Data collecting is necessary to some organizations such as nuclear power plants and earthquake bureaus, which have very small databases. Traditional data collecting is to obtain necessary data from internal and external data-sources and join all data together to create a homogeneous huge database. Because collected data may be untrusty, it can disguise really useful patterns in data. In this paper, breaking away traditional data collecting mode that deals with internal and external data equally, we argue that the first step for utilizing external data is to identify quality data in data-sources for given mining tasks. Pre- and post-analysis techniques are thus advocated for generating quality data. <br /
While noting the importance of data quality, existing process mining methodologies (i) do not provid...
A big organization may have multiple branches spread across different locations. Processing of data ...
Large and over the years grown databases are a persistent concern in the field of data quality. Data...
Data are important for making decisions. However, the quality of the data affects the quality of dec...
We are living in a world of information abundance, surplus, and access. We have technologies to acqu...
Large and over the years grown databases are a persistent concern in the field of data quality. Data...
Large and over the years grown databases are a persistent concern in the field of data quality. Data...
Modern organisations consider data to be their lifeblood. The potential benefits of data-driven anal...
International audienceAs data types and data structures change to keep up with evolving technologies...
The purpose of this article is to present modern approaches to data storage and processing, as well ...
Abstract: Independent from the concrete definition of the term “data qual-ity ” consistency always p...
Nowadays, activities and decisions making in an organization is based on data and information obtain...
While noting the importance of data quality, existing process mining methodologies (i) do not provid...
Abstract The quality of discovered association rules is commonly evaluated by interestingness measur...
Pre-processing data on the dataset is often neglected, but it is an important step in the data minin...
While noting the importance of data quality, existing process mining methodologies (i) do not provid...
A big organization may have multiple branches spread across different locations. Processing of data ...
Large and over the years grown databases are a persistent concern in the field of data quality. Data...
Data are important for making decisions. However, the quality of the data affects the quality of dec...
We are living in a world of information abundance, surplus, and access. We have technologies to acqu...
Large and over the years grown databases are a persistent concern in the field of data quality. Data...
Large and over the years grown databases are a persistent concern in the field of data quality. Data...
Modern organisations consider data to be their lifeblood. The potential benefits of data-driven anal...
International audienceAs data types and data structures change to keep up with evolving technologies...
The purpose of this article is to present modern approaches to data storage and processing, as well ...
Abstract: Independent from the concrete definition of the term “data qual-ity ” consistency always p...
Nowadays, activities and decisions making in an organization is based on data and information obtain...
While noting the importance of data quality, existing process mining methodologies (i) do not provid...
Abstract The quality of discovered association rules is commonly evaluated by interestingness measur...
Pre-processing data on the dataset is often neglected, but it is an important step in the data minin...
While noting the importance of data quality, existing process mining methodologies (i) do not provid...
A big organization may have multiple branches spread across different locations. Processing of data ...
Large and over the years grown databases are a persistent concern in the field of data quality. Data...