Leonardo C. Botega

Methodology for Data and Information Quality Assessment in the Context of Emergency Situational Awareness

Situation Assessment (SA) approaches aim to provide powerful resources to support decision makers in enhancing their Situational Awareness (SAW). The process of SA in emergency response systems is of utmost importance once the information acquired and inferred from human reports is used to support the deployment of tactics and resources to attend incidents. However, operators of such systems may face informational barriers leading to an erroneous SAW and consequently jeopardize the assessment process if they are not handled. One of such barriers in this context is the presence of low-quality data or information. Hence, a challenging issue in this field is to determine how to generate, score, update and represent data and information quality cues to support operators to reason under uncertainties and improve their understanding about an ongoing situation. The state of the art in this area presents a research gap regarding methodologies for the information quality assessment which can be used in the emergency management domain. Also, there is a lack of approaches that interface with different levels of situational information during an assessment routine. Hence, in order to enhance operators situational awareness, a new methodology is presented to improve the capabilities of SA systems by enriching knowledge about situations with reliable metadata. Such methodology, named Information Quality Assessment Methodology in the Context of Emergency situational awareness, is composed by: elicitation of data and information quality requirements; definition of functions and metrics to quantify quality dimensions, such as completeness, timeliness, consistency, relevance and uncertainty; and the representation of situational information by the instantiation of a situation model, which can be consumed by an ontology. Finally, a case study is addressed to verify the applicability of the methodology using data and information from a robbery event. The results obtained show situational models with qualified information that feed SA systems, enabling them to be aware of information quality.

Conceptual Framework to Enrich Situation Awareness of Emergency Dispatchers

Computer-Aided Dispatch (CAD) systems provide powerful resources to support emergency operators (dispatchers) in their activity. However, these dispatchers can work under heavy stress, which can lead to failure to get necessary information, resulting in unsuccessful response to calls. One challenging issue to better support operators in stressing calls is to determine how to generate, score and represent informational quality cues to help them to reason under uncertainties and improve their understanding about an ongoing situation (situational awareness - SAW). In such a context, the poor knowledge about the entities involved in a situation and what is really going on may lead to wrong decision-making. One of the gaps in the state-of-the-art research in this area is the lack of a common ground regarding information quality. This is due to domain-specific demands and the absence of a comprehensive framework of information quality that interface with different levels of knowledge during a situation assessment cycle. Hence, in order to improve dispatchers’ situational awareness, we present a new conceptual framework to support decision making in emergency call situations by enriching situations knowledge with reliable metadata and successive reassessments of information quality. The framework’s requirements elicitation was carried out with police experts as well as the definition and application of information quality scoring criteria and the representation of such scores along with a semantic knowledge representation model. The framework application on real robbery reporting calls has indicated very positive results.

SAW-Oriented User Interfaces for Emergency Dispatch Systems

Situational awareness (SAW) is a concept widely spread in application areas that require critical decision-making, such as in emergency dispatching systems. SAW is related to the level of consciousness that an individual or team has to a situation. SAW-oriented UI for critical systems require specialized user interfaces to provide operators a dynamic understanding of what is happening in an environment. The information to be managed by such interfaces affects the way operators in an emergency dispatch system acquire, maintain and recover SAW. A challenging issue on the design of SAW-oriented interfaces is how the human-system interaction process can be redesigned for the enhancement of SAW considering environments with potential large scale heterogeneous multi sensors data in complex, ever-changing situations. The problem is increased when such information is subject to uncertainty, which may compromise the acquisition of the situational awareness. Also, humans are expected to make decisions based on their own understanding of what is going on, which allied to experience and expertise can be valuable assets to be used to process refinement during the construction of an incremental knowledge. The goal of this paper is to introduce a conceptual framework to create specialized interfaces that support the participation of operators in the process of SAW acquisition. Such SAW-oriented interface presents a tight integration between the operator and the other phases of an assessment process, such as information quality assessment, information fusion and information visualization. A robbery event report, in an emergency dispatch system, is used as a case study to demonstrate practical and promising results of the applicability of our solution.

Towards Semantic Fusion Using Information Quality Awareness to Support Emergency Situation Assessment

Information Fusion is the integration of synergic information to support high-level decision-making. Emergency management systems are applications that may take advantage of such integration by supporting system’s operators on developing situation awareness (SAW) and dealing with the critical and dynamic nature of real emergency scenarios. Semantic models help to describe and to determine synergy among entities that may be useful for fusion and situation assessment routines (SA). In this context, the awareness of information quality issues can enrich even more the knowledge that humans and system hold about situations. The objective of this paper is to present advances towards a new semantic fusion approach supported by information quality inferences and semantic web concepts to improve the assessments about emergency situations and hence supporting situation awareness. A previous fusion approach based on a syntactic integration with quality indexes is used to illustrate the improvements on information fusion results with the semantic models.

A Model to Promote Interaction between Humans and Data Fusion Intelligence to Enhance Situational Awareness

The operator of a Command & Control C2 system has a crucial role on the improvement of information that is processed through data fusion engines to provide Situational Awareness SAW. Through direct access to data transformations, operators can improve information quality, by reducing uncertainty, according to their skills and expertise. Uncertainty, in this work, is considered an adverse condition, which can make the real information less accessible. Although relevant solutions have been reported in the literature on innovative user interfaces and approaches for quality-aware knowledge representation, these are concerned mostly on transforming the way information is graphically represented and on quantitatively mapping the quality-aware knowledge acquired from systems, respectively. There are few studies that deal more specifically with accessibility for decision-makers in safety-critical situations, such as C2, considering the aspect of data uncertainty. This paper presents a model to help researchers to build uncertainty-aware interfaces for C2 systems, produced by both data fusion and human reasoning over the information. Combined to environmental and personal factors, a tailored and enriched knowledge can be built, interchangeable with systems intelligence. A case study on the monitoring of a conflict among rival soccer fans is being implemented for the validation of the proposed solution.

Augmented reality and tangible user interfaces integration for enhancing the user experience

The integration of post-wimp computer interfaces arises as an alternative to meet individual limitations of each one, considering both interaction components and feedbacks to users. Tangible interfaces can present restrictions referring to physical space on tabletop architectures, which limits the manipulation of objects and deprecates the interactive process. Hence, this paper proposes the integration of techniques of mobile Augmented Reality with tabletop tangible architecture for blending real and virtual components on its surface, aiming to make the interactive process richer, seamless and more complete.

Quality-aware human-driven information fusion model

Situational Awareness (SAW) is a widespread concept in areas that require critical decision-making and refers to the level of consciousness that an individual or team has about a situation. A poor SAW can induce humans to failures in the decision-making process, leading to losses of lives and property damage. Data fusion processes present opportunities to enrich the knowledge about situations by integrating heterogeneous and synergistic data from different sources and transforming them into more meaningful subsidies for decision-making. However, a problem arises when information is subject to problems concerning its quality, especially when humans are the main sources of data (HUMINT). Motivated by the informational demand from the emergency management domain and by the limitations and challenges of the state of the art, this work proposes and describes a new information fusion model, called Quantify (Quality-aware Human-Driven Information Fusion Model), whose main contribution is the exhaustive use of the quality information management throughout the fusion process to parameterize and to guide the work of humans and systems. To validate the model, an emergency situation assessment system prototype was developed, called ESAS (Emergency Situation Assessment Systems). Then, experts from the Sao Paulo State Police (PMESP) tested the prototypes and the system was evaluated using SART (Situation Awareness Rating Technique), which showed higher rates of SAW using the Quantify model, compared to the model from the state-of-the-art, especially in questions relating to the components of resource supply and situational understanding.

Objects Assessment Approach Using Natural Language Processing and Data Quality to Support Emergency Situation Assessment

Situation Awareness (SAW) is a cognitive process that is defined by the perception of relevant elements present in a monitored environment (e.g., people, objects, vehicles, places), the understanding of their meaning (i.e., what they are doing) and the projection of their statuses in the near future. In the domain of emergency management, the data employed to the process of acquisition and maintenance of SAW are provided by several sources, using different formats and different classifications, such as: images from security cameras, reports made to the emergency response center, posts in social networks and several physical sensors, such as: positional, altitude and movement. Data from this sources, if well processed and understood by a specialist, may contribute to the decision-making process, supporting the establishment of emergency response tactics and a better allocation of operational resources. The acquisition of SAW demands the characterization of the ongoing situation. Typically, knowing exactly what is going on demands exhaustive routines of intelligent data assessment. In the emergency management domain, it means to better explore and analyze what the humans say about the events. This paper presents a general architecture that integrates objects and situational assessment for the emergency management domain and a specific process for the objects assessment using natural language processing (NLP) and semantic practices, to better identify relevant elements that may be useful for the situation assessment routines, such as information fusion. Known approaches are limited due to the absence of data quality analysis as part of the process, undesirable when decision makers need to rely on emergency information. Preliminary results of a case study of an intelligent object assessment of a robbery situation reported in Brazilian Portuguese demonstrate the advantages and practical particularities of our solution.

Towards semantic fusion using information quality and the assessment of objects and situations to improve emergency situation awareness

Information Fusion is the integration of synergic information to support cognition and high-level processing. Emergency management systems may take advantage of such integration and better support human operators in the development of Situational Awareness (SAW) for decision-making. The critical and dynamic nature of real emergency scenarios impose challenges to reveal, integrate and derive useful information for decision processes. The problem increases when humans are the main source of data, leading to information quality issues, such as imprecision, inconsistency and uncertainty. Current syntactical-only fusion approaches are limited regarding the assessment of situational meaning and human language nuances. Semantic models help to describe and to apply relationships among entities that may be useful for a net centric fusion and Situation Assessment (SA) routines. The objective of this paper is to present advances towards a new semantic fusion approach supported by information quality inferences and semantic web concepts to improve the SA about emergency situations and hence supporting SAW. For such, a new architecture is presented to integrate objects and situation assessment approaches by syntactical and semantic means. A previous fusion approach based on a syntactic integration with quality indexes is used to illustrate the improvements on information fusion results with the semantic models.

Multi-criteria Fusion of Heterogeneous Information for Improving Situation Awareness on Emergency Management Systems

Information Fusion is the synergic integration of data from different sources for the support to decision-making. The emergency management systems predominance of such application has driven the development to new and better sensors, new methods, for data processing and architectures that promote access, composition, refinement and information handling, with the active participation of specialists as data providers and specialists of the systems. In this scenario of data fusion, uncertainty of diverse natures can be aggregated to both data and information at different levels of the process, creating distorted information to the specialist. As a result the situation awareness and cognitive process can be affected leading to poor quality support to decision-making as a generalization of information quality, uncertainty need to be reduced to improve awareness about the situation of interest. The objective of our work is the mitigation of uncertainty propagated by other quality attributes such as information completeness, so specialists can be able to convey an improved understanding. For such, a new fusion framework fed by multi-criteria parameterization, including information quality measures and its semantics, is depicted as an engine to build more accurate information from diverse sensed possibilities. A case study with a situation assessment application is in course to validate the effectiveness of the generated solution. Preliminary and promising results are discussed as a more valuable tool to support decision-making.