Interactive exploration of data with visual metaphors

Visual Analytics (VA) is an approach to data analysis by means of visual manipulation of data representation, which relies on innate human abilities of perception and cognition. Even though current visual toolkits in the Business Analytics (BA) domain have improved the effectiveness of data exploration, analysis and reporting, their features are often not intuitive, and can be confusing and difficult to use. Moreover, visualizations generated from these toolkits are mostly accessible to specialist users. Thus, there is a need for analytic environments that support data exploration, interpretation and communication of insight that do not add to the cognitive load of the analyst and their non-technical clients. In this conceptual paper, we explore the potential of primary metaphors, which arise out of human lived and sensory-motor experiences, in the design of immersive visual analytics environments. Primary metaphors provide ideas for representation of time, space, quantity, similarity, actions and team work. Using examples developed in our own work, we also explain how to combine such metaphors to create complex and cognitively acceptable visual metaphors, such as 3D data terrains that approximate our intuition of reality and create opportunities for data to be viewed, navigated, explored, touched, changed, discussed, reported and described to others, individually or collaboratively.

Confocal laser scanning microscopy elucidation of the micromorphology of the leaf cuticle and analysis of its chemical composition

Electron microscopy techniques such as transmission electron microscopy (TEM) and scanning electron microscopy (SEM) have been invaluable tools for the study of the micromorphology of plant cuticles. However, for electron microscopy, the preparation techniques required may invariably introduce artefacts in cuticle preservation. Further, there are a limited number of methods available for quantifying the image data obtained through electron microscopy. Therefore, in this study, optical microscopy techniques were coupled with staining procedures and, along with SEM were used to qualitatively and quantitatively assess the ultrastructure of plant leaf cuticles. Leaf cryosections of Triticum aestivum (wheat), Zea mays (maize), and Lupinus angustifolius (lupin) were stained with either fat-soluble azo stain Sudan IV or fluorescent, diarylmethane Auramine O and were observed under confocal laser scanning microscope (CLSM). For all the plant species tested, the cuticle on the leaf surfaces could be clearly resolved in many cases into cuticular proper (CP), external cuticular layer (ECL), and internal cuticular layer (ICL). Novel image data analysis procedures for quantifying the epicuticular wax micromorphology were developed, and epicuticular waxes of L. angustifolius were described here for the first time. Together, application of a multifaceted approach involving the use of a range of techniques to study the plant cuticle has led to a better understanding of cuticular structure and provides new insights into leaf surface architecture.

Preference mining techniques for customer behavior analysis

The thesis has studied a number of critical problems in data mining for customer behavior analysis and has proposed novel techniques for better modeling of the customers’ decision making process, more efficient analysis of their travel behavior, and more effective identification of their emerging preference.

A study and a directory of energy consumption data sets of buildings

Energy consumption data are required to perform analysis, modelling, evaluation, and optimisation of energy usage in buildings. While a variety of energy consumption data sets have been examined and reported in the literature, there is a lack of a comprehensive categorisation and analysis of the available data sets. In this study, an overview of energy consumption data of buildings is provided. Three common strategies for generating energy consumption data, i.e., measurement, survey, and simulation, are described. A number of important characteristics pertaining to each strategy and the resulting data sets are discussed. In addition, a directory of energy consumption data sets of buildings is developed. The data sets are collected from either published papers or energy related organisations. The main contributions of this study include establishing a resource pertaining to energy consumption data sets and providing information related to the characteristics and availability of the respective data sets; therefore facilitating and promoting research activities in energy consumption data analysis.

Farmland prices, structural breaks and panel data

Previous time series evidence has indicated that farmland prices and cash rents are not cointegrated, a finding at odds with the present value model of farmland prices. We argue that this failure to find cointegration may be due to low power of tests and to the presence of structural change representing a shifting risk premium on farmland investments. To accommodate this possibility, we use panel unit root and cointegration methods that are more powerful than conventional time series methods and allow for breaks in the cointegration relationship. Our results, based on a large panel covering 31 US states between 1960 and 2000, suggest that the present value model of farmland prices cannot be rejected. © Oxford University Press and Foundation for the European Review of Agricultural Economics 2007; all rights reserved.

A secure cloud computing based framework for big data information management of smart grid

Smart grid is a technological innovation that improves efficiency, reliability, economics, and sustainability of electricity services. It plays a crucial role in modern energy infrastructure. The main challenges of smart grids, however, are how to manage different types of front-end intelligent devices such as power assets and smart meters efficiently; and how to process a huge amount of data received from these devices. Cloud computing, a technology that provides computational resources on demands, is a good candidate to address these challenges since it has several good properties such as energy saving, cost saving, agility, scalability, and flexibility. In this paper, we propose a secure cloud computing based framework for big data information management in smart grids, which we call 'Smart-Frame.' The main idea of our framework is to build a hierarchical structure of cloud computing centers to provide different types of computing services for information management and big data analysis. In addition to this structural framework, we present a security solution based on identity-based encryption, signature and proxy re-encryption to address critical security issues of the proposed framework.

Networking for big data

Big data is an emerging hot research topic due to its pervasive application in human society, such as government, climate, finance, and science. Currently, most research work on big data falls in data mining, machine learning, and data analysis. However, these amazing top-level killer applications would not be possible without the underneath support of networking due to their extremely large volume and computing complexity, especially when real-time or near-real-time applications are demanded.

Influence of body mass index on prescribing costs and potential cost savings of a weight management programme in primary care

Objectives Prescribed medications represent a high and increasing proportion of UK health care funds. Our aim was to quantify the influence of body mass index (BMI) on prescribing costs, and then the potential savings attached to implementing a weight management intervention.

Methods Paper and computer-based medical records were reviewed for all drug prescriptions over an 18-month period for 3400 randomly selected adult patients (18–75 years) stratified by BMI, from 23 primary care practices in seven UK regions. Drug costs from the British National Formulary at the time of the review were used. Multivariate regression analysis was applied to estimate the cost for all drugs and the ‘top ten’ drugs at each BMI point. This allowed the total and attributable prescribing costs to be estimated at any BMI. Weight loss outcomes achieved in a weight management programme (Counterweight) were used to model potential effects of weight change on drug costs. Anticipated savings were then compared with the cost programme delivery. Analysis was carried out on patients with follow-up data at 12 and 24 months as well as on an intention-to-treat basis. Outcomes from Counterweight were based on the observed lost to follow-up rate of 50%, and the assumption that those patients would continue a generally observed weight gain of 1 kg per year from baseline.

Results The minimum annual cost of all drug prescriptions at BMI 20 kg/m2 was £50.71 for men and £62.59 for women. Costs were greater by £5.27 (men) and £4.20 (women) for each unit increase in BMI, to a BMI of 25 (men £77.04, women £78.91), then by £7.78 and £5.53, respectively, to BMI 30 (men £115.93 women £111.23), then by £8.27 and £4.95 to BMI 40 (men £198.66, women £160.73). The relationship between increasing BMI and costs for the top ten drugs was more pronounced. Minimum costs were at a BMI of 20 (men £8.45, women £7.80), substantially greater at BMI 30 (men £23.98, women £16.72) and highest at BMI 40 (men £63.59, women £27.16). Attributable cost of overweight and obesity accounted for 23% of spending on all drugs with 16% attributable to obesity. The cost of the programme was estimated to be approximately £60 per patient entered. Modelling weight reductions achieved by the Counterweight weight management programme would potentially reduce prescribing costs by £6.35 (men) and £3.75 (women) or around 8% of programme costs at one year, and by £12.58 and £8.70, respectively, or 18% of programme costs after two years of intervention. Potential savings would be increased to around 22% of the cost of the programme at year one with full patient retention and follow-up.

Conclusion Drug prescriptions rise from a minimum at BMI of 20 kg/m2 and steeply above BMI 30 kg/m2. An effective weight management programme in primary care could potentially reduce prescription costs and lead to substantial cost avoidance, such that at least 8% of the programme delivery cost would be recouped from prescribing savings alone in the first year.

Effective Data Management Enables Intelligent Utility Management

Instrumentation and automation plays a vital role to managing the water industry. These systems generate vast amounts of data that must be effectively managed in order to enable intelligent decision making. Time series data management software, commonly known as data historians are used for collecting and managing real-time (time series) information. More advanced software solutions provide a data infrastructure or utility wide Operations Data Management System (ODMS) that stores, manages, calculates, displays, shares, and integrates data from multiple disparate automation and business systems that are used daily in water utilities. These ODMS solutions are proven and have the ability to manage data from smart water meters to the collaboration of data across third party corporations. This paper focuses on practical, utility successes in the water industry where utility managers are leveraging instantaneous access to data from proven, commercial off-the-shelf ODMS solutions to enable better real-time decision making. Successes include saving $650,000 / year in water loss control, safeguarding water quality, saving millions of dollars in energy management and asset management. Immediate opportunities exist to integrate the research being done in academia with these ODMS solutions in the field and to leverage these successes to utilities around the world.

RWater – A Cyber Enabled Tool For Hydrologic Modeling And Analysis

Existing distributed hydrologic models are complex and computationally demanding for using as a rapid-forecasting policy-decision tool, or even as a class-room educational tool. In addition, platform dependence, specific input/output data structures and non-dynamic data-interaction with pluggable software components inside the existing proprietary frameworks make these models restrictive only to the specialized user groups. RWater is a web-based hydrologic analysis and modeling framework that utilizes the commonly used R software within the HUBzero cyber infrastructure of Purdue University. RWater is designed as an integrated framework for distributed hydrologic simulation, along with subsequent parameter optimization and visualization schemes. RWater provides platform independent web-based interface, flexible data integration capacity, grid-based simulations, and user-extensibility. RWater uses RStudio to simulate hydrologic processes on raster based data obtained through conventional GIS pre-processing. The program integrates Shuffled Complex Evolution (SCE) algorithm for parameter optimization. Moreover, RWater enables users to produce different descriptive statistics and visualization of the outputs at different temporal resolutions. The applicability of RWater will be demonstrated by application on two watersheds in Indiana for multiple rainfall events.

PDA, Circulation, and Over-Budget Requests: Harnessing Data to Inform Library Strategies

This article presents the data-rich findings of an experiment with enlisting patron-driven/demand-driven acquisitions (DDA) of ebooks in two ways. The first experiment entailed comparison of DDA eBook usage against newly ordered hardcopy materials’ circulation, both overall and ebook vs. print usage within the same subject areas. Secondly, this study experimented with DDA ebooks as a backup plan for unfunded requests left over at the end of the fiscal year.

PDA, Circulation, and Over-Budget Requests: Harnessing Data to Inform Library Strategies

This article presents the data-rich findings of an experiment with enlisting patron-driven/demand-driven acquisitions (DDA) of ebooks in two ways. The first experiment entailed comparison of DDA eBook usage against newly ordered hardcopy materials’ circulation, both overall and ebook vs. print usage within the same subject areas. Secondly, this study experimented with DDA ebooks as a backup plan for unfunded requests left over at the end of the fiscal year.

An Environmental Analysis Corroborating PDA and the Winthrop Example

Libraries seek active ways to innovate amidst macroeconomic shifts, growing online education to help alleviate ever-growing schedule conflicts as students juggle jobs and course schedules, as well as changing business models in publishing and evolving information technologies. Patron-driven acquisition (PDA), also known as demand-driven acquisition (DDA), offers numerous strengths in supporting university curricula in the context of these significant shifts. PDA is a business model centered on short-term loans and subsequent purchases of ebooks resulting directly from patrons' natural use stemming from their discovery of the ebooks in library catalogs where the ebooks' bibliographic records are loaded at regular intervals established between the library and ebook supplier. Winthrop University's PDA plan went live in October 2011, and this article chronicles the philosophical and operational considerations, the in-library collaboration, and technical preparations in concert with the library system vendor and ebook supplier. Short-term loan is invoked after a threshold is crossed, typically number of pages or time spent in the ebook. After a certain number of short-term loans negotiated between the library and ebook supplier, the next short-term loan becomes an automatic purchase after which the library owns the ebook in perpetuity. Purchasing options include single-user and multi-user licenses. Owing to high levels of need in college and university environments, Winthrop chose the multi-user license as the preferred default purchase. Only where multi-user licenses are unavailable does the automatic purchase occur with single-user title licenses. Data on initial use between October 2011 and February 2013 reveal that of all PDA ebooks viewed, only 30% crossed the threshold into short-term loans. Of all triggered short-term loans, Psychology was the highest-using. Of all ebook views too brief to trigger short-term loans, Business was the highest-using area. Although the data are still too young to draw conclusions after only a few months, thought-provoking usage differences between academic disciplines have begun to emerge. These differences should be considered in library plans for the best possible curricular support for each academic program. As higher education struggles with costs and course-delivery methods libraries have an enduring lead role.

Using Interlibrary Loan Data as a Section Tool: ILL Trails Provide Collection Clues

This article describes analyzing Interlibrary Loan data to help inform collection management decision and offers guidance for formulating policies for discerning borrowed titles indicative of gaps in the library from special-interest pursuits beyond the scope of the university curriculum.