Within this paper, we describe a neuroinformatics project (called "NeuroScholar," http://www.neuroscholar.org/) that enables researchers to examine, manage, manipulate, and use the information contained within the published neuroscientific literature. The project is built within a multi-level, multi-component framework constructed with the use of software engineering methods that themselves provide code-building functionality for neuroinformaticians. We describe the different software layers of the system. First, we present a hypothetical usage scenario illustrating how NeuroScholar permits users to address large-scale questions in a way that would otherwise be impossible. We do this by applying NeuroScholar to a "real-world" neuroscience question: How is stress-related information processed in the brain? We then explain how the overall design of NeuroScholar enables the system to work and illustrate different components of the user interface. We then describe the knowledge management strategy we use to store interpretations. Finally, we describe the software engineering framework we have devised (called the "View-Primitive-Data Model framework," [VPDMf]) to provide an open-source, accelerated software development environment for the project. We believe that NeuroScholar will be useful to experimental neuroscientists by helping them interact with the primary neuroscientific literature in a meaningful way, and to neuroinformaticians by providing them with useful, affordable software engineering tools.

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Influence Maximization problem, selection of a set of users in a social network to maximize the influence spread, has received ample research attention in the social network analysis domain due to its practical applications. Although the problem has been extensively studied, existing works have neglected the location’s popularity and importance along with influential users for product promotion at a particular region in Location-based Social Networks. Real-world marketing companies are more interested in finding suitable locations and influential users in a city to promote their product and attract as many users as possible. In this work, we study the joint selection of influential users and locations within a target region from two complementary perspectives; general and specific location type selection perspectives. The first is to find influential users and locations at a specified region irrespective of location type or category. The second perspective is to recommend locations matching location preference in addition to the target region for product promotion. To address general and specific location recommendations and influential users, we propose heuristic-based methods that effectively find influential users and locations for product promotion. Our experimental results show that it is not always an optimal choice to recommend locations with the highest popularity values, such as ratings, check-ins, and so, which may not be a true indicator of location popularity to be considered for marketing. Our results show that not only influential users are helpful for product promotion, but suitable influential locations can also assist in promoting products in the target region.

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