Data Science Camp

Last Saturday (October 24, 2015) was SF Bay ACM's annual Data Science Camp Silicon Valley. The venue was the Town Hall in PayPal's intergalactic headquarters on 2161 North 1st Street in San Jose, nestled between eBay and Apple's future Car Division, just one block from Comet, Foveon, Lumileds, Peaxy, and Toyota's research lab.

From the voting on the sessions, it appears that the event attracted data scientists from all over the US, with a large number of participants people who had taken the Coursera classes on big data, machine learning, and data science and were now wondering how to progress from Coursera to a job (82 votes). As Sara Kalisin from Intel noted, when companies try out analytics, they do not really know what to do with the result and end up not staffing the project because the benefit is less than the employee's salary. In addition to the session on "Coursera to job," Sara also led a session with title "How to showcase data
science impact" (15 votes).

At the beginning of the day, Joseph Rickert and Robert Horton of Microsoft gave the tutorial "Introduction to R for Machine Learning." R, which goes back to Bell Labs in 1976, has become the most widely used data analysis software. It is undergoing an astonishing growth and today has about 36,820 functions. The CRAN repository has a solution for almost all data analysis problems.

Originally, R was a statistical package with statistical tools acting on observations stored in arrays. There were different packages, like Weka, for mining large data sets stored in files with machine learning to classify patterns and make predictions. However, today R has all the machine learning functionality on top of the original statistical tools. This has been possible because today a serious workstation has at least 64 GB of RAM, which allows to store big data in arrays.

When the data is too large to fit in memory, it can be partitioned into blocks which can be processed sequentially or in parallel. However, this capability is not available with the free version of R and requires the expensive commercial enterprise version. Robert Horton announced that SQL Server 2016 will support the server-side execution of R functions. This means that the data no longer will have to be moved across the network for analysis.

After the sponsored lunch, John Park from HP led a double session with title "Malware Classification +
ML + Crowd Sourcing" (46+44 votes). The amount of malware injected every day is mind-boggling. He uses an algorithm called Nilsimsa Hash on the binary files and uses natural language processing and classifiers trained through crowd-sourcing to find the malware.

Another very popular session with the title "I have data. Now What? IOT wearable space in life sciences. How to analyze the data for individual users. How do we build that" (117 votes) was led by Lee Courtney from Qurasense. Representatives from large international financial institutions and the health care industry participated in this interactive session. The only company permanently storing all data and continuously mining all of it was a manufacturer of set-top boxes for the cable industry.

For everybody else, storing the data was just too dangerous because of the flood of malware, while Hadoop has no security. This requires ingesting the data, mining it, then deleting it. Because the HDFS data ingest is very slow and each file must be stored in three copies, as little data as possible is preserved. At the end of the session, Lee Courtney summarized the top three unsolved problems for big data as

1. no security
2. no security
3. poor ingest performance

As a note, there are file systems with excellent security and supporting HDFS. They have a POSIX interface, so it is not necessary to move the data at all.

Moving the data is a big problem that will not go away, as illustrated by this figure by DataCore. Until about eight years ago, processors kept getting faster. However, when the wall of physical transistor shrinking was hit, the clock rates actually became slower to control the heat generation and allow for more cores on each die. With the increasing number of cores—for example, a typical workstation processor has eight cores each with two hyperthreads, for a total of 16 cores—less IO bandwidth is available to each CPU: the number of pins on the chip is still the same, and there is a growing IO gap.

Some people believe that cloud computing and virtual machines solve the problem. However, for storage this is an illusion. Indeed, according to the Hennessy/Patterson rules of thumb, for general computing the utilization rate is about 0.20–0.25 [HOHY14]. For storage servers, the utilization rate is 0.6–0.8 [AFG+10], therefore statistical multiplexing is less useful because with the OS and hypervisor overheads a processor is maxed out. The IO gap comes on top of this!

[AFG+10] Michael Armbrust, Armando Fox, Rean Griffith, Anthony D Joseph, Randy Katz, Andy Konwinski, Gunho Lee, David Patterson, Ariel Rabkin, Ion Stoica, et al. A view of cloud computing. Communications of the ACM, 53(4):50–58, 2010.

[HOHY14] Md. Iqbal Hossain (Older) and Md. Iqbal Hossain (Younger). Dynamic scaling of a web-based application in a cloud architecture. Master’s thesis, School of Information and Communication Technology, KTH Royal Institute of Technology, Stockholm, 2014.

Sony to bring 4K tech to surveillance cameras

If the recent flow of billions of dollars in VC capital into the data storage and analysis industry is any indication, we have evolved into compulsive data packrats. However, even billions of people cannot type all the data we hoard. It takes color imaging to produce exabytes of data. Millions of selfies and cat movies contribute to the data stash, but only machines can create "new" data at exabyte scale.

One of the most prolific kind of data generation machines are the surveillance video camera systems. With the relentless widening of the social gap, a larger proportion of the population is evolving into desperate sub-proletarians with nothing to lose. This increases home robberies and is triggering a boom for home video security systems.

On February 25 we wrote on purple disks from Western Digital (the corresponding disks from Seagate have a turquoise label) optimized for surveillance video. Unfortunately, the images the police sends to the neighbors asking for help in identifying thieves are often too blurry to clearly recognize a perpetrator.

Around 2015, Sony plans to put its 4K-resolution technology in its surveillance cameras, which will boast significantly improved picture quality. Even zoomed-in images will appear sharp. Larger CMOS sensors will be employed, and software to make effective use of the images will be developed.

This quadrupling of video image resolution will be a bonanza for the data storage industry, as the global market for surveillance cameras will grow from ¥700 billion in 2013 to nearly ¥1 trillion in 2015 ($6.791 billion to $9.402 billion).

Nikkei article

Purple disk

Last year I wrote about Western Digital color-coding their hard disk drives to make it easier to find the optimal drive for an application. We saw that blue is for everyday use (about 40 hours per week in a PC), black for high performance, green for low power, and red for NAS (continuous operation, low power, vibration tolerance, error correction, streaming).

Now Western Digital has a new line color-coded purple. Purple is similar to red: it is aimed at surveillance video and meant to be always on, be deployed in bunches (vibration control), and used for streaming (different cache optimization). Although you could use red disks for video surveillance, the purple disks have an AllFrame firmware technology that reduces video frame loss.

Seagate's corresponding Surveillance HDD line has a turquoise label.

Energy footprint of the digital economy

Back in 2009 we looked at the carbon footprint of ripping color documents for digital presses and published the result in the EI 2010 paper "Font rendering on a GPU-based raster image processor." Assuming the raster image processor is run at maximum capacity, the state of the art system at the time consumed 38,723 KWh and generated 23,234 Kg of CO₂. By using GPUs, we were able to rip the same data with 10,804 KWh respectively 6,483 Kg of CO₂. At the time we thought saving 16,751 Kg of CO₂ per year per RIP was a pretty cool result, but at the end the product never shipped, despite — or maybe because — it was much lower cost. (See the paper for the details of the calculations.)

This month the Digital Power Group published the white-paper "The cloud begins with coal: big data, big networks, big infrastructure, and big power." The work was sponsored by the National Mining Association and the American Coalition for Clean Coal Electricity, which explains why some of the numbers appear a little optimistic in terms of the coal needed to keep the smart phones running and serving contents, but even if we divide the numbers by 5 to make them a little more realistic, the numbers are quite staggering when we add everything up. It turns out, that a smart phone requires as much coal as a small refrigerator. Cloud computing will consume an ever increasing fraction of our total energy consumption. This is a good reason to work on more efficient and greener storage systems.

Data Privacy

During the ascent of Nazism in Europe in the decade before world war II, the Swiss banks introduced secret bank accounts to hide the identity of persecuted customers from infiltrated spies working at the banks or loitering in their lobbies. After the war, this mechanism was abused by the banks to assist tax evaders and consequently has been largely dismantled.

Apparently, the Swiss have been able to maintain their reputation as a discreet country. An echo effect of the leak of an American agency's penchant for snooping everybody's data, is that more entities are now storing their data in Switzerland, although data storage in Switzerland is about 25% more expensive than in the neighboring EU countries. "Our customers know that money can be replaced — but sensitive data can not," says Mateo Meier of Artmotion, a data center in Zürich. Switzerland's know-how, political stability and adequate infrastructure are ideal conditions to store data securely, he says.

History does not repeat itself, and the Swiss have learned from the mistakes related to bank secrecy: there are no privacy rights for suspected felons and their data.

Newspaper article: Schweizer Datentresore sind nicht sicher