Investment in information technology (IT) has been growing rapidly and one key reason for investing in IT is to improve information quality (IQ). Timeliness is an important IQ dimension that often needs to be improved for decision making. Especially in the era of big data, timeliness becomes more valued because of challenges of massive data size and high dimensionality. Many financial analyses ...

Along with data being increasingly viewed as a critical asset and the rise of “Big Data” with its potential uses, research of data and information quality has grown in importance to become a distinct area within information systems. Particularly with the ever more rapid changes in technology and adoption of datadriven decision-making, at this juncture it is important to take stock of data and i...

Intelligent transport system (ITS) has large potentials on road safety applications as well as nonsafety applications. One of the big challenges for ITS is on the reliable and cost-effective vehicle communications due to the large quantity of vehicles, high mobility, and bursty traffic from the safety and non-safety applications. In this paper, we investigate the use of dedicated shortrange com...

This paper reflects on six years developing semantic data quality tools and curation systems for both largescale social sciences data collection and a major web of data hub. This experience has led the author to believe in using organisational value as a mechanism for automation of data quality management to deal with Big Data volumes and variety. However there are many challenges in developing...

Data governance is a subject that is becoming increasingly important in business and government. In fact, good governance data allows improved interactions between employees of one or more organizations. Data quality represents a great challenge because the cost of non-quality can be very high. Therefore the use of data quality becomes an absolute necessity within an organization. To improve th...

In Big data era, information integration often requires abundant data extracted from massive data sources. Due to a large number of data sources, data source selection plays a crucial role in information integration, since it is costly and even impossible to access all data sources. Data Source selection should consider both efficiency and effectiveness issues. For efficiency, the approach shou...

The quality aspects of spatial data are very important in the decision-making process. However, the quality inspection of spatial data is still dependent on manual checking, and there is an urgent need to develop an automatic or semi-automatic generic system for spatial data quality inspection. In this paper, we present a general framework that automatically copes with spatial data quality insp...