Modern business intelligence systems depend highly on high quality data. The core of data quality management is to identify all possible sources of data quality problems. To achieve this goal, an extensive metadata infrastructure is the most promising solution. Through theoretical metadata model...

The quality of public data made available by governments is a crucial factor in achieving greater transparency and also for such data to be used effectively by society. Several models of quality evaluation of open data portals have been proposed in recent years. The vast majority of these models...

Maintaining a stable data warehouse becomes quite a challenge if discipline is not applied to code development, code changes, code performance, system resource usage and configuration of integration specification. As the size of the data warehouse increases the value it brings to an organization...

This paper presents ongoing research conducted through collaboration between the University of Arkansas at Little Rock and the Arkansas Department of Education to develop an entity resolution and identity management system. The process includes a multi-phase approach consisting of data-quality...

Most large enterprises requiring operational business processes utilize several thousand instances of legacy, upgraded, cloud-based, and/or acquired information management applications. With the advent of Big Data, Business Intelligence (BI) systems, receive unconsolidated data from a wide-range...

The analysis of inpatient comorbidities is important for hospital management, epidemiological studies, and health services research and planning. This paper aims to study the evolution of coded comorbidities in a nationwide administrative database. Specifically, data from Portuguese hospitals...

The data quality assessment is a critical task in Intensive Care Units (ICUs). In the ICUs the patients are continuously monitored and the values are collected in real-time through data streaming processes. In the case of ventilation, the ventilator is monitoring the patient respiratory system...

Ensuring data quality in Linked Open Data is a complex process as it consists of structured information supported by models, ontologies and vocabularies and contains queryable endpoints and links. In this paper, the authors first propose an objective assessment framework for Linked Data quality....

In this article, the authors design two metric sets considering Richness and Correctness based on a quasi-formal conceptual representation. They also design a novel metric set on overlapped instances of different KBs to make the metric results comparable. Finally, they use random sampling...

In certain situations, syntactically valid, but incorrect, data entered into a database can result in near-immediate, catastrophic financial losses for an organization. Examples include: omitting zeros in prices of goods on e-commerce sites; and financial fraud where data is directly entered...