![]() ![]() Many organizations collect large amounts of data to support their business and decision making processes. The data collected from various sources may have data quality problems in it. Project based on Schotime's branch of PetaPoco. Simple microORM that maps the results of a query onto a POCO object. Checkout the Wiki for more documentation. There is no mapping setup needed for this (query only) scenario. These kinds of issues become prominent when various databases are integrated. This works by mapping the column names to the property names on the User object. The integrated databases inherit the data quality problems that were present in the source database. Code: Imports NPoco Imports System.Configuration Public Class NPocoTest Public Sub doTest () Using db As IDatabase New Database (ConfigurationManager.AppSettings ('mydbconnectionstring'), ) Dim app As App db. The data in the integrated systems need to be cleaned for proper decision making. Cleansing of data is one of the most crucial steps. In this research, focus is on one of the major issue of data cleansing i.e. “duplicate record detection” which arises when the data is collected from various sources. As a result of this research study, comparison among standard duplicate elimination algorithm (SDE), sorted neighborhood algorithm (SNA), duplicate elimination sorted neighborhood algorithm (DE-SNA), and adaptive duplicate detection algorithm (ADD) is provided. A prototype is also developed which shows that adaptive duplicate detection algorithm is the optimal solution for the problem of duplicate record detection. For approximate matching of data records, string matching algorithms (recursive algorithm with word base and recursive algorithm with character base) have been implemented and it is concluded that the results are much better with recursive algorithm with word base.īig data analytics helps us to find potentially valuable knowledge, but as the size of the dataset increases, the computing cost also grows exponentially. In our previous work, BotCluster, we had designed a pre-processing filtering pipeline, including whitelist filter and flow loss-response rate (FLR) filter, for data reduction, which intended to wipe out irrelative noises and reduce the computing overhead. IDatabase.FetchMultiple(Type types, object cb. Code Issues 47 Pull requests 8 Actions Projects 0 Wiki Security. using System using using using using System. I will flesh this article out later, but here is some code as an example. However, we still face a data redundancy phenomenon in which some of the same feature vectors repeatedly emerged. I think that would be great as thats the most IO intensive with multiple results and thus longer running queries Thanks. How to do CRUD operations with NPoco in Umbraco 8. In this paper, we propose a data compacting approach aimed to reduce the input volume and keep enough representative feature vectors to fit DBSCAN's (Density-based spatial clustering of applications with noise) criteria. It purges the redundant vectors according to a purging threshold and keeps the primary representatives. ![]()
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