Leveraging cross-media analytics to detect events and mine opinions for emergency management

2017 ◽  
Vol 41 (4) ◽  
pp. 487-506 ◽  
Author(s):  
Wei Xu ◽  
Lingyu Liu ◽  
Wei Shang
Keyword(s):  
Decision Support ◽  
Emergency Management ◽  
Real Time ◽  
Event Detection ◽  
Opinion Mining ◽  
Time Response ◽  
Content Type ◽  
Cross Media ◽  
Emergency Event ◽  
Emergency Events

Purpose Timely detection of emergency events and effective tracking of corresponding public opinions are critical in emergency management. As media are immediate sources of information on emergencies, the purpose of this paper is to propose cross-media analytics to detect and track emergency events and provide decision support for government and emergency management departments. Design/methodology/approach In this paper, a novel emergency event detection and opinion mining method is proposed for emergency management using cross-media analytics. In the proposed approach, an event detection module is constructed to discover emergency events based on cross-media analytics, and after the detected event is confirmed as an emergency event, an opinion mining module is used to analyze public sentiments and then generate public sentiment time series for early warning via a semantic expansion technique. Findings Empirical results indicate that a specific emergency can be detected and that public opinion can be tracked effectively and efficiently using cross-media analytics. In addition, the proposed system can be used for decision support and real-time response for government and emergency management departments. Research limitations/implications This paper takes full advantage of cross-media information and proposes novel emergency event detection and opinion mining methods for emergency management using cross-media analytics. The empirical analysis results illustrate the efficiency of the proposed method. Practical implications The proposed method can be applied for detection of emergency events and tracking of public opinions for emergency decision support and governmental real-time response. Originality/value This research work contributes to the design of a decision support system for emergency event detection and opinion mining. In the proposed approaches, emergency events are detected by leveraging cross-media analytics, and public sentiments are measured using an auto-expansion of the domain dictionary in the field of emergency management to eliminate the misclassification of the general dictionary and to make the quantization more accurate.

Multidimensional mining of public opinion in emergency events

2020 ◽  
Vol 38 (3) ◽  
pp. 545-560
Author(s):  
Qingqing Zhou ◽  
Ming Jing
Keyword(s):  
Public Opinion ◽  
Opinion Mining ◽  
Content Type ◽  
The Public ◽  
Fine Grained ◽  
Multiple Dimensions ◽  
Emergency Event ◽  
Comprehensive Information ◽  
Opinion Expression ◽  
Emergency Events

Purpose The suddenness, urgency and social publicity of emergency events lead to great impacts on public life. The deep analysis of emergency events can provide detailed and comprehensive information for the public to get trends of events timely. With the development of social media, users prefer to express opinions on emergency events online. Thus, massive public opinion information of emergencies has been generated. Hence, this paper aims to conduct multidimensional mining on emergency events based on user-generated contents, so as to obtain finer-grained results. Design/methodology/approach This paper conducted public opinion analysis via fine-grained mining. Specifically, public opinion about an emergency event was collected as experimental data. Secondly, opinion mining was conducted to get users’ opinion polarities. Meanwhile, users’ information was analysed to identify impacts of users’ characteristics on public opinion. Findings The experimental results indicate that public opinion is mainly negative in emergencies. Meanwhile, users in developed regions are more active in expressing opinions. In addition, male users, especially male users with high influence, are more rational in public opinion expression. Originality/value To the best of the authors’ knowledge, this is the first research to identify public opinion in emergency events from multiple dimensions, which can get in-detail differences of users’ online expression.

An approach to evaluation of emergency plans for unconventional emergency events based on soft fuzzy rough set

Kybernetes ◽  
2016 ◽  
Vol 45 (3) ◽  
pp. 461-473 ◽  
Author(s):  
Sun Bingzhen ◽  
Ma Weimin
Keyword(s):  
Decision Making ◽  
Emergency Management ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Management Decision ◽  
Fuzzy Rough Set ◽  
Emergency Plans ◽  
Content Type ◽  
Emergency Events

Purpose – The purpose of this paper is to present a new method for evaluation of emergency plans for unconventional emergency events by using the soft fuzzy rough set theory and methodology. Design/methodology/approach – In response to the problems of insufficient risk identification, incomplete and inaccurate data and different preference of decision makers, a new model for emergency plan evaluation is established by combining soft set theory with classical fuzzy rough set theory. Moreover, by combining the TOPSIS method with soft fuzzy rough set theory, the score value of the soft fuzzy lower and upper approximation is defined for the optimal object and the worst object. Finally, emergency plans are comprehensively evaluated according to the soft close degree of the soft fuzzy rough set theory. Findings – This paper presents a new perspective on emergency management decision making in unconventional emergency events. Also, the paper provides an effective model for evaluating emergency plans for unconventional events. Originality/value – The paper contributes to decision making in emergency management of unconventional emergency events. The model is useful for dealing with decision making with uncertain information.

A proactive decision making framework for condition-based maintenance

2015 ◽  
Vol 115 (7) ◽  
pp. 1225-1250 ◽  
Author(s):  
Alexandros Bousdekis ◽  
Babis Magoutas ◽  
Dimitris Apostolou ◽  
Gregoris Mentzas
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Literature Review ◽  
Real Time ◽  
Condition Based Maintenance ◽  
Sensor Data ◽  
Extensive Literature ◽  
Decision Space ◽  
Content Type ◽  
Extensive Literature Review

Purpose – The purpose of this paper is to perform an extensive literature review in the area of decision making for condition-based maintenance (CBM) and identify possibilities for proactive online recommendations by considering real-time sensor data. Based on these, the paper aims at proposing a framework for proactive decision making in the context of CBM. Design/methodology/approach – Starting with the manufacturing challenges and the main principles of maintenance, the paper reviews the main frameworks and concepts regarding CBM that have been proposed in the literature. Moreover, the terms of e-maintenance, proactivity and decision making are analysed and their potential relevance to CBM is identified. Then, an extensive literature review of methods and techniques for the various steps of CBM is provided, especially for prognosis and decision support. Based on these, limitations and gaps are identified and a framework for proactive decision making in the context of CBM is proposed. Findings – In the proposed framework for proactive decision making, the CBM concept is enriched in the sense that it is structured into two components: the information space and the decision space. Moreover, it is extended in a way that decision space is further analyzed according to the types of recommendations that can be provided. Moreover, possible inputs and outputs of each step are identified. Practical implications – The paper provides a framework for CBM representing the steps that need to be followed for proactive recommendations as well as the types of recommendations that can be given. The framework can be used by maintenance management of a company in order to conduct CBM by utilizing real-time sensor data depending on the type of decision required. Originality/value – The results of the work presented in this paper form the basis for the development and implementation of proactive Decision Support System (DSS) in the context of maintenance.

Conceptual Risk-Based Decision Support Methodology for Improved Near Real-Time Response to WDS Failures

2009 ◽  
Author(s):  
J. Bicik ◽  
C. Makropoulos ◽  
D. Joksimović ◽  
Z. Kapelan ◽  
M. S. Morley ◽  
...  
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Time Response

scholarly journals Data Challenges With Real-Time Safety Event Detection And Clinical Decision Support (Preprint)

2018 ◽  
Author(s):  
Eric Steven Kirkendall ◽  
Yizhao Ni ◽  
Todd Lingren ◽  
Matthew Leonard ◽  
Eric S Hall ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Real Time ◽  
Clinical Decision Support ◽  
Event Detection ◽  
Infusion Pump ◽  
Clinical Decision ◽  
Time Data ◽  
Real Time Data ◽  
Safety Event

BACKGROUND The continued digitization and maturation of health care information technology has made access to real-time data easier and feasible for more health care organizations. With this increased availability, the promise of using data to algorithmically detect health care–related events in real-time has become more of a reality. However, as more researchers and clinicians utilize real-time data delivery capabilities, it has become apparent that simply gaining access to the data is not a panacea, and some unique data challenges have emerged to the forefront in the process. OBJECTIVE The aim of this viewpoint was to highlight some of the challenges that are germane to real-time processing of health care system–generated data and the accurate interpretation of the results. METHODS Distinct challenges related to the use and processing of real-time data for safety event detection were compiled and reported by several informatics and clinical experts at a quaternary pediatric academic institution. The challenges were collated from the experiences of the researchers implementing real-time event detection on more than half a dozen distinct projects. The challenges have been presented in a challenge category-specific challenge-example format. RESULTS In total, 8 major types of challenge categories were reported, with 13 specific challenges and 9 specific examples detailed to provide a context for the challenges. The examples reported are anchored to a specific project using medication order, medication administration record, and smart infusion pump data to detect discrepancies and errors between the 3 datasets. CONCLUSIONS The use of real-time data to drive safety event detection and clinical decision support is extremely powerful, but it presents its own set of challenges that include data quality and technical complexity. These challenges must be recognized and accommodated for if the full promise of accurate, real-time safety event clinical decision support is to be realized.

scholarly journals Data Challenges With Real-Time Safety Event Detection And Clinical Decision Support

10.2196/13047 ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. e13047 ◽  
Author(s):  
Eric Steven Kirkendall ◽  
Yizhao Ni ◽  
Todd Lingren ◽  
Matthew Leonard ◽  
Eric S Hall ◽  
...  
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Clinical Decision Support ◽  
Event Detection ◽  
Clinical Decision ◽  
Safety Event
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