Identifying the same internet user across devices or over time is often infeasible. This presents a problem for online experiments, as it precludes person-level randomization. Randomization must instead be done using imperfect proxies for people, like cookies, email addresses, or device identifiers. Users may be partially treated and partially untreated as some of their cookies are assigned to the test group and some to the control group, complicating statistical inference. We show that the estimated treatment effect in a cookie-level experiment converges to a weighted average of the marginal effects of treating more of a user's cookies. If the marginal effects of cookie treatment exposure are positive and constant, it underestimates the true person-level effect by a factor equal to the number of cookies per person. Using two separate datasets---cookie assignment data from Atlas and advertising exposure and purchase data from Facebook---we empirically quantify the differences between cookie and person-level advertising effectiveness experiments. The effects are substantial: cookie tests underestimate the true person-level effects by a factor of about three, and require two to three times the number of people to achieve the same power as a test with perfect treatment assignment.
{"title":"People and Cookies: Imperfect Treatment Assignment in Online Experiments","authors":"Dominic Coey, Michael C. Bailey","doi":"10.1145/2872427.2882984","DOIUrl":"https://doi.org/10.1145/2872427.2882984","url":null,"abstract":"Identifying the same internet user across devices or over time is often infeasible. This presents a problem for online experiments, as it precludes person-level randomization. Randomization must instead be done using imperfect proxies for people, like cookies, email addresses, or device identifiers. Users may be partially treated and partially untreated as some of their cookies are assigned to the test group and some to the control group, complicating statistical inference. We show that the estimated treatment effect in a cookie-level experiment converges to a weighted average of the marginal effects of treating more of a user's cookies. If the marginal effects of cookie treatment exposure are positive and constant, it underestimates the true person-level effect by a factor equal to the number of cookies per person. Using two separate datasets---cookie assignment data from Atlas and advertising exposure and purchase data from Facebook---we empirically quantify the differences between cookie and person-level advertising effectiveness experiments. The effects are substantial: cookie tests underestimate the true person-level effects by a factor of about three, and require two to three times the number of people to achieve the same power as a test with perfect treatment assignment.","PeriodicalId":20455,"journal":{"name":"Proceedings of the 25th International Conference on World Wide Web","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89314200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The pervasiveness of mobile technologies today have facilitated the creation of massive crowdsourced and geotagged data from individual users in real time and at different locations in the city. Such ubiquitous user-generated data allow us to infer various patterns of human behavior, which help us understand the interactions between humans and cities. In this study, we focus on understanding users economic behavior in the city by examining the economic value from crowdsourced and geotaggged data. Specifically, we extract multiple traffic and human mobility features from publicly available data sources using NLP and geo-mapping techniques, and examine the effects of both static and dynamic features on economic outcome of local businesses. Our study is instantiated on a unique dataset of restaurant bookings from OpenTable for 3,187 restaurants in New York City from November 2013 to March 2014. Our results suggest that foot traffic can increase local popularity and business performance, while mobility and traffic from automobiles may hurt local businesses, especially the well-established chains and high-end restaurants. We also find that on average one more street closure nearby leads to a 4.7% decrease in the probability of a restaurant being fully booked during the dinner peak. Our study demonstrates the potential of how to best make use of the large volumes and diverse sources of crowdsourced and geotagged user-generated data to create matrices to predict local economic demand in a manner that is fast, cheap, accurate, and meaningful.
{"title":"Understanding User Economic Behavior in the City Using Large-scale Geotagged and Crowdsourced Data","authors":"Yingjie Zhang, Beibei Li, Jason I. Hong","doi":"10.1145/2872427.2883066","DOIUrl":"https://doi.org/10.1145/2872427.2883066","url":null,"abstract":"The pervasiveness of mobile technologies today have facilitated the creation of massive crowdsourced and geotagged data from individual users in real time and at different locations in the city. Such ubiquitous user-generated data allow us to infer various patterns of human behavior, which help us understand the interactions between humans and cities. In this study, we focus on understanding users economic behavior in the city by examining the economic value from crowdsourced and geotaggged data. Specifically, we extract multiple traffic and human mobility features from publicly available data sources using NLP and geo-mapping techniques, and examine the effects of both static and dynamic features on economic outcome of local businesses. Our study is instantiated on a unique dataset of restaurant bookings from OpenTable for 3,187 restaurants in New York City from November 2013 to March 2014. Our results suggest that foot traffic can increase local popularity and business performance, while mobility and traffic from automobiles may hurt local businesses, especially the well-established chains and high-end restaurants. We also find that on average one more street closure nearby leads to a 4.7% decrease in the probability of a restaurant being fully booked during the dinner peak. Our study demonstrates the potential of how to best make use of the large volumes and diverse sources of crowdsourced and geotagged user-generated data to create matrices to predict local economic demand in a manner that is fast, cheap, accurate, and meaningful.","PeriodicalId":20455,"journal":{"name":"Proceedings of the 25th International Conference on World Wide Web","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88535050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Semantic Web systems provide open interfaces for end-users to access data via a powerful high-level query language, SPARQL. But users unfamiliar with either the details of SPARQL or properties of the target dataset may find it easier to query by example -- give examples of the information they want (or examples of both what they want and what they do not want) and let the system reverse engineer the desired query from the examples. This approach has been heavily used in the setting of relational databases. We provide here an investigation of the reverse engineering problem in the context of SPARQL. We first provide a theoretical study, formalising variants of the reverse engineering problem and giving tight bounds on its complexity. We next explain an implementation of a reverse engineering tool for positive examples. An experimental analysis of the tool shows that it scales well in the data size, number of examples, and in the size of the smallest query that fits the data. We also give evidence that reverse engineering tools can provide benefits on real-life datasets.
{"title":"Reverse Engineering SPARQL Queries","authors":"M. Arenas, G. I. Diaz, Egor V. Kostylev","doi":"10.1145/2872427.2882989","DOIUrl":"https://doi.org/10.1145/2872427.2882989","url":null,"abstract":"Semantic Web systems provide open interfaces for end-users to access data via a powerful high-level query language, SPARQL. But users unfamiliar with either the details of SPARQL or properties of the target dataset may find it easier to query by example -- give examples of the information they want (or examples of both what they want and what they do not want) and let the system reverse engineer the desired query from the examples. This approach has been heavily used in the setting of relational databases. We provide here an investigation of the reverse engineering problem in the context of SPARQL. We first provide a theoretical study, formalising variants of the reverse engineering problem and giving tight bounds on its complexity. We next explain an implementation of a reverse engineering tool for positive examples. An experimental analysis of the tool shows that it scales well in the data size, number of examples, and in the size of the smallest query that fits the data. We also give evidence that reverse engineering tools can provide benefits on real-life datasets.","PeriodicalId":20455,"journal":{"name":"Proceedings of the 25th International Conference on World Wide Web","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73745926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gary Soeller, Karrie Karahalios, Christian Sandvig, Christo Wilson
Maps have long played a crucial role in enabling people to conceptualize and navigate the world around them. However, maps also encode the world-views of their creators. Disputed international borders are one example of this: governments may mandate that cartographers produce maps that conform to their view of a territorial dispute. Today, online maps maintained by private corporations have become the norm. However, these new maps are still subject to old debates. Companies like Google and Bing resolve these disputes by localizing their maps to meet government requirements and user preferences, i.e., users in different locations are shown maps with different international boundaries. We argue that this non-transparent personalization of maps may exacerbate nationalistic disputes by promoting divergent views of geopolitical realities. To address this problem, we present MapWatch, our system for detecting and cataloging personalization of international borders in online maps. Our system continuously crawls all map tiles from Google and Bing maps, and leverages crowdworkers to identify border personalization. In this paper, we present the architecture of MapWatch, and analyze the instances of border personalization on Google and Bing, including one border change that MapWatch identified live, as Google was rolling out the update.
{"title":"MapWatch: Detecting and Monitoring International Border Personalization on Online Maps","authors":"Gary Soeller, Karrie Karahalios, Christian Sandvig, Christo Wilson","doi":"10.1145/2872427.2883016","DOIUrl":"https://doi.org/10.1145/2872427.2883016","url":null,"abstract":"Maps have long played a crucial role in enabling people to conceptualize and navigate the world around them. However, maps also encode the world-views of their creators. Disputed international borders are one example of this: governments may mandate that cartographers produce maps that conform to their view of a territorial dispute. Today, online maps maintained by private corporations have become the norm. However, these new maps are still subject to old debates. Companies like Google and Bing resolve these disputes by localizing their maps to meet government requirements and user preferences, i.e., users in different locations are shown maps with different international boundaries. We argue that this non-transparent personalization of maps may exacerbate nationalistic disputes by promoting divergent views of geopolitical realities. To address this problem, we present MapWatch, our system for detecting and cataloging personalization of international borders in online maps. Our system continuously crawls all map tiles from Google and Bing maps, and leverages crowdworkers to identify border personalization. In this paper, we present the architecture of MapWatch, and analyze the instances of border personalization on Google and Bing, including one border change that MapWatch identified live, as Google was rolling out the update.","PeriodicalId":20455,"journal":{"name":"Proceedings of the 25th International Conference on World Wide Web","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72722232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Giridhari Venkatadri, Oana Goga, Changtao Zhong, Bimal Viswanath, K. Gummadi, Nishanth R. Sastry
On most current websites untrustworthy or spammy identities are easily created. Existing proposals to detect untrustworthy identities rely on reputation signals obtained by observing the activities of identities over time within a single site or domain; thus, there is a time lag before which websites cannot easily distinguish attackers and legitimate users. In this paper, we investigate the feasibility of leveraging information about identities that is aggregated across multiple domains to reason about their trustworthiness. Our key insight is that while honest users naturally maintain identities across multiple domains (where they have proven their trustworthiness and have acquired reputation over time), attackers are discouraged by the additional effort and costs to do the same. We propose a flexible framework to transfer trust between domains that can be implemented in today's systems without significant loss of privacy or significant implementation overheads. We demonstrate the potential for inter-domain trust assessment using extensive data collected from Pinterest, Facebook, and Twitter. Our results show that newer domains such as Pinterest can benefit by transferring trust from more established domains such as Facebook and Twitter by being able to declare more users as likely to be trustworthy much earlier on (approx. one year earlier).
{"title":"Strengthening Weak Identities Through Inter-Domain Trust Transfer","authors":"Giridhari Venkatadri, Oana Goga, Changtao Zhong, Bimal Viswanath, K. Gummadi, Nishanth R. Sastry","doi":"10.1145/2872427.2883015","DOIUrl":"https://doi.org/10.1145/2872427.2883015","url":null,"abstract":"On most current websites untrustworthy or spammy identities are easily created. Existing proposals to detect untrustworthy identities rely on reputation signals obtained by observing the activities of identities over time within a single site or domain; thus, there is a time lag before which websites cannot easily distinguish attackers and legitimate users. In this paper, we investigate the feasibility of leveraging information about identities that is aggregated across multiple domains to reason about their trustworthiness. Our key insight is that while honest users naturally maintain identities across multiple domains (where they have proven their trustworthiness and have acquired reputation over time), attackers are discouraged by the additional effort and costs to do the same. We propose a flexible framework to transfer trust between domains that can be implemented in today's systems without significant loss of privacy or significant implementation overheads. We demonstrate the potential for inter-domain trust assessment using extensive data collected from Pinterest, Facebook, and Twitter. Our results show that newer domains such as Pinterest can benefit by transferring trust from more established domains such as Facebook and Twitter by being able to declare more users as likely to be trustworthy much earlier on (approx. one year earlier).","PeriodicalId":20455,"journal":{"name":"Proceedings of the 25th International Conference on World Wide Web","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80282735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We present the malicious CAPTCHA attack, allowing a rogue website to trick users into unknowingly disclosing their private information. The rogue site displays the private information to the user in obfuscated manner, as if it is a CAPTCHA challenge; the user is unaware that solving the CAPTCHA, results in disclosing private information. This circumvents the Same Origin Policy (SOP), whose goal is to prevent access by rogue sites to private information, by exploiting the fact that many websites allow display of private information (to the user), upon requests from any (even rogue) website. Information so disclosed includes name, phone number, email and physical addresses, search history, preferences, partial credit card numbers, and more. The vulnerability is common and the attack works for many popular sites, including nine out of the ten most popular websites. We evaluated the attack using IRB-approved, ethical user experiments.
{"title":"Tell Me About Yourself: The Malicious CAPTCHA Attack","authors":"Nethanel Gelernter, A. Herzberg","doi":"10.1145/2872427.2883005","DOIUrl":"https://doi.org/10.1145/2872427.2883005","url":null,"abstract":"We present the malicious CAPTCHA attack, allowing a rogue website to trick users into unknowingly disclosing their private information. The rogue site displays the private information to the user in obfuscated manner, as if it is a CAPTCHA challenge; the user is unaware that solving the CAPTCHA, results in disclosing private information. This circumvents the Same Origin Policy (SOP), whose goal is to prevent access by rogue sites to private information, by exploiting the fact that many websites allow display of private information (to the user), upon requests from any (even rogue) website. Information so disclosed includes name, phone number, email and physical addresses, search history, preferences, partial credit card numbers, and more. The vulnerability is common and the attack works for many popular sites, including nine out of the ten most popular websites. We evaluated the attack using IRB-approved, ethical user experiments.","PeriodicalId":20455,"journal":{"name":"Proceedings of the 25th International Conference on World Wide Web","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82156309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The public cloud "infrastructure as a service" market possesses unique features that make it difficult to predict long-run economic behavior. On the one hand, major providers buy their hardware from the same manufacturers, operate in similar locations and offer a similar menu of products. On the other hand, the competitors use different proprietary "fabric" to manage virtualization, resource allocation and data transfer. The menus offered by each provider involve a discrete number of choices (virtual machine sizes) and allow providers to locate in different parts of the price-quality space. We document this differentiation empirically by running benchmarking tests. This allows us to calibrate a model of firm technology. Firm technology is an input into our theoretical model of price-quality competition. The monopoly case highlights the importance of competition in blocking "bad equilibrium" where performance is intentionally slowed down or options are unduly limited. In duopoly, price competition is fierce, but prices do not converge to the same level because of price-quality differentiation. The model helps explain market trends, such the healthy operating profit margin recently reported by Amazon Web Services. Our empirically calibrated model helps not only explain price cutting behavior but also how providers can manage a profit despite predictions that the market "should be" totally commoditized.
公共云“基础设施即服务”市场具有独特的特点，很难预测长期的经济行为。一方面，主要供应商从相同的制造商那里购买硬件，在相似的地点运营，并提供类似的产品菜单。另一方面，竞争对手使用不同的专有“结构”来管理虚拟化、资源分配和数据传输。每个供应商提供的菜单包含离散数量的选择(虚拟机大小)，并允许供应商定位在价格-质量空间的不同部分。我们通过运行基准测试来记录这种差异。这使我们能够校准企业技术模型。企业技术是我们的价格质量竞争理论模型的一个输入。垄断案例突出了竞争在阻止“不良均衡”方面的重要性，在这种均衡中，业绩被故意拖慢或选择被过度限制。在双寡头垄断中，价格竞争是激烈的，但由于价格质量的差异，价格不会趋同于同一水平。该模型有助于解释市场趋势，比如亚马逊网络服务(Amazon Web Services)最近公布的健康的营业利润率。我们的经验校准模型不仅有助于解释降价行为，还有助于解释供应商如何在市场“应该”完全商品化的预测下管理利润。
{"title":"Competition on Price and Quality in Cloud Computing","authors":"Cinar Kilcioglu, Justin M. Rao","doi":"10.1145/2872427.2883043","DOIUrl":"https://doi.org/10.1145/2872427.2883043","url":null,"abstract":"The public cloud \"infrastructure as a service\" market possesses unique features that make it difficult to predict long-run economic behavior. On the one hand, major providers buy their hardware from the same manufacturers, operate in similar locations and offer a similar menu of products. On the other hand, the competitors use different proprietary \"fabric\" to manage virtualization, resource allocation and data transfer. The menus offered by each provider involve a discrete number of choices (virtual machine sizes) and allow providers to locate in different parts of the price-quality space. We document this differentiation empirically by running benchmarking tests. This allows us to calibrate a model of firm technology. Firm technology is an input into our theoretical model of price-quality competition. The monopoly case highlights the importance of competition in blocking \"bad equilibrium\" where performance is intentionally slowed down or options are unduly limited. In duopoly, price competition is fierce, but prices do not converge to the same level because of price-quality differentiation. The model helps explain market trends, such the healthy operating profit margin recently reported by Amazon Web Services. Our empirically calibrated model helps not only explain price cutting behavior but also how providers can manage a profit despite predictions that the market \"should be\" totally commoditized.","PeriodicalId":20455,"journal":{"name":"Proceedings of the 25th International Conference on World Wide Web","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82705249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aaron Cahn, Scott Alfeld, P. Barford, S. Muthukrishnan
Web cookies are used widely by publishers and 3rd parties to track users and their behaviors. Despite the ubiquitous use of cookies, there is little prior work on their characteristics such as standard attributes, placement policies, and the knowledge that can be amassed via 3rd party cookies. In this paper, we present an empirical study of web cookie characteristics, placement practices and information transmission. To conduct this study, we implemented a lightweight web crawler that tracks and stores the cookies as it navigates to websites. We use this crawler to collect over 3.2M cookies from the two crawls, separated by 18 months, of the top 100K Alexa web sites. We report on the general cookie characteristics and add context via a cookie category index and website genre labels. We consider privacy implications by examining specific cookie attributes and placement behavior of 3rd party cookies. We find that 3rd party cookies outnumber 1st party cookies by a factor of two, and we illuminate the connection between domain genres and cookie attributes. We find that less than 1% of the entities that place cookies can aggregate information across 75% of web sites. Finally, we consider the issue of information transmission and aggregation by domains via 3rd party cookies. We develop a mathematical framework to quantify user information leakage for a broad class of users, and present findings using real world domains. In particular, we demonstrate the interplay between a domain's footprint across the Internet and the browsing behavior of users, which has significant impact on information transmission.
{"title":"An Empirical Study of Web Cookies","authors":"Aaron Cahn, Scott Alfeld, P. Barford, S. Muthukrishnan","doi":"10.1145/2872427.2882991","DOIUrl":"https://doi.org/10.1145/2872427.2882991","url":null,"abstract":"Web cookies are used widely by publishers and 3rd parties to track users and their behaviors. Despite the ubiquitous use of cookies, there is little prior work on their characteristics such as standard attributes, placement policies, and the knowledge that can be amassed via 3rd party cookies. In this paper, we present an empirical study of web cookie characteristics, placement practices and information transmission. To conduct this study, we implemented a lightweight web crawler that tracks and stores the cookies as it navigates to websites. We use this crawler to collect over 3.2M cookies from the two crawls, separated by 18 months, of the top 100K Alexa web sites. We report on the general cookie characteristics and add context via a cookie category index and website genre labels. We consider privacy implications by examining specific cookie attributes and placement behavior of 3rd party cookies. We find that 3rd party cookies outnumber 1st party cookies by a factor of two, and we illuminate the connection between domain genres and cookie attributes. We find that less than 1% of the entities that place cookies can aggregate information across 75% of web sites. Finally, we consider the issue of information transmission and aggregation by domains via 3rd party cookies. We develop a mathematical framework to quantify user information leakage for a broad class of users, and present findings using real world domains. In particular, we demonstrate the interplay between a domain's footprint across the Internet and the browsing behavior of users, which has significant impact on information transmission.","PeriodicalId":20455,"journal":{"name":"Proceedings of the 25th International Conference on World Wide Web","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90345347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mobile page load times are an order of magnitude slower compared to non-mobile pages. It is not clear what causes the poor performance: the slower network, the slower computational speeds, or other reasons. Further, most Web optimizations are designed for non-mobile browsers and do not translate well to the mobile browser. Towards understanding mobile Web page load times, in this paper we: (1) perform an in-depth pairwise comparison of loading a page on a mobile versus a non-mobile browser, and (2) characterize the bottlenecks in the mobile browser {em vis-a-vis} non-mobile browsers. To this end, we build a testbed that allows us to directly compare the low-level page load activities and bottlenecks when loading a page on a mobile versus a non-mobile browser. We find that computation is the main bottleneck when loading a page on mobile browsers. This is in contrast to non-mobile browsers where networking is the main bottleneck. We also find that the composition of the critical path during page load is different when loading pages on the mobile versus the non-mobile browser. A key takeaway of our work is that we need to fundamentally rethink optimizations for mobile browsers.
{"title":"An In-depth Study of Mobile Browser Performance","authors":"Javad Nejati, A. Balasubramanian","doi":"10.1145/2872427.2883014","DOIUrl":"https://doi.org/10.1145/2872427.2883014","url":null,"abstract":"Mobile page load times are an order of magnitude slower compared to non-mobile pages. It is not clear what causes the poor performance: the slower network, the slower computational speeds, or other reasons. Further, most Web optimizations are designed for non-mobile browsers and do not translate well to the mobile browser. Towards understanding mobile Web page load times, in this paper we: (1) perform an in-depth pairwise comparison of loading a page on a mobile versus a non-mobile browser, and (2) characterize the bottlenecks in the mobile browser {em vis-a-vis} non-mobile browsers. To this end, we build a testbed that allows us to directly compare the low-level page load activities and bottlenecks when loading a page on a mobile versus a non-mobile browser. We find that computation is the main bottleneck when loading a page on mobile browsers. This is in contrast to non-mobile browsers where networking is the main bottleneck. We also find that the composition of the critical path during page load is different when loading pages on the mobile versus the non-mobile browser. A key takeaway of our work is that we need to fundamentally rethink optimizations for mobile browsers.","PeriodicalId":20455,"journal":{"name":"Proceedings of the 25th International Conference on World Wide Web","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90372984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Felipe Pezoa, Juan L. Reutter, F. Suárez, M. Ugarte, D. Vrgoc
JSON -- the most popular data format for sending API requests and responses -- is still lacking a standardized schema or meta-data definition that allows the developers to specify the structure of JSON documents. JSON Schema is an attempt to provide a general purpose schema language for JSON, but it is still work in progress, and the formal specification has not yet been agreed upon. Why this could be a problem becomes evident when examining the behaviour of numerous tools for validating JSON documents against this initial schema proposal: although they agree on most general cases, when presented with the greyer areas of the specification they tend to differ significantly. In this paper we provide the first formal definition of syntax and semantics for JSON Schema and use it to show that implementing this layer on top of JSON is feasible in practice. This is done both by analysing the theoretical aspects of the validation problem and by showing how to set up and validate a JSON Schema for Wikidata, the central storage for Wikimedia.
{"title":"Foundations of JSON Schema","authors":"Felipe Pezoa, Juan L. Reutter, F. Suárez, M. Ugarte, D. Vrgoc","doi":"10.1145/2872427.2883029","DOIUrl":"https://doi.org/10.1145/2872427.2883029","url":null,"abstract":"JSON -- the most popular data format for sending API requests and responses -- is still lacking a standardized schema or meta-data definition that allows the developers to specify the structure of JSON documents. JSON Schema is an attempt to provide a general purpose schema language for JSON, but it is still work in progress, and the formal specification has not yet been agreed upon. Why this could be a problem becomes evident when examining the behaviour of numerous tools for validating JSON documents against this initial schema proposal: although they agree on most general cases, when presented with the greyer areas of the specification they tend to differ significantly. In this paper we provide the first formal definition of syntax and semantics for JSON Schema and use it to show that implementing this layer on top of JSON is feasible in practice. This is done both by analysing the theoretical aspects of the validation problem and by showing how to set up and validate a JSON Schema for Wikidata, the central storage for Wikimedia.","PeriodicalId":20455,"journal":{"name":"Proceedings of the 25th International Conference on World Wide Web","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91521248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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