A computer can indeed be a metaphor for man, and then it becomes relevant to discover whether man is all bits on the inside. But an alternative to metaphor is at hand.

–– A Newell and H A Simon,
“Human Problem Solving, 1972 "

The near simultaneous emergence of cloud computing and big data analytics has
brought a new set of challenges. Organizations have started to replace their
own infrastructure with large computing clusters hosted by cloud providers such as Amazon and Google, and they buy these services using complex pricing mechanisms. The cloud providers currently provide computing power, while the application hosts are interested in completion of their jobs, irrespective of the computing power needed to complete those jobs. The missing link between the demand in terms of job completion and the supply in the form of computing power is a key challenge. Research is being done to address it. Reports claim that many algorithms are proposed to sort the jobs in the order of their marginal values, i.e., value-to-resource ratio, similar to the greedy knapsack algorithm, and then schedule a job if it can be completed within its deadline. Here the assumption is that the jobs are provided along with their completion values and resource requirements, and that the cloud providers can choose the sequence and resources with which to schedule the jobs. These approximate algorithms are easy to describe. The analysis uses the formulation of the problem as a linear program and applies dual fitting technique to prove the approximation factor. In the near future, it is quite conceivable that the models may evolve and prove to be a key contribution, even more so than the algorithms and the underlying analysis. That is understandable for works in new and rapidly evolving fields, especially considering what exactly should be measured is likely to evolve as well.

A musical emotion brain computer interface is an EEG-based device that could be used for humans to communicate emotions nonverbally. This has applications for cases where individuals are prevented from communicating verbally due to loss of motor control. It is interesting because of its potential development to understand how animals may feel or communicate. However, the development of this interface goes some way in facilitating the expression of human feelings and emotions nonverbally, and to support emotion communication in affective computing. The research has application across many areas, including for those who communicate non-linguistically. It has the potential for deployment in creative projects, wearable computing applications, and interspecies communication, and helps to facilitate further development of neuro-linguistic representational systems.

Thyagaraju G S and U P Kulkarni, in the first paper, “Design and Implementation of Prototyping Simulator of Context-Aware Applications”, present a GUI-based prototyping simulator that allows end-users to visually design a wide variety of prototypes of context-aware applications, including those based on if-then rules, temporal-spatial relationships and environmental personalization. The prototyping simulator is tested for designing a prototype of reading room, coffee shop, living room, classroom and meeting room. The conflict-resolving algorithms, which are embedded in the toolkit to resolve the conflict among users, devices and sensors, are designed using a hybrid of fuzzy logic, Bayesian probability and rough set theory. The system can also be used as a testbed for newly designed conflict-resolving algorithms.

In the second paper, “Event-Based Data Gathering from Master Section Head by Mobile Agent in Homogeneous Sensor Networks”, the authors, S Karthikeyan and S Jayashri, try to monitor applications on wireless sensor networks when an event is sensed to notify the user via message. The mobile agent-based approach, as the paper says, aids in conserving the energy of the network, thereby maximizing the lifetime of the nodes, and further outperforms the traditional client-server architecture method in terms of execution and energy consumption. The underlying idea is that the data is not transferred from the clusterhead to the sink, instead from the nodes where events have occurred through mobile agents.

Sanjay Jain and Kapil Sharma, in the last paper, “Identifying Optimum Inventory by Using Genetic Algorithm with Special Reference to Automobile Sector”, make an attempt to optimize the inventory level of an organization by using genetic algorithm in MATLAB 7.6 based on the data obtained from a leading automobile dealer. Preventive maintenance vehicles are considered for the study by applying VED and FMS criteria. The inventory management approach has achieved its objectives of minimization of total supply chain cost and the determination of the products, which would help the supplier manage additional holding cost or shortage cost.

-- C R K Prasad
Consulting Editor