Managing big data experiments on smartphones

The explosive number of smartphones with ever growing sensing and computing capabilities have brought a paradigm shift to many traditional domains of the computing field. Re-programming smartphones and instrumenting them for application testing and data gathering at scale is currently a tedious and time-consuming process that poses significant logistical challenges. Next generation smartphone applications are expected to be much larger-scale and complex, demanding that these undergo evaluation and testing under different real-world datasets, devices and conditions. In this paper, we present an architecture for managing such large-scale data management experiments on real smartphones. We particularly present the building blocks of our architecture that encompassed smartphone sensor data collected by the crowd and organized in our big data repository. The given datasets can then be replayed on our testbed comprising of real and simulated smartphones accessible to developers through a web-based interface. We present the applicability of our architecture through a case study that involves the evaluation of individual components that are part of a complex indoor positioning system for smartphones, coined Anyplace, which we have developed over the years. The given study shows how our architecture allows us to derive novel insights into the performance of our algorithms and applications, by simplifying the management of large-scale data on smartphones

Using exploratory factor analysis in information system (IS) research

This paper is part of a field study that explored the impact of Information System implementation on Organisational Performance by examining the concept of IS effectiveness and by exploring how businesses arrive at the conclusion that the undertaking is successful or unsuccessful. Many statistical techniques have been used for the inference of conclusions. This paper will explain in brief the methodology followed and the exploratory factor analysis (EFA) conducted for the measurement of the construct if IS effectiveness. Following all tests on correlations and a number of extraction methods the final solution comprised 13 factors representing the independent variables and 4 factors representing the dependent variables. The results from our analysis provide insight into the IS evaluation field of research and provide new scales for the measurement of IS effectiveness

Investigation into the variations of moisture content of two buildings constructed with light earth walls

This paper briefly describes the background to light earth buildings and details a series of moisture measurements undertaken upon the clay and straw, (light earth) constructed walls of two UK based buildings. The methodology of measurement that was based upon previous studies undertaken on walls made from straw bales is described. A novel ‘in-wall’ wet heating system used in one of the two buildings allows the investigation of the effects of direct wall heating upon the distribution of moisture in the walls. The influence of exterior and interior temperature and humidity are described as are the variations in moisture migration introduced by the in-wall heating system. It was concluded that both buildings have exterior wall moisture content readings that indicate little risk of degradation due to interior wall moisture levels (although the Studio walls do exhibit higher and if suffered over long time periods, dangerous moisture readings for part of the measurement period)

A Model that Predicts the Material Recognition Performance of Thermal Tactile Sensing

Tactile sensing can enable a robot to infer properties of its surroundings, such as the material of an object. Heat transfer based sensing can be used for material recognition due to differences in the thermal properties of materials. While data-driven methods have shown promise for this recognition problem, many factors can influence performance, including sensor noise, the initial temperatures of the sensor and the object, the thermal effusivities of the materials, and the duration of contact. We present a physics-based mathematical model that predicts material recognition performance given these factors. Our model uses semi-infinite solids and a statistical method to calculate an F1 score for the binary material recognition. We evaluated our method using simulated contact with 69 materials and data collected by a real robot with 12 materials. Our model predicted the material recognition performance of support vector machine (SVM) with 96% accuracy for the simulated data, with 92% accuracy for real-world data with constant initial sensor temperatures, and with 91% accuracy for real-world data with varied initial sensor temperatures. Using our model, we also provide insight into the roles of various factors on recognition performance, such as the temperature difference between the sensor and the object. Overall, our results suggest that our model could be used to help design better thermal sensors for robots and enable robots to use them more effectively.Comment: This article is currently under review for possible publicatio

Temporal Data Modeling and Reasoning for Information Systems

Temporal knowledge representation and reasoning is a major research field in Artificial Intelligence, in Database Systems, and in Web and Semantic Web research. The ability to model and process time and calendar data is essential for many applications like appointment scheduling, planning, Web services, temporal and active database systems, adaptive Web applications, and mobile computing applications. This article aims at three complementary goals. First, to provide with a general background in temporal data modeling and reasoning approaches. Second, to serve as an orientation guide for further specific reading. Third, to point to new application fields and research perspectives on temporal knowledge representation and reasoning in the Web and Semantic Web