Using Contextual Graphs as a Decision-making Tool in the Process of Hiring Candidates

Poor selection of employees can be a first step towards a lack of motivation, poor performance, and high turnover, to name a few. It's no wonder that organizations are trying to find the best ways to avoid these slippages by finding the best possible person for the job. Therefore, it is very important to understand the context of hiring process to help to understand which recruiting mistakes are most damaging to the organization in order to reduce the recruiting challenges faced by Human resource managers by building their capacity to ensure optimal HR performance. This paper initiates a research about how Contextual Graphs Formalism can be used for improving the decision making in the process of hiring potential candidates. An example of a typical procedure for visualization of recruiting phases is presented to show how to add contextual elements and practices in order to communicate the recruitment policy in a concrete and memorable way to both hiring teams and candidates.

A Knowledge Preservation and Re-Use Tool Based on Context-Driven Reasoning

This article describes a knowledge preservation and re-use tool designed to capture the knowledge of a specific individual at the US National Science Foundation, for later retrieval by successors after his retirement. The system is designed in a Q&A format, where it is sufficiently intelligent to ask for clarifying questions. The primary objective was to create a system that would result in acceptance of the system by the users. The domain of interest to be preserved and re-used was programmatic knowledge about the NSF Industry/University Collaborative Research Centers (I/UCRC) Program, and more specifically, the knowledge of its long-time director, Dr. Alex Schwarzkopf. The system is called AskAlex and it uses a trio of techniques to accomplish its objectives. Contextual graphs (CxG) are used as the basic knowledge representation structure. CxG’s are assisted by a search engine and an ontology of terms to help find the proper contextual graph that can best answer the question being asked. Evaluations with users and potential users generally confirm our selection and provided some guidance for improvements in the system.

Modelling Context-Aware Security for Electronic Health Records

The Internet has proven to be the most convenient and demanding facility for various types of businesses and transactions for the past few years. In recent years, business information systems have expanded into networks, encompassing partners, suppliers, and customers. There has been a global availability (Anderson, 2001; BSI Global, 2003) of resources over the Internet to satisfy different needs in various fields. The availability factor has called for various security challenges in fields where information is very valuable and not meant for all. Potential threats to information and system security come from a variety of sources. These threats may result in violations to confidentiality, interruptions in information integrity, and possible disruption in the delivery of services. So it is essential to manage the flow of information over the network with the required level of security. There are many security technologies and models that have been introduced which are capable of realizing the functions and objectives of information system security. This article first gives a brief overview of what we term basic security policies of an integrated security model. Then it suggests context-based security policies for a health organization scenario using contextual graphs augmented with details about specific security actions, which relate to the security policies enumerated in the integrated security model. The plan of the article is as follows. We first overview the three concepts in detail and briefly describe the concept of contextual (meta-policy) graphs. We then develop a context-based security meta-policy for securing patient records based on the security policies overviewed and discuss related work, before concluding the paper.

Context-Based Explanations for E-Collaboration

With new findings about context available now, a new insight is possible on past problems abandoned previously by lack of a relevant solution at that time, like incremental knowledge acquisition, practice learning and explanation generation. Previously, they were considered as distinct problems. Now their integration in the task at hand of the user offers new options, especially for e-collaboration. Hereafter, the article is organized in the following way. First, we comment briefly previous works on explanations in order to point out what is reusable. Second, we discuss explanation generation potentialities in a context-based formalism called contextual graphs. Finally, we show what explanations can bring in e-collaboration, maybe more than in face-to-face collaboration.