Uncertainty reduction in big data catalogue for information product quality evaluation

Big Data Quality: From Content to Context

Over the last 20 years, and particularly with the advent of Big Data and analytics, the research area around Data and Information Quality (DIQ) is still a fast growing research area. There are many views and streams in DIQ research, generally aiming at improving the effectiveness of decision making in organizations. Although there are a lot of researches aimed at clarifying the role of BIG data...

Learning ELM-Tree from big data based on uncertainty reduction

A challenge in big data classification is the design of highly parallelized learning algorithms. One solution to this problem is applying parallel computation to different components of a learning model. In this paper, we first propose an extreme learning machine tree (ELM-Tree) model based on the heuristics of uncertainty reduction. In the ELM-Tree model, information entropy and ambiguity are ...

Near-Real-Time OGC Catalogue Service for Geoscience Big Data

Geoscience data are typically big data, and they are distributed in various agencies and individuals worldwide. Efficient data sharing and interoperability are important for managing and applying geoscience data. The OGC (Open Geospatial Consortium) Catalogue Service for the Web (CSW) is an open interoperability standard for supporting the discovery of geospatial data. In the past, regular OGC ...

Understanding Big Data Quality for Maximum Information Usability

Perspectives of Big Data Quality in Smart Service Ecosystems (Quality of Design and Quality of Conformance)

Despite the increasing importance of data and information quality, current research related to Big Data quality is still limited. It is particularly unknown how to apply previous data quality models to Big Data. In this paper we review Big Data quality research from several perspectives and apply a known quality model with its elements of conformance to specification and design in the context o...