My work life has been quite busy lately and I haven't had a chance to sit down and blog. I have been touring around London and some parts of the northern England consulting and organizing some training here and there. Luckily I have had the chance to do some work on Imagick and the 2.2.0 beta release is getting closer. The internal structure was completely restructured and broken down into several smaller files. During this time Imagick was adapted to follow the PHP Coding Standards more closely. Still a work in progress
Scribe is a server for aggregating log data streamed in real time from a large number of servers. It is designed to be scalable, extensible without client-side modification, and robust to failure of the network or any specific machine.
This package extends the DiskSim disk-simulation environment from Carnegie Mellon University to provide limited support for solid-state-disk (SSD) simulation. This is not a simulator for any specific SSD, but rather a simulator for an idealized SSD that is parameterized by the properties of NAND flash chips such as read, write, erase latency, number of chips, connectivity, and chip bandwidth. DiskSim is primarily a trace-driven simulator, but it has some functionality for generating random workloads. This add-on is delivered as a source subdirectory of DiskSim, along with a patch file to modify an existing DiskSim source tree to be compatible with the add-on.
Do you wish you could live all the time in the clean, logical .NET environment instead of the messy, chaotic real world? Would you rather develop web apps than play with your children? Well, that sounds pretty unhealthy to us. We're not looking for total obsessive weirdos - just programmers hungry for the challenge of developing for a site that sells over seventeen units a day!
The increasing proliferation of online shopping and purchasing has naturally led to a growth in the popularity of comparison-shopping search engines, popularly known as “shopbots”. We extend the one-product-at-a-time search approach used in current shopbot implementations to consider purchasing plans for a bundle of items. Our approach leverages bundle-based pricing and promotional deals frequently offered by online merchants to extract substantial savings. Interestingly, our approach can also identify “freebies” that consumers can obtain at no extra cost. We also develop a model to extend the capability of the current recommendation algorithms that are mainly based on collaborative filtering and item-to-item similarity techniques, to incorporate product price and savings as an additional important factor in making recommendations to shoppers. We develop a practical algorithm that can be employed when the number of items is large or when the real-time nature of shopbot applications dictates quick response rates to consumer queries. A detailed experimental analysis with real-world data from major retailers suggests that the proposed models can provide significant savings for bundle purchasing consumers, and frequently identify freebies for consumers. Together the results underscore the potential benefits that can accrue by incorporating our models into current shopbot systems.
Possibly, if you are not residing in one of the countries listed, not attending an accredited university or not a member of one of the student organizations that we're connected with. But keep checking back, as we're working on adding more ways to verify your student status all the time.
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