Topic
Data aggregator
About: Data aggregator is a research topic. Over the lifetime, 2615 publications have been published within this topic receiving 40265 citations.
Papers published on a yearly basis
Papers
More filters
TL;DR: A paper based on the survey of UWSN with data aggregation to highlight its benefits and limitations and to build interest of research fraternity towards future challenges identified on the basis of survey of existing approaches.
64 citations
TL;DR: This study presents an up to date survey of major contributions to the security solutions in data aggregation which mainly use soft computing techniques, including fuzzy logic, swarm intelligence, genetic algorithm and neural networks.
Abstract: In wireless sensor networks (WSN), data aggregation using soft computing methods is a challenging issue because of the security factors. When a node is compromised, it is easy for an adversary to inject false data and mislead the aggregator to accept false readings. Therefore there is a need for secure data aggregation. Although sufficient works on the survey of data aggregation in WSNs are done, it seems less satisfactory in terms of maintaining a secured data aggregation, and measuring accurate values. This study presents an up to date survey of major contributions to the security solutions in data aggregation which mainly use soft computing techniques. Here, classification of protocols is done according to the soft computing technique as: fuzzy logic, swarm intelligence, genetic algorithm and neural networks. Accuracy, energy consumption, cost reduction and security measures are the metrics used for the classification. Finally, the authors provide a comparative study of all aggregation techniques.
63 citations
TL;DR: This paper analyzes the data aggregation problem in CI construction from the point of view of information loss and proposes distance-based and entropy-based aggregation models for constructing CIs based on the ''minimum information loss'' principle.
Abstract: Composite indicators (CIs) have been widely accepted as a useful tool for performance comparisons, public communication and decision support in a wide spectrum of fields, e.g. economy, environment and knowledge/information/innovation. The quality and reliability of a CI depend heavily on the underlying construction scheme where data aggregation is a major step. This paper analyzes the data aggregation problem in CI construction from the point of view of information loss. Based on the ''minimum information loss'' principle, the distance-based and entropy-based aggregation models for constructing CIs are presented. The entropy-based aggregation model has also been extended to deal with qualitative data. It is shown that the proposed aggregation models have close relationships with several popular MCDA aggregation methods in CI construction, although our proposed models seem to be more flexible while more complex in application. Two case studies are presented to illustrate the use of the proposed aggregation models.
63 citations
TL;DR: The results indicate that all of these geographic information characteristics have significant impacts on decision performance, and many interactions are present among the factors.
Abstract: Geographic information systems (GIS) have taken on an increasingly important role supporting decision making in many organizations. GIS have been used to support a breadth of tasks including oil and mineral exploration, facility location, logistics support, and facilities management decisions. The effectiveness of GIS as a decision support tool comes primarily from the visual display of data in the form of maps. When presenting information as a geographic map, the level of data aggregation potentially affects aspects of task complexity such as information load and the potential for pattern recognition by the user. Other task attributes expected to be related to data aggregation effects include problem size, the degree of data dispersion, and users' spatial orientation skills.
We conducted an experiment to study these effects and their interactions. Subjects used a GIS including map-based information characterized by different levels of problem size, data dispersion, and data aggregation. Spatial orientation skill was examined as a covariate in the experimental treatments. The results indicate that all of these geographic information characteristics have significant impacts on decision performance. Moreover, many interactions are present among the factors. We evaluate these interactions in order to derive implications for practice and for future research.
63 citations
TL;DR: An optimal method based on linear programming to add noise to individual locations that preserves the distribution of a disease is developed and applied to patients in New York County, New York, showing that privacy is guaranteed while moving patients 25—150 times less than aggregation by zip code.
Abstract: Datasets describing the health status of individuals are important for medical research but must be used cautiously to protect patient privacy. For patient data containing geographical identifiers, the conventional solution is to aggregate the data by large areas. This method often preserves privacy but suffers from substantial information loss, which degrades the quality of subsequent disease mapping or cluster detection studies. Other heuristic methods for de-identifying spatial patient information do not quantify the risk to individual privacy. We develop an optimal method based on linear programming to add noise to individual locations that preserves the distribution of a disease. The method ensures a small, quantitative risk of individual re-identification. Because the amount of noise added is minimal for the desired degree of privacy protection, the de-identified set is ideal for spatial epidemiological studies. We apply the method to patients in New York County, New York, showing that privacy is guaranteed while moving patients 25—150 times less than aggregation by zip code.
63 citations