Scalable Facility Location for Massive Graphs on Pregel-like Systems

We propose a new scalable algorithm for facility location. Facility location is a classic problem, where the goal is to select a subset of facilities to open, from a set of candidate facilities F , in order to serve a set of clients C. The objective is to minimize the total cost of opening facilities plus the cost of serving each client from the facility it is assigned to. In this work, we are interested in the graph setting, where the cost of serving a client from a facility is represented by the shortest-path distance on the graph. This setting allows to model natural problems arising in the Web and in social media applications. It also allows to leverage the inherent sparsity of such graphs, as the input is much smaller than the full pairwise distances between all vertices. To obtain truly scalable performance, we design a parallel algorithm that operates on clusters of shared-nothing machines. In particular, we target modern Pregel-like architectures, and we implement our algorithm on Apache Giraph. Our solution makes use of a recent result to build sketches for massive graphs, and of a fast parallel algorithm to find maximal independent sets, as building blocks. In so doing, we show how these problems can be solved on a Pregel-like architecture, and we investigate the properties of these algorithms. Extensive experimental results show that our algorithm scales gracefully to graphs with billions of edges, while obtaining values of the objective function that are competitive with a state-of-the-art sequential algorithm

APIs and Your Privacy

Application programming interfaces, or APIs, have been the topic of much recent discussion. Newsworthy events, including those involving Facebook’s API and Cambridge Analytica obtaining information about millions of Facebook users, have highlighted the technical capabilities of APIs for prominent websites and mobile applications. At the same time, media coverage of ways that APIs have been misused has sparked concern for potential privacy invasions and other issues of public policy. This paper seeks to educate consumers on how APIs work and how they are used within popular websites and mobile apps to gather, share, and utilize data. APIs are used in mobile games, search engines, social media platforms, news and shopping websites, video and music streaming services, dating apps, and mobile payment systems. If a third-party company, like an app developer or advertiser, would like to gain access to your information through a website you visit or a mobile app or online service you use, what data might they obtain about you through APIs and how? This report analyzes 11 prominent online services to observe general trends and provide you an overview of the role APIs play in collecting and distributing information about consumers. For example, how might your data be gathered and shared when using your Facebook account login to sign up for Venmo or to access the Tinder dating app? How might advertisers use Pandora’s API when you are streaming music? After explaining what APIs are and how they work, this report categorizes and characterizes different kinds of APIs that companies offer to web and app developers. Services may offer content-focused APIs, feature APIs, unofficial APIs, and analytics APIs that developers of other apps and websites may access and use in different ways. Likewise, advertisers can use APIs to target a desired subset of a service’s users and possibly extract user data. This report explains how websites and apps can create user profiles based on your online behavior and generate revenue from advertiser-access to their APIs. The report concludes with observations on how various companies and platforms connecting through APIs may be able to learn information about you and aggregate it with your personal data from other sources when you are browsing the internet or using different apps on your smartphone or tablet. While the paper does not make policy recommendations, it demonstrates the importance of approaching consumer privacy from a broad perspective that includes first parties and third parties, and that considers the integral role of APIs in today’s online ecosystem

The Business and Culture of Social Media: In Search of the People Formerly Known As the Audience

This presentation addresses three transformations: the transformation of the audience; of advertising models; and of media businesses. The talk describes how they were transformed first by digital technology, and how they are now being transformed by social media. It goes on to describe what we call the "three economies" which govern the era of social media and proposes some research needed in order to understand and to monetize the audiences of this era

Reactions of Generation Y to Luxury Hotel Twitter Promotions

Social media refers to the means of interactions among people in which they create, share, and exchange information and ideas in virtual communities and networks (Merriam-Webster, 2013). Social media marketing refers to the process of gaining website traffic or attention through social media sites (Evans, 2008). In today’s society, social media refers mainly to websites including (but not limited to) Facebook, Twitter, LinkedIn, and Google+. The most popular and fastest growing of these social media venues is Twitter. Twitter was founded in 2006 by Jack Dorsey, Evan Williams, and Biz Stone. Since 2006, almost 200 million users across the globe have joined the site; over 140 million tweets are “tweeted” daily (Picard, 2011). Twitter took advantage of a niche in the market, allowing 140 characters to express an idea or emotion. Twitter has changed the media world as a news source, tweeting real-time information from stories that arise (Picard, 2011). In the lodging industry, methods of social media to promote hotels are becoming more popular. Twitter, in particular, has emerged as a “moment of truth” for a hotel, demonstrating how instantly and tactfully hotels react to the thoughts and opinions of former, current, and potential guests. Studies have also suggested that “online social life mirrors offline relationships in many ways” (Moore, p. 440). Therefore, Twitter accounts should be viewed as an extension of the hospitality business, in particular lodging, echoing the relationship a customer would feel upon arrival to the hotel. Hotel marketing teams have reached “great success by driving demand to hotels through increased online advertising and web optimization” (Chipkin, 2013). This has increased overall customer views of the hotel without affecting the rate strategy of the property or brand. Twitter presence could, potentially, help a patron decide between two hotels, “If a promotion, experience or package is unique, it definitely works to generate bookings and helps put you first in a consumer’s mind when they are choosing between two or three hotels,” says Rachel Harrison of Hyatt Andaz (Chipkin, 2013). Hotel companies worldwide are investing in their social media networks. Certain hotels (i.e. W Barcelona) are even hiring social media and marketing managers whose responsibilities include instant Twitter feedback (Appendix 1). The purpose behind this investment is to maximize these social media accounts, creating feedback from all potential guests, allowing them to react to both positive and negative word of mouth. Social media managers have recently encountered an opportunity; Generation Y is becoming a target demographic. As Generation Y enters the workforce and begins a career, the exposure to hotel brands and types will increase. Luxury hotel stays are becoming more financially reachable to these Generation Y guests because of their career advancements (Fields, 2013). This study will serve to evaluate the added benefits from the adoption of social media channels, particularly Twitter

The Usage of Personal Data as Content in Integrated Marketing Communications

Personal user data has proven extremely valuable for firms in the digital age. The wealth of data available to firms has provided unprecedented access into the world of the consumer. Companies hoping to capitalize on their user's data have turned to several interesting outlets. This research addresses the repurposing of user data as content in marketing. By analyzing four cases of data presented as marketing communications across two companies, this research provides new insights into the public release of private user data for marketing purposes. Four cases of personal data used in marketing communications were chosen specifically for their time proximity, characteristics of the sending firms, and their disparate outcomes. These instances of marketing communications, two by Spotify and two by Netflix, were released during November and December of 2017 and each resulted in a diverse range of public opinion. An analysis of these cases was conducted using the comprehensive framework of integrated marketing communications (Tafesse & Kitchen, 2017). There is a significant difference in the perceptual outcomes of integrated marketing communication campaigns which display user data as content. This analysis provides insights into the characteristics of marketing communications and how their outcomes fit into broader marketing strategies. These case studies provide opportunities for marketers to improve their campaigns in line with their desired audience outcome. Patterns of scope, strategy, mode, and outcome do not suggest success or failure in the context of marketing communications, but rather a set of insights marketers should keep in mind when pursuing communication strategies which harness personal user data.No embargoAcademic Major: Marketin

An exploratory study to design an adaptive hypermedia system for online-advertisement

The revolutionary world of the World Wide Web has created an open space for a multitude of fields to develop and propagate. One of these major fields is advertisement. Online advertisement has become one of the main activities conducted on the web, heavily supported by the industry. Importantly, it is one of the main contributors to any businesses’ income. However, consumers usually ignore the great majority of adverts online. This research paper studies the field of online advertisement, by conducting an exploratory study to understand end users’ needs for targeted online advertisement using adaptive hypermedia techniques. Additionally, we explore social networks, one of the booming phenomena of the web, to enhance the appropriateness of the advertising to the users. The main current outcome of this research is that end users are interested in personalised advertisement that tackles their needs and that they believe that the use of social networks and social actions help in the contextualisation of advertisement