Ai Web Scraping
HTTP Rotating & Static Proxies
- 40 million IPs for all purposes
- 195+ locations
- 3 day moneyback guarantee
Developing AI-Based Solution for Web Scraping – Dataversity
Click to learn more about author Aleksandras Šulženko.
As the buzz around artificial intelligence and machine learning keeps increasing, there are few tech-based companies who have yet to try their hand at it. We decided to take the plunge into AI and ML last year.
We started small. Instead of attempting to implement machine learning models in all stages of our solutions for data acquisition, we split everything into manageable pieces. We applied the Pareto principle – what would take the least effort but provide the greatest benefit? For us, the answer was AI-based fingerprinting.
Thus, late last year we unveiled a new type of proxy: Next-Gen Residential Proxies. They are our machine learning-based innovation in the proxy industry. Since we had fairly little experience with AI and ML beforehand, we gathered machine learning experts and Ph. D researchers as the advisory board.
We were successful in our efforts of developing AI-powered dynamic fingerprinting. Such an addition to our arsenal of solutions for web scraping makes it easy for anyone to reach 100% data acquisition success rates.
Additionally, we are in the beta testing phase of our Adaptive HTML parser. In the foreseeable future, Next-Gen Residential Proxies will allow you to gather structured data in JSON from any e-commerce product page.
These solutions arose from necessity. Getting IP blocked by a server is an unfortunate daily reality of web scraping. As websites are making strides in the enhancement of flagging algorithms with behavioral and fingerprinting-based detection, our hope lies in using machine learning to maximize data acquisition success rate.
Note:The tips and details outlined below are a theoretical exploration for those who want to try out web scraping. Before engaging in web scraping, consult your lawyers or legal team.
Understanding Bot Detection: Search Engines
It shouldn’t be surprising that gaining access to search engine scraping is rife with opportunity. However, search engines know full well about all the advantages gained by scraping them. Thus, they often use possibly the most sophisticated anti-bot measures available.
Throughout the experience of our clients, we always notice the same issue with search engine –low scraping success rates. Search engines are quite trigger-happy when labeling activity as bot-like.
Newest developments in anti-bot technology include two important improvements: behavioral detection and fingerprinting-based detection. Simply changing user agents and IP addresses to completely avoid blocks is a thing of the past. Web scraping needs to be more advanced.
Once the data is collected it is then matched with known bot fingerprints. Avoiding fingerprinting is not easy. According to research, an average browser can be tracked for 54 days, and a quarter of those can be easily tracked for over 100 days.
Thus, there is no surprise that the process of maintaining a high success rate in search engine scraping is challenging. It’s a cat-and-mouse game where the cat has been enhanced by cybernetics. Upgrading the mouse with AI would go a long way for most businesses in this area.
E-commerce platforms are a completely different beast. Paths to data in search engine scraping are very short. Generally, they consist of sending queries directly to the engine in question and downloading the entire page. After that, some valuable data has already been extracted.
However, for e-commerce platforms, a vast amount of constantly changing product pages will need to be scraped to acquire usable data. A lot more daily browsing will need to be done to extract all the requisite data points.
Therefore, e-commerce platforms have more opportunities to detect bot-like behavior. Often they will add more extensive behavioral and fingerprinting-based detection into the mix to flag bots more quickly and accurately.
Intuitively, there’s an understanding that bots will browse in a different manner from humans. Often, speed and accuracy will be the standout features of a bot. After all, just slowing down a bot to human browsing speeds and mannerisms (or even slower! ) would be a considerable victory.
Machine learning is almost always used in behavioral detection as a comparison model is required. Data on human browsing patterns is collected and fed to a machine learning model. After enough data has been digested by the machine learning algorithm, it can begin making reasonably accurate predictions.
Human and bot behavior tracking can take numerous routes through both client and server-side features. These will often include:
Pages per sessionTotal number of requestsUser journey (humans will behave in a less orderly fashion)Average time between two pagesResources loaded and suspicious use (for example, browsers sending JS requests but being unable to draw GUI, disabling CSS, etc. )Average scroll depthMouse movements and clicksKeys pressed
Someone attempting to circumvent sophisticated fingerprinting-based tracking techniques will have to do two things: a lot of trial-and-error testing to reverse-engineer the machine learning model and its triggers, and create a crawling pattern that would be both evasive and effective.
Trial-and-error testing will be extremely costly. Lots of proxies will receive temporary or even permanent bans before enough data is acquired to get a decent understanding of the model at play.
The e-commerce-platform-specific crawling pattern, of course, will be developed out of an understanding of the model used to flag bot-like activity. Unfortunately, the process will be an endless effort of tinkering around with settings, receiving bans, and changing parameters to get the most out of each crawl.
AI-Powered Dynamic Fingerprinting
What is the best way to combat AI- and ML-based anti-bot algorithms? Creating an AI- and ML-based crawling algorithm. Good data is not hard to come by as the success and failure points are very cut-and-dry.
Anyone who has done web scraping in the past should already have a decent collection of fingerprints that might be considered valuable. These fingerprints can be stored into a database, labeled, and provided as training data.
However, testing and validation is going to be a little more difficult. Not all fingerprints are created equal and some might receive blocks more frequently than others. Collecting data on success rates per fingerprint and creating a feedback loop will greatly enhance the AI over time.
That’s exactly what you can do using Next-Gen Residential Proxies. They find the most effective fingerprints that result in the least number of blocks without supervision. Our version of AI-powered dynamic fingerprinting involves a feedback loop that can use trial-and-error results to discover combinations of user agents, browsers, and OS that will have a better chance at bypassing detection.
AI-powered dynamic fingerprinting solves the primary problem of e-commerce platform scraping: enhanced bot activity detection algorithms. With some injection of AI and machine learning, our proverbial mouse might just stand a chance against the cat.
These technologies, of course, would be helpful for anyone who does web scraping at scale. Understanding how fingerprinting impacts block rates is one of the most important theories for those who do web scraping.
Our next step is fixing a different web scraping pain point: parsing. Developing and maintaining a specialized parser for every different target takes a substantial amount of effort. We’d rather leave that to AI.
What we found is that building an adaptive parser requires a ton of labeled data but doesn’t require feedback loops as complex as with AI-powered dynamic fingerprinting. Getting the training data was simple, if a little boring.
We contracted help to manually label all the fields in an e-commerce product page and fed the training data to our parsing machine learning model. Eventually, after some data validation and testing, it reached a stage of allowing its users to deliver data from e-commerce product pages with reasonable accuracy.
Introducing AI and machine learning to proxy management is going to become an inevitability at some point in the near future. Real-time bot protection has already begun creating ML models that attempt to separate bots from humans. Manually managing all aspects of proxies (e. g., user agents, rotation, etc. ) might become simply too inefficient and costly.
To some, building AI and machine learning models might seem like a daunting task. However, web crawling is a game with countless moving parts. You don’t have to create one overarching overlord-like ML model that would do everything. Start by tending to the smaller tasks (such as dynamic user agent creation). Eventually, you will be able to build the entire web crawling system out of small ML-based models.
HTTP Rotating & Static Proxies
- 40 million IPs for all purposes
- 195+ locations
- 3 day moneyback guarantee
AI web scraping augments data collection – SearchEnterpriseAI
Web scraping automates the data gathering process and refines the data pipeline, but it requires careful attention to choosing the right tools, languages and programs.
Web scraping has been around almost as long as there has been a web to scrape. The technology forms the cornerstone of search services like Google and Bing and can extract large amounts of data.
Data collection on the web tends to be at the mercy of how it is presented, and many sites actively discourage web scraping. However, developers can create web scraping applications in languages such as Python or Java to help bring data into a variety of AI applications. It is crucial for developers to carefully think about pipelines they use for acquiring their data. Each step of this process — getting the right data, cleaning it and then organizing it into the appropriate format for their needs — must be reviewed.
These pipelines are a continual work in progress. The perfect web scraping pipeline for today may have to be completely revamped for tomorrow. Knowing this, there are a variety of tools and best practices that can help automate and refine these pipelines and keep organizations on the right path.
Web scraping applications and AI
Web scraping involves writing a software robot that can automatically collect data from various webpages. Simple bots might get the job done, but more sophisticated bots use AI to find the appropriate data on a page and copy it to the appropriate data field to be processed by an analytics application.
AI web scraping-based use cases include e-commerce, labor research, supply chain analytics, enterprise data capture and market research, said Sarah Petrova, co-founder at Techtestreport. These kinds of applications rely heavily on data and the syndication of data from different parties. Commercial applications use web scraping to do sentiment analysis about new product launches, curate structured data sets about companies and products, simplify business process integration and predictively gather data.
One specific web scraping project includes curating language data for non-English natural language processing (NLP) models or capturing sports statistics for building new AI models for fantasy sports analysis. Burak Özdemir, a web developer based in Turkey, used web scraping to build a neural network model for NLP tasks in Turkish.
Though web scraping has existed for a long time, the use of AI for web extraction has become a game changer.
Sayid ShabeerChief product officer, HighRadius
“Although there are so many pretrained models that can be found online for English, it’s much harder to find a decent data set for other languages, ” Özdemir said. He has been experimenting with scraping Wikipedia and other platforms that have structured text to train and test his models — and his work can provide a framework for others looking to develop and train NLP in non-English languages.
The tools of web scraping
There is a variety of tools and libraries that developers can use to jumpstart their web scraping projects. Primarily, Python has web scraping technology readily available via online libraries.
Python plays a significant role in AI development with focus on web scraping, Petrova said. She recommended considering libraries like Beautiful Soup, lxml, MechanicalSoup, Python Requests, Scrapy, Selenium and urllib.
Each tool has its own strength and they can often complement one another. For example, Scrapy is an open source and collaborative framework for extracting data that is useful for data mining, monitoring and automated testing. Beautiful Soup is a Python library for pulling data out of HTML and XML files. Petrova said she deploys it for modeling scrape scripts as the library provides simple methods and Pythonic idioms for navigating, searching and modifying a parse tree.
Augmenting data with web scraping
AI algorithms are often developed on the front end to learn which sections of a webpage contain fields such as product data, review or price. Petrova noted that combining web scraping with AI, the process of data augmentation can become more efficient.
“Web scraping, especially smart, AI-driven, data extraction, cleansing, normalization and aggregation solutions, can significantly reduce the amount of time and resources organizations have to invest in data gathering and preparation relative to solution development and delivery, ” said Julia Wiedmann, machine learning research engineer, at Diffbot, a structured web search service.
Petrova said common data augmentation techniques include:
extrapolation (relevant fields are updated or provided with values);
tagging (common records are tagged to a group, making it easier to understand and differentiate for the group);
aggregation (using mathematical values of averages and means — values are estimated for relevant fields, if needed); and
probability techniques (based on heuristics and analytical statistics — values are populated based on the probability of events).
Using AI for resilient scraping
Websites are built to be human-readable and not machine-readable, which makes it hard to extract at scale and across different page layouts. Anyone who has tried to aggregate and maintain data knows what a difficult task this can be — whether it be a manually compiled database with typos, missing fields, and duplicates or the variability of online content publication practices, Wiedmann said.
Her team has developed AI algorithms that use the same cues as a human to detect the information that should be scraped. She has also found that it is important to integrate outputs into applied research or test environments first. There can be hidden variability tied to the publication practices of the sources. Data quality assurance routines can help minimize the manual data maintenance.
“Designing systems that minimize the amount of manual maintenance will reduce errors and data misuse, ” Wiedmann said.
Improving data structure
AI can also structure data collected with web scraping to improve the way it can be used by other applications.
“Though web scraping has existed for a long time, the use of AI for web extraction has become a game changer, ” said Sayid Shabeer, chief product officer at HighRadius, an AI software company.
Traditional web scraping can’t extract structured data from unstructured documents automatically, but recent advancements built AI algorithms that work in data extraction in a similar fashion to humans and that continue to learn as well. Shabeer’s team used these types of bots for extracting remittance information for cash applications from retail partners. The web aggregation engine regularly logs into retailer websites and looks for remittance information. Once the information becomes available, the virtual agents automatically capture the remittance data and provide it in a digital format.
From there, a set of rules can be applied to further enhance the quality of data and bundle it with the payment information. AI models allow the bots to master a variety of tasks rather than have them focus on just one process.
To build these bots, Shabeer’s team collated the common class names and HTML tags that are used on various retailer’s websites and fed these into the AI engine. This was used as training data to ensure that the AI engine could handle any new retailer portals that were being added with minimal to no manual intervention. Over time, the engine became more and more capable of extracting data without any intervention.
Limitations of web scraping
In a recent case in which LinkedIn tried to prevent HiQ Labs from scraping its data for analytics purposes, the U. S. Supreme Court has ruled that web scraping for analytics and AI can be legal. However, there are still a variety of ways that websites may not intentionally or accidentally break web scraping applications.
Petrova said that some of the common limitations she has encountered include:
Scraping at scale. Scraping a single page is straightforward, but there are challenges in scraping millions of websites, including managing the codebase, collecting data and maintaining a data warehouse.
Pattern changes. Each website periodically changes its user interface.
Anti-scraping technologies. Some websites use anti-scraping technologies.
Honeypot traps. Some website designers put honeypot traps inside websites to detect web spiders and deliver false information. This can involve generating links that normal users can’t see but crawlers can.
Quality of data. Records that do not meet the quality guidelines will affect the overall integrity of the data.
Browser vs. back end
Web scraping is generally done by a headless browser that can scour webpages independent of any human activity. However, there are AI chatbot add-ons that scrape data as a background process running in the browser that can help users find new information. These front-end programs use AI to decide how to communicate the appropriate information to a user.
Marc Sloan, co-founder and CEO at Scout, an AI web scraping chatbot, said they originally did this by using a headless browser in Python that pulled webpage content via a network of proxies. Information was extracted from the content using a variety of techniques. Sloan and his team used Spacy to extract entities and relations from unstructured text into knowledge graphs using Neo4j. Convolutional networks were used to identify features such as session type, session similarity and session endpoints.
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What is AI Scoring in Sleep Medicine? – EnsoData
EnsoSleep is an AI scoring software solution that automates sleep event detection with 90 percent agreement. AI scored sleep studies are ready for a clinician’s overview and a discussion with a patient in about 15 minutes.
As an artificial intelligence (AI) solution in medicine, we’re often asked a lot of general questions about our AI scoring software, and how it works. Some folks ask about our AI’s capabilities: how fast is it? Is it consistent? What does it actually do? How does it work? Many in the sleep industry wonder what’s different between the autoscoring products they’ve seen before and our EnsoSleep AI scoring. To answer these questions, we compiled an AI Scoring FAQ with the most common questions we hear from sleep professionals interested in our AI technology.
What is AI?
Artificial Intelligence (AI) is the general field of study at the intersection of statistics, software development and theoretical computer science. AI developers ideate different applicable algorithmic approaches using large data sets to create machines with a certain level of intelligence. AI is often thought of as a robot, but a calculator, a scheduling assistant, an electric thermostat, and even your old Tamagotchi could be considered basic AI. The goal of many AI technologies is to solve problems the same way humans might, but with more accuracy and efficiency.
What is Machine Learning (ML)?
Machine Learning (ML) refers to a collection of methods that enables an algorithm to execute specific tasks without being explicitly programmed. This is achieved by taking advantage of previously observed data, and through a training process, allows the ML algorithm to learn relationships within the observed data in order to make predictions on newly collected data. As a result, an ML-powered algorithm will continue to improve over time.
What is Waveform AI?
Waveforms are used in healthcare to monitor, diagnose, and treat patients. The electric signal pulsing up and down on the monitor next to a patient’s bed is waveform data. Heartbeats on an EKG, eye movements through an EOG, and brain waves through an EEG all output as waveform data, with over 1. 5 billion waveforms run per year globally across specialties. EnsoData’s Waveform AI leads the world in reading and understanding these waveforms. The first waveform AI product on the market was EnsoSleep, a sleep scoring AI built on over 500, 000 clinical studies. You can see the other AI products in healthcare down below.
What is AI Sleep Scoring?
AI sleep scoring is the automated event detection and analysis of PSG or HSAT data performed by an AI scoring software, like EnsoSleep. The AI does this by analyzing the waveform data of sleep tests and providing an AI scored study for a registered clinician to review, all in a fraction of the time it takes to perform the task manually.
How effective is AI scoring?
People also ask, how consistent is AI? How accurate is it? Our EnsoSleep AI scoring requires a clinician’s review, and during those reviews we consistently agree with the overscorers at an average rate of 90 percent, with some clinicians agreeing at higher rates.
How fast can an RPSGT review an AI scored PSG test?
EnsoSleep identifies patterns and events in polysomnography tests (PSGs) for clinical diagnosis, replacing a workflow that currently requires hours from clinicians to manually mark reams of complex data by hand. This results in very efficient turnaround times. For most customers, clinicians can review an EnsoSleep AI scored PSG in around 15 minutes, compared to an hour on average for a test scored from scratch.
Can your AI score HSATs?
Yes, EnsoSleep works for home sleep apnea tests (HSATs). In fact, AI scoring makes at-home testing more scalable and profitable for providers as it helps open up precious time for other activities.
How fast can AI score an HSAT?
For all tests, the connection levels on the user end impact speeds, but EnsoSleep can score HSATs in about 15 minutes, including upload, scoring, and download times. Click on the image below to read a case study of how this was implemented at a 4-location sleep clinic.
In the case of Dr. Bijwadia and Synaptrx Sleep, this swift turnaround time significantly improved the patient turnaround times.
What’s the difference between autoscoring technologies and AI scoring technology?
AI scoring technology is not like the old autoscoring, which was based on algorithms written by programmers to follow “if-this-then-that” rules on an event by event basis. With EnsoSleep AI scoring, we’re leveraging machine learning and deep learning in our algorithms. We’ve trained our system on hundreds of thousands of studies that were scored by RPSGTs and interpreted by board certified physicians. As a result, the AI scoring is a much more sophisticated, consistent, and adaptable solution than the other autoscoring solutions.
How does AI sleep scoring compare to outsourcing sleep study scoring?
Sleep scoring solutions powered by AI drive better turnaround times for understaffed sleep centers. In this case study, see how a 4-location sleep practice reduced patient times from weeks to same-day results: “Leveraging Cutting Edge Sleep scoring AI Saves iSleep Time. ”
Can AI provide sleep staging?
Yes, our EnsoSleep AI does provide sleep stage scoring.
That’s all great, but is there a video I can share with my team that easily explains EnsoSleep’s AI scoring?
How can AI help the field of Sleep Medicine?
In a world in which 80% of sleep apnea patients have yet to find out if they are suffering from the condition the field of healthcare must choose to adopt necessary technology to speed up the process. EnsoSleep is a sleep scoring software designed to fill that immense need.
What is the sleep apnea scoring criteria for the AI?
Our AI scoring helps identify many sleep issues, including obstructive sleep apnea (OSA), periodic limb movements (PLMS) and other sleep disorders that are identified during a PSG or HSAT. Our AI reduces manual sleep analysis time allowing technologists to focus on the parts of the study that can lead to important diagnosis, such as heart arrhythmias or abnormal EEG.
Is the AI scoring FDA cleared?
The FDA cleared EnsoSleep in 2017. This early clearance was also one of the earliest AI-based devices in medicine, earning accolades in the Benjamens, et. al. study: “The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. ”
Do you partner with anyone in the sleep industry?
EnsoData partners with medical device, wearable, and EMR companies to further streamline integration and adoption of sleep scoring AI amongst the sleep community. For specific partnership queries, send us a message via our contact page.
What are the key benefits of using AI scoring?
The key benefits to using AI scoring are simple: consistency, simplicity, and speed.
EnsoSleep provides more consistent results, faster turnaround for patients, and a better patient experience.
Our solution minimizes downstream costs of untreated conditions and increases access to personalized treatment.
EnsoSleep is interoperable with the leading medical and wearable devices, so there is no change to the workflows clinicians use today when implementing EnsoData’s technology.
What do your customers think of your AI scoring?
Customers love working with EnsoData: the company has 96% customer retention and has scaled to work with more than 400 clinics in the past 2 years.
What is the cost of your AI scoring?
Great question! Our pricing is designed to help you maximize your team’s time. If you’d like to know more about our tiered pricing options, please request a demo with one of our team’s account executives.
Do you have a sleep scoring manual?
We do have a user manual to help our users navigate our software, but we pride ourselves on fitting seamlessly into your workflow.
Do you have any other resources on AI scoring?
There are a handful of other great pages on our site to check out:
Check out our EnsoSleep White Paper, the first major asset outlining our waveform AI scoring solution for sleep clinics.
Dive into our educational sleep webinars. We have a variety of topics designed to help everyone in the sleep field.
AI-Powered software has transformed a number of sleep clinics. Dive into our EnsoSleep case studies to read each story.
Want to see our published scientific papers in sleep research? Check out our publications page for articles dating back to our inception in 2015.
We hope this helps you understand artificial intelligence sleep scoring a bit more, but if we didn’t answer your question, please, let us know what it is in the form below. Thank you.
Frequently Asked Questions about ai web scraping
Does web scraping use AI?
Web scraping involves writing a software robot that can automatically collect data from various webpages. Simple bots might get the job done, but more sophisticated bots use AI to find the appropriate data on a page and copy it to the appropriate data field to be processed by an analytics application.Jun 29, 2020
What is AI scraping?
AI sleep scoring is the automated event detection and analysis of PSG or HSAT data performed by an AI scoring software, like EnsoSleep.
Can you r for web scraping?
Web scraping has a set process that works like this, generally: Access a page from R. Instruct R where to “look” on the page. Convert data in a usable format within R using the rvest package.Oct 24, 2018