A closed-loop recycling process is being investigated (using membrane technology) to remove soils from industrial surface cleaning Operations. This recycling process combines cleaning chemical (aqueous and semi-aqueous formulations), cleaning equipment and closedloop recyding methods tb minimize soil loading in a parts cleaning process. Closed-loop recycling and the need for it is the primary t...

Sensor networks have shown tremendous growth in many domains such as environmental monitoring. The data captured from the physical world through these sensor devices, however, tend to be incomplete, noisy, and unreliable. Traditional data cleaning techniques cannot be applied to such data as they do not take into account the strong spatial and temporal correlations typically present in sensor d...

The Log-structured File System (LFS) transforms random writes to a huge sequential one to provide superior write performance on storage devices. However, LFS inherently suffers from overhead incurred by cleaning segments. Specifically, when file system utilization is high and the system is busy, write performance of LFS degenerates significantly due to high cleaning cost. Also, in the newer fla...

The integration of information is an important area of research in databases. The duplicate elimination problem of detecting database records that are approximate duplicates, but not exact duplicates, which describe the same real world entity, is an important data cleaning problem. To ensure high data quality, data warehouse must cleanse data by detecting and eliminating the redundant data. Dur...

Data mining is a part of a process called KDD-Knowledge Discovery in Databases. This process consists basically of steps that are performed before carrying out data mining, such as data selection, data cleaning, pre-processing, and data transformation [1, 2]. There may be thousands or millions of records that have to be read and to extract the rules for, but the question is what will happen if ...

A Data Scientist typically performs a number of tedious and time-consuming steps to derive insight from a raw data set. The process usually starts with data ingestion, cleaning, and transformation (e.g. outlier removal, missing value imputation), then proceeds to model building, and finally a presentation of predictions that align with the end-users objectives and preferences. It is a long, com...

The importance of metadata has been broadly referred in the last years, mainly in the field of data warehousing and decision support systems. Contemporarily, in the adjacent field of data quality, several approaches and tools have been set out for the purpose of data profiling and cleaning. However, little effort has been made in order to formally specify metrics and techniques for data quality...

Background: Fear of dental care prevents dental cleaning behavior and increases the prevalence of dental caries. This study was conducted to determine the effect of Trans-Theoretical Model constructs and fear of dental care on the dental cleaning behavior of students. <span style="font-family...

Good data preparation is a key prerequisite to successful data mining [P99]. Conventional wisdom suggests that data preparation takes about 60 to 80% of the time involved in a data mining exercise [R97]. There have been good reviews of the problems associated with data preparation [F97, HS98 and MS97]. However the data cleaning aspect of data preparation is regarded as involving major human inp...

A probabilistic framework is introduced for reducing the inherent uncertainty of trajectory data collected for RFID-monitored objects. The framework represents the position of an object at each instant as a random variable over the set of possible locations. The probability density function of this random variable is initialized according to an a-priori probability distribution, and then revise...