Inconsistent contexts are death-wounds which usually result in context-aware applications’ incongruous behaviors and users’ perplexed feelings, therefore the benefits of context-aware computing will become less believed. This problem occurs in most sensor based applications due to the intrinsic drawbacks of fallible physical sensors which can only detect some evidence of real world’s situations...

1 Mphil Scholar, 2 Assistant Professor, Department Of Computer Science , Nadar Saraswathi College (India) ABSTRACT Cloud storage services are commercially more popular due to their amount of advantages. Most of the cloud service provider provides services like infrastructure management, data storage services on 24/7 through any devices at anywhere. To provide this ubiquitous always on service m...

Gaussian Mixture Model (GMM) is one of the most popular data clustering methods which can be viewed as a linear combination of different Gaussian components. In GMM, each cluster obeys Gaussian distribution and the task of clustering is to group observations into different components through estimating each cluster’s own parameters. The ExpectationMaximization algorithm is always involved in su...

Models undergo a variety of transformations throughout development. One of the key transformations is merge, used when developers need to combine a set of models with respect to the overlaps between them. A major question about model transformations in general, and merge in particular, is what consistency properties are preserved across the transformations and what consistency properties may ne...

We analyze consumption and asset pricing with recursive preferences given by Kreps–Porteus stochastic differential utility (K–P SDU). We show that utility depends on two state variables: current consumption and a second variable (related to the wealth–consumption ratio) that captures all information about future opportunities. This representation of utility reduces the internal consistency cond...

We model learning in a continuous-time Brownian setting where there is prior ambiguity. The associated model of preference values robustness and is timeconsistent. The model is applied to study optimal learning when the choice between actions can be postponed, at a per-unit-time cost, in order to observe a signal that provides information about an unknown parameter. The corresponding optimal st...

One of the major challenges in distributed systems is establishing consistency among replicated data in a timely fashion. While the consistent ordering of events has been extensively researched, the time span to reach a consistent state is mostly considered an effect of the chosen consistency model, rather than being considered a parameter itself. This paper argues that it is possible to give g...

To achieve scalability, modern Internet services often rely on distributed databases with consistency models for transactions weaker than serializability. At present, application programmers often lack techniques to ensure that the weakness of these consistency models does not violate application correctness. We present criteria to check whether applications that rely on a database providing on...