Big Ideas for Small Business Report

Big Ideas for Small Business is a national peer network led by the National League of Cities (NLC) that aims to accelerate efforts by local governments to support small businesses and encourage entrepreneurship.  This direct peer-to-peer engagement expands the capacity of city staff to explore common challenges, share proven strategies, and collaborate on new approaches for creating a more business-friendly city.  The Big Ideas for Small Business toolkit discusses important strategies for how local leaders can be better advocates for small businesses. Our report provides guidance on creating ecosystems that support small business growth; reorganizing city resources to better meet the needs of small businesses; and providing business owners with access to new sources of capital. Specific strategies highlighted in this report explain how to:Connect Small Businesses to Information and ResourcesEstablish a Small Business Resource Center Advocate for Small Businesses via Community-Led Councils or CommitteesProactively Engage the Local Business CommunityProvide Platforms for NetworkingCreate Incubator SpacesCelebrate Successful BusinessesDevelop One-Stop-Shops and Express Lanes at City Hall Streamline City Regulations and the Inspection ProcessHelp Small Businesses Build a Web PresenceSupport Microlending and CrowdfundingEncourage Local Small Businesses to Bid for City Contracts

Building Near-Real-Time Processing Pipelines with the Spark-MPI Platform

Advances in detectors and computational technologies provide new opportunities for applied research and the fundamental sciences. Concurrently, dramatic increases in the three Vs (Volume, Velocity, and Variety) of experimental data and the scale of computational tasks produced the demand for new real-time processing systems at experimental facilities. Recently, this demand was addressed by the Spark-MPI approach connecting the Spark data-intensive platform with the MPI high-performance framework. In contrast with existing data management and analytics systems, Spark introduced a new middleware based on resilient distributed datasets (RDDs), which decoupled various data sources from high-level processing algorithms. The RDD middleware significantly advanced the scope of data-intensive applications, spreading from SQL queries to machine learning to graph processing. Spark-MPI further extended the Spark ecosystem with the MPI applications using the Process Management Interface. The paper explores this integrated platform within the context of online ptychographic and tomographic reconstruction pipelines.Comment: New York Scientific Data Summit, August 6-9, 201

‘Everything the Data Touches Is Our Kingdom’:Market Power of ‘Data Ecosystems’

Companies such as Google and Facebook are not merely conglomerates of Internet-based services which just so happen to process personal data. They should instead be conceptualized as ‘data ecosystems’ and treated as such. Data ecosystems are companies which collect and monetize personal data through a network of widely diverging internet-based services, for the overarching purpose of targeted advertising. Contrasted with traditional conglomerates, a data ecosystem is unique since all of its different branches are interconnected through a single shared resource: personal data. Consequently, this ecosystem structure grants strong sources of market power. Network effects of personal data, throughout the entire ecosystem, lead to services being constantly updated and personalized with increasing accuracy, while simultaneously enhancing the monetization strategy of targeted advertising. Meanwhile, data ecosystems’ reach across the Internet means that consumers cannot realistically choose not to participate, nor find suitable competitors for each service. Finally, data ecosystems have strong incentives to expand into additional markets: conglomerate mergers are an essential strategy to reinforce their sources of market power. Data ecosystems enjoy a unique form of market power which has been seriously underestimated in the past. A new approach that fully appreciates their unique structure and market power is therefore required

Simplifying Big Data Analytics System with A Reference Architecture

The internet and pervasive technology like the Internet of Things (i.e. sensors and smart devices) have exponentially increased the scale of data collection and availability. This big data not only challenges the structure of existing enterprise analytics systems but also offer new opportunities to create new knowledge and competitive advantage. Businesses have been exploiting these opportunities by implementing and operating big data analytics capabilities. Social network companies such as Facebook, LinkedIn, Twitter and Video streaming company like Netflix have implemented big data analytics and subsequently published related literatures. However, these use cases did not provide a simplified and coherent big data analytics reference architecture as well as currently, there still remains limited reference architecture of big data analytics. This paper aims to simplify big data analytics by providing a reference architecture based on existing four use cases and subsequently verified the reference architecture with Amazon and Google analytics services