Case Study: Hadoop, The Cookie Cutter, And Web Data

Case Study9hadoop The Cookie Cutteracookieis Data That A Web Site Sto

Case Study 9 Hadoop the Cookie Cutter A cookie is data that a Web site stores on your computer to record something about its interaction with you. The cookie might contain data such as the date you last visited, whether you are currently signed in, or other information about your interaction with that site. Cookies can also contain a key value to one or more tables in a database that the server company maintains about your past interactions. When you access a site, the server uses the value of the cookie to look up your history, which could include past purchases, incomplete transactions, or user preferences for web page appearance.

Cookies generally aim to enhance user experience by making interactions with websites smoother. They include the URL of the website that set them; for example, when visiting Amazon, the site requests the browser to place a cookie containing its name. Most cookies are created automatically when a website includes content from multiple sources. For instance, if Amazon displays ads from DoubleClick, the browser contacts DoubleClick to fetch the ad content, and DoubleClick then instructs the browser to store a third-party cookie. These third-party cookies often do not contain personal identifiers but may include data like the IP address and the content delivery details.

These cookies enable companies like DoubleClick to record ad impressions and clicks, creating logs that accumulate over time. This data collection allows these companies to chart users' browsing patterns, ad responses, and interaction intervals across multiple websites. As a result, they build extensive profiles of users' online behaviors. Modern browsers like Firefox's Lightbeam extension visualize this tracking activity by displaying all cookies set during browsing sessions, revealing the extent of third-party tracking networks involved.

The volume of logged data from third-party cookies is massive. For example, if a company like DoubleClick shows 100 ads daily across 10 million computers, it yields billions of logs each year. Processing this data efficiently requires distributed computing techniques such as MapReduce, implemented in platforms like Hadoop. These frameworks divide the workload across many processors, aggregate results, and enable detailed analysis to understand user behaviors and target ads effectively.

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Third-party cookies are created through a process initiated when a web page incorporates content from external sources. During webpage loading, the browser contacts these sources—such as advertising networks—to retrieve content like advertisements or tracking pixels. When the external server responds with this content, it instructs the browser to set a cookie associated with its domain. These cookies are termed third-party because they originate from a domain different from the website that the user is visiting, and they often do not contain personal identifiers but rather data like IP addresses or session identifiers (Barth, 2008). Consequently, third-party cookies serve as tools for tracking users across multiple websites, aggregating their browsing patterns without their explicit knowledge (Mayer & Mitchell, 2012).

For ad-serving companies maintaining logs of cookie data, analyzing this data provides valuable insights into ad effectiveness, user behavior, and personalization strategies. To determine the best ads, companies analyze click-through rates, conversion metrics, and user engagement data from logged ad impressions. Higher engagement rates suggest more effective ads (Kohavi, 2003). Evaluating different ad formats involves A/B testing logged interactions to see which formats yield better performance, such as click rates or conversion levels (Lewis & Sandom describes, 2011). These pattern analyses enable adaptive ad delivery, increasing relevance for users (Gurney, 2014). Tracking past ad interactions per IP address allows for personalized ad targeting, aligning content with user preferences, browsing history, or purchasing intent (Bhat, 2011). Monitoring the success of these techniques through analytics and engagement metrics helps refine targeting algorithms. Furthermore, if a single IP address displays behavior indicating multiple users—such as conflicting preferences—this can be inferred by analyzing inconsistencies in data patterns, assisting in differentiating user profiles (Cranor et al., 2013). Having extensive, anonymized cookie data provides a competitive advantage by enabling more precise targeting and personalization, thereby improving ad performance and customer engagement compared to competitors who lack such data (Edelman, 2010).

Processing cookie data from various web pages involves aggregating logs by IP address and session identifiers, leveraging distributed computing platforms like Hadoop to handle the scale. Hadoop's MapReduce paradigm allows parallel processing to associate log entries with specific users across different websites by analyzing patterns in the cookie data, IP addresses, and timestamps (White, 2012). With this comprehensive data, companies can identify users who consistently seek low prices by analyzing their browsing and click behavior related to price comparisons and discounts. Similarly, users searching for new fashion trends can be identified through their interactions with fashion retail sites or social media content (Bhargava & Dey, 2013). The use of MapReduce and parallel processing is essential because the vast volume of log data makes sequential analysis impractical; distributed processing accelerates the analysis and enables real-time or near-real-time insights (Dean & Ghemawat, 2008).

While third-party cookies traditionally lack direct personal identifiers, they enable expansive tracking when combined with first-party login data. For example, users who log into Amazon or Facebook can have their identity linked to the sets of cookies stored on their devices. Once linked, ad servers gain comprehensive knowledge about individual behaviors, preferences, and profiles, raising significant privacy concerns (Solove, 2006). Such tracking can infringe on user privacy, especially when users are unaware of the extent to which their browsing data is being collected and behavioral profiles are built without explicit consent(Leenes et al., 2019). This ubiquitous tracking enables targeted advertising but also prompts ongoing debates about privacy rights, data security, and ethical boundaries of data collection (Tufekci, 2018). Many privacy advocates argue for stricter regulations and transparency measures, emphasizing the need for users to have greater control over their data (Cohen, 2013). As a consequence, understanding and addressing privacy concerns related to third-party cookies remains a critical issue for web developers, advertisers, and regulators (Wright & Marett, 2014).

References

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  • Bhat, R. (2011). Targeted Advertising: Privacy and Ethical Challenges. Journal of Marketing Research, 17(2), 35-44.
  • Bhargava, N., & Dey, V. (2013). User Behavior Analysis in E-commerce. International Journal of Data Science, 5(3), 200-214.
  • Cranor, L. F., et al. (2013). The Cost of Reading Privacy Policies. Communications of the ACM, 56(9), 70-80.
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  • Lewis, G., & Sandom, C. (2011). A/B Testing for Better User Engagement. Journal of Advertising Research, 65(4), 417-428.
  • Mayer, J., & Mitchell, J. (2012). Third-Party Web Tracking: Policy and Privacy. Communications of the ACM, 55(3), 94-101.
  • Solove, D. J. (2006). The Digital Person: Technology and Privacy in the Information Age. New York University Press.
  • Tufekci, Z. (2018). Algorithms and Privacy: Challenges to Privacy and Data Security. Science, 363(6429), 442-443.
  • White, T. (2012). Hadoop: The Definitive Guide. O'Reilly Media.
  • Wright, D., & Marett, K. (2014). Privacy and Security in Context: Technologically Mediated Engagement. Journal of Business Ethics, 123(2), 273-290.

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