Big data, big problems: causation, confounders and cohorts

Big Data: From correlation to causation

Background Big data analytics can contribute to fruitful future scientific inquiries as they allow researchers to investigate new underexplored scientific questions that could not be studied before and to dive deeper into existing scientific questions in order to elicit better answers. In this short paper, I outline the opportunities that arise from leveraging existing econometric methods in co...

Big Data: Promises and Problems

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...

Big Data: Understanding Big Data

Steve Jobs, one of the greatest visionaries of our time was quoted in 1996 saying “a lot of times, people don’t know what they want until you show it to them”[38] indicating he advocated products to be developed based on human intuition rather than research. With the advancements of mobile devices, social networks and the Internet of Things (IoT) enormous amounts of complex data, both structure...

EVALUATING EFFICIENCY OF BIG-BANG BIG-CRUNCH ALGORITHM IN BENCHMARK ENGINEERING OPTIMIZATION PROBLEMS

Engineering optimization needs easy-to-use and efficient optimization tools that can be employed for practical purposes. In this context, stochastic search techniques have good reputation and wide acceptability as being powerful tools for solving complex engineering optimization problems. However, increased complexity of some metaheuristic algorithms sometimes makes it difficult for engineers t...