ML and CV in data extraction. A new factor
Contents of article:
- ML with CV in data extraction: AI-basics explained by Dexodata
- ML with data extraction zoomed in on
- CV as next data extraction frontiers
People and data entered the zettabyte era in the mid 2010s. At that moment, volumes of info exceeded 1021 bytes, i.e. a zettabyte. As experts from UBS foresee, around 2030 there will be 660 zettabytes on the internet. We at Dexodata, as an ecosystem of proxies for data extraction, welcome these exponential developments. Expanding info means extra users approaching us and buying residential and mobile proxies for data collection.
Questions arise, i.e. how could humans, even armed with automated data harvesting tools with proxies, scrape such enormous datasets? Our brain faces difficulties when attempting to simply imagine one sixtillion bytes. Collecting those pools looks notably ever-more challenging for human species, but not for computer vision (CV) and machine learning (ML) as AI subtypes.
ML with CV in data extraction: AI-basics explained by Dexodata
In the capacity of an ecosystem with geo targeted proxies, we realize the importance of comprehending what words convey. Insightful discussions mandate all terminologies being clarified. Artificial intelligence (AI) as an umbrella term along with machine learning and computer vision has special connotations when web data extraction processes are at stake. Let’s delve into peculiarities:
- AI in data extraction. AI refers to smart computer systems, performing tasks that require human intelligence. AI can understand overall structures of websites, identify relevant patterns, make high-level decisions about the scraping process.
- Being a subset of AI, ML data extraction describes algorithms and statistical models, enabling machines to perform tasks without explicit programming. That means, ML-driven data extraction solutions would learn and improve from past and ongoing “professional” experiences. In data extraction, such options could automatically adjust to alterations within website structures, content, anti-scraping measures, while controlling scraping routines. After that, ML is in the right position to assume responsibility for data analysis, normalization, even decision-making based on extracted data.
- As the name implies, CV represents next-gen approaches of visual content assessment regarding data extraction. It aids in obtaining and interpreting info from images and videos as well as in understanding graphical layouts of web pages.
ML with data extraction zoomed in on
Automated data extraction, previously seen as a thing of the future in comparison with manual copy-pasting, no longer can satisfy modern needs. Rigorous once-and-for-all set patterns that can be modified only by hand or via another outdated algorithm, functioning on a straightforward “if, then” basis, typically fail within today’s environment. It is too problematic to foresee all obstacles while bundling data extraction tools without ML, including:
| Barrier | Situation |
| IP restrictions | To prevent websites from restricting or rate-limiting IPs it's advisable to employ a strategy of using different IPs per each single request, closely monitoring your scraper. Machine learning is helpful concerning scheduling and reacting here |
| CAPTCHAs | That good-old impediment necessitates integrating third-party CAPTCHA-handling solutions or writing your own one. Both objectives could engage ML |
| Dynamic site content | Up-to-date presences often utilize client-side rendering technologies, e.g. JS, for producing dynamic content, necessitating extra ML-measures when it comes to web scraping |
| Limited rates | To safeguard their servers, websites may restrict the volumes of requests clients can initiate within specific timeframes. Manipulations with endpoints, headers, proxy origins, other parameters might help. Self-evolving algorithms will handle them with increased speed |
| Page structure modifications | Modifications regarding a website's design or HTML structure can pose challenges for scrapers in accurately identifying and selecting elements, unless ML gets involved |
| Honeypots | These elements or links are concealed, intended solely for automated script access. Engaging with honeypots might result in red flags. Self-learning algorithms might be helpful in avoiding traps |
| Browser-based fingerprinting | By collecting and analyzing browser details, this method creates a distinctive identifier to monitor users, creating formidable stumbling blocks for info collection scripts to overcome. ML algorithms will be quicker in “face-changing” practices |
The list is not extensive. Blockers might also include required credentials, slow page loading speed (hindering harvesters), the fact that non-browser user-agents can be swiftly identified, you name it. There already exist quite a few ready-to-use intelligent data extraction solutions in various niches for neutralizing them. Whatever eventual picks will be, buy residential and mobile proxies, as even smart options still need this base for building upon.
CV as next data extraction frontiers
ML elements are commonplace in web data extraction domains (ok, they soon will be). CV is a different matter. This is a game changer. Paradoxically, despite perceptions of CV as a contemporary advancement, it is rooted in extensive research spanning several decades. During the mid-1960s, MIT introduced "Project MAC," an abbreviation for “Project on Mathematics and Computation”. Its origins can be traced back to the XXth century, commencing with Herman Hollerith's tabulator sorter, reaching its pinnacle with his card punch machine. CV can be viewed as a recent manifestation of Hollerith's groundbreaking discovery, representing an AI branch dedicated to instructing computers in interpreting 2D/3D images. Building upon that capability, CV manifests a major breakthrough.
If one discusses “conventional” ML, most imagine texts, tables, number rows, code lines, etc. Yet, there are much greater info-obtaining potentials when purely digital content gets involved. Hard facts prove that point:
- Visual info constitutes 90% of data conveyed towards brains. That is why humans love pictures as well as videos;
- Per the Harvard Design Magazine, 750 billion images are there on WWW. CV makes them accessible for data extraction, analysis, interpretation;
- Advanced CV software turns videos into fields for data extraction, too. There is a lot to grab, as back in 2022, on YouTube alone, there were 800 million videos.
Screen scraping enabled by geo targeted proxies, together with other formats of visual data extraction via CV, transforms those information goldmines into full-fledged viable intellectual digital assets. All industries, even “conservative” ones, capitalize on this trend.
| Domain | Scenarios of CV |
| Finance |
Paper checks, invoices, contracts, agreements summarized via CV |
| Automotive | Self-driving vehicles working, evolving, initially trained on CV |
| HealthTech | CV-enabled readings of CT examinations, MRI research, ultrasound visual data |
| Manufacturing | Scanning bases with barcodes, conducting QA checks, inspecting packaging through CV |
When it comes to less regulated domains, e.g. e-commerce data extraction, data scraping on YouTube, or brand protection activities via CV and rotating proxies, options get even wider. CV could analyze contexts, translate images into datasets, even read emotions for marketing campaigns.
No matter what your industry or intended use case might be, unleashing full data extraction potentials of ML, coupled with CV, will compel you to buy residential and mobile proxies. Apply Dexodata’s pool with 1M+ whitelisted ethically sourced IPs from America, Canada, Great Britain, major EU member states, Ukraine, Belarus, Kazakhstan, Chile, Turkey, Japan, among 100+ available countries. Our promise revolves around 100% compatibility with intelligent software, 99% uptime, top-notch customer support, reasonable pricing plans, from $3.65 per 1Gb or $0.3 per port. We help ML- and CV-driven efforts all over the globe!
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