Visual analysis of social networks in space and time using smartphone logs

We designed and applied novel interactive visualisation to investigate how social networks - derived from smartphone logs - are embedded in time and space. Social networks were identified through direct calls between participants and calls to mutual contacts of participants. Direct contact between participants was sparse and deriving networks through mutual contacts helped enrich the social networks. Our resulting interactive visualisation tool offers four linked and co-ordinated views of spatial, temporal, individual and social network aspects of the data. Brushing and altering techniques help us investigate how these aspects relate. We also simultaneously display some demographic and attitudinal variables to help add context to the behaviours we observe. Using these techniques, we were able to characterise spatial and temporal aspects of participants' social networks and suggest explanations for some of them. We reflect on the extent to which such analysis helps us understand social communication behaviour

Supporting simulation in industry through the application of grid computing

An increased need for collaborative research, together with continuing advances in communication technology and computer hardware, has facilitated the development of distributed systems that can provide users access to geographically dispersed computing resources that are administered in multiple computer domains. The term grid computing, or grids, is popularly used to refer to such distributed systems. Simulation is characterized by the need to run multiple sets of computationally intensive experiments. Large scale scientific simulations have traditionally been the primary benefactor of grid computing. The application of this technology to simulation in industry has, however, been negligible. This research investigates how grid technology can be effectively exploited by users to model simulations in industry. It introduces our desktop grid, WinGrid, and presents a case study conducted at a leading European investment bank. Results indicate that grid computing does indeed hold promise for simulation in industry

Crisis Analytics: Big Data Driven Crisis Response

Disasters have long been a scourge for humanity. With the advances in technology (in terms of computing, communications, and the ability to process and analyze big data), our ability to respond to disasters is at an inflection point. There is great optimism that big data tools can be leveraged to process the large amounts of crisis-related data (in the form of user generated data in addition to the traditional humanitarian data) to provide an insight into the fast-changing situation and help drive an effective disaster response. This article introduces the history and the future of big crisis data analytics, along with a discussion on its promise, challenges, and pitfalls

Library Resources: Procurement, Innovation and Exploitation in a Digital World

The possibilities of the digital future require new models for procurement, innovation and exploitation. Emma Crowley and Chris Spencer describe the skills staff need to deliver resources in hybrid and digital environments. The chapter demonstrates the innovative ways that librarians use to procure and exploit the wealth of resources available in a digital world. They also describe the technological developments that can be adopted to improve workflow processes and they highlight the challenges faced on this fascinating journey

Text Mining with HathiTrust: Empowering Librarians to Support Digital Scholarship Research

This workshop will introduce attendees to text analysis research and the common methods and tools used in this emerging area of scholarship, with particular attention to the HathiTrust Research Center. The workshop\u27s train the trainer curriculum will provide a framework for how librarians can support text data mining, as well as teach transferable skills useful for many other areas of digital scholarly inquiry. Topics include: introduction to gathering, managing, analyzing, and visualizing textual data; hands-on experience with text analysis tools, including the HTRC\u27s off-the-shelf algorithms and datasets, such as the HTRC Extracted Features; and using the command line to run basic text analysis processes. No experience necessary! Attendees must bring a laptop

Wrangle Your Data Like a Pro With the Data Processing Power of Python

Management, delivery, and marketing of library resources and collections necessitate interaction with a plethora of data from many sources and in many forms. Accessing and transforming data into meaningful information or different formats used in library automation can be time consuming, but a working knowledge of a programming language can improve efficiency in many facets of librarianship. From processing lists to creating extensible markup language (XML), from editing machine-readable cataloging (MARC) records before upload to automating statistical reports, the Python programming language and third-party application programming interfaces (APIs) can be used to accomplish both behind-the-scenes tasks and end-user facing projects. Creating programmatic solutions to problems requires an understanding of potential. Here we summarize the data sources, flows, and transformations used to accomplish existing projects at Mercer University and the College of Charleston. Foundational programming techniques are explained and resources for learning Python are shared