An incident information management framework based on data integration, data mining, and multi-criteria decision making

Peng, Yi; Zhang, Yong; Tang, Yu; Li, Shiming

doi:10.1016/j.dss.2010.11.025

Cited by 144 publications

(74 citation statements)

References 38 publications

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“…This problem deals with a setting where a set of parties with private inputs wishes to jointly compute some function of their inputs. This joint computation should have the property that the parties learn the correct output and nothing else, even if some of the parties maliciously collude to obtain more information [7]. Clearly, a protocol is needed to solve privacy-preserving distributed data mining problems.…”

Section: IImentioning

confidence: 99%

A Case Study on Mining Security Issues & Remedies in Privacy Preservation

Kumar¹

2017

IJRASET

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The development in data mining technology brings serious threat to the individual information. The objective of privacy preserving data mining (PPDM) is to safeguard the sensitive information contained in the data. The unwanted disclosure of the sensitive information may happen during the process of data mining results. In this study we identify four different types of users involved in mining application i.e. data source provider, data receiver, data explorer and determiner decision maker. We differentiate each type of user's responsibilities and privacy concerns with respect to sensitive information. We'd like to provide useful insights into the study of privacy preserving data mining. This paper presents a comprehensive noise addition technique for protecting individual privacy in a data set used for classification, while maintaining the data quality. We add noise to all attributes, both numerical and categorical, and both to class and non-class, in such a way so that the original patterns are preserved in a perturbed data set. Our technique is also capable of incorporating previously proposed noise addition techniques that maintain the statistical parameters of the data set, including correlations among attributes. Thus the perturbed data set may be used not only for classification but also for statistical analysis.

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Section: IImentioning

confidence: 99%

A Case Study on Mining Security Issues & Remedies in Privacy Preservation

Kumar¹

2017

IJRASET

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“…City incident information management system can integrate data mining methods to provide a comprehensive assessment of the impact of natural disasters on the agricultural production and rank disaster affected areas objectively and assist governments in disaster preparation and resource allocation [132].…”

Section: Data Mining In City Governancementioning

confidence: 99%

Data Mining for the Internet of Things: Literature Review and Challenges

Chen

Deng

Wan

et al. 2015

International Journal of Distributed Sensor Networks

405

215

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The massive data generated by the Internet of Things (IoT) are considered of high business value, and data mining algorithms can be applied to IoT to extract hidden information from data. In this paper, we give a systematic way to review data mining in knowledge view, technique view, and application view, including classification, clustering, association analysis, time series analysis and outlier analysis. And the latest application cases are also surveyed. As more and more devices connected to IoT, large volume of data should be analyzed, the latest algorithms should be modified to apply to big data. We reviewed these algorithms and discussed challenges and open research issues. At last a suggested big data mining system is proposed.

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“…system [13] is a Decision Support System (DSS, [14]) for crisis management that exploits information retrieved from a large quantity and several types of data sources available in a target geographical area, in order to detect critical situations and command the corresponding reactions including guiding rescue teams or delivering emergency information to the population via the Secure! app.…”

Section: Application On a Crisis Management Systemmentioning

confidence: 99%

“…Previous studies and frameworks such as [12], [14] tackle the management of crisis data coming from different heterogeneous sources, trying to figure out common features and merging them into a format that can be presented to the crisis management operator or to authorities. The authorities or the operators that want to i) interpret this large amount of near real-time data, or ii) analyse them for a-posteriori analysis, cannot examine each individual available information.…”

Section: Introductionmentioning

confidence: 99%

Presenting the Proper Data to the Crisis Management Operator: A Relevance Labelling Strategy

Zoppi

Ceccarelli

Lollini

et al. 2016

2016 IEEE 17th International Symposium on High Assurance Systems Engineering (HASE)

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Abstract-The large availability of smart portable devices and the growing interest in developing Internet of Things (IoT) oriented software components make several heterogeneous data available for analysis purposes. In the context of Crisis Management Systems, this means that people owning mobile devices when involved in natural disasters or terroristic attacks may be considered information sources as the classical ones, e.g., sensors or surveillance cameras. Including the information from the citizens in the situational analysis processes comes with two main issues that need to be addressed: i) the source could deliver wrong data (voluntarily or by mistake) that damage the integrity and the correctness of the analysis, and ii) a significant amount of heterogeneous data need to be selected, filtered and aggregated, to provide to the operator a real-time snapshot of the situation depicted using only credible and relevant information. In this paper, we define and implement a relevance labeling strategy able to process information coming from heterogeneous sources aimed at crisis situations and to provide to the human operator all the details he needs. We include provisions for detecting and removing redundancies and misleading data that can slow down or compromise the process and the a-posteriori analysis. The filtering strategy is last applied to events collected for the Secure! crisis management service-based system, showing its application to three scenarios related to real crisis situations happened in the last year.

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An incident information management framework based on data integration, data mining, and multi-criteria decision making

Cited by 144 publications

References 38 publications

A Case Study on Mining Security Issues & Remedies in Privacy Preservation

A Case Study on Mining Security Issues & Remedies in Privacy Preservation

Data Mining for the Internet of Things: Literature Review and Challenges

Presenting the Proper Data to the Crisis Management Operator: A Relevance Labelling Strategy

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