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The current trend in Big Data analytics and in particular health information technology is toward building sophisticated models, methods and tools for business, operational and clinical intelligence. However, the critical issue of data quality required for these models is not getting the attention it deserves. The purpose of this paper is to highlight the issues of data quality in the context of Big Data health care analytics.

The insights presented in this paper are the results of analytics work that was done in different organizations on a variety of health data sets. The data sets include Medicare and Medicaid claims, provider enrollment data sets from both public and private sources, electronic health records from regional health centers accessed through partnerships with health care claims processing entities under health privacy protected guidelines.

Assessment of data quality in health care has to consider: first, the entire lifecycle of health data; second, problems arising from errors and inaccuracies in the data itself; third, the source(s) and the pedigree of the data; and fourth, how the underlying purpose of data collection impact the analytic processing and knowledge expected to be derived. Automation in the form of data handling, storage, entry and processing technologies is to be viewed as a double-edged sword. At one level, automation can be a good solution, while at another level it can create a different set of data quality issues. Implementation of health care analytics with Big Data is enabled by a road map that addresses the organizational and technological aspects of data quality assurance.

The value derived from the use of analytics should be the primary determinant of data quality. Based on this premise, health care enterprises embracing Big Data should have a road map for a systematic approach to data quality. Health care data quality problems can be so very specific that organizations might have to build their own custom software or data quality rule engines.

Today, data quality issues are diagnosed and addressed in a piece-meal fashion. The authors recommend a data lifecycle approach and provide a road map, that is more appropriate with the dimensions of Big Data and fits different stages in the analytical workflow.

To present a Mobile Scanning System for digitizing three‐dimensional (3D) models of real‐world terrain.

A combination of sensors (video, laser range, positioning, orientation) is placed on a mobile platform, which moves past the scene to be digitized. Data fusion from the sensors is performed to construct an accurate 3D model of the target environment.

The developed system can acquire accurate models of real‐world environments in real time, at resolutions suitable for a variety of tasks.

Treating the individual subsystems of the mobile scanning system independently yields a robust system that can be easily reconfigured on the fly for a variety of scanning scenarios.

The purpose of this paper is to observe the effect of cheap cow horn ash particles (CHAp) filler as a possible replacement for expensive fillers on the mechanical properties of polyester-banana peduncle fibre (BPF) composites were evaluated using standard procedures.

Composite was developed using CHAp as a filler component, polyester resin and BPF, with the filler of varying percentage weights (5%, 10%, 15% and 20%), at particle sizes of 125 µm, using hand lay-up technique. The physicochemical properties of CHAp were examined through x-ray fluorescence (XRF), X-ray diffractometer (XRD), transmission electron microscopy, scanning electron microscope, energy dispersion spectrometric analysis (EDS) and density. Mechanical properties of the developed composites were also examined.

The results showed that the tensile properties and impact strength of the composites reduced marginally with the incorporation of the cow horn ash particle as a filler. However, the flexural strength of the composites increased progressively with the incorporation of BPF as the fibre loading increased. The major constituents of CHAp were CaO from XRF study, calcite (CaCO₃) from XRD study and Ca in EDS study in accordance with the analytical parameter, which showed a major component of calcium. The high value of CaCO₃ in CHAp improved flexural and impact strengths of the composites. CHAp presented around solid and irregular shape particle characteristic of most fillers with an average particle size of 98.13 nm. The tensile and flexural strengths of the polyester matrix composites obtained at 7.5% BPF: 7.5% CHAp was 117.87 MPa depicting satisfactory mechanical characteristics.

Generally, cow horn ash particle exhibited adequate filler component potential in composite production in keeping with its property effects on the mechanical properties of polyester-BPF composites.