Big Data Optimization : Algorithmic Framework for Data Analysis Guided by Semantics

Linked data, enterprise data, data models, big data streams, neural networks, data infrastructures, deep learning, data mining, web of data, signal processing, smart cities,

Sources of data for analysis included study-level data from the Cohorts for Heart and Aging Research in Genomic Epidemiology Birth Gene (CHARGE-BIG) Study (49 studies, n = 180

The programming language most widely used in data mining is Python, primarily because of its ver- satility and the number of libraries that are available for data processing,

The convergence of the optimization procedure for both cavity I and II is more clearly appreciated in Fig. It can be deduced from these data that the convergence is achieved in

Optimization of the dynamic data types to reduce the number of memory accesses and memory footprint of the dynamic data structures used in the application (e.g., linked

By means of the evolutionary multi-objective optimisation approach presented in Chapter 6, artificial data sets are generated to cover reachable regions in different dimensions of

 The expansionary monetary policy measures have had a negative impact on net interest margins both via the reduction in interest rates and –less powerfully- the flattening of the

However, missing data imputation in networking explores the ability of missing data imputation algorithms when the information was lost due to communication problems,