Anonymization Techniques for Preserving Data Quality in Participatory Sensing

2016 ◽  
Author(s):  
Tishna Sabrina ◽  
Manzur Murshed ◽  
Anindya Iqbal
Keyword(s):  
Data Quality ◽  
Participatory Sensing

scholarly journals Improving Data Quality with an Accumulated Reputation Model in Participatory Sensing Systems

Sensors ◽  
2014 ◽  
Vol 14 (3) ◽  
pp. 5573-5594 ◽  
Author(s):  
Ruiyun Yu ◽  
Rui Liu ◽  
Xingwei Wang ◽  
Jiannong Cao
Keyword(s):  
Data Quality ◽  
Participatory Sensing ◽  
Sensing Systems ◽  
Reputation Model

A THEORETICAL FRAMEWORK OF DATA QUALITY IN PARTICIPATORY SENSING: A CASE OF MHEALTH

2015 ◽  
Vol 77 (18) ◽  
Author(s):  
Andita Suci Pratiwi ◽  
Syarulnaziah Anawar
Keyword(s):  
Data Quality ◽  
Theoretical Framework ◽  
Participatory Sensing ◽  
Sensing System

Research in data quality is important in participatory sensing area to provide integrity of the data contributed by participants in mHealth participatory campaign. Many factors can influence the integrity of data contribution. One of major concerns is the possibility of data truthfulness of being uncertain due to incompleteness, imprecision, vagueness, and fragmentary. In participatory sensing, the interpretation of data quality is rather loose and there is no established theoretical framework that represents the elements of data quality in mHealth participatory sensing system.  Therefore, the objective of this paper is two-fold: First, to investigate the variables of data quality that suits participatory sensing system. Second to propose a theoretical framework of data quality in mHealth participatory sensing. The finding will serve a guideline of data quality in mhealth participatory sensing.

A Review of Failure Handling Mechanisms for Data Quality Measures

2012 ◽  
Author(s):  
Nurul A. Emran ◽  
Noraswaliza Abdullah ◽  
Nuzaimah Mustafa
Keyword(s):  
Data Quality ◽  
Quality Measures ◽  
Failure Handling

The Role of Technological Change in the Long-run Global Economy Forecasting

2013 ◽  
pp. 97-116 ◽  
Author(s):  
A. Apokin
Keyword(s):  
Technological Change ◽  
Data Quality ◽  
Long Range ◽  
Global Economy ◽  
World Economy ◽  
Qualitative Approaches ◽  
Long Run ◽  
The World ◽  
National Economies

The author compares several quantitative and qualitative approaches to forecasting to find appropriate methods to incorporate technological change in long-range forecasts of the world economy. A?number of long-run forecasts (with horizons over 10 years) for the world economy and national economies is reviewed to outline advantages and drawbacks for different ways to account for technological change. Various approaches based on their sensitivity to data quality and robustness to model misspecifications are compared and recommendations are offered on the choice of appropriate technique in long-run forecasts of the world economy in the presence of technological change.

Bankruptcy Model Construction and its Limitation in Input Data Quality

2019 ◽  
Vol 10 (2) ◽  
pp. 117-125
Author(s):  
Dana Kubíčková ◽  
◽  
Vladimír Nulíček ◽  
Keyword(s):  
Data Quality ◽  
Normal Distribution ◽  
Input Data ◽  
Model Construction ◽  
The Third ◽  
Third Stage ◽  
Discriminant Analyses ◽  
One Year ◽  
The University ◽  
Multivariate Discriminant

The aim of the research project solved at the University of Finance and administration is to construct a new bankruptcy model. The intention is to use data of the firms that have to cease their activities due to bankruptcy. The most common method for bankruptcy model construction is multivariate discriminant analyses (MDA). It allows to derive the indicators most sensitive to the future companies’ failure as a parts of the bankruptcy model. One of the assumptions for using the MDA method and reassuring the reliable results is the normal distribution and independence of the input data. The results of verification of this assumption as the third stage of the project are presented in this article. We have revealed that this assumption is met only in a few selected indicators. Better results were achieved in the indicators in the set of prosperous companies and one year prior the failure. The selected indicators intended for the bankruptcy model construction thus cannot be considered as suitable for using the MDA method.

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