HLIF and LLIF can benefit from the advances in enterprise computing, but there are few ... both at the hardware (i.e. components and sensors) and the software (i.e. ... Cloud services store outputs, access information, support processing, and ...
Decisions-to-Data using Level 5 Information Fusion Erik Blasch Air Force Research Laboratory, Information Directorate, Rome, NY, 13441
ABSTRACT Over the last decade, there has been interest in presenting information fusion solutions to the user and ways to incorporate visualization, interaction, and command and control. In this paper, we explore Decisions-to-Data (D2D) in information fusion design: (1) sensing: from data to information (D2I) processing, (2) reporting: from human computer interaction (HCI) visualizations to user refinement (H2U), and (3) disseminating: from collected to resourced (C2R) information management. D2I supports net-centric intelligent situation awareness that includes processing of information from non-sensor resources for mission effectiveness. H2U reflects that completely automated systems are not realizable requiring Level 5 user refinement for efficient decision making. Finally, C2R moves from immediate data collection to fusion of information over an enterprise (e.g., data mining, database queries and storage, and source analysis for pedigree). By using D2I, H2U, and C2R concepts, they serve as informative themes for future complex information fusion interoperability standards, integration of man and machines, and efficient networking for distribution user situation understanding. Keywords: Information Fusion, Data to Decisions, Virtual Worlds, Data Fusion Information Group, Enterprise, Info. Management
1. INTRODUCTION The paradigm of the conference is multi-sensor interoperability, integration and networking for persistent intelligence, surveillance, and reconnaissance (ISR) [1, 2]. A growing trend is to look at methods of data-to-decisions; however, we view it as Decisions-to-Data (D2D). Information fusion seeks to reduce uncertainty, associate data, and enable knowledge elucidation through data valuation. Uncertainty Enterprise Reporting comes from many sources including sensors, entities, and the environment and the subsequent processing over interpretation, context, language, and users [3]. Assessing the quality of merged and combined information requires objective and subjective uncertainty measures, reasoning, and system design [4]. Figure 1 demonstrates that Info Mgt Processes information fusion (sensing) is a function of access to the Da! data through the network (enterprise), information management processes [5], and coordination with the user (reporting) [6, 7, 8]. Future successes of information . fusion system designs over streaming data will be Network impacted by information management (e.g., cloud-enabled distributed network environment) and end user Figure 1: Information Fusion in the Enterprise. coordination (e.g., distributed clients). i
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From the seminal book on information fusion [9], the Joint Directors of Laboratories (JDL) model was proposed [10]. Subsequent revisions [11, 12] to the model incorporate new directions such as context [13]. The JDL model was revised for the proposed Data Fusion Information Group (DFIG) model [3, 14]. Key elements of contemporary information fusion melding include: (1) sensing: mission awareness of data to information, (2) reporting: human interfaces to user involvement, and (3) dissemination: collected to resourced information management. Currently, a common theme is data to decisions (D2D) over joint data management (JDM) [15,16]; however this is proposed as a bottom-up solution; whereas a top-down perspective (e.g. evidence-based queries)
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is also needed from decisions-to-data (D2D). In this paper, we revisit the development of an information fusion architecture motivated from system design solutions based on information management, enterprise technologies, and user interaction. Current advances in available processing, sensor collection, data storage, and data distribution have afforded more complex, distributed, and operational information fusion systems (IFSs). IFSs notionally consist of low-level information fusion (LLIF) (e.g., data collection, registration [17], and target tracking association in time and space [18]) and high-level information fusion (HLIF) (e.g., situational awareness [19], threat assessment [20], user coordination [21], and mission control [22]). HLIF challenges [23] include: resource management [24], network-centric architectures [25], and spectrum sharing [26]; which are elements of a cloud computing environment of access, storage, and retrieval. Contemporary HLIF research focuses on information management [27] and systems design [28]. HLIF and LLIF can benefit from the advances in enterprise computing, but there are few reports that bring together these technologies, none the less document their impact on operational decision-making. Current contemporary topics of interest include security [29], service-oriented computing [30, 31], and integrated intelligence (such as the Open Geospatial Consortium (OGC) [32, 33]). There are examples of Google’s Cloud Fusion service [34] which brings information together, but the hosting and linking of information provides a common repository that still leaves the user with the goal of associating data and deriving the value of information. One example from Google Fusion is the linking of people to a location; however, there is little in the way of determining the quality, credibility, availability, quantity, and type of data that is needed to combine the information in a meaningful way to make more informed decisions. The future command and control (C2) systems for intelligence analysts [35] situation awareness require methods in HLIF for the creation and maintenance of data, displays for decision making [36], and reduction in mental workload [37]. As an example of information management challenges, one is spatial image analysis to include: data storage, parallel computation, high bandwidth communications, automatic pattern recognition, and human interfaces [38]. Section 2 covers D2I modeling. Section 3 presents H2U methods such as virtual worlds. Section 4 describes collected to resourced (C2R) information management. Section 5 discusses the recent trends in enterprise cloud computing and implications for the management of information fusion. An example application is presented in Section 6 for video tracking and Section 7 provides conclusions.
2. DATA TO INFORMATION (D2I) MODELING 2.1 High-Low Fusion Level Distinctions Information fusion is a technique to combine multiple sources of data, distributions [39], or information over various system-level processes as described in the Data Fusion Information Group (DFIG) model [3, 14, 24], depicted in Figure 2. In the DFIG model, the goal was to separate the data fusion and Resource Management (RM) functions and highlight the user involvement. RM is divided into sensor control (L4), user refinement (L5), and platform placement/resource collection (L6), and to meet mission objectives. Data fusion includes object (L1), Situation (L2) and impact (L3) assessment such as sense-making of threats, course of actions, game-theoretic decisions, and intent analysis to help refine the estimation and information needs for different actions. RM can be aided by enterprise computing aspects of data acquisition, access, recall, and storage services. Info Fusion Real
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Figure 2: DFIG Information Fusion model (L = Level). Information fusion across all levels includes many uncertainty sources, methods, and measures [40]. As a challenge, both at the hardware (i.e. components and sensors) and the software (i.e. algorithms and networks) layers add to the complexity of system design. Recent efforts include the uncertainty representation and reasoning evaluation framework
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(URREF) working group [http://eturwg.c4i.gmu.edu] [4] that is looking at enterprise level analysis over hardware and software uncertainty representations to standardize terminology for downstream information fusion processes. High-Level Information Fusion (HLIF) (as referenced to levels beyond Level 1) is the ability of a fusion system, through knowledge, expertise, and understanding to: capture awareness and complex relations, reason over past and future events, use direct sensing exploitations and tacit reports, and discern the usefulness and intention of results to meet system-level goals. Designs of real-world information fusion systems imply distributed information source coordination (network), organizational concepts (command), and environmental understanding (context). Additionally, there is a need for automated processes that provide functionality in support of user decision processes, particularly at higher levels requiring reasoning and inference which is typically done by a human. For example, a cloud-enabled service can greatly enhance attributes of timeliness, availability, usability, and relevance which benefit both LLIF and HLIF though situation awareness [41]. The DFIG model and enterprise computing services share a common goal to provide information (over the cloud) for situation awareness. Cloud services store outputs, access information, support processing, and provide dissemination over asynchronous services. Using the DFIG paradigm, Level 4 (sensor management) could use a cloud service to access information, Level 5 (user refinement) can be the end-user applications that query information, and Level 6 (mission management) can provide filtering and control of information dissemination to the correct user estimates. Inherent in the analysis is that Level 0 (data preprocessing) is that data is already resident in the cloud environment. Next, we discuss situation awareness and assessment in an enterprise network (i.e., cloud enabled) to focus on information processing. 2.2 Situation Awareness There are two main groups addressing situational information: the engineering information fusion community (i.e. Situation Assessment [SA]) and the human factors community (i.e. Situation Awareness [SAW]). SAW is a mental state while SA supports (e.g. fusion products) that state which requires a common transformation between the two representations. Given the developments of SAW and SA, we combine the ideas into an integrated information fusion situation awareness (IFSA) model in which the role of SA stratifies the object/event analysis. The IFSA combines elements of the community models; SAW reference model with the DFIG elements of a combined L2/L3 analysis and user refinement (L5). The IFSA model is presented in Figure 3. Information Fusion
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