. H., and Cosar, A. (2007). Genetic algorithm for the multiplequery optimization problem. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 37(1), 147-153. [12] Y. Jia and Z. Shao. A Benchmark for Hive, PIG and Hadoop,2009 https://issues.apache.org/jira/browse/HIVE. [13] Mehta, M. and DeWitt, D.J. (1995). Managing intraoperator parallelism in parallel database systems. VLDB (382-394). [14] Beynon, M., et al. (2002). Processing large-scale multidimensional data in parallel and distributed environments. Parallel Computing, 28(5), 827-859. [15] Jarke, M. (1985). Common subexpression isolation in multiple query optimization. In Query Processing in Database Systems (pp. 191-205). Springer Berlin Heidelberg. [16] Chen, G., et al. (2011). Optimization of sub-query processing in distributed data integration systems. Journal of Network and Computer Applications, 34(4), 1035-1042. [17] S. Cluet and G. Moerkotte. On the Complexity of Generating Optimal Left-Deep Processing Trees with Cross Products. International Conference on DatabaseTheory, 1995. [18] J. Dean and S. Ghemawat. MapReduce: Simpli_ed Data Processing on Large Clusters. Operating Systems Design and Implementation, 2004. [19] A. Ganapathi, Y. Chen, A. Fox, R. Katz, and D. Patterson. Statistics-Driven Workload Modeling for the Cloud. SMDB 2010, 2010. [20] G. Graefe. Query Evaluation Techniques for Large Databases. ACM Computing Surveys 25:2, p. 73-170, 1993. [21] M. Jarke and J. Koch. Query Optimization in Database Systems. ACM Computing Surveys, 1984. [22] Y. Jia and Z. Shao. A Benchmark for Hive, PIG and Hadoop, 2009. https://issues.apache.org/jira/browse/HIVE-396. [23] B. J. Oommen and M. Thiyagarajah. Rectangular Attribute Cardinality Map: A New Histogram-like Technique for Query Optimization. Proceedings of the International Database Engineering and Applications Symposium , IDEAS'99, Montreal, Canada, 1999. [24] V. Poosala, Y. E. Ioannidis, P. J. Haas, and E. J. Shekita. Improved Histograms for Selectivity Estimation of Range Predicates. Proceedings of the ACM SIGMOD International Conference on Management of Data, 1996.	23-29
5.	Optimizing the Performance of HADOOP Using Index-Based Join Operation Authors: Anshul Shrivas, Deepak Paranjpe Abstract - Data refers to technologies and initiatives that involve data that is too diverse, fast-changing or massive for conventional technologies, skills and infra- structure to address efficiently is called Big Data. Hive is a data warehouse infrastructure tool, well suited for query processing and data analysis. Hive is gaining popularity for its SQL like query language HiveQL and for supporting majority of the SQL operations in relational database management systems (RDBMS). Being the expensive operation in RDBMS, join has been the focus of many query optimization techniques to improve performance of database systems. We investigate such techniques for join operations in Hive and develop a two-way join algorithm for queries in HiveQL. When a query requires only a small subset of data selected by a predicate in the WHERE clause, the brute-force method which scans the entire tables results in poor performance for redundant disk I/Os, and irrelevant maps initiation in case the query is issued using the MapReduce. In this work, we implement the proposed index-based join technique and integrate it in Hive. To add our extension, we obtain Hive architecture details by reverse engineering the code and map our design to the conceptual optimization flow. To evaluate the performance, after setting up the environment, we run relevant test queries on datasets generated using the industry standard benchmark, TPC-H. Our results indicate significant performance gain over relatively large data or highly selective queries. Keywords : Big Data, Hive, Hadoop, Indexing, Join. References- [1] Antony, S., Chakka, P., Jain, N., J., Liu, Murthy, R., Sarma, J. S., Thusoo, A., Zhang, N “Hive – A Petabyte Scale Data Warehouse Using Hadoop,” IEEE 26th Intl. Conf. Data Engineering (ICDE), Long Beach, CA, 2010, pp. 996 – 1005. [2] Apache Hadoop [Online]. Available: http://hadoop.apache.org/ [3] Dean, J., Ghemawat, S. “MapReduce: Simplified Data Processing on Large Clusters,” Mag. Commun. ACM 50thanniversary, vol. 51, issue 1, 2008, pp.107-113 [4] http://www.hadooptpoint.com/introduction-hive/ [5] Yue Liu1,6,7 , Songlin Hu1 “DGFIndex for Smart Grid: Enhancing Hive with a Cost-Effective multidimensional Range Index” 40th International Conference on Very Large Data Bases, September 1st - 5th 2014, Hangzhou, China. [6] ANTLR [Online]. Available: http://www.antlr.org/ [7] An, M., Wang, W., Wang, Y., “Using Index in the MapReduce Framework, ”, 12th Intl. Asia Pacific Web Conf. (APWEB),Beijing, China, 2010, pp. 52-58 [8] Dean, J., Ghemawat, S. “MapReduce: Simplified DataProcessing on Large Clusters,” Mag. Commun. ACM 50th anniversary, vol. 51, issue 1, 2008, pp.107-113 [9]Capriolo, E., Rutherglen, J., Wampler, D. Programming Hive: Data Warehouse and Query Language for Hadoop, 1st ed, O'Reilly Media, 2012 [10] TPC-H[Online]. http://www.tpc.org/tpch/ [11]HIVE 1694[Online]. Available: https://issues.apache.org/jira/browse/HIVE-1694 [12] Hive index design doc [Online]. Available: https://cwiki.apache.org/confluence/display/Hive/IndexDe [13] Hive JIRA [Online]. Available: https://issues.apache.org/jira/browse/HIVE [14] HIVE-1644 [Online]. Available: https://issues.apache.org/jira/browse/HIVE-1644 [15] N. Jain, L. Tang, “Join strategies in Hive”, Facebook, Rep. Hadoop summit 2011, 2011 [Online]. [16] Li, Z., Ross, K. A. “Fast joins using join indices”, in The International Journal on Very Large Data Bases, vol. 8, issue 1, 1999, pp.1–24 [17] Lou, W., Ren, K., Wang, C., Wang, Q. Privacy-Preserving Public Auditing for Storage Security in Cloud Computing, Proc. 30th IEEE Int'l Conf. Computer Communications (INFOCOM 10), IEEE Press, San Diego, CA, 2010, pp. 525– 533. [18] S. Madden: “From Databases to Big Data,” IEEE Internet Computer., vol.16, issue 3, pp. 4-6, May-June, 2012 [19] MapReduce Tutorial [Online]. Available (date): http://hadoop.apache.org/docs/mapreduce/r0.22.0/mapred_tut orial.html [20]MongoDB[Online]. Available: http://www.mongodb.org/ [21]Neo4j[Online]. Available(write date): http://www.neo4j.org/ [22] Gruenheid, A., Mark, L., Omnecinski, E. “Query Optimization using column statistics in Hive,” in Proc. 15th Symp. Intl. Database Engineering & Applications (IDEAS), Lisbon, Portugal, 2011, pp. 97-105, 2011	30-35
6.	Load Balancing Techniques in Cloud Computing Authors: Aakash Dhar Badgaiyan, Rajendra Arakh	30-35