speaker: prof. sten f. andler director, infofusion research program
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Challenges in Information Fusion Technology Capabilities for Modern Intelligence and Security Problems. Speaker: Prof. Sten F. Andler Director, Infofusion Research Program University of Skövde, Skövde, Sweden (*) Author: Dr. James Llinas Center for Multisource Information Fusion - PowerPoint PPT PresentationTRANSCRIPT
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Challenges in Information Fusion Technology Capabilities
for Modern Intelligence and Security Problems
Speaker: Prof. Sten F. AndlerDirector, Infofusion Research Program
University of Skövde, Skövde, Sweden (*)Author: Dr. James Llinas
Center for Multisource Information FusionUniversity at Buffalo, Buffalo, New York, USA
(*)
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Key Information Fusion Challenges Driven by Operational Problems and Modern IT
• Heterogeneity of Data, Information• Common Referencing and Data Association
Impacts• Dealing with Semantics• The Entry of Graphical Methods• Architecting Systems and Analytic Frameworks
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Heterogeneity of Data/Information
• Observational– “Hard” Sensor Data and “Soft” linguistic/reported/unstructured Data
• Open-source & Social Media– Issues: Mostly in linguistic form; Trust, Volume, Formats, Modalities
• Contextual differences– Issues: Format, Middleware reqmt, dynamics, relevance
• Ontological differences– Issues: Multiple-ontology cases, semantics, dynamics, relevance
• Learned knowledge– Issues: integrating inductive and other inferencing procedures
Heterogeneity from modern IT capabilities/problems and networked systemsLack of reliable a priori knowledge to support dynamic deductively-based reasoning “Weak Knowledge” problems
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• Soft (linguistic) data -- New preprocessing Front Ends: requirement for semantically robust Text Extraction/NLP processes– Marginally available today– If not extracted, properly labeled entities never enter the Fusion process– If not tagged with some level of (reliable) uncertainty/confidence, entity
uncertainty not considered• Confounds both Common Referencing and Data Association
• Exploiting Contextual Data requires Middleware to condition data in a form useable by Fusion process (native form-to-useable form)– Can also require hybrid algorithms, eg context-aided Kalman Filter designs
• In networked systems, there can be multiple Ontological versions being used– Creates a need for ontological normalization (Common Referencing function)– Also impacts Data Association; inconsistent nomenclature will prevent feasible
associations• Information learned in real-time creates a Level 4 Knowledge Management
functional requirement, and real-time adaptation that can include dealing with out-of-sequence evidence (retrospective adaptation)
Some Impacts due to Data Heterogeneity
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• Common Referencing– Temporal alignment within streaming Soft data feeds is challenging
• Dealing with linguistic tense: past/present/future– Impacts correct Temporal Reasoning
» Creates a need for agile Temporal Reasoning
– Networked environments open the possibility for inconsistent forms of uncertainty representation• Creates a need for uncertainty transforms, normalization methods
• Data Association– Major impact due to Soft (linguistic) data and availability of
Relational links• Association now of higher dimension: Entities/attributes and inter-entity
Relations — becomes a Graph Association problem• New scoring functions required; eg Relational similarity
Some further Impacts regarding Common Referencing and Data Association
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Representative Impacts regarding Common Referencing and Data Association, cont.
G. Tauer, R. Nagi, M. Sudit, The graph association problem: Mathematical models and a lagrangian heuristic, Naval Research Logistics (NRL) Volume 60, Issue 3, pages 251–268, April 2013
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• Graphs as a Representational Form– The standard for language representation– Deals with Entities and Relations– Quantitatively-based; visually manageable
• Graph-based Analytics– Framework for Data Association as shown– Evidential searching/matching (supports query-based,
discovery-based analysis)• Variety of Graph-Matching paradigms, issues
– Stochastic due to tagged uncertainties in graph elements– Incremental to handle streaming real-time data– Large scale to handle “Big Data”; eg Cloud-based
Representative Impacts regarding Graphical Forms and Operations
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• Optimal strategies for semantic “control” – control of semantic complexities– Rigorous control of Ontologies– Controlled vs Uncontrolled Languages• Eg Battle Management Language
– Robust Text Extraction, NLP– Role of Human Mediators in system architecture• Speed (automation) vs semantic accuracy
• Semantic Uncertainty• Vague predicates; issue of Truth—leads to 3-valued forms of
Uncertainty Representation
Some further Impacts regarding Semantics
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• Many problems are “Weak Knowledge” problems wherein the extent of reliable a priori dynamic knowledge about the domain is limited
• This motivates an approach that must combine deductive and inductive (or abductive) methods in an effective way– These tend to require technologies that support discovery and
learning-based hypothesis-formulation strategies• Methods such as Complex Event Processing, Probabilistic
Argumentation, Graph-based Relational Learning are some of the new inferencing methods being studied.
Some Impacts regarding System Architectures and Analytical Frameworks
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* Integrating the Data Fusion and Data Mining Processes Ed Waltz, Natl Symp on Sensor and Data Fusion, 2004
Earliest Thoughts on Combining Inductive and Deductive Inferencing for Fusion*
Representative Architectures: Inductive + Deductive
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Representative Architectures: Hard and Soft Fusion Processes; Disparate Analytic Tools
encingn
encingon
on
-gon
Hard (sensor) fusion
Enterprise Service Bus
Core Enterprise
Servces
Information (Evidence) Services
(Sensor) Data and Computational
Services
Evidence and Entity -estimate Foraging Services
SensemakingServices
Intel Cell –or –Company Opns Intell Support Team
Analytic SupportServices
Soft (intel) fusion
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Summary• Requirements for Data and Information Fusion Processes and
Systems have gone far beyond the goal of estimating properties and geometries of entities– Dealing with complex Semantics, inter-entity Relations, Social Media
and other Contextual effects, complex Temporal dynamics, and Heterogeneous Data have made the design of IF Systems a markedly new challenge.
• Incremental advances and accomplishments are being realized but there is much to be done
• Major advances are needed in dealing with more complex inferencing challenges to support efficient learning and discovery processes.
• New partnerships are needed across various multidisciplinary areas in order to address these new complexities