ENTRIES TAGGED "algorithm"
The importance of data science tools that let organizations easily combine, deploy, and maintain algorithms
Data science often depends on data pipelines, that involve acquiring, transforming, and loading data. (If you’re fortunate most of the data you need is already in usable form.) Data needs to be assembled and wrangled, before it can be visualized and analyzed. Many companies have data engineers (adept at using workflow tools like Azkaban and Oozie), who manage1 pipelines for data scientists and analysts.
A workflow tool for data analysts: Chronos from airbnb
A raw bash scheduler written in Scala, Chronos is flexible, fault-tolerant2, and distributed (it’s built on top of Mesos). What’s most interesting is that it makes the creation and maintenance of complex workflows more accessible: at least within airbnb, it’s heavily used by analysts.
Job orchestration and scheduling tools contain features that data scientists would appreciate. They make it easy for users to express dependencies (start a job upon the completion of another job), and retries (particularly in cloud computing settings, jobs can fail for a variety of reasons). Chronos comes with a web UI designed to let business analysts3 define, execute, and monitor workflows: a zoomable DAG highlights failed jobs and displays stats that can be used to identify bottlenecks. Chronos lets you include asynchronous jobs – a nice feature for data science pipelines that involve long-running calculations. It also lets you easily define repeating jobs over a finite time interval, something that comes in handy for short-lived4 experiments (e.g. A/B tests or multi-armed bandits).
The cycle of good, bad, and stable has happened at every layer of the stack. It will happen with big data, too.
First, technology is good. Then it gets bad. Then it gets stable.
This has been going on for a long time, likely since the invention of fire, knives, or the printed word. But I want to focus specifically on computing technology. The human race is busy colonizing a second online world and sticking prosthetic brains — today, we call them smartphones — in front of our eyes and ears. And stacks of technology on which they rely are vulnerable.
When we first created automatic phone switches, hackers quickly learned how to blow a Cap’n Crunch whistle to get free calls from pay phones. When consumers got modems, attackers soon figured out how to rapidly redial to get more than their fair share of time on a BBS, or to program scripts that could brute-force their way into others’ accounts. Eventually, we got better passwords and we fixed the pay phones and switches.
We moved up the networking stack, above the physical and link layers. We tasted TCP/IP, and found it good. Millions of us installed Trumpet Winsock on consumer machines. We were idealists rushing onto the wild open web and proclaiming it a new utopia. Then, because of the way the TCP handshake worked, hackers figured out how to DDOS people with things like SYN attacks. Escalation, and router hardening, ensued.
We built HTTP, and SQL, and more. At first, they were open, innocent, and helped us make huge advances in programming. Then attackers found ways to exploit their weaknesses with cross-site scripting and buffer overruns. They hacked armies of machines to do their bidding, flooding target networks and taking sites offline. Technologies like MP3s gave us an explosion in music, new business models, and abundant crowd-sourced audiobooks — even as they leveled a music industry with fresh forms of piracy for which we hadn’t even invented laws. Read more…
Wikidata's structure vs. diverse knowledge, and a look at the many factors behind Netflix's recommendations.
A critic says Wikidata could undermine Wikipedia's localized information. Also, Netflix explains why its recommendation engine is much more complicated than most people realize.
A merging of artificial intelligence and healthcare is tougher than many realize.
People will eventually get better care from artificial intelligence, but for now, we should keep the algorithms focused on the data that we know is good and keep the doctors focused on the patients.
Dynamic pricing angers some Uber users, Hadoop hits 1.0, a possible set back for open-access research.
Uber's dynamic pricing worked as intended on New Year's Eve, but not everyone is happy about that. Elsewhere, Hadoop reaches the 1.0 milestone and proposed legislation seeks to repeal an open-access research policy.
How data and algorithms help doctors make use of real-time data.
Predictive Medical Technologies says its new system can use real-time, intensive care unit monitoring data to predict cardiac arrest and other events up to 24 hours ahead of time. CEO Bryan Hughes discusses the system and the application of diagnostic data in this interview.