Data that exists in fixed field in a record is called as structured data and putting away such data into database is broadly expanding to strengthen keyword query yet result lists do not give successful responses to keyword query and subsequently it is hard from user’s point of view. It is useful to grasp such kind of queries which gives results with low positioning. Here we determine identification of such queries to discover power of search performed in reply of query and characteristics of such hard query is identified by considering building blocks of the database and result list. One applicable issue of database is the existence of missing data and it can be resolved by imputation. Here an inTeractive Retrieving-Inferring data imPutation method (TRIP) is utilized which accomplishes retrieving and inferring in successive manner to fill the missing attribute values in the database. TRIP can also analyze optimal scheduling scheme in Deterministic Data Imputation (DDI). Filling missing values in such successive manner, we can improve the precision of imputation. So by considering imputation along with identification of power of query performance over the database, we can achieve successful improvements in the query results.

Keyword Query; Database; Query Performance; Deterministic Data Imputation

[1] N. Sarkas, S. Paparizos, and P. Tsaparas, “Structured
annotations of web queries,” in Proc. ACM SIGMOD
Int. Conf. Manage. Data, Indianapolis, IN, USA, pp. 771–
782,2010.
[2] Ganti, Y. He, and D. Xin, “Keyword++: A framework to improve keyword search over entity databases,” in Proc. VLDB Endowment, Singapore, vol. 3, no. 1–2, pp. 711–722, Sept. 2010,
[3] V. Hristidis, L. Gravano, and Y. Papakonstantinou, “Efficient IRstyle keyword search over relational databases,” in Proc. 29th VLDB Conf., Berlin, Germany, pp. 850–861,2003.
[4] G. Bhalotia, A. Hulgeri, C. Nakhe, S. Chakrabarti, and S. Sudarshan, “Keyword searching and browsing in databases
using BANKS,” in Proc. 18th ICDE, San Jose, CA, USA,
pp. 431–440,2002.
[5] Nandi and H. V. Jagadish, “Assisted querying using instant response interfaces,” in Proc. SIGMOD 07, Beijing, China, pp. 1156–1158.
[6] E. Demidova, P. Fankhauser, X. Zhou, and W. Nejdl, “DivQ: Diversification for keyword search over structured databases,” in Proc. SIGIR’ 10, Geneva, Switzerland, pp. 331–338.
[7] J. A. Aslam and V. Pavlu, “Query hardness estimation
using Jensen-Shannon divergence among multiple scoring
functions,” in Proc. 29th ECIR, Rome, Italy, pp. 198– 209,2007.
[8] Shiwen Cheng, Arash Termehchy, and Vagelis Hristidis, “Efficient Prediction of Difficult Keyword Queries over Databases”, vol. 26, no. 6, June 2014.
[9] J. D. Gibbons and S. Chakraborty, Nonparametric Statistical
Inference. New York, NY: Marcel Dekker, 1992.
[10] G. Batista and M. Monard. An analysis of four missing data treatment methods for supervised learning. Applied Artificial Intelligence, 17(5-6):519–533, 2003
[11] Zhixu Li, Lu Qin, Hong Cheng, Xiangliang Zhang, and Xiaofang Zhou, “TRIP: An Interactive Retrieving-Inferring Data Imputation Approach,” IEEE Transaction 2015.
[12] E. Yom-Tov, S. Fine, D. Carmel, and A. Darlow,“Learning to estimate query difficulty: Including applications to missing content detection and distributed information retrieval,” in Proc. 28th Annu. Int. ACM SIGIR Conf. Research Development Information Retrieval, Salvador, Brazil, pp. 512–519,2005.
[13] Y. Zhou and B. Croft, “Ranking robustness: A novel framework to predict query performance,” in Proc. 15th ACM Int. CIKM, Geneva, Switzerland, pp. 567–574,2006.
[14] J. Kim, X. Xue, and B. Croft, A probabilistic retrieval model for semistructured data, in Proc. ECIR, Tolouse, France, pp. 228239, 2009.
[15] J.-J. Shen, C.-C. Chang, and Y.-C. Li. Combined association rules for dealing with missing values. Journal of Information Science, 33(4):468–480, 2007.
[16] Z. Li, M. A. Sharaf, L. Sitbon, S. Sadiq, M. Indulska, and X. Zhou. Webput: Efficient web-based data imputation. In WISE, pages 243–256, 2012.
[17] S. Brin. Extracting patterns and relations from the world wide web. The World Wide Web and Databases, pages 172–183, 1999.
[18] Z. Li, M. A. Sharaf, L. Sitbon, X. Du, and X. Zhou. Core: A context-aware relation extraction method for relation completion. IEEE Transactions on Knowledge and Data Engineering, page 1, 2013.