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Information Curation Layer

Information Curation Layer. Amjad Usman 2014-08-27. Outline. Introduction Motivation & Challenges Related Work Proposed Idea Workflow Inter/Intra Layered Communication Tools & Technologies Uniqueness. Introduction. Behavior. Activities. Context. Low Level Activities. Context

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Information Curation Layer

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  1. Information Curation Layer Amjad Usman 2014-08-27

  2. Outline • Introduction • Motivation & Challenges • Related Work • Proposed Idea • Workflow • Inter/Intra Layered Communication • Tools & Technologies • Uniqueness

  3. Introduction Behavior Activities Context Low Level Activities Context Modeling Behavior Modeling & Analysis Exercise Pattern Diet/Food Pattern Sleeping Pattern Lifelog Repository

  4. Motivation

  5. Challenges Resolving the heterogeneity of diverse sources of data Context Modeling Fusing different types of context information Conversion and verification of different types of context information Designing abstract model to represent different behaviors of a user Behavior Modeling Identifying features and their relationships among behavior elements Recognition of behavioral patterns from the model

  6. Related Work Context Modeling | Behavior Modeling and Analysis

  7. Related Work Context Modeling | Behavior Modeling & Analysis

  8. Limitations of the existing work Single input modality Lack of uniform contextual model Lack of context based semantic integrity check Unable to relate or perform fusion for behavioral pattern identification

  9. Proposed Architecture (Abstract View) High level Context awareness Service layer • Behavior Modeling and Analysis Behavior Analysis Mediator Behavior Data Processing Behavior Modeling Context Awareness and Modeling Life Log Repository Rule base HDFS Data Access Interface Context Fusion Mappers & Transformer Context Verification Context Interpreter Intermediate Data

  10. Proposed Architecture (Detailed View) High level Context awareness Service layer • Behavior Modeling and Analysis Behavior Analysis Behavior Data Processing Mediator Pattern Identification Behavior Model Picker Model Populator Response Handler Request Handler Behavior Recognition Behavior Modeling Model Store Extractor Behavior Feature Identification Behavior Descriptor Report Generation Behavior Modeler Context Awareness and Modeling Rule base Life Log Repository Context Interpreter Matching Activities Decision Propagator Mappers & Transformer HDFS Data Access Interface Context Fusion Context Verification • Rule-Filtering Activity Retriever Activity Transformer Horizontal Fusion Consistency Check Intermediate Data Vertical Fusion Existence Check Query Generator Mapping Files Life log Ontology

  11. Proposed Architecture (Functional view) High level Context awareness Service layer Behavioral Data Lifelog Data Response • Behavior Modeling and Analysis Request Behavior Analysis Behavior Data Processing Mediator Pattern Identification Response Handler Request Handler Behavior Model Picker Model Popolator Behavior Recognition Model Behavior Modeling Model Store Behavior Extractor Behavior Feature Identification Behavior Descriptor Report Generation Behavior Modeler Lifelog Data Extracted Data Context Awareness and Modeling Matching Rules Rule base Life Log Repository Context Interpreter Matching Activities Verified Context Decision Propagator Mappers & Transformer HDFS Data Access Interface Context Fusion Context Verification • Rule-Filtering Activity Retriever Activity Transformer Horizontal Fusion Consistency Check Intermediate Data Low-level Activities Vertical Fusion Existence Check Query Generator Mapping Files Life log Ontology • High Level Context Fused Context Low-level Activities In OWL format

  12. Context Modeling and Awareness - Scenario Low Level Activities High Level Context Person: ABC Activity: Sitting Activity Time: 9AM Location: Lab Research Work Person: ABC Activity: Sitting Activity Time: 10AM Location: Prof. Office Office Work Meeting Person: ABC Activity: Eating Activity Time: 12PM Location: Dorm Lunch

  13. Context Modeling and Awareness - Scenario Methodology Output Input Mappers & Transformer Activity Transformer • Generate Mappings • Store Mappings • Extract XML Concepts • Find Mappings • Convert OWL Format OWL Format XML Format Mapping Files Life log Ontology Low Level Activities Person: Mr J Activity: Sitting Activity Time: 9AM Location: Lab <activity> <detectedBy>Sensor</detectedBy> <hasName>Mr J</hasName> <activityName>Sitting</activityName> <time>8 AM</time> <location>Lab</location> </activity> Sitting Person: Mr J Activity: Sitting Activity Time: 10AM Location: Prof. Office Mr J Lab Person: Mr J Activity: Eating Activity Time: 12PM Location: Dorm Activity 9AM Person PersonName Sensor Location Value Category Time

  14. Context Modeling and Awareness - Scenario Methodology Output Input Mappers & Transformer Activity Transformer • Generate Mappings • Store Mappings • Extract XML Concepts • Find Mappings • Convert OWL Format OWL Format XML Format Mapping Files Life log Ontology Low Level Activities Person: Mr J Activity: Sitting Activity Time: 9AM Location: Lab <activity> <detectedBy>Sensor</detectedBy> <hasName>Mr J</hasName> <activityName>Sitting</activityName> <time>10 AM</time> <location>Prof Office</location> </activity> Sitting Person: Mr J Activity: Sitting Activity Time: 10AM Location: Prof. Office Mr J Prof Office Person: Mr J Activity: Eating Activity Time: 12PM Location: Dorm Activity 10AM Person PersonName Sensor Location Value Category Time

  15. Context Modeling and Awareness - Scenario Methodology Output Input Mappers & Transformer Activity Transformer • Generate Mappings • Store Mappings • Extract XML Concepts • Find Mappings • Convert OWL Format OWL Format XML Format Mapping Files Life log Ontology Low Level Activities Person: Mr J Activity: Sitting Activity Time: 9AM Location: Lab <activity> <detectedBy>Sensor</detectedBy> <hasName>Mr J</hasName> <activityName>Eating</activityName> <time>12 PM</time> <location>Dormitory</location> </activity> Eating Person: Mr J Activity: Sitting Activity Time: 10AM Location: Prof. Office Mr J Dormitory Person: Mr J Activity: Eating Activity Time: 12PM Location: Dorm Activity 12PM Person PersonName Sensor Location Value Category Time

  16. Context Modeling and Awareness - Scenario Methodology Output Input Context Interpreter Decision Propagator • Query Generation • Activities Retrieval • Maintain Log • Rules Extraction • Rules Filtration 1 2 • Rule-Filtering Activity Retriever 3 4 5 Query Generator 1 Life Log Repository • Select ?PersonId ?PersonName ?activity ?time ?location ?date • Where ?activity :hasName ?activity • ?activity :hasperformer ?person • ?activity :hasTime ?time • ?activity :hasDate ?date 2 Sitting Sitting Sitting Eating 3 Mr J Mr J Mr J Mr J Lab Prof Office Dormitory Lab Activity Activity Activity Activity 9AM 12PM 10AM 9AM 5 4 IF User:= Student ⋀ Activity := Sitting ⋀ Location := Lab ← Context:= Research Work Person Person Person Person PersonName PersonName PersonName PersonName Sensor Sensor Sensor Sensor Location Location Location Location Value Value Value Value Category Category Category Category Time Time Time Time

  17. Context Modeling and Awareness - Scenario Methodology Output Input Context Interpreter Decision Propagator • Query Generation • Activities Retrieval • Maintain Log • Rules Extraction • Rules Filtration 1 2 • Rule-Filtering Activity Retriever 3 4 5 Query Generator 1 Life Log Repository • Select ?PersonId ?PersonName?activity ?time ?location ?date ?context • Where ?activity :hasName ?activity • ?activity :hasperformer ?person • ?activity :hasTime ?time • ?activity :hasDate ?date. ?activity :hasContext ?context 2 Sitting Sitting Sitting Eating 3 Mr J Mr J Mr J Mr J Prof Office Lab Dormitory Prof Office Activity Activity Activity Activity 10AM 9AM 12PM 10AM Person Person Person Person PersonName PersonName PersonName PersonName 5 4 IF User:= Student ⋀ Activity := Sitting ⋀ Location := Prof. Office ← Context:= Meeting Sensor Sensor Sensor Sensor Location Location Location Location Value Value Value Value Category Category Category Category Time Time Time Time

  18. Context Modeling and Awareness - Scenario Methodology Output Input Context Interpreter Decision Propagator • Query Generation • Activities Retrieval • Maintain Log • Rules Extraction • Rules Filtration 1 2 • Rule-Filtering Activity Retriever 3 4 5 Query Generator 1 • Select ?PersonId ?PersonName ?activity ?time ?location ?date ?context • Where ?activity :hasName ?activity • ?activity :hasperformer ?person • ?activity :hasTime ?time • ?activity :hasDate ?date. ?activity :hasContext ?context Life Log Repository Eating Sitting Sitting Eating 2 Mr J Mr J Mr J Mr J 3 Prof Office Dormitory Lab Dormitory Activity Activity Activity Activity 9AM 12PM 12PM 10AM Person Person Person Person PersonName PersonName PersonName PersonName Sensor Sensor Sensor Sensor Location Location Location Location Value Value Value Value 5 4 IF Activity := Eating ⋀ Location := Dormitory ⋀ Time:= (12 to 3PM) ← Context:= Lunch Category Category Category Category Time Time Time Time

  19. Context Modeling and Awareness - Scenario Context Fusion Methodology Output Input Horizontal Fusion • Extract Attributes • Compare Information • Store Information • Infer new context 1 2 Vertical Fusion 3 4 1 3 2 Vertical Fusion 1 1 3 2 Vertical Fusion 1 3 2 Vertical Fusion

  20. Context Modeling and Awareness - Scenario Context Fusion Methodology Output Input Horizontal Fusion • Extract Attributes • Compare Information • Store Information • Infer new context 1 2 Vertical Fusion 3 4 1 3 2 1 Horizontal Fusion 4 IF Date := ABC ⋀ Context := Reaserach Work ⋀ Meeting ⋀ Lunch ← Context:= Office Work

  21. Context Modeling and Awareness - Scenario Methodology Output Input • Syntax Checking • Semantics Checking • Duplication Checking • Store Information Life Log Repository Existence Check Consistency Check IsSyntaxOk ??? IsSemanticsOK??? Duplicate Exists ??? Consistent Context Yes Yes No Context Verification Consistency Check No Status: Yes No Show Message Discard Activity Show Message Existence Check Activity already exists Error: Incorrect Date Format Error: No property with this name exists

  22. Behavior Modeling and Analysis - Scenario Recommendation Manager Feeling Over weight User: ABC, Activity: Exercise, Date: Today Time: 12:15:00 Location: KHUGym Person: abc Behavior: Weight Gain Behavior Modeling & Analysis Check Request Type Running Cycling Walking 12:10:00 12:30:00 12:05:00 Core III: Service Layer Request for: Exercise Behavior

  23. Behavior Modeling and Analysis - Scenario Recommendation Manager Feeling Over weight User: ABC, Activity: Exercise, Date: Today Time: 12:15:00 Location: KHUGym Person: abc Behavior: Weight Gain Behavior Modeling & Analysis Check Request Type Running Cycling Walking 12:10:00 12:30:00 12:05:00 Core III: Service Layer Request for: Exercise Behavior

  24. Behavior Modeling and Analysis – Scenario Exercise Context Scenario Service layer • Behavior Modeling and Analysis . Behavior Analysis Behavior Data Processing Mediator Pattern Identification Response Handler Request Handler Behavior Model Picker Model Popolator HDFS Data Access Interface Behavior Recognition Behavior Modeling Model Store Intermediate Data Extractor Behavior Feature Identification Behavior Descriptor Report Generation Behavior Modeler Visualization User:abc, activity: exercise, Date: today Different Presentation Styles User: ABC, Activity: Exercise, Date: Today, Time: 12:15:00, Location: KHUGym • Did the user abc do exercise today? Request Type: Context Request Handler Check Request Type Request Service Layer Response Handler Extractor Exercise activity data of user abc performed today (2014:08:27) from lifelog Request Type: Behavior Context : Exercise Running Cycling Walking 12:10:00 12:30:00 12:05:00

  25. Behavior Modeling and Analysis – Scenario Long/Short-term Behavior Service layer • Behavior Modeling and Analysis . Behavior Analysis Behavior Data Processing Mediator Pattern Identification Response Handler Request Handler Behavior Model Picker Model Popolator HDFS Data Access Interface Behavior Recognition Behavior Modeling Model Store Intermediate Data Extractor Behavior Feature Identification Behavior Descriptor Report Generation Behavior Modeler Behavior Data Processor Request Type: Context Data Request Handler Check Request Type Data Service Layer Response Handler Extractor Request Past exercise history of user ABC extracted from Life log repository • Weekly exercise behavior of user abc? Request Type: Behavior (Short/Long) User: abc, Type: Short, Behavior: Exercise

  26. Behavior Modeling and Analysis - Scenario Service layer • Behavior Modeling and Analysis . Behavior Analysis Behavior Data Processing Mediator Pattern Identification Response Handler Request Handler Behavior Model Picker Model Populator HDFS Data Access Interface Behavior Recognition Behavior Modeling Model Store Intermediate Data Extractor Behavior Feature Identification Behavior Descriptor Report Generation Behavior Modeler Request: Short-term Exercise Behavior Model Populator Response Handler Exercise Model populated with Data Past exercise history of user ABC Exercise Model Model Picker Sleep Exercise Food Model Selected Behavioral Model Store (Repository)

  27. Behavior Modeling and Analysis - Scenario Service layer • Behavior Modeling and Analysis . Behavior Analysis Behavior Data Processing Mediator Pattern Identification Response Handler Request Handler Behavior Model Picker Model Popolator Behavior Recognition HDFS Data Access Interface Behavior Modeling Model Store Extractor Behavior Feature Identification Behavior Descriptor Intermediate Data Report Generation Behavior Modeler Pattern Identification [Exercise Behavior] When..?? Where..?? How long..?? Model Populator Time Pattern Location Pattern Duration Pattern Behavioral Data Evening (5/7) ~ 71% Avg. Exercise Duration35-40 minutes Morning (2/7) ~ 29% Behavior Recognition Visualization

  28. Inter/Intra Layered Communication Core III: Service Curation Layer Core IV: Supporting Layer Visualization Recommendation Manager Reasoner and Predictor Context / Behavior (short-term & Long-term) Core II: Information Curation Layer High Level Context-awareness Low-level Context Low Level Context-awareness HDFS Data Access Interface Intermediate Data Structured Data

  29. Tools and Technologies • Knowledge Representation • RDF / RDFS / OWL 2 • Protégé as IDE • Programming Language & API • Java • Jena, Twitter • Query & Rule Language • SPARQL • SWRL

  30. Uniqueness and Contribution • Context integration from multiple and diverse sources • Unified ontological context representation • Fusion of contextual information • Two-phase Match Making algorithm • Horizontal and Vertical Fusion • Ontological Behavior Representation Model • Long-term / Short-term Behavior Recognition/Identification

  31. References [Rodríguez2014] Rodríguez, N. D., Cuéllar, M. P., Lilius, J., & Calvo-Flores, M. D.. A survey on ontologies for human behavior recognition. ACM Computing Surveys (CSUR), 46(4), 43. [Lane2014] Lane, Nicholas D., et al. "BeWell: Sensing Sleep, Physical Activities and Social Interactions to Promote Wellbeing." Mobile Networks and Applications (2014): 1-15. [Kim2013] Kim, P. H., and Giunchiglia, F. Lifelog Data Model and Management: Study on Research Challenges. International Journal of Computer Information Systems and Industrial Management, 115-125. [Kim2012] Kim, P. H., and Giunchiglia, F. Life logging practice for human behavior modeling. In IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2873-2878. [Lane2011] Nicholas D. Lane, et. al. BeWell: A Smartphone Application to Monitor, Model and Promote Wellbeing, Pervasive Health 2011-- 5th International ICST Conference on Pervasive Computing Technologies for Healthcare, Dublin. [Chen2011] Chen, L., Nugent, C., Biswas, J., and Hoey, J. An ontology-based context-aware approach for behavior analysis. In Activity Recognition in Pervasive Intelligent Environments, vol. 4. Atlantis Press, Paris, France, 2011, pp. 127--148. [Ribboni2011a] Daniele Riboniand Claudio Bettini.. OWL 2 modeling and reasoning with complex human activities. Pervasive Mobile Computing 7, 3, 379–395.

  32. References [Ribboni2011b] Riboni, D., & Bettini, C. (2011). COSAR: hybrid reasoning for context-aware activity recognition. Personal and Ubiquitous Computing, 15(3), 271-289. [Lee2010] S. Lee, G. Gong, and S.-g. Lee. (2010). Entity-Event Lifelog Ontology Model (EELOM) for Lifelog Ontology Schema Definition. (APWEB). [Santosa2010] A. C. Santosa, J. M. P. Cardosob, D. R. Ferreiraa. Providing User Context for Mobile and Social Networking Applications”, Pervasive and Mobile Computing 01/2010; 6:324-341. [Longbing2010] Longbing Cao, In-depth Behavior Understanding and Use:the Behavior Informatics Approach, Information Science, 180(17); 3067-3085, 2010. [Lee2009] S. Lee, G. Gong, and S. Lee LifeLogOn: Log on to Your Lifelog Ontology! In Info Proceedings of the 8th International Semantic Web Conference (ISWC). [Chein2009] Chien, B. C., Tsai, H. C., & Hsueh, Y. K. (2009). CADBA: a context-aware architecture based on context database for mobile computing. In Ubiquitous, Autonomic and Trusted Computing. Symposia and Workshops on (pp. 367-372). IEEE. [Cao2008] Cao, Longbing. "Behavior informatics and analytics: Let behavior talk." Data Mining Workshops, 2008. ICDMW'08. IEEE International Conference on. IEEE, 2008.

  33. ⋀ Location Thank You!

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