Inferring Latent Patterns in Air Quality from Urban Big Data

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...

From Data Quality to Big Data Quality

This article investigates the evolution of data quality issues from traditional structured data managed in relational databases to Big Data. In particular, The paper examines the nature of the relationship between Data Quality and several research coordinates that are relevant in Big Data, such as the variety of data types, data sources and application domains, focusing on maps, semistructured ...

Inferring Latent Structure From Mixed Real and Categorical Relational Data

We consider analysis of relational data (a matrix), in which the rows correspond to subjects (e.g., people) and the columns correspond to attributes. The elements of the matrix may be a mix of real and categorical. Each subject and attribute is characterized by a latent binary feature vector, and an inferred matrix maps each row-column pair of binary feature vectors to an observed matrix elemen...

Urban Air Quality Management

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...