Predicting large scale fine grain energy consumption

Today a large volume of energy-related data have been continuously collected. Extracting actionable knowledge from such data is a multi-step process that opens up a variety of interesting and novel research issues across two domains: energy and computer science. The computer science aim is to provide energy scientists with cutting-edge and scalable engines to effectively support them in their daily research activities. This paper presents SPEC, a scalable and distributed predictor of fine grain energy consumption in buildings. SPEC exploits a data stream methodology analysis over a sliding time window to train a prediction model tailored to each building. The building model is then exploited to predict the upcoming energy consumption at a time instant in the near future. SPEC currently integrates the artificial neural networks technique and the random forest regression algorithm. The SPEC methodology exploits the computational advantages of distributed computing frameworks as the current implementation runs on Spark. As a case study, real data of thermal energy consumption collected in a major city have been exploited to preliminarily assess the SPEC accuracy. The initial results are promising and represent a first step towards predicting fine grain energy consumption over a sliding time window

Finding Influential Users in Social Media Using Association Rule Learning

Influential users play an important role in online social networks since users tend to have an impact on one other. Therefore, the proposed work analyzes users and their behavior in order to identify influential users and predict user participation. Normally, the success of a social media site is dependent on the activity level of the participating users. For both online social networking sites and individual users, it is of interest to find out if a topic will be interesting or not. In this article, we propose association learning to detect relationships between users. In order to verify the findings, several experiments were executed based on social network analysis, in which the most influential users identified from association rule learning were compared to the results from Degree Centrality and Page Rank Centrality. The results clearly indicate that it is possible to identify the most influential users using association rule learning. In addition, the results also indicate a lower execution time compared to state-of-the-art methods

PABED A Tool for Big Education Data Analysis

Cloud computing and big data have risen to become the most popular technologies of the modern world. Apparently, the reason behind their immense popularity is their wide range of applicability as far as the areas of interest are concerned. Education and research remain one of the most obvious and befitting application areas. This research paper introduces a big data analytics tool, PABED Project Analyzing Big Education Data, for the education sector that makes use of cloud-based technologies. This tool is implemented using Google BigQuery and R programming language and allows comparison of undergraduate enrollment data for different academic years. Although, there are many proposed applications of big data in education, there is a lack of tools that can actualize the concept into practice. PABED is an effort in this direction. The implementation and testing details of the project have been described in this paper. This tool validates the use of cloud computing and big data technologies in education and shall head start development of more sophisticated educational intelligence tools

Wildbook: Crowdsourcing, computer vision, and data science for conservation

Photographs, taken by field scientists, tourists, automated cameras, and incidental photographers, are the most abundant source of data on wildlife today. Wildbook is an autonomous computational system that starts from massive collections of images and, by detecting various species of animals and identifying individuals, combined with sophisticated data management, turns them into high resolution information database, enabling scientific inquiry, conservation, and citizen science. We have built Wildbooks for whales (flukebook.org), sharks (whaleshark.org), two species of zebras (Grevy's and plains), and several others. In January 2016, Wildbook enabled the first ever full species (the endangered Grevy's zebra) census using photographs taken by ordinary citizens in Kenya. The resulting numbers are now the official species census used by IUCN Red List: http://www.iucnredlist.org/details/7950/0. In 2016, Wildbook partnered up with WWF to build Wildbook for Sea Turtles, Internet of Turtles (IoT), as well as systems for seals and lynx. Most recently, we have demonstrated that we can now use publicly available social media images to count and track wild animals. In this paper we present and discuss both the impact and challenges that the use of crowdsourced images can have on wildlife conservation.Comment: Presented at the Data For Good Exchange 201

Where's Wally?:in search of citizen perspectives on the smart city

This paper builds upon an earlier conference publication by the authors, offering contributions based on a systematic literature review and qualitative study. The paper begins by drawing attention to the paucity of “citizen”—more appropriately, “situated”—perspectives on what a smart city should and could be. The paper then addresses that absence by detailing a research project that explored how people in London, Manchester, and Glasgow responded to the smart city concept. Participants were asked questions regarding their prior familiarity with the phrase “smart city”, their thoughts relating to what it means for a city to be smart, and what a “true” smart city might mean to them. The paper analyses and offers a synthesis of the responses collected throughout the research with the dominant rhetoric about smart cities, as identified through a recent systematic literature review, thereby providing a critical assessment of the values underlying the smart city. It aims to explore and present some of the expectations that citizens hold for their cities’ politicians, policy makers, planners, academics, and technology companies. We believe that these perspectives from citizens can be used to inform responsible development, spatially and socially inclusive technologies, and ultimately more resilient cities

The US Transit Bus Manufacturing Industry

Manufacturing buses for the US transit market has been a challenging business over the last several decades. It is a small market with volatile demand. Many manufacturers have gone bankrupt, left the market, or been acquired by competitors. Manufacturers of transit buses in the US must comply with a wide range of operational and design regulations. The most salient policy areas include regulating emissions, disabled access, procurement, alternative fuels, the Altoona Test, pooled purchases and piggybacking, spare ratios, workforce training, minimum useful life, Buy America, and research & development (R&D). The purpose of this report is to provide policy makers with an update on the state of the industry, an analysis of how government policies are impacting the industry, and suggestions for policies that can help the industry move forward and thrive to best serve the transit-riding public