Challenging Data Models and Data Confidentiality Through “Pay-As-You-Go” Approach Entity Resolution

Abstract

Problem importance: Predictive analytics seems to be an exceptionally complex and vital concern in domains like computer science, biology, agriculture, business, and national security. When big data applications were indeed accessible, highly efficient cooperation processes are often meaningful. Simultaneously time, new subjective norms originate when the high quantities of data will conveniently assert confidential data. This paper has reviewed two complementary huge issues: data integration and privacy, the ER “pay-as-you-go” approach (Whang et al. in IEEE Trans Knowl Data Eng 25(5):1111–1124 (2012) [1]) in which it explores how the developments of ER is maximized to short-term work. Stepwise ER problem (Whang and Molina in PVLDB 3(1):1326–1337 (2010) [2]) is not even a unique process; it is done concurrently by the better usage of information, schemes, and applications. Joint ER problem with multiple independent datasets are fixed in collaboration (Whang and Molina in ICDE (2012) [3]) and the problem of ER with inconsistencies (Whang et al. in VLDB J 18(6):1261–1277 (2009) [4]). To overcome the research gap in the existing system, the proposed research work addresses an entity resolution (ER) problem that tends to address the records in databases referring to a certain complex real-time entity.