Data fusion in data federation using modified discriminative Markov logic networks

Abstract

The quality integrated data is crucial for data mining process. The existing approaches are used trust your friends and cry with wolves principle to resolve the data conflicts. These principles are taking the value of a preferred source and taking the most frequent value. However, it is a challenge for data integration to choose the most trustworthy data source and it is arbitrary to trust only certain source. To mitigate above issues, Data Fusion in Data Federation using Modified Discriminative Markov Logic Networks (DF-MDMLN) approach is proposed. Data fusion is to resolve the data conflicts among the data from different heterogeneous databases by utilizing multiangle features and knowledge of discriminative Markov Logic Network (MLN). The data fusion is used to improve the precision and recall of the end users' data set. E-shopping for computer peripherals application is considered for experimentation to analyze the performance of DF-MDMLN approach. Experiments on E-shopping data sets show the effectiveness of DF-MDMLN approach. It is observed that the precision and recall of data fusion has been improved by 40% and 27% respectively.

A Systematic Approach For Data Cleansing Process of Geospatial Data to Perform Application Specific Data Analytics

“…[6] The proposed research mainly concentrates on the data cleaning and enriching activities of geospatial for further use. [3] The following are the steps followed in doing the process.…”

“…Collecting the data of the objects based on the movement on the surface and converting them into required format either in the form of tables, paragraphs etc.., and [1] Data Fusion, Filtering and pre-processing In this step data fusion will be done by collecting data from multiple sources and integrating the required data as per the nee of the application. [3] Once Data Fusion is done, the filtering operation is carried out where the data collected in fusion will be considered in the form of sub sets as a portion of data and segregated data based on the requirement.…”

“…Means based on the need it will be partitioned and stored, based on the requirement to gather data it will be directly searched in that portion in order to save the time and get the accurate data for further processing. [3] Spatial Data Analytics This is the next step after entire input spatial data is ready as per the problem definition. This spatial data can be further used for the analytics to solve real time problems.…”