Advances in data representation for hard/soft information fusion

Information fusion is becoming increasingly human-centric. While past systems typically relegated humans to the role of analyzing a finished fusion product, current systems are exploring the role of humans as integral elements in a modular and extensible distributed framework where many tasks can be accomplished by either human or machine performers. For example, "participatory sensing" campaigns give humans the role of "soft sensors" by uploading their direct observations or as "soft sensor platforms" by using mobile devices to record human-annotated, GPS-encoded high quality photographs, video, or audio. Additionally, the role of "human-in-the-loop", in which individuals or teams using advanced human computer interface (HCI) tools such as stereoscopic 3D visualization, haptic interfaces, or aural "sonification" interfaces can help to effectively engage the innate human capability to perform pattern matching, anomaly identification, and semantic-based contextual reasoning to interpret an evolving situation. The Pennsylvania State University is participating in a Multi-disciplinary University Research Initiative (MURI) program funded by the U.S. Army Research Office to investigate fusion of hard and soft data in counterinsurgency (COIN) situations. In addition to the importance of this research for Intelligence Preparation of the Battlefield (IPB), many of the same challenges and techniques apply to health and medical informatics, crisis management, crowd-sourced "citizen science", and monitoring environmental concerns. One of the key challenges that we have encountered is the development of data formats, protocols, and methodologies to establish an information architecture and framework for the effective capture, representation, transmission, and storage of the vastly heterogeneous data and accompanying metadata -- including capabilities and characteristics of human observers, uncertainty of human observations, "soft" contextual data, and information pedigree. This paper describes our findings and offers insights into the role of data representation in hard/soft fusion.

[1]  Mark H. Hansen,et al.  Participatory sensing - eScholarship , 2006 .

[2]  James Llinas,et al.  A framework for dynamic hard/soft fusion , 2008, 2008 11th International Conference on Information Fusion.

[3]  Sanjay Jha,et al.  A hybrid sensor network for cane-toad monitoring , 2005, SenSys '05.

[4]  Rakesh Nagi,et al.  Enhancements to high level data fusion using graph matching and state space search , 2010, Inf. Fusion.

[5]  Gary Anthes,et al.  Happy Birthday, RDBMS! , 2010, Commun. ACM.

[6]  John T. Rickard,et al.  Hierarchical Higher Level Data Fusion using Fuzzy Hamming and Hypercube Clustering , 2008, J. Adv. Inf. Fusion.

[7]  A. Dan,et al.  Information as a Service: Modeling and Realization , 2007, International Workshop on Systems Development in SOA Environments (SDSOA'07: ICSE Workshops 2007).

[8]  Florian Probst Ontological Analysis of Observations and Measurements , 2006, GIScience.

[9]  Rick Cattell,et al.  Scalable SQL and NoSQL data stores , 2011, SGMD.

[10]  James Llinas,et al.  Handbook of Multisensor Data Fusion , 2001 .

[11]  D. Dubois,et al.  On Possibility/Probability Transformations , 1993 .

[12]  Rakesh Nagi,et al.  Soft information, dirty graphs and uncertainty representation/processing for situation understanding , 2010, 2010 13th International Conference on Information Fusion.

[13]  Wolfram Wöß,et al.  Sharing Data on the Grid using Ontologies and distributed SPARQL Queries , 2007, 18th International Workshop on Database and Expert Systems Applications (DEXA 2007).

[14]  Avelyn Davidson,et al.  Happy Birthday , 1997 .

[15]  John Davidson,et al.  Ogc® sensor web enablement:overview and high level achhitecture. , 2007, 2007 IEEE Autotestcon.

[16]  D. L. Hall,et al.  Mathematical Techniques in Multisensor Data Fusion , 1992 .

[17]  David A. Schum,et al.  Assessing the Competence and Credibility of Human Sources of Intelligence Evidence: Contributions from Law and Probability , 2007 .

[18]  Kirk Martinez,et al.  Environmental Sensor Networks: A revolution in the earth system science? , 2006 .

[19]  Donna B. Stoddard,et al.  Getting IT right. , 2004, Harvard business review.

[20]  Matt Welsh,et al.  CodeBlue: An Ad Hoc Sensor Network Infrastructure for Emergency Medical Care , 2004 .

[21]  Alessandro Margara,et al.  Processing flows of information: From data stream to complex event processing , 2012, CSUR.

[22]  Christoph Stasch,et al.  New Generation Sensor Web Enablement , 2011, Sensors.

[23]  Tracy Gardner,et al.  UML Modelling of Automated Business Processes with a Mapping to BPEL4WS , 2003 .

[24]  Lotfi A. Zadeh,et al.  Fuzzy logic = computing with words , 1996, IEEE Trans. Fuzzy Syst..

[25]  David L. Hall,et al.  Test and evaluation of soft/hard information fusion systems: A test environment, methodology and initial data sets , 2010, 2010 13th International Conference on Information Fusion.

[26]  W. E. Galloway,et al.  Reply to the comments of W. Helland-Hansen on "Towards the standardization of sequence stratigraphy" by Catuneanu et al. (Earth-Sciences Review 92(2009)1-33) , 2009 .

[27]  Tim O'Reilly,et al.  Web Squared: Web 2.0 Five Years On , 2009 .

[28]  Rajiv Khosla,et al.  Multi-layered Distributed Agent Ontology for Soft Computing Systems , 2003, KES.

[29]  David L. Hall,et al.  A multi-agent infrastructure for hard and soft information fusion , 2011, Defense + Commercial Sensing.

[30]  Marc-Thomas Schmidt,et al.  The Enterprise Service Bus: Making service-oriented architecture real , 2005, IBM Syst. J..

[31]  Neal Leavitt,et al.  Will NoSQL Databases Live Up to Their Promise? , 2010, Computer.

[32]  Michael Mertens,et al.  Tracking and Data Fusion for Ground Surveillance , 2014 .

[33]  Jim Gray,et al.  A Conversation with Jim Gray , 2003, ACM Queue.

[34]  Michael Stonebraker,et al.  SQL databases v. NoSQL databases , 2010, CACM.

[35]  Alfred O. Hero,et al.  Sensor Management: Past, Present, and Future , 2011, IEEE Sensors Journal.

[36]  Moshe Kam,et al.  Evidence combination for hard and soft sensor data fusion , 2011, 14th International Conference on Information Fusion.

[37]  David L. Hall,et al.  Human-Centered Information Fusion: Artech House Electronic Warfare Library , 2010 .

[38]  James Llinas,et al.  Network and infrastructure considerations for hard and soft information fusion processes , 2012, 2012 15th International Conference on Information Fusion.

[39]  David L. Hall,et al.  Human cognitive and perceptual factors in JDL level 4 hard/soft data fusion , 2012, Defense + Commercial Sensing.

[40]  Jun Han,et al.  A Conceptual Framework for Unified and Comprehensive SOA Management , 2009, ICSOC Workshops.

[41]  Gregory F. Cooper,et al.  The Computational Complexity of Probabilistic Inference Using Bayesian Belief Networks , 1990, Artif. Intell..

[42]  George Percivall,et al.  Ogc® sensor web enablement:overview and high level achhitecture. , 2007 .