A Survey of Bayesian Statistical Approaches for Big Data

The modern era is characterised as an era of information or Big Data. This has motivated a huge literature on new methods for extracting information and insights from these data. A natural question is how these approaches differ from those that were available prior to the advent of Big Data. We present a review of published studies that present Bayesian statistical approaches specifically for Big Data and discuss the reported and perceived benefits of these approaches. We conclude by addressing the question of whether focusing only on improving computational algorithms and infrastructure will be enough to face the challenges of Big Data

The Turn of the Month Anomaly in Amman Stock Exchange: Evidence and Implications

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A step foreword historical data governance in information systems

From major companies and organizations to smaller ones around the world, databases are now one of the leading technologies for supporting most of organizational information assets. Their evolution allows us to store almost anything often without determining if it is in fact relevant to be saved or not. Hence, it is predictable that most information systems sooner or later will face some data management problems and consequently the performance problems that are unavoidably linked to. In this paper we tackle the data management problem with a proposal for a solution using machine-learning techniques, trying to understand in an intelligent manner the data in a database, according to its relevance for their users. Thus, identifying what is really important to who uses the system and being able to distinguish it from the rest of the data is a great way for creating new and efficient measures for managing data in an information system.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013