Some papers (continue 1)

1. Architectures for context

• Context: not just more text; effective only when it is shared (communication); emerging in dialog
• Interpretation of intention depends on mutually available context;
• Something is context because of the way it is used in interpretation, not due to its inherent properties;
• Context-aware computing: the design of computing mechanism that can use characterizations of some standard aspects of the user’s setting (place, people, and things) as a context for interaction.
• Analyze context’s features, compare three types of models (widget, networked service, and blackboards) and evaluate four factors (efficiency, configurability, robustness and simplicity).

2. The context toolkit; Understanding and using context

• Define the context, and design the context widget and the shapes of GUI widget.

3. Engineering context aware enterprise systems

• Analyze context toolkit.
•  Generate “What we need is an infrastructure that supports dynamic composition of modular context components at runtimeâ€?.
• Analyze other main models.

4. Perceptual components for context aware computing

• Based on ontology, model user’s activity by a set of roles and relations.
•  Different composition of roles and relations correspond to situations with context.
• Basic concepts of context

5. Context is key

• Propose a structured, flexible approach to context.
• Analyze problems of context models: part of process, holistic treatments, mismatch between user and system, discover resources and users, services, and then integrate them into a useful experience.
• Define context by a set of entities: literal value, real world, a set of roles, relationship, and situation (specific configuration of entities, roles, or relationships).
• Propose system architecture.

6. Some motivations and possible approaches to a semantics of adaptive systems

• Intuitions about the relation between context and adaptation.
• - Adaptive behaviors: a collection of possible behaviors selected according to a context (state of the system, and limited history);
• Situation identification -> process-correct behavior (occur by a given route);
• - Separate adaptation logic from behavior logic;
• - Seam – objects of real interest

7. Modeling context information in pervasive computing systems

• Using UML model context;

8. The disappearing computer

• Summarize the current challenges of pervasive computing

9. Foundations for a theory of contextors

• The system introduces the definition, description and compositions of context.

10. Categorization and modeling of quality in context information

• Propose QoCI (quality of contextual information).
• Categorize the current context models: set-category, directed-graph, first-logic, preferences and user’s profile

11. Advanced interaction in context

• TEA:

1. TEA’s objectivity: develop an awareness-enabling add-on component for mainstream mobile computing and communication devices; 
2. TEA’s add-on is responsible for the continuous dynamic profiling of the user’s place-activity (or context). Such information can be used for controlling in coming streams, annotating outgoing streams, or for setting device controls.
3. TEA component relies on multiple primitive cues, extracted from an open collection of low-cost sub-semantic sensors.
4. The general TEA objectivity is to assess which sensors can be combined effectively for context-awareness, which methods are required to relate sensor data to situations, and how complex contexts can be constructed from simple ones.
5. Architecture:

• Sensor: [physical: electronic hardware components that measure physical parameters;] [logical: all the information gathered from the host of the awareness component]
• Cues: [Solve calibration problems. A cue is regarded as a function taking the value of a single sensor up to a certain time as input and providing symbolic or sub-symbolic output.] Context: [a set of 2D vectors ]
• Scripting: [including context information in application. Supported semantics are entering, leaving and while in a context

   6.  There is more context than location Sensor (physical and logical) -> cues

• To develop new functionality with added value for the user and still keep the interaction mechanism simple and straightforward. 
•  The more the device knows about the user, the task and the environment, the better the support is for the user and the more the interface can become invisible.
• Context awareness as knowledge about the user’s and IT device’s state, including surroundings, situation, and location to a less extent.

12. Sentient computing

• A real intuitive physical action initiates an appropriate response, made possible by an understanding computer system in which location and status data extends throughout the physical environment.

13. Towards quality data: an attribute-based approach

• Apply a set of quality parameters to evaluate quality of attributes of an entity

14. Prediction intelligence in context-aware applications

• The retrieval of contextual information depends on spatial variants, time, history of interaction, and a range of factors that are not provided explicitly, but do exit implicitly in the ambient environment
• The concept of predicting the whole context on the level of abstract contextual identifiers with online algorithms integrated in the mobile device is novel
• Observable as the variable of interest related to a set of entities. It implicitly maintains specific knowledge concerning its environment

15. Application design for wearable and context-aware computers

16. A middleware infrastructure for active spaces

17. Business rules and object role modeling

• ORM: a method for designing and querying database models at the conceptual level, where the application is described in terms readily understood by users.
• ORM has no attributes.
• ORM constraints: irreflexivity, intransitivity and acyclicity.
• ORM conceptual schemas comprise fact types, constraints and derive (logic and arithmetic) rules.