Home > Archives > IJSRST162216
Efficient Sort Search on Massive Data
Authors(3) :-Nandhini A, Kanimozhi. R, Noureen P. T
Efficient top-N retrieval of records from a database has been an active research field for many years. The problem from a real world application point of view has the order of records according to some similarity function on an attribute is not unique. Many researchers have same values in several attributes and thus their ranking in those attributes is arbitrary (based on random choice).For instance, in large person databases many individuals have the same first name, the same date of birth, or live in the same city. Existing algorithms are ill-equipped to handle such cases efficiently. We introduce a Dynamic TMS searcher, which retrieves larger chunks of records from the sorted lists using fixed limits, and which focuses its efforts on records that are ranked high in more than one ordering and thus are more promising candidates. We experimentally show that our method outperforms Dynamic Sorting Algorithm (DSA) for top-k retrieval in those very common cases where we used with dynamically scheduling the resources based on the data which are provided with , this efficient short search algorithm along with the massive data retrieval on a very fine tuple data's can be of a different dataset. Here in this project we are going to use these logics for the need of solution in the field of medical research, where there are many manageable databases that are been used in a common path for the end of healthy need and the retrieval of solution for the cause of illness to a human being.
Nandhini A, Kanimozhi. R, Noureen P. T
Massive data, Indexing, Top-k retrieval, Dataset, Attribute, Sorted list
- R. Fagin, R. Kumar, and D. Sivakumar, “Efficient similarity search and classification via rank aggregation,” in Proc. ACM SIGMOD Int. Conf. Manage. Data, 2003, pp. 301–312.
- G. Das, D. Gunopulos, N. Koudas, and D. Tsirogiannis, “Answering top-k queries using views.” in Proc. 32nd Int. Conf. Very Large Data Bases., pp. 451–462, 2006.
- H. Bast, D. Majumdar, R. Schenkel, M. Theobald, and G. Weikum, “Io-top-k: Index-access optimized top-k query processing,” in Proc. 32nd Int. Conf. Very Large Data Bases, 2006, pp. 475–486.
- R. Fagin, A. Lotem, and M. Naor, “Optimal aggregation algorithms for middleware,” in Proc. 20th ACM SIGMOD-SIGACTSIGART Symp. Principles Database Syst., 2001, pp. 102–113.
- R. Akbarinia, E. Pacitti, and P. Valduriez, “Best position algorithms for top-k queries,” in Proc. 33rd Int. Conf. Very Large Databases, 2007, pp. 495–506.
Published in : Volume 2 | Issue 2 | March-April 2016
Date of Publication : 2016-04-30
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 37-40
Manuscript Number : IJSRST162216
Publisher : Technoscience Academy
PRINT ISSN : 2395-6011
ONLINE ISSN : 2395-602X
Cite This Article :
Nandhini A, Kanimozhi. R, Noureen P. T, "Efficient Sort Search on Massive Data", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 2, Issue 2, pp.37-40, March-April-2016.
Journal URL : http://ijsrst.com/IJSRST162216