Abstract Covariate balance is a conventional key diagnostic for methods estimating causal effects from observational studies. Recently, there is an emerging interest in directly incorporating covariate balance in the estimation. We study a recently proposed entropy maximization method called...

Abstract Propensity score weighting is a tool for causal inference to adjust for measured confounders in observational studies. In practice, data often present complex structures, such as clustering, which make propensity score modeling and estimation challenging. In addition, for clustered...

Purpose – The purpose of this paper is to develop customer satisfaction indices for the services provided by inspectors in certain departments of Al‐Ain Municipality, the United Arab Emirates. Design/methodology/approach – The methodology is based on customer satisfaction models with...

Ambient intelligence is an emerging platform that provides advances in sensors and sensor networks, pervasive computing, and artificial intelligence to capture the real time climate data. This result continuously generates several exabytes of unstructured sensor data and so it is often called...

The term Ambient Intelligence (AmI) encompasses other technologies such as ubiquitous communication, pervasive computing and ubiquitous computing. Hospitals can improve their working by monitoring the health of the patients and performing automatic analysis of various and health parameters...

The technological revolution integrating multiple information sources and extension of computer science in different sectors led to the explosion of the data quantities, which reflects the scaling of vo-lumes, numbers and types. These massive increases have resulted in the development of new...

This article describes how the time taken to execute a query and return the results, increase exponentially as the data size increases, leading to more waiting times of the user. Hadoop with its distributed processing capability is considered as an efficient solution for processing such large...

The Big Data, due to its complicated and diverse nature, poses a lot of challenges for extracting meaningful observations. This sought smart and efficient algorithms that can deal with computational complexity along with memory constraints out of their iterative behavior. This issue may be...

This article gives insight in a running dissertation at the University in Potsdam. Point of discussion is the spatial and temporal distribution of emergencies of German fire brigades that have not sufficiently been scientifically examined. The challenge is seen in Big Data: enormous amounts of...

In the era of the Internet of Things and Big Data modern cars have become mobile electronic systems or computers on wheels. Car sensors record a multitude of car and traffic related data as well as environmental parameters outside the vehicle. The data recorded are spatio-temporal by nature...