Data Cleaning and Semantic Improvement in Biological Databases

Data Cleaning and Semantic Improvement in Biological Databases

Public genomic and proteomic databases can be affected by a variety of errors. These errors may involve either the description or the meaning of data (namely, syntactic or semantic errors). We focus our analysis on the detection of semantic errors, in order to verify the accuracy of the stored information. In particular, we address the issue of data constraints and functional dependencies among...

Semantic Improvement of Deductive Databases

We assume that there is a deductive database hD; C i, where D is a set of stratiied rules and facts and C a set of constraints. Querying a database means returning all ground instances of the query which are true in the well deened standard model MD induced by D. Given a query Q it is transformed into a new query Q 0 , such that Q 0 will be less costly to be evaluated than Q, and such that Q 0 ...

Quantitative Data Cleaning for Large Databases

Data collection has become a ubiquitous function of large organizations – not only for record keeping, but to support a variety of data analysis tasks that are critical to the organizational mission. Data analysis typically drives decision-making processes and efficiency optimizations, and in an increasing number of settings is the raison d’etre of entire agencies or firms. Despite the importan...

Data Cleaning and Matching of Institutions in Bibliographic Databases

Bibliographic databases are very important for a variety of tasks for governments, academic institutions and businesses. These include assessing research output of institutions, performance evaluation of academics and compiling university rankings. However, incorrect or incomplete data in such databases can compromise any analysis and lead to poor decisions and financial loss. In this paper we ...

Correlation-Based Methods for Biological Data Cleaning