Högskolan i Skövde

Data Science offers a set of powerful approaches for making new discoveries from large and complex data sets. It combines aspects of mathematics, statistics, machine learning, etc. to turn vast amounts of data into new insights and knowledge. However, the sole use of automatic data science techniques for large amounts of complex data limits the human user’s possibilities in the discovery process, since the user is estranged from the process of data exploration. This chapter describes the importance of Information Visualization (InfoVis) and visual analytics (VA) within data science and how interactive visualization can be used to support analysis and decision-making, empowering and complementing data science methods. Moreover, we review perceptual and cognitive aspects, together with design and evaluation methodologies for InfoVis and VA.

In this survey, 105 papers related to interactive clustering were reviewed according to seven perspectives: (1) on what level is the interaction happening, (2) which interactive operations are involved, (3) how user feedback is incorporated, (4) how interactive clustering is evaluated, (5) which data and (6) which clustering methods have been used, and (7) what outlined challenges there are. This article serves as a comprehensive overview of the field and outlines the state of the art within the area as well as identifies challenges and future research needs.

This paper presents findings about visual representations of cause and effect relationship's direction, strength, and uncertainty based on an online user study. While previous researches focus on accuracy and few attributes, our empirical user study examines accuracy and the subjective ratings on three different attributes of a cause and effect relationship edge. The cause and effect direction was depicted by arrows and tapered lines; causal strength by hue, width, and a numeric value; and certainty by granularity, brightness, fuzziness, and a numeric value. Our findings point out that both arrows and tapered cues work well to represent causal direction. Depictions with width showed higher conjunct accuracy and were more preferred than that with hue. Depictions with brightness and fuzziness showed higher accuracy and were marked more understandable than granularity. In general, depictions with hue and granularity performed less accurately and were not preferred compared to the ones with numbers or with width and brightness.

Research on information fusion and situation management within the military domain, is often focused on data-driven approaches for aiding decision makers in achieving situation awareness. We have in a companion paper identified situation recognition as an important topic for further studies on knowledge-driven approaches. When developing new algorithms it is of utmost importance to have data for studying the problem at hand (as well as for evaluation purposes). This often become a problem within the military domain as there is a high level of secrecy, resulting in a lack of data, and instead one often needs to resort to artificial data. Many tools and simulation environments can be used for constructing scenarios in virtual worlds. Most of these are however data-centered, that is, their purpose is to simulate the real-world as accurately as possible, in contrast to simulating complex scenarios. In high-level information fusion we can however often assume that lower-level problems have already been solved - thus the separation of abstraction - and we should instead focus on solving problems concerning complex relationships, i.e. situations and threats. In this paper we discuss requirements that research on situation recognition puts on simulation tools. Based on these requirements we present a component-based simulator for quickly adapting the simulation environment to the needs of the research problem at hand. This is achieved by defining new components that define behaviors of entities in the simulated world.

Situation recognition is an important problem to solve for introducing new capabilities in surveillance applications. It is concerned with recognizing a priori defined situations of interest, which are characterized as being of temporal and concurrent nature. The purpose is to aid decision makers with focusing on information that is known to likely be important for them, given their goals. Besides the two most important problems: knowing what to recognize and being able to recognize it, there are three main problems coupled to real time recognition of situations. Computational complexity — we need to process data and information within bounded time. Tractability — human operators must be able to easily understand what is being modelled. Expressability — we must be able to express situations at suitable levels of abstraction. In this paper we attempt to lower the computational complexity of a Petri net based approach for situation.

Situation recognition is an important problem within the surveillance domain, which addresses the problem of recognizing a priori defined patterns of interesting situations that may be of concurrent and temporal nature, and which possibly are occurring in the present flow of data and information. There may be many viable approaches, with different properties, for addressing this problem however, something they must have in common is good efficiency and high performance. In order to determine if a potential solution has these properties, it is a necessity to have access to test and development environments. In this paper we present DESIRER, a development environment for working with situation recognition, and for evaluating and comparing different approaches.

Situation recognition is an important problem to address for developing newcapabilities in the surveillance domain.  It is concerned with recognizing a priori defined situations of interest, which can be of concurrent and temporal nature, possibly occurring in a continuous flow of data and information. It is however a complex task to manually define what constitutes an interesting situation, and we therefore investigate the possibility of using genetic algorithms for evolving Petri nets for situation recognition. Our results show that: (1) it is possible to evolve complex Petri nets, (2) it is possible to increase the performance of manually  designed Petri nets, and (3) a dynamic genome representation consisting of  complex genes is beneficial compared to a representation consisting of bit strings.

Situation recognition – the task of tracking states and identifying situations - is a problem that is important to look into for aiding decision makers in achieving enhanced situation awareness. The purpose of situation recognition is, in contrast to producing more data and information, to aid decision makers in focusing on information that is important for them, i.e. to detect potentially interesting situations. In this paper we explore the applicability of a Petri net based approach for modeling and recognizing situations, as well as for managing the hypothesis space coupled to matching situation templates with the present stream of data.

The process of tracking and identifying developing situations is an ability of importance within the surveillance domain. We refer to this as situation recognition and believe that it can enhance situation awareness for decision makers. Situation recognition requires that many subproblems are solved. For instance, we need to establish which situations are interesting, how to represent these situations, and which inferable events and states that can be used for representing them. We also need to know how to track and identify situations and how to determine the correlation between present information about situations with knowledge. For some of these subproblems, data-driven approaches are suitable, whilst knowledge-driven approaches are more suitable for others. In this paper we discuss our current research efforts and goals concerning template-based situation recognition. We provide a categorization of approaches for situation recognition together with a formalization of the template-based situation recognition problem. We also discuss this formalization in the light of a pick-pocket scenario. Finally, we discuss future directions for our research on situation recognition. We conclude that situation recognition is an important problem to look into for enhancing the overall situation awareness of decision makers.

In the military aviation domain, the decisionmaker, i.e. the pilot, often has to process huge amounts of information in order to make correct decisions. This is further aggravated by factors such as time-pressure, high workload and the presence of uncertain information. A support system that aids the pilot to achieve his/her goals has long been considered vital for performance progress in military aviation. Research programs within the domain have studied such support systems, though focus has not been on team collaboration. Based on identified challenges of assessing team situation awareness we suggest an approach to future military aviation support systems based on information fusion. In contrast to most previous work in this area, focus is on supporting team situation awareness, including team threat evaluation. To deal with these challenges, we propose the development of a situational adapting system, which presents information and recommendations based on the current situation.

In 1995, the MedView project, based on a co-operation between computing science and clinical medicine was initiated. The overall goal of the project was to develop models, methods and tools to support clinicians in their daily diagnostic work. As part of MedView, two information visualisation tools were developed and tested as solutions to the problem of visualising clinical experience derived from large amounts of clinical data. The first tool (The Cube) was based on the idea of dynamic three-dimensional (3D) parallel diagrams, an idea similar to the notion of 3D parallel co-ordinates. The Cube was developed to enhance the clinician's ability to intelligibly analyse existing patient material and to allow for pattern recognition and statistical analysis. The second tool (SimVis) was based on a similarity assessment-based interaction model for exploring data, and was designed to help clinicians to classify and cluster clinical examination data. User interaction was supported by 3D visualisation of clusters and similarity measures. Both tools were tested on a knowledge base containing about 1500 examinations obtained from different clinics. Clinical practice indicated that the basic ideas are conceptually appealing to the involved clinicians as the tools can be used for generating and testing of hypotheses.

 Background: Information technology (IT) support for remote collaboration of geographically distributed communities of practice (CoP) in health care must deal with a number of sociotechnical aspects of communication within the community. In the mid-1990s, participants of the Swedish Oral Medicine Network (SOMNet) began discussing patient cases in telephone conferences. The cases were distributed prior to the conferences using PowerPoint and email. For the technical support of online CoP, Semantic Web technologies can potentially fulfill needs of knowledge reuse, data exchange, and reasoning based on ontologies. However, more research is needed on the use of Semantic Web technologies in practice.

Objectives: The objectives of this research were to (1) study the communication of distributed health care professionals in oral medicine; (2) apply Semantic Web technologies to describe community data and oral medicine knowledge; (3) develop an online CoP, Swedish Oral Medicine Web (SOMWeb), centered on user-contributed case descriptions and meetings; and (4) evaluate SOMWeb and study how work practices change with IT support.

Methods: Based on Java, and using the Web Ontology Language and Resource Description Framework for handling community data and oral medicine knowledge, SOMWeb was developed using a user-centered and iterative approach. For studying the work practices and evaluating the system, a mixed-method approach of interviews, observations, and a questionnaire was used.

Results: By May 2008, there were 90 registered users of SOMWeb, 93 cases had been added, and 18 meetings had utilized the system. The introduction of SOMWeb has improved the structure of meetings and their discussions, and a tenfold increase in the number of participants has been observed. Users submit cases to seek advice on diagnosis or treatment, to show an unusual case, or to create discussion. Identified barriers to submitting cases are lack of time, concern about whether the case is interesting enough, and showing gaps in one’s own knowledge. Three levels of member participation are discernable: a core group that contributes most cases and most meeting feedback; an active group that participates often but only sometimes contribute cases and feedback; and a large peripheral group that seldom or never contribute cases or feedback.

Conclusions: SOMWeb is beneficial for individual clinicians as well as for the SOMNet community. The system provides an opportunity for its members to share both high quality clinical practice knowledge and external evidence related to complex oral medicine cases. The foundation in Semantic Web technologies enables formalization and structuring of case data that can be used for further reasoning and research. Main success factors are the long history of collaboration between different disciplines, the user-centered development approach, the existence of a “champion” within the field, and nontechnical community aspects already being in place.

We present a research program for identifying, modeling, and making use of generic pragmatic patterns for clinical knowledge management that support evidence-based medicine (EBM). Part of this program is SOMWeb, a system based on Semantic Web technologies, which is used for knowledge sharing and dissemination within an oral medicine community. A study of the use of SOMWeb has been conducted as the first step in the elicitation of important contextual factors and communicative activities involved in knowledge sharing processes in oral medicine. One such activity, community discussion activation, is described using consultation patterns together with the collaboration patterns of [5]. The general need for context-aware health information systems and the prospective use of approaches within Pragmatic Web in the pursuit of EBM are also discussed.

SOMWeb is an online collaboration system based on Semantic Web technologies, which is used for knowledge sharing and dissemination within an oral medicine community in Sweden. Based on a previous study of the use of SOMWeb, general patterns of interaction and communicative activities involved in community collaboration have been identified. The patterns for one such activity, distance consultation, are described and modeled using techniques from the Pragmatic Web. It is also shown how patterns could inform system design

Founded on the Semantic Web technologies OWL and RDF, SOMWeb is an online community of practice that is used for knowledge sharing and dissemination within an oral medicine community in Sweden. It is shown how patterns for communication and collaboration within SOMWeb can be identified and represented in OWL, in terms of knowledge components, such as ontologies describing domain knowledge, user models, and organization models. It is described how patterns could be put into use and inform the design of future versions of SOMWeb.

In a net-based society, clinicians can come together for cooperative work and distance learning around a common medical material. This requires suitable techniques for cooperative knowledge management and user interfaces that are adapted to both the group as a whole and to individuals. To support distributed management and sharing of clinical knowledge, we propose the development of an intelligent web community for clinicians within oral medicine. This virtual meeting place will support the ongoing work on developing a digital knowledge base, providing a foundation for a more evidence-based oral medicine. The presented system is founded on the use and development of web services and standards for knowledge modelling and knowledge-based systems. The work is conducted within the frame of a well-established cooperation between oral medicine and computer science.

Modern healthcare's need for knowledge sharing and bridging the research–practice gap requires new forms of collaboration, in which clinicians of varying clinical and research expertise work together over geographical and organisational borders. To support such distributed communities of practice (CoPs), an understanding of their collaboration processes, outcomes, challenges and enablers is needed. The article examines these issues through a case study of a long-running CoP, the Swedish Oral Medicine Network (SOMNet). SOMNet's main form of collaboration is monthly telephone conference meetings centred on case consultations. Cases are submitted by the clinicians via a Web-based system. The methods used were interviews, observations, and a questionnaire. The work adds to previous research by studying a distributed CoP explicitly focused on supporting the transfer of scientific results from researchers to practitioners. We found that the regular meetings give a rhythm to the community. The centrality of cases means an immediate benefit for the submitter while the community is provided an authentic context for learning. SOMNet yields opportunities for help and learning for diverse expertise levels; the type of benefits is affected by the participant's degree of oral medicine knowledge and collaboration involvement. There are challenges in accommodating varying levels of expertise and encouraging those less experienced to participate. Enablers of the collaboration include the participation of experts, meeting facilitators and well-adapted ITs.

This article describes the modeling of clinical examinations in oral medicine using OWL. Based on experiences from our previous work and knowledge model, requirements for an ontology for examinations in oral medicine are identified. OWL can be used to address most, but not all, of the requirements. We found a lack of guidance for several design choices and for development of OWL ontologies at different levels of sophistication. However, using OWL gives us the ability to come back and refine the knowledge model after initial deployment.

To support knowledge sharing between practitioners of oral medicine throughout Sweden, the SOMWeb system for community collaboration has been constructed, based on Semantic Web technologies. A large function of the system is to support the discussion of difficult and interesting cases at a monthly telephone conference. From our observations of these meetings, a the users’ responses to a questionnaire, and discussions with users, we find that many of the improvements that can be made to enable further use of the system relate to adding the use of context. Some initial thoughts on what would be relevant to represent in relation to context are given, such as the experience of the practitioner, the reason for adding a case to the system, and the current activity of the user. The long-term aims of this work is to identify aspects of context relevant to online communities of practice in healthcare, and to investigate how these contexts can be modeled using Semantic Web technologies.

Medical ontologies like GALEN, the FMA or SNOMED represent a kind of “100% certain” medical knowledge which is not inherent to all medical sub-domains. Clinical radiology uses computerized imaging techniques to make the human body visible and interprets the imaging findings in a clinical context delivering a textual report. For clinical radiology few standardized vocabularies are available. We examined the definitions given in the glossary of terms for thoracic radiology published by the Fleischner Society. We further classified these terms with regard to their definitions in terms of (a) describing visible structures on the image itself, (b) referring to ontological entities of the body (anatomical or pathological), and (c) terms imposing knowledge on structures visible on the image, epistemologically representing ontological entities of the body. Each ontological/epistemological definition was rated on a scale of vague/weak-sound/strong and put in context with the evaluation comments for the use of the terms given in the glossary itself. The result of this distinction shows that clinical radiology uses many terms referring to ontological entities valid for representation in a medical ontology. However, many epistemological terms exist in the terminology which impose epistemological knowledge on ontological entities. The analysis of the evaluation comments reveals that terms classified as sound (ontologically) and strong (epistemologically) are evaluated higher than terms bearing vague or weak definitions. On the basis of this, we argue that the distinction between ontological and epistemological definitions is necessary in order to construct epistemologically-sensitive application ontologies for medical sub-domains, like clinical radiology, where knowledge is fragmented in terms of description, inferred from a description, concluded on the basis of imaging, or other additional information with varying degrees of certainty.

This paper describes how Semantic Web technologies have been used in an online community for knowledge sharing between clinicians in oral medicine in Sweden. The main purpose of this community is to serve as repository of interesting and difficult cases, and as a support for monthly teleconferences. All information regarding users, meetings, news, and cases is stored in RDF. The community was built using the Struts framework and Jena was used for interacting with RDF.

This paper describes how Semantic Web technologies have been used in an online community for knowledge sharing between clinicians in oral medicine in Sweden. The main purpose of this community is to serve as repository of interesting and difficult cases, and as a support for monthly teleconferences. All information regarding users, meetings, news, and cases is stored in RDF. The community was built

Military fighter pilots have to make suitable decisions fast in an environment where continuously increasing flows of information from sensors, team members and databases are provided. Not only do the huge amounts of data aggravate the pilots’ decision making process: time-pressure, presence of uncertain data and high workload are factors that can worsen the performance of pilot decision making. In this paper, initial ideas of how to support the pilots accomplishing their tasks are presented. Results from interviews with two fighter pilots are described as well as a discussion about how these results can guide the design of a military fighter pilot decision support system, with focus on team cooperation.

Advancements in technology and the need for improving the pilots' working situations have stimulated the growth of automated functions within the fighter aircraft domain. Functions that aid the pilots perform their tasks and to make decisions fast in an often rapidly changing environment have been introduced with the ultimate aim of easing the pilots' workloads and improving their situation awareness. However, both positive and negative effects of automating pilots' tasks have been documented, such as decreased workload and skill degradation, and it has been acknowledged that it is important to consider the needs of the human operator when designing automated functions. Human-Centred Automation (HCA) has been suggested as an approach for designing automated systems that brings forward the positive effects of automation. However, how to apply the concept of HCA in light of the specific prerequisites and demands of the modern fighter aircraft domain has not been fully investigated. This paper presents the results from interviews made together with fighter aircraft system developers, where the concept of HCA was investigated. Their opinions of the applicability of the concept of HCA within the fighter aircraft domain is represented as well as their thoughts about identified HCA guidelines in relation to the proposed support system. Conclusions drawn from the study are that HCA is indeed an important concept to consider within the domain as well as that the proposed guidelines could be of great use during the design process. Additionallly, insight into how to design automated support systems with the concept of HCA in mind is given.

It has long been considered crucial to develop decision support systems that aid fighter pilots achieve their goals. Such systems often require automation of tasks formerly performed manually by the pilots, in situations characterized by huge amounts of (possibly uncertain and incomplete) sensor data and contextual information, time-pressure and dynamically changing tasks. Thus, careful investigations must be performed so as to develop such systems that provide accurate support for their users. This paper reports on the findings concerning research within the field of human-centred automation as well as presents empirical results concerning the applicability of automation guidelines when designing information fusion based support systems in the fighter aircraft domain.

Motivation – To guide the development of human-centred automation within the fighter aircraft domain.

Research approach – Identified human-centred automation guidelines have been analysed in relation to existing fighter aircraft automated functions together with system developers at Saab Aeronautics.

Findings/Design – The results show that the human-centred automation guidelines have been considered during the development process. From these results, implications for the design of guidelines and for the design of automated systems in the aircraft domain are drawn.

Research limitations/Implications – Deeper analysis of how automated functions can support pilots in future fighter aircraft is needed since the proposed guidelines are too general for the military fighter aircraft domain. Thus, future work involves an evaluation of the guidelines together with fighter aircraft system developers and/or military strategists. Such analysis must be carried out with specific automated functions in mind.

Originality/Value – By comparing with existing automated functions, the research makes contributions to HCA guidelines to be used in the fighter aircraft domain. Suggestions of human-centred automation improvements within the fighter aircraft domain are presented. The analysis has also identified differences between the proposed guidelines and parts of the studied implementation examples.

Take away message – The HCA guidelines must be adapted according to the specific tasks that the automated functions are intended to assist the operators with. To adapt the automation according to the level of experience of the operators as well as to expand the cooperative automation functions between aircraft in a team have been identified as future directions for automation improvements within the fighter aircraft domain.

Threat evaluation (TE) is concerned with determining the intent, capability and opportunity of detected targets. To their aid, military operators use support systems that analyse incoming data and make inferences based on the active evaluation framework. Several interface and interaction guidelines have been proposed for the implementation of TE systems; however there is a lack of research regarding how to make these systems transparent to their operators. This paper presents the results from interviews conducted with TE operators focusing on the need for and possibilities of improving the transparency of TE systems through the visualization of uncertainty and the presentation of the system rationale. © 2013 Springer-Verlag Berlin Heidelberg.

To investigate the impact of visualizing car uncertainty on drivers' trust during an automated driving scenario, a simulator study was conducted. A between-group design experiment with 59 Swedish drivers was carried out where a continuous representation of the uncertainty of the car's ability to autonomously drive during snow conditions was displayed to one of the groups, whereas omitted for the control group. The results show that, on average, the group of drivers who were provided with the uncertainty representation took control of the car faster when needed, while they were, at the same time, the ones who spent more time looking at other things than on the road ahead. Thus, drivers provided with the uncertainty information could, to a higher degree, perform tasks other than driving without compromising with driving safety. The analysis of trust shows that the participants who were provided with the uncertainty information trusted the automated system less than those who did not receive such information, which indicates a more proper trust calibration than in the control group.

We present an empirical study where the effects of three levels of system transparency of an automated target classification aid on fighter pilots’ performance and initial trust in the system were evaluated. The levels of transparency consisted of (1) only presenting text–based information regarding the specific object (without any automated support), (2) accompanying the text-based information with an automatically generated object class suggestion and (3) adding the incorporated sensor values with associated (uncertain) historic values in graphical form. The results show that the pilots needed more time to make a classification decision when being provided with display condition 2 and 3 than display condition 1. However, the number of correct classifications and the operators’ trust ratings were the highest when using display condition 3. No difference in the pilots’ decision confidence was found, yet slightly higher workload was reported when using display condition 3. The questionnaire results report on the pilots’ general opinion that an automatic classification aid would help them make better and more confident decisions faster, having trained with the system for a longer period.

In the era of big data, it is imperative to assist the human analyst in the endeavor to find solutions to ill-defined problems, i.e. to “detect the expected and discover the unexpected” (Yi et al., 2008). To their aid, a plethora of analysis support systems is available to the analysts. However, these support systems often lack visual and interactive features, leaving the analysts with no opportunity to guide, influence and even understand the automatic reasoning performed and the data used. Yet, to be able to appropriately support the analysts in their sense-making process, we must look at this process more closely. In this paper, we present the results from interviews performed together with data analysts from the automotive industry where we have investigated how they handle the data, analyze it and make decisions based on the data, outlining directions for the development of analytical support systems within the area.

This paper demonstrates how to explore and visualize different types of structure in data, including clusters, anomalies, causal relations, and higher order relations. The methods are developed with the goal of being as automatic as possible and applicable to massive, streaming, and distributed data. Finally, a decentralized learning scheme is discussed, enabling finding structure in the data without collecting the data centrally. 

In this paper, a precise description of the threat evaluation process is presented. This is followed by a review describing which parameters that have been suggested for threat evaluation in an air surveillance context throughout the literature, together with an overview of different algorithms for threat evaluation. Grounded in the findings from the literature review, a threat evaluation system have been developed. The system is based on a Bayesian network approach, making it possible to handle imperfect observations. The structure of the Bayesian network is described in detail. Finally, an analysis of the system’s performance as applied to a synthetic scenario is presented.

 Threat evaluation is a high-level information fusion problem of high importance within the military domain. This task is the foundation for weapons allocation, where assignment of blue force (own) weapon systems to red force (enemy) targets is performed. In this paper, we compare two fundamentally different approaches to threat evaluation: Bayesian networks and fuzzy inference rules. We conclude that there are pros and cons with both types of approaches, and that a hybrid of the two approaches seems both promising and viable for future research.

The problem of allocating defensive weapon resources to hostile targets is an optimization problem of high military relevance. The need for obtaining the solutions in real-time is often overlooked in existing literature. Moreover, there  does not exist much research aimed at comparing the performance of different  algorithms for weapon-target allocation. We have implemented a suite of  metaheuristic algorithms for solving the static weapon-target allocation problem, and compare their real-time performance on a large set of problem instances using the open source testbed SWARD. The compared metaheuristic algorithms are ant colony optimization, genetic algorithms, and particle swarm optimization. Additionally, we have compared the quality of the generated allocations to those generated by a well-known maximum marginal return algorithm. The results show that the metaheuristic algorithms perform well on small- and medium-scale problem sizes, but that real-time requirements limit their usefulness for large search spaces.

Threat evaluation is the process in which threat values are assigned to detected targets, based upon the inferred capabilities and intents of the targets to inflict damage to blue force defended assets. This is a high-level information fusion process of high importance, since the calculated threat values are used as input when blue force weapon systems are allocated to the incoming targets, a process often referred to as weapon allocation. Threat values can be calculated from a number of different parameters, such as the position of the closest point of approach (CPA) with respect to blue force defended assets, time required to reach the CPA, the target’s velocity, and its type. A number of algorithms for calculating threat values have been suggested throughout literature, however, criteria to evaluate the performance of such algorithms seem to be lacking. In this paper, we discuss different ways to assess the performance of threat evaluation algorithms. In specific, we describe how threat evaluation algorithms can be compared to each other, using a survivability criterion. Survivability is measured by running the threat evaluation algorithms on simulated scenarios and using the resulting threat values as input to a weapon allocation module. Depending on how well the threat evaluation is performed, the ability of the blue force weapon systems to eliminate the incoming targets will vary (and thereby also the survivability of the defended assets).

Threat evaluation is the process in which threat values are assigned to detected targets, based upon the inferred capabilities and intents of the targets to inflict damage to blue force defended assets. This is a high-level information fusion process of high importance, since the calculated threat values are used as input when blue force weapon systems are allocated to the incoming targets, a process often referred to as weapon allocation. Threat values can be calculated from a number of different parameters, such as the position of the closest point of approach (CPA) with respect to blue force defended assets, time required to reach the CPA, the target's velocity, and its type. A number of algorithms for calculating threat values have been suggested throughout literature, however, criteria to evaluate the performance of such algorithms seem to be lacking. In this paper, we discuss different ways to assess the performance of threat evaluation algorithms. In specific, we describe an implemented testbed in which threat evaluation algorithms can be compared to each other, based on a survivability criterion. Survivability is measured by running the threat evaluation algorithms on simulated scenarios and using the resulting threat values as input to a weapon allocation module. Depending on how well the threat evaluation is performed, the ability of the blue force weapon systems to eliminate the incoming targets will vary (and thereby also the survivability of the defended assets). Our obtained results for two different threat evaluation algorithms are presented and analyzed.

The allocation of weapons to targets (such as missiles and hostile aircrafts) is a well-known resource allocation problem within the field of operations research. It has been proven that this problem, in general, is NP-complete. For this reason, optimal solutions to the static weapon-target allocation (WTA) problem can not be obtained in real-time for large-scale problems. We try to find the limit for how large problems that can be solved optimally in real-time by exhaustive search algorithms through running empirical experiments. We also propose a heuristic genetic algorithm for solving larger-scale problems.

In this paper we describe a data mining approach for detection of anomalous vessel behaviour. The suggested approach is based on Bayesian networks which have two important advantages compared to opaque machine learning techniques such as neural networks: (1) possibility to easily include expert knowledge into the model, and (2) possibility for humans to understand and interpret the learned model. Our approach is implemented and tested on synthetic data, where initial results show that it can be used for detection of single-object anomalies such as speeding.

Prediction of the enemy's intention is a main issue of threat analysis, and, hence, will be an important part of the C2-systems of tomorrow. A technique that can be useful for this kind of predictions is Bayesian networks (BNs). We have developed a BN for prediction of the enemy's tactical intention, and the implemented BN has been integrated into a ground target simulation framework. The general problem of how to find appropriate prior distributions for BNs has been addressed by developing a tool for data collection, which may make it easier to come up with appropriate prior distributions, by learning conditional probability tables from collected cases, i.e. parameter learning