An Exploration of Search Code

April 9, 2021

Software engineer Bard de Geode posts an exercise in search coding on his blog—“Building a Full-Text Search Engine in 150 Lines of Python Code.” He has pared down the thousands and thousands of lines of code found in proprietary search systems to the essentials. Of course, those platforms have many more bells and whistles, but this gives one an idea of the basic components. Navigate to the write-up for the technical details and code snippets that I do not pretend to follow completely. The headings de Geode walks us through include Data, Data preparation, Indexing, Analysis, Indexing the corpus, Searching, Relevancy, Term frequency, and Inverse document frequency. He concludes:

“You can find all the code on Github, and I’ve provided a utility function that will download the Wikipedia abstracts and build an index. Install the requirements, run it in your Python console of choice and have fun messing with the data structures and searching. Now, obviously this is a project to illustrate the concepts of search and how it can be so fast (even with ranking, I can search and rank 6.27m documents on my laptop with a ‘slow’ language like Python) and not production grade software. It runs entirely in memory on my laptop, whereas libraries like Lucene utilize hyper-efficient data structures and even optimize disk seeks, and software like Elasticsearch and Solr scale Lucene to hundreds if not thousands of machines. That doesn’t mean that we can’t think about fun expansions on this basic functionality though; for example, we assume that every field in the document has the same contribution to relevancy, whereas a query term match in the title should probably be weighted more strongly than a match in the description. Another fun project could be to expand the query parsing; there’s no reason why either all or just one term need to match.”

Fore more information, de Geode recommends curious readers navigate to MonkeyLearn’s post “What is TF-IDF?” and to an explanation of “Term Frequency and Weighting” posted by Stanford’s NLP Group. Happy coding.

Cynthia Murrell, April 9, 2021

Microsoft Adds Semantic Search to Azure Cognitive Search: Is That Fast?

April 9, 2021

Microsoft is adding new capabilities to its cloud-based enterprise search platform Azure Cognitive Search, we learn from “Microsoft Debuts AI-Based Semantic Search on Azure” at Datanami. We’re told the service offers improved development tools. There is also a “semantic caption” function that identifies and displays a document’s most relevant section. Reporter George Leopold writes:

“The new semantic search framework builds on Microsoft’s AI at Scale effort that addresses machine learning models and the infrastructure required to develop new AI applications. Semantic search is among them. The cognitive search engine is based on the BM25 algorithm, (as in ‘best match’), an industry standard for information retrieval via full-text, keyword-based searches. This week, Microsoft released semantic search features in public preview, including semantic ranking. The approach replaces traditional keyword-based retrieval and ranking frameworks with a ranking algorithm using deep neural networks. The algorithm prioritizes search results based on how ‘meaningful’ they are based on query relevance. Semantics-based ranking ‘is applied on top of the results returned by the BM25-based ranker,’ Luis Cabrera-Cordon, group program manager for Azure Cognitive Search, explained in a blog post. The resulting ‘semantic answers’ are generated using an AI model that extracts key passages from the most relevant documents, then ranks them as the sought-after answer to a query. A passage deemed by the model to be the most likely to answer a question is promoted as a semantic answer, according to Cabrera-Cordon.”

By Microsoft’s reckoning, the semantic search feature represents hundreds of development years and millions of dollars in compute time by the Bing search team. We’re told recent developments in transformer-based language models have also played a role, and that this framework is among the first to apply the approach to semantic search. There is one caveat—right now the only language the platform supports is US English. We’re told that others will be added “soon.” Readers who are interested in the public preview of the semantic search engine can register here.

Cynthia Murrell, April 9, 2021

Autonomy: Some Search History

April 6, 2021

I want to offer a happy quack to The Register, an online information service, for links to Autonomy documents. The slow moving legal carnival train is nearing its destination. “Everything You Need to Know about the HPE v Mike Lynch High Court Case” provides a useful summary of the trial. In addition, the article includes links to a number of fascinating documents. These provide some helpful insights into the challenges vendors of enterprise search and content processing systems face. Furthermore, the documents make clear that enterprise software can be a business challenge. The sales cycle is difficult. Installing and optimizing the software are challenges. Plus keeping the customer’s expectations for a solution in line with the realities of the solution often require the intellectual skills of big time wizards. Why are these documents relevant in 2021?

First, some vendors of search and content processing systems ignore the realities exposed in these documents.

Second, today’s customers are fooled by buzzwords and well crafted demonstrations. The actual system may be “different.”

Third, the users of today’s systems are likely to find themselves struggling to locate and make sense of information they know is available in the organization.

But marketing and complex interactions among software and service vendors and their partners are fascinating. Are similar practices in play today?

That’s an interesting question to consider.

Stephen E Arnold, April 6, 2021

Google Ad King Assembles Ad Free Search Engine

April 5, 2021

The heart of Google’s revenue is targeted ads. Despite the tech giant’s code of conduct, the company became a profit-driven corporate beast. Sridhar Ramaswamy was once Google’s advertising king, but he became disillusioned with the corporate beast. His biggest qualms were how Google’s obsessions with growth affected everything in the company, including user privacy and search quality.

Maybe Ramaswamy was inspired by DuckDuckGo when he decided to build a new search engine without ads and data tracking. Forbes details Ramaswamy’s career move in the article, “After Building Google’s Advertising Business, This Founder Is Creating An Ad-Free Alternative.”

His new search engine is called Neeva and his fellow Google cofounder Vivek Raghunathan invested in the new search startup. Instead of relying on ad revenue, Ramaswamy wants Neeva to be subscription based. His plan is for users to pay $5-10 a month to see non-sponsored search results.

Privacy is a major concern for users and the current Internet of things is hardly secure. Neeva comes at a time when users are demanding better regulations and better technology securing their information. There could also be a growing demand for unpolluted search results. Larry Page and Sergey Brin even wrote in their famous Stanford research paper that search engines driven by ad revenue will not ultimately meet consumers’ needs, because they will be biased by advertisements.

Neeva already has many investors, but tech experts doubt it will do much damage to Google:

“Search engine experts doubt Neeva will be able to do much damage to Google, at least in the short term. Some say Google’s gravitational pull is too strong for users to leave. Arun Kumar, CTO at Interpublic Group of Companies, Inc. a New York-based advertising holding company, says while Neeva might ‘find a few takers, but you’re not going to shake the kingdom.’”

Money is the driving force behind Google and user’s needs. Why pay for something when it is free in other places-biased or not?

Whitney Grace, April 5, 2021

Xooglers Have Google DNA When It Comes to Search

March 22, 2021

I spotted this story: “Ex-Google Employees Come Up with Their Own Privacy-Focused Search Engine.” The hook is that two Xooglers (former Google employees) are beavering away on a new search engine. The details appear in the write up. What I noticed was that users will have to pay to play. Plus, in order to become a subscriber, certain personal information will be required. Here’s a selection of the data the “privacy focused search engine” will possess:

  • Email address
  • Phone number
  • Location information
  • Name
  • User settings
  • IP address
  • Information you save in your ‘spaces.’
  • Payment information
  • The operating system or device
  • Mailing address
  • Cookie identifiers
  • Information regarding your contacts
  • The browser type and version you use
  • Pages that you visit

You can take the Xooglers out of Google, but it seems you cannot take the Google out of Xooglers. I particularly like the useful information which can be extracted from these data and nifty analyses like cross correlation. And that browser history! Yep, very interesting.

The privacy focused phrase is tasty too.

Stephen E Arnold, March 22, 2021

The Duck Confronts Googzilla

March 18, 2021

You have heard of David and Goliath? What about the duck and Googzilla? No. Navigate to “DuckDuckGo Calls Out Google over User Data Collection.” The metasearch engine wants everyone to know that Google does not define “privacy” the way the duck crowd does. The write up states:

DuckDuckGo says Google tried its best to hide its data collection practices, until it was no longer possible for them to keep it private. ‘After months of stalling, Google finally revealed how much personal data they collect in Chrome and the Google app. No wonder they wanted to hide it,’ DuckDuckGo said in a series of tweets. ‘Spying on users has nothing to do with building a great web browser or search engine. We would know (our app is both in one).’

Everyone is entitled to an opinion.

However, it is interesting to consider the question, “What happens next?”

  1. Google can ignore the duck. Eric Schmidt is no longer explaining that Qwant keeps him awake at night because that service is a heck of a threat. So, meh.
  2. Google takes steps to make life slight more interesting for the DuckDuckGo. There are some possibilities which are fun to ponder; for example, hasta la vista to links from the GOOG to the duck or Google works its magic within its walled garden. There’s a lot of content that lives within the Google ecosystem and when it is blocked or gifted with added latency, the scope may be a surprise to some.
  3. Google goes on the offensive just as it has with Microsoft. Imagine Google’s CEO suggesting that Microsoft’s CEO is dragging red herrings to the monopoly party. What could Google’s minions identify as information of value about DuckDuckGo, its traffic, and its index coverage? Interesting to ponder.

The tale of David and Goliath is an enduring one. The duck versus Googzilla might lack legendary status of brave David, but the confrontation might be a surprising one. Ducks are fierce creatures, but may have to punch above their weight to cause Googzilla pain.

Stephen E Arnold, March 18, 2021

Google and Microsoft Are Fighting. But a Battle May Loom between Coveo and Service Now

March 18, 2021

The 2021 cage match line ups are interesting. The Google – Microsoft dust up is a big deal. Google says Microsoft is using its posture on news as a way to blast rock and roll fog around the egregious security breaches for SolarWinds and Exchange Server.

But that fog could obscure a bout between Coveo (a smart search company) and Service Now (a Swiss Army knife of middleware, including Attivio search. Both companies invoke the artificial intelligence moniker. Both covet enterprise customers. Both want to extend their software into large organizations.

Service Now makes it plans clear in “Service Now Adds New AI and Low-Code Development Features.” The write up states:

[A user conference in Quebec] … also introduces AI Search, underpinned by technology acquired in ServiceNow’s purchase of Attivio. AI Search delivers intelligent search results and actionable information, complementing Quebec’s Engagement Messenger that extends self-service to third-party portals to enable AI search, knowledge management, and case interactions. Also new in Quebec is the aforementioned virtual agent, which delivers AI-powered conversational experiences for IT incident resolution.

From my vantage point, the AI is hand waving. Search has quite a few moving parts, and human involvement is necessary whether smart software is involved or not.

What Service Now has, however, is a meta-play; that is, it offers numerous management services. If properly set up and resourced could reduce the pain of some utility functions. Search is the mother of all utility services.

Coveo is a traditional enterprise search vendor. The company has targeted numerous business functions as likely customers; for example, customer support and marketing.

But niche vendors of utilities have to be like the “little engine that could.”

This may not be the main event like Google versus Microsoft, but it will be an event to watch.

Stephen E Arnold, March 18, 2021

Search Engines: Bias, Filters, and Selective Indexing

March 15, 2021

I read “It’s Not Just a Social Media Problem: How Search Engines Spread Misinformation.” The write up begins with a Venn diagram. My hunch is that quite a few people interested in search engines will struggle with the visual. Then there is the concept that typing in a search team returns results are like loaded dice in a Manhattan craps game in Union Square.

The reasons, according to the write up, that search engines fall off the rails are:

  • Relevance feedback or the Google-borrowed CLEVER method from IBM Almaden’s patent
  • Fake stories which are picked up, indexed, and displayed as value infused,

The write up points out that people cannot differentiate between accurate, useful, or “factual” results and crazy information.

Okay, here’s my partial list of why Web search engines return flawed results:

  1. Stop words. Control the stop words and you control the info people can find
  2. Stored queries. Type what you want but get the results already bundled and ready to display.
  3. Selective spidering. The idea is that any index is a partial representation of the possible content. Instruct spiders to skip Web sites with information about peanut butter, and, bingo, no peanut butter information
  4. Spidering depth. Is the bad stuff deep in a Web site? Just limit the crawl to fewer links?
  5. Spider within a span. Is a marginal Web site linking to sites with info you want killed? Don’t follow links off a domain.
  6. Delete the past. Who looks at historical info? A better question, “What advertiser will pay to appear on old content?” Kill the backfile. Web indexes are not archives no matter what thumbtypers believe.

There are other methods available as well; for example, objectionable info can be placed in near line storage so that results from questionable sources display with latency or slow enough to cause the curious user to click away.

To sum up, some discussions of Web search are not complete or accurate.

Stephen E Arnold, March 15, 2021

Search and Privacy: Those Log Files Are Tempting However

March 11, 2021

Search has been a basic Internet function since its inception, but when it was first invented protecting users’ privacy was not a concern.  Nowadays a simple search reveals users’ interests, locations, and much more information that can be sold or stolen.  TechRadar explains why search needs to be redesigned with privacy as the top priority: “Why We Need To Rebuild Internet Search, Putting User Privacy First.”

Early Internet developers wanted to make money from their new invention in order to build new technology.  Investors and developers were happy, because there was a profit.  Early Internet advertising, however, transformed into a big privacy problem today:

“Problems later emerged because what started out as a quick fix to a short-term problem turned into a central part of the internet’s architecture. Like anything else in tech, engineers quickly went to work optimizing advertising to be as efficient as possible, stumbling into a situation where the world’s biggest and most powerful companies were suddenly incentivized to gather more and more personal data on users to sell advertising. This resulted in algorithms to maximize engagement on content sites that prioritized instinctive and emotional decisions – or “fast thinking” as the Nobel Prize winner in behavioral economics Daniel Kahneman calls it.”

The information superhighway has turned into a giant consumerism tool that spreads fake news, radicalization, pushes unneeded products and services, and feeds on peoples’ insecurities.  Driving sales to stir the economy is one thing, but the spread of misinformation and radicalization leads to dangerous situations, including the recent coup attempt on Washington D.C. and constant backfires against science.

User-experience drives technology design and development, so any new search protocols must have today’s ease of use.  Currently multi-party computation (MPC) replicates blockchain-like technology so it protects users’ privacy.   Selected computers directly access encrypted data without knowing anything about the data, dubbed zero-knowledge computation. 

Zero-knowledge computation is a good solution to protecting user privacy, but there is a big problem preventing more development: money.  Advertisers and businesses love the current search system, because it feeds their bottom line.  Most users do not protect their data, but if they demanded more privacy protections then organizations would invest more money in that area. 

Whitney Grace, March 11, 2021

Elastic and Its Approach to Its Search Business

February 16, 2021

This blog post is about Elastic, the Shay Banon information retrieval company, not Amazon AWS Elastic services. Confused yet? The confusion will only increase over time because the “name” Elastic is going to be difficult to keep intact due to Amazon’s ability to erode brand names.

But that’s just one challenge the Elastic search company founded by the magic behind Compass Search. An excellent analysis of Elastic search’s challenges appears in “Elastic Has Stretched the Patience of Many in Open Source. But Is There Room for a Third Way?”

The write up quotes an open source expert as saying:

Let’s be really clear – it’s a move from open to proprietary as a consequence of a failed business model decision…. Elastic should have though their revenue model through up front. By the time the team made the decision to open source their code, the platform economy existed and their decisions to open source ought to
have been aligned to an appropriate business model.

I circled this statement in the article:

Sympathy for Elastic’s position comes from a perhaps unexpected source. Matt Assay, principal at Elastic’s bête noire AWS, believes it’s time to revisit the idea of “shared source”, a licensing scheme originally dreamed up by Microsoft two decades ago as an answer to the then-novel open source concept. In shared source, code is open – as in visible – but its uses are restricted… The heart of the problem is about who gets to profit from open source software. To help resolve that problem, we just might need new licensing.

Information retrieval is not about precision and recall, providing answers to users, or removing confusion about terms and product names — search is about money. Making big bucks from a utility service continues to lure some and smack down others. Now it is time to be squishy and bouncy I suppose.

Stephen E Arnold, February 16, 2021

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