Data Completeness Measures

Missing values compensation in duplicates detection using hot deck method

Duplicate record is a common problem within data sets especially in huge volume databases. The accuracy of duplicate detection determines the efficiency of duplicate removal process. However, duplicate detection has become more challenging due to the presence of missing values within the records where during the clustering and matching process, missing values can cause records deemed similar to be inserted into the wrong group, hence, leading to undetected duplicates. In this paper, duplicate detection improvement was proposed despite the presence of missing values within a data set through Duplicate Detection within the Incomplete Data set (DDID) method. The missing values were hypothetically added to the key attributes of three data sets under study, using an arbitrary pattern to simulate both complete and incomplete data sets. The results were analyzed, then, the performance of duplicate detection was evaluated by using the Hot Deck method to compensate for the missing values in the key attributes. It was hypothesized that by using Hot Deck, duplicate detection performance would be improved. Furthermore, the DDID performance was compared to an early duplicate detection method namely DuDe, in terms of its accuracy and speed. The findings yielded that even though the data sets were incomplete, DDID was able to offer a better accuracy and faster duplicate detection as compared to DuDe. The results of this study offer insights into constraints of duplicate detection within incomplete data sets.

Relocation of public healthcare network of a large city in the event of a major earthquake: a combined methodological analysis

Purpose

The aim of this research is to improve the responsiveness of the healthcare network of a large city to a major earthquake, by applying a combined methodology to reduce human suffering and death.

Design/methodology/approach

Scenario analysis, a non-linear programming (NLP) model, and the analytical network process are sequentially applied to find the “best location pattern”.

Findings

When considering the occurrence of major earthquakes in cities with high population density, as a rule of thumb, the location of healthcare facilities should prioritize areas characteristically overcrowded and/or that were built based on poor standards of seismic resistance.

Research limitations/implications

The proposed research design does not include a cost criterion in the set of decision variables involved. Furthermore, the results derived from the NLP-model are restricted by the input simulation data.

Practical implications

The performance of the “best location pattern” is compared with the current location of healthcare facilities in terms of their distances to the affected zones. Metropolis areas worldwide with similar conditions to the city under consideration could be benefited from applying the general methodology for relocation of healthcare facilities described in this research.

Originality/value

This research implements a diverse combination of methodologies to examine the problem of relocating of healthcare facilities in a large city in the wake of an assumed earthquake. In addition, to the best of authors' knowledge, this is the first study of its kind that proposes improvements in the responsiveness of the healthcare facilities' network in the city in question.