Development of context aware system based on Bayesian network driven context reasoning method and ontology context modeling

2015 IEEE 39th Annual Computer Software and Applications Conference

2015

Abstract-Context-aware systems perform adaptive changes in several ways. One way is for the system developers to encompass all possible context changes in a context-aware application and embed them into the system. However, this may not suit situations where the system encounters unknown contexts. In such cases, system inferences and adaptive learning are used whereby the system executes one action and evaluates the outcome to self-adapts/self-learns based on that. Unfortunately, this iterative approach is time-consuming if high number of actions needs to be evaluated. By contrast, our framework for context-aware systems finds the best action for unknown context through concurrent multi-action evaluation and self-adaptation which reduces significantly the evolution time in comparison to the iterative approach. In our implementation we show how the context-aware multi-action system can be used for a context-aware evaluation for database performance tuning.

Section: A Context-aware Application Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Context-Aware Framework for Performance Tuning via Multi-action Evaluation

2015 IEEE 39th Annual Computer Software and Applications Conference

2015

“…Having considered many different context-aware models [18,19,20,21,22] context-aware framework in [23] Fig.1 shows the adoption of the context-aware framework in [23] for the experiment-based performance tuning. Following the definition in [6] the DBS is nominated as the entity that is of concern and the user workload W and the resource usage R is defined as the contexts describing the state of the DBS (i.e.…”

Section: Experiment-based Performance Tuning Using Context-awarementioning

confidence: 99%

Performance tuning of database systems using a context-aware approach

2014 9th International Conference on Computer Engineering &Amp; Systems (ICCES)

2014

Abstract-Database system performance problems have a cascading effect into all aspects of an enterprise application. Database vendors and application developers provide guidelines, best practices and even initial database settings for good performance. But database performance tuning is not a one-off task. Database administrators have to keep a constant eye on the database performance as the tuning work carried out earlier could be invalidated due to multitude of reasons. Before engaging in a performance tuning endeavor, a database administrator must prioritize which tuning tasks to carry out first. This prioritization is done based on which tuning action would yield highest performance benefit. However, this prediction may not always be accurate. Experiment-based performance tuning methodologies have been introduced as an alternative to prediction-based performance tuning approaches. Experimenting on a representative system similar to the production one allows a database administrator to accurately gauge the performance gain for a particular tuning task. In this paper we propose a novel approach to experiment-based performance tuning with the use of a context-aware application model. Using a proof-of-concept implementation we show how it could be used to automate the detection of performance changes, experiment creation and evaluate the performance tuning outcomes for mixed workload types through database configuration parameter changes.

“…Context-aware systems react to a context change by executing an action, while what action to execute is determined by the context inference. A context-aware application does context inference on the basis of the so-called 5W1H (Where, When, What, Who, Why, How) factors [6]. Expanding on this, context-aware applications look at the who's, where's, when's and what's (that is, what the user is doing) of entities and use this information to determine why the situation is occurring [7].…”

Section: Introductionmentioning

confidence: 99%

Context-Aware Approach for Determining the Threshold Price in Name-Your-Own-Price Channels

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

2016

Abstract. Key feature of a context-aware application is the ability to adapt based on the change of context. Two approaches that are widely used in this regard are the context-action pair mapping where developers match an action to execute for a particular context change and the adaptive learning where a context-aware application refines its action over time based on the preceding action's outcome. Both these approaches have limitation which makes them unsuitable in situations where a context-aware application has to deal with unknown context changes. In this paper we propose a framework where adaptation is carried out via concurrent multi-action evaluation of a dynamically created action space. This dynamic creation of the action space eliminates the need for relying on the developers to create context-action pairs and the concurrent multi-action evaluation reduces the adaptation time as opposed to the iterative approach used by adaptive learning techniques. Using our reference implementation of the framework we show how it could be used to dynamically determine the threshold price in an e-commerce system which uses the name-your-own-price (NYOP) strategy.