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Context-Awareness based on Lifelog

Context-Awareness based on Lifelog. Sangkeun Lee Intelligent Database Systems Lab. Seoul National University. Introduction. The whole story begins with my survey ‘A Survey of Context-Aware Systems’ Context & Context-awareness General Process in Context-Aware Systems

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Context-Awareness based on Lifelog

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  1. Context-Awareness based on Lifelog Sangkeun Lee Intelligent Database Systems Lab. Seoul National University

  2. Introduction • The whole story begins with my survey ‘A Survey of Context-Aware Systems’ • Context & Context-awareness • General Process in Context-Aware Systems • History of Context-aware Systems • Generations of Context-aware Systems • Trend Analysis • Let’s briefly revisit the survey first

  3. Context & Context-aware Systems • Context • Any information that can be used to characterize the situation of entities relevant to the interaction betweena user and an application (Dey and Abowd ,2001) • Context-aware Systems • The systems that use context to provide relevant information and/or services to the user, where relevancy depends on the user’s task (Dey and Abowd ,2001)

  4. General Process in Context-Aware Systems • Generally, processing context-awareness can be summarized into following four steps • Sense • Getting values from context sources (e.g. sensors) • Context-sources doesn’t have to be hardware devices • (e.g. virtual sensors, other applications) • Abstraction • Context Aggregation • Temperatures of previous 10 minutes -> Very Cold • Context Interpretation • GPS signal -> City name • (Recognize) • Understanding current context & trigger the most suitable action • Action • Better user’s experience

  5. Trend Analysis

  6. Insights from the Survey • What makes context-Awareness so difficult? • Realizing useful Application/Domain Specific context-aware systems is not a big problem • Using simple, non-flexible context models • In a restricted & known scenarios (e.g. museum, …) • Not a ‘true’ context-awareness for a ubiquitous environment • However, realizing a flexible and general context-aware system is a big problem • If you use ontological context model, • then you get flexibility and expressiveness of representing context information, • but recognizing (understanding) the represented context data causes computational costs, less scalability – not practical!

  7. Approach to achieve feasibility and flexibility at the same time? • As I mentioned earlier, context-aware systems are • The systems that use context to provide relevant information and/or services to the user, where relevancy depends on the user’s task (Dey and Abowd ,2001) • Recommender system • Recommender systems or recommendation engines form or work from a specific type of information filtering system technique that attempts to present information items that are likely to be of interest to the user. (Wikipedia) • Similar ! but there are many working systems • They use user’s ratings, logs, previous histories, …. • By getting hints from recommender systems that are practically used in many domains, we present a practical way of processing context-awareness by separating ‘Recognize’ step into two steps ‘Collect’ & ‘Match’ • Existing approach : Sense - Abstraction – Recognize – Action (Analytic) • Practical Approach : Collect - Sense – Abstraction - Match – Action (Empiric)

  8. Sense t1 Profile t2 t7 t3 t8 t9 Information t4 t5 Query t10 t2 t11 t4 t12 Service t6 Context Practical Context-awareness –an Empiric Approach • Match Collect

  9. Practical Context-awareness –an Empiric Approach • Is it reasonable to do this? • Yes! • Context history can be used to establish trends and predict future context values. [Anind K. Dey and Gregory D. Abowd, 2001] • “Context history is generally believed to be useful, but it is rarely used” [Guanling Chen and David Kotz, 2001] • “Context histories may be used to establish trends and predict future context values.” [Matthias Baldauf, 2007] • Many systems keeps recording context history (although they do not utilize them) • Context Toolkit, CoBrA, CASS, SOCAM and CORTEX save sensed context data persistently in a database.

  10. Collect & Match • My research consists of two parts • Collect • Sense and store context histories • LifeLogOn: Log on to Your Lifelog Ontology! (ISWC ‘09) • Entity-Event Lifelog Ontology Model (EELOM)for Lifelog Ontology Schema Definition (APWEB ‘10) • LifeLogOn: A Practical Lifelog System for Building and Exploiting Lifelog Ontology (UMC ‘10) • Match • Find the most suitable information/services based on current context & context histories (logs) using available techniques such as • Collaborative filtering, Context-aware collaborative filtering, contents based matching, Simple If-then rules, many other heuristics • A flexible framework for context-aware matching is needed

  11. Music Listening Logs GPS Logs Phone Call Logs E-mail History Schedules Movie Watching Logs *SematnicRelationships Collect – LifeLogOn: A Lifelog System • Integrate currently available logs from different devices and create semantic relationships among logs • Transforms relational log data and metadata into instance-level Ontology and stores in knowledge base Music Listening Logs Movie Watching Logs GPS Logs Phone Call Logs E-mail History Schedules LifeLogOn

  12. Collect – ‘Entity-Event Lifelog Ontology Model’ • EELOM consists of domain, entity and events • A domain is a group of entities and events, and it has a domain name for representing it. • An entity is a thing which has a separate existence and can be uniquely identified (e.g. a person, a location, a device, a timestamp) An entity is represented as a set of attributes. Entities can be shared among domains. • An event is an activity or interaction that can be uniquely identified. It is represented as a set of entities, and each entity has its role for the event. • Why not RDF? OWL? • - It does not allow complex relationships (e.g. hierarchy of entities), • but it is still flexible and expressive enough to cover logs of many domains • Easy to construct index structure by considering everything as entities & events

  13. Bright Time@1 640KB Home.jpg Photo@1 Location Oldpop 55:24:66 Atmosphere Music@2 640x480 OtherContext Location Time@2 OtherContext Yellow Submarine Singing Location@2 Hiphop User@1 SonyCD2 Play@2 take@1 Seoul,Korea Groove Hyori’s back Beatles Music@1 Play@1 Location@1 Kangwon 12:31:42 Chunchon Yesterday 10 minute Hyori Kpop Korea Korea Album Seoul Seoul Genre Music Music Music Male Matt Artist Time Time User User Date Date 110 Stop Play BPM Title 90 12 30 12 02 01 27 9 Music Domain Collect – LifeLogOnOverview GPS Log from Cell phone AMG Metadata Last.fm Music Log RelationalLog Data iTunes Music Log Log to Ontology Mapper Log–Ontology Schema Mapping CDDB Metadata Temperature Log from Forecast.com Ontology Schema Definition Tool OntologySchema LifeLogOn Instance-Level Ontology Generator OntologyGenerator Visualization & Browsing Tool Ontology Instances

  14. What can we use LifeLogOn for? • You can create your own lifelog ontology without understanding any ontology languages • You can search something not sure about • Find a song you listened at your birthday party and but only know the filename of the photos at the party • Find photos that you took when you are talking with your friend on the phone, saying "It's so beautiful here!" • Find any events and entities those you know their context information such as time, location,... • ... even more

  15. We have the context history collection… • Many issues remain • Building a lifelog ontology using real data set • Implementing Loggers(maybe on iPhone, …) • Log Ontology Abstraction/Summarization • Better Visualization • Entity Matching, …

  16. Sense t1 Profile t2 t7 t3 t8 t9 Information t4 t5 Query t10 t2 t11 t4 t12 Service t6 Context Let’s Get Back to Context-awareness • Match Collect

  17. Paper Reading • Context-Aware Collaborative Filtering System: Predicting the User’s Preference in the Ubiquitous Computing Environment - Annie Chen, 2005 • predicts a user’s preference in different context situations based on past experiences • 시간 상황 정보를 고려한 협업 필터링을 이용한 음악 추천 – 2009, 이동주 • Context-Aware Recommendation by Aggregating User Context–2009, 신동민 • Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions–2005,GediminasAdomavicius • And so on, …

  18. Problem Definition • Recommender System • Estimating ratings for the items that have not been seen by a user • User (explicitly/implicitly - logs) rates Item • Input: User, Ratings Output: Item • Context-aware Recommender System (similar but different!) • Predicting a user’s preference in different context situations based on past experiences • User (explicitly/implicitly) rates Item + each rating has Context Info • Input: User, Current Context Output: Item

  19. Problem Definition • Context-awareness based on Lifelog Ontology • In our Lifelog Ontology, there are no classification of users, items, contexts… • All the things in the ontology are Events & Entities

  20. Problem Definition • Assume that the system knows the current context (situation) • Context-aware applications are such as • Find out the most suitable songs to the current user • Find out the most suitable person to be invited to the current location • Find out the most similar songs to the song that currently user’s listening to • Find out the most related anything to the current situation • … • Relatedness can be defined in various ways • We can conclude that a context-aware system based on lifelogs is a system that allows dynamically & flexibly composing recommendation based on lifelogs

  21. Context-Awareness Based on Lifelogs • Our Lifelog model • Lifelog Ontology LO is a set of events EV • A context history is represented by a event EV that are composed of a set of entities EN • e.g. {Sangkeun Lee, Yesterday, 2009/12/24, Summer, Seoul, SNU} • A current context C is represented by a set of entities EN • e.g. {2009/12/25, Hyo-ri Lee, Winter, Hungry, Happy, Busan, …}

  22. Simple Flexible Match Algorithm (on-going work) Similar User Based on logs User User’s rating Songs Rating prediction based on similar user’s raitings Collaborative Filtering (e.g. Music recommendation)

  23. Simple Flexible Match Algorithm (on-going work) Event Song Song Event Time Song User User Location Entity … Entity … … … … • Generalized Model • Raiting is one possible relatedness between users & songs • User similarity is one possible relatedness between users • Entity – Entity Relatedness • Can be calculated by similarity of events related to each entity • Is it only suitable for same type of entities? Not sure.

  24. Simple Flexible Match Algorithm 1 (on-going work) E6 E1 E6 E1 E5 C E5 C E2 E2 E4 E3 E4 E3 Similar Entity Entity relatedness in current context Entity Entity relatedness in current context Events Predict relatedness based on similar entity’s relatedness E6 E1 E5 C E2 E4 E3 Collaborative Context-aware Filtering

  25. Simple Flexible Match Algorithm 2 (on-going work) E6 E1 Current Context can be represented in a set of entities (Assume that all the entities are the categorical values) E6 E1 E6 E5 C E1 E6 E1 E2 E6 E5 CH1 E1 E6 E5 CH1 E2 E1 E6 E5 CH1 E2 E1 E5 CH1 E2 E5 CH1 E2 E4 E3 E4 E3 E5 CH1 E2 E4 E3 E2 E4 E3 E4 E3 E4 E3 E4 E3 Event ranking based on similarity to current context Entity Rank can be calculated by weighted sum • Context-Aware Filtering • Find out the most related entities to current context • Context Similarity • Can be defined as an aggregation of entity relatedness • Entity Rank • The most related entity to current context

  26. Simple Flexible Match Algorithm (on-going work) • Context-Aware Filtering • Flexible? • Context Similarity, Entity Relatedness can be defined in various ways by controlling Weight parameters of context similarity, weight parameters of entity relatedness • You can decide what to recommend (Any entity can be recommended)

  27. Simple Match is Not the Only Way – Graph Traverse • Assume that Current Context is • {Matt Lee, DSC_2128, 2009/07/23, 018-2144-8842} • Result • Entity: matt183/018-2144-8842/ • Entity: SNU. Main Gate/37:27:14.60N:126:57:12.33E/37:27:14.60N:126:57:12.33E/ • Entity: GS24-TB/ • Entity: Green House/37:27:27.29N:126:57:62.52E/37:27:27.29N:126:57:62.52E/ • Entity: Jay Yeon/Female/yeon@korea.pe.kr/101-1106 Suwon apt. Suwon Si, Republic Of Korea/Deejay09/010-4255-5541/ • Entity: 2009/01/04/ • Entity: Lazy Rhapsody/The Duke: Creole Rhapsody (1931-1932) (disc 1)/Duke Ellington & His Orchestra/196440/ • Entity: SNU. Rear Gate/37:27:13.79N:126:57:18.23E/37:27:13.79N:126:57:18.23E/ • Entity: 12:35:45/ • Entity: Warren Gong/gongstock@europa.snu.ac.kr/138-3 Naksung apt. BongChun dong, Kwanak-gu, Seoul Si, Republic Of Korea/gongfit/019-258-8163/ • Entity: SNU Library/37:27:72.11N:126:57:11.32E/37:27:72.11N:126:57:11.32E/

  28. Simple Match is Not the Only Way – Simple If-then Rules • Simple if then rules • sometimes much powerful and useful then other recommendation algorithm • Many context-aware actions can be defined as simple if-then rules • Alert me when ‘Sangkeun Lee’ is in ‘IDS Lab’ • Turn on light when ‘Sangkeun Lee’ arrives at home • …. • CAUTION : should not require complex reasoning or inference

  29. Conclusion & Discussion • The Ideal Context-awareness • Find out the most related anything to the current situation • Considering everything useful • Ignoring information noise • …

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