A Survey on Keyword Interrogation Implication on Document Vicinity Based on Location
Akshay A. Bhujugade, Dattatraya V. Kodavade , , ,
Affiliations A Survey on Keyword Interrogation Implication on Document Vicinity Based on Location
:10.22362/ijcert/2017/v4/i11/xxxx [UNDER PROCESS]
Keyword suggestions are the basic feature of the search engine and it accesses relevant information. The naive user doesn’t know how to express their queries; keyword suggestion in web search assists users to access relevant information without any prior knowledge of how to express in queries. The keyword suggestion module can use the current location of a user and retrieve documents which are near to user location. The Euclidean distance is measured for user location and the documents locations. Accordingly the edge weight adjustment is done referring initial K-D graph. The keyword-document graph is used to map the keyword queries and the spatial distance between the resulting documents and the user location. The graph is browsed in random walk with restart, for calculating the highest score for better keyword query suggestion. The paper discusses techniques for the keyword suggestions and also about location-aware keyword query suggestion framework and improved partition based algorithm.
Akshay A. Bhujugade and Dattatraya V. Kodavade (2017). A Survey on Keyword Interrogation Implication on Document Vicinity Based on Location. International Journal of Computer Engineering In Research Trends, 4(11), 514-518. Retrieved from http://ijcert.org/ems/ijcert_papers/V4I1108.pdf
 Shuyao Qi, Dingming Wu, and Nikos Mamoulis “Location Aware Keyword Query Suggestion Based on Document Proximity,” IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 28, NO. 1, JANUARY 2016.
 D. Wu, M. L. Yiu, and C. S. Jensen, “Moving spatial keyword queries: Formulation, methods, and analysis,” ACM Trans. Database Syst., vol. 38, no. 1, pp. 7:1–7:47, 2013.
 Y. Lu, J. Lu, G. Cong, W. Wu, and C. Shahabi, “Efficient algorithms and cost models for reverse spatial-keyword k-nearest neighbor search,” ACM Trans. Database Syst., vol. 39, no. 2, pp. 13:1–13:46, 2014.
 R. Baeza-Yates, C. Hurtado, and M. Mendoza, “Query recommendation using query logs in search engines”, in Extending Database Technology, pp.588–596, 2004.
 P. Berkhin, “Bookmark-coloring algorithm for personalized pagerank computing,” Internet Math., vol. 3, pp. 41–62, 2006.
 N. Craswell and M. Szummer, “Random walks on the click graph,” in Proc. 30th Annu. Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval , pp. 239–246, 2007.
 H. Cao, D. Jiang, J. Pei, Q. He, Z. Liao, E. Chen, and H. Li, “Context-aware query suggestion by mining click-through and session data,” in Proc. 14th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, pp. 875–883,2008.
 M. P. Kato, T. Sakai, and K. Tanaka, “When do people use query suggestion Inf. Retr., vol. 16, no. 6, pp. 725–746, 2013.
 H. Tong, C. Faloutsos, and J.-Y. Pan, “Fast random walk with restart and its applications,” in Proc. 6th Int. Conf. Data Mining, pp. 613–622, 2006.
 N. Craswell and M. Szummer, “Random walks on the click graph,” in Proc. 30th Annu. Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, pp. 239–246, 2007.
 Y. Fujiwara, M. Nakatsuji, M. Onizuka, and M. Kitsuregawa, “Fast and exact top-k search for random walk with restart,” Proc. VLDB Endowment, vol. 5, no. 5, pp. 442–453, Jan. 2012.
 V. Swathi ,D. Saidulu , B. Chandrakala,” Enabling Secure and Effective Spatial Query Processing on the Cloud using Forward Spatial Transformation,” International Journal of Computer Engineering In Research Trends.,vol.4,no.7,pp.301-307, July2017.