Mary Meeker (KPCB); Internet Trends 2017; Kleiner, Perkins, Caulfield, Byers (KPCB); 2017-05-31; 555 slides; landing ← kpcb.com/InternetTrends.
yes
Using Big Data in Real-Life Online Marketing: Personality-targeted and tailored advertising on Facebook. S. Matz, A.F. Chan, V. Popov, D. Stillwell, M. Kosinski. poster. Association for Psychological Science Convention (APS), 2014.
Michal Kosinski, publications.
Microservices and Teams at Amazon; Jan Stenberg; In InfoQ; 2015-12-31.
tl;dr → Amazon’s methods are where it’s at, the cutting edge, the bees knees, da bomb!
Chris Munns (Amazon) Microservices at Amazon; In I Love APIs 2015 Conference; 2015; 42 slides; separately noted.
| Microservices | SOA |
|---|---|
| Substantial function requires an RPC, a network call | Substantial function requires an RPC, a network call. |
| Many very small components | Fewer more sophisticated components |
| Business logic lives inside a single service domain | Business logic can live across multiple domains |
| Wire protocol must be HTTP JSON or (less preferably) XML | Wire protocol is the Enterprise Service BUS (ESB), as a layer between services |
| (RPC) type API driven with client-deployed SDKs/Library. | Middleware (?) |
Neal Ford (ThoughtWorks); Building Microservice Architectures; In Some Venue; 2014; 80 slides.
tl;dr → Enterprise Service Bus (ESB) rides again, but with Agile, Conway, Java, JSON, HTTP, REST, CI/CD & DevOps!
Sam Newman; Building Microservices: Designing Fine-Grained Systems; O’Reilly Media; preview edition; WHEN?; 102 pages; free sample (final edition); 25 pages; Amazon: kindle: $31, paper: $42+SHT; O’Reilly: pdf: $43, paper: $50+SHT.
and others
Separately noted.
Jason Bau, Jonathan Mayer, Hristo Paskov, John C. Mitchell; A Promising Direction for Web Tracking Countermeasures; In Proceedings of Some Conference; 2013; 5 pages.
Machine Learning: A Promising Direction for Web Tracking Countermeasures; 19 slides.
tl;dr → Automatically curated (learned) block lists; minimum publishable unit, no followup.
Web tracking continues to pose a vexing policy problem. Surveys have repeatedly demonstrated substantial consumer demand for control mechanisms, and policymakers worldwide have pressed for a Do Not Track system that effectuates user preferences. At present, however, consumers are left in the lurch: existing control mechanisms and countermeasures have spotty effectiveness and are difficult to use.
We argue in this position paper that machine learning could enable tracking countermeasures that are effective and easy to use. Moreover, by distancing human expert judgments, machine learning approaches are both easy to maintain and palatable to browser vendors. We briefly explore some of the promise and challenges of machine learning for tracking countermeasures, and we close with preliminary results from a prototype implementation.
The Changing Digital Landscape: Where Things are Heading; Lee Rainie (Pew Research Center); Presented at Tencent Media Summit, Beijing, China; 2015-11-12; 36 slides.
Separately noted.
David Patterson (U.C. Berkeley); How to have a Bad Career in Research, Academia Pre-PhD and Post-PhD (& How to Give a Bad Talk); 2015-11-18 ← ChRlHT.
Kassem Fawaz, Huan Feng, Kang G. Shin (University of Michigan); Anatomization and Protection of Mobile Apps’ Location Privacy Threats; In Proceedings of the 24th USENIX Security Symposium; 2015-08-14; 34 slides; landing.
