A practical example of how anomaly detection makes complex data problems easier to solve.
As new tools for distributed storage and analysis of big data are becoming more stable and widely known, there is a growing need for discovering best practices for analytics at this scale. One of the areas of widespread interest that crosses many verticals is anomaly detection.
At its best, anomaly detection is used to find unusual, rarely occurring events or data for which little is known in advance. Examples include changes in sensor data reported for a variety of parameters, suspicious behavior on secure websites, or unexpected changes in web traffic. In some cases, the data patterns being examined are simple and regular and, thus, fairly easy to model.
Anomaly detection approaches start with some essential but sometimes overlooked ideas about anomalies:
- Anomalies are defined not by their own characteristics but in contrast to what is normal.
- Before you can spot an anomaly, you first have to figure out what “normal” actually is.
This need to first discover what is considered “normal” may seem obvious, but it is not always obvious how to do it, especially in situations with complicated patterns of behavior. Best results are achieved when you use statistical methods to build an adaptive model of events in the system you are analyzing as a first step toward discovering anomalous behavior. Read more…
The Lambda Architecture has its merits, but alternatives are worth exploring.
Nathan Marz wrote a popular blog post describing an idea he called the Lambda Architecture (“How to beat the CAP theorem“). The Lambda Architecture is an approach to building stream processing applications on top of MapReduce and Storm or similar systems. This has proven to be a surprisingly popular idea, with a dedicated website and an upcoming book. Since I’ve been involved in building out the real-time data processing infrastructure at LinkedIn using Kafka and Samza, I often get asked about the Lambda Architecture. I thought I would describe my thoughts and experiences.
What is a Lambda Architecture and how do I become one?
The Lambda Architecture looks something like this:
Data from the Internet of Things makes an integrated data strategy vital.
The Internet of Things (IoT) is more than a network of smart toasters, refrigerators, and thermostats. For the moment, though, domestic appliances are the most visible aspect of the IoT. But they represent merely the tip of a very large and mostly invisible iceberg.
IDC predicts by the end of 2020, the IoT will encompass 212 billion “things,” including hardware we tend not to think about: compressors, pumps, generators, turbines, blowers, rotary kilns, oil-drilling equipment, conveyer belts, diesel locomotives, and medical imaging scanners, to name a few. Sensors embedded in such machines and devices use the IoT to transmit data on such metrics as vibration, temperature, humidity, wind speed, location, fuel consumption, radiation levels, and hundreds of other variables. Read more…
Why my understanding of AI is different from yours.
Editor’s note: this post is part of our Intelligence Matters investigation.
Let me start with a secret: I feel self-conscious when I use the terms “AI” and “artificial intelligence.” Sometimes, I’m downright embarrassed by them.
Before I get into why, though, answer this question: what pops into your head when you hear the phrase artificial intelligence?
For the layperson, AI might still conjure HAL’s unblinking red eye, and all the misfortune that ensued when he became so tragically confused. Others jump to the replicants of Blade Runner or more recent movie robots. Those who have been around the field for some time, though, might instead remember the “old days” of AI — whether with nostalgia or a shudder — when intelligence was thought to primarily involve logical reasoning, and truly intelligent machines seemed just a summer’s work away. And for those steeped in today’s big-data-obsessed tech industry, “AI” can seem like nothing more than a high-falutin’ synonym for the machine-learning and predictive-analytics algorithms that are already hard at work optimizing and personalizing the ads we see and the offers we get — it’s the term that gets trotted out when we want to put a high sheen on things. Read more…
More visible at Health Privacy Summit than Health Datapalooza.
On the first morning of the biggest conference on data in health care–the Health Datapalooza in Washington, DC–newspapers reported a bill allowing the Department of Veterans Affairs to outsource more of its care, sending veterans to private health care providers to relieve its burdensome shortage of doctors.
There has been extensive talk about the scandals at the VA and remedies for them, including the political and financial ramifications of partial privatization. Republicans have suggested it for some time, but for the solution to be picked up by socialist Independent Senator Bernie Sanders clinches the matter. What no one has pointed out yet, however–and what makes this development relevant to the Datapalooza–is that such a reform will make the free flow of patient information between providers more crucial than ever.
Researchers and startups are building tools that enable feature discovery.
Why do data scientists spend so much time on data wrangling and data preparation? In many cases it’s because they want access to the best variables with which to build their models. These variables are known as features in machine-learning parlance. For many0 data applications, feature engineering and feature selection are just as (if not more important) than choice of algorithm:
Good features allow a simple model to beat a complex model.
(to paraphrase Alon Halevy, Peter Norvig, and Fernando Pereira)
The terminology can be a bit confusing, but to put things in context one can simplify the data science pipeline to highlight the importance of features:
Feature Engineering or the Creation of New Features
A simple example to keep in mind is text mining. One starts with raw text (documents) and extracted features could be individual words or phrases. In this setting, a feature could indicate the frequency of a specific word or phrase. Features1 are then used to classify and cluster documents, or extract topics associated with the raw text. The process usually involves the creation2 of new features (feature engineering) and identifying the most essential ones (feature selection).