August 16, 2016
In an exclusive interview, Yippy’s head of enterprise search reveals that Yippy launched an enterprise search technology that Google Search Appliance users are converting to now that Google is sunsetting its GSA products.
Yippy also has its sights targeting the rest of the high-growth market for cloud-based enterprise search. Not familiar with Yippy, its IBM tie up, and its implementation of the Velocity search and clustering technology? Yippy’s Michael Cizmar gives some insight into this company’s search-and-retrieval vision.
Yippy ((OTC PINK:YIPI) is a publicly-trade company providing search, content processing, and engineering services. The company’s catchphrase is, “Welcome to your data.”
The core technology is the Velocity system, developed by Carnegie Mellon computer scientists. When IBM purchased Vivisimio, Yippy had already obtained rights to the Velocity technology prior to the IBM acquisition of Vivisimo. I learned from my interview with Mr. Cizmar that IBM is one of the largest shareholders in Yippy. Other facets of the deal included some IBM Watson technology.
This year (2016) Yippy purchased one of the most recognized firms supporting the now-discontinued Google Search Appliance. Yippy has been tallying important accounts and expanding its service array.
John Cizmar, Yippy’s senior manager for enterprise search
Beyond Search interviewed Michael Cizmar, the head of Yippy’s enterprise search division. Cizmar found MC+A and built a thriving business around the Google Search Appliance. Google stepped away from on premises hardware, and Yippy seized the opportunity to bolster its expanding business.
I spoke with Cizmar on August 15, 2016. The interview revealed a number of little known facts about a company which is gaining success in the enterprise information market.
Cizmar told me that when the Google Search Appliance was discontinued, he realized that the Yippy technology could fill the void and offer more effective enterprise findability. He said, “When Yippy and I began to talk about Google’s abandoning the GSA, I realized that by teaming up with Yippy, we could fill the void left by Google, and in fact, we could surpass Google’s capabilities.”
Cizmar described the advantages of the Yippy approach to enterprise search this way:
We have an enterprise-proven search core. The Vivisimo engineers leapfrogged the technology dating from the 1990s which forms much of Autonomy IDOL, Endeca, and even Google’s search. We have the connector libraries THAT WE ACQUIRED FROM MUSE GLOBAL. We have used the security experience gained via the Google Search Appliance deployments and integration projects to give Yippy what we call “field level security.” Users see only the part of content they are authorized to view. Also, we have methodologies and processes to allow quick, hassle-free deployments in commercial enterprises to permit public access, private access, and hybrid or mixed system access situations.
With the buzz about open source, I wanted to know where Yippy fit into the world of Lucene, Solr, and the other enterprise software solutions. Cizmar said:
I think the customers are looking for vendors who can meet their needs, particularly with security and smooth deployment. In a couple of years, most search vendors will be using an approach similar to ours. Right now, however, I think we have an advantage because we can perform the work directly….Open source search systems do not have Yippy-like content intake or content ingestion frameworks. Importing text or an Oracle table is easy. Acquiring large volumes of diverse content continues to be an issue for many search and content processing systems…. Most competitors are beginning to offer cloud solutions. We have cloud options for our services. A customer picks an approach, and we have the mechanism in place to deploy in a matter of a day or two.
Connecting to different types of content is a priority at Yippy. Even through the company has a wide array of import filters and content processing components, Cizmar revealed that Yippy is “enhanced the company’s connector framework.”
I remarked that most search vendors do not have a framework, relying instead on expensive components licensed from vendors such as Oracle and Salesforce. He smiled and said, “Yes, a framework, not a widget.”
Cizmar emphasized that the Yippy IBM Google connections were important to many of the company’s customers plus we have also acquired the Muse Global connectors and the ability to build connectors on the fly. He observed:
Nobody else has Watson Explorer powering the search, and nobody else has the Google Innovation Partner of the Year deploying the search. Everybody tries to do it. We are actually doing it.
Cizmar made an interesting side observation. He suggested that Internet search needed to be better. Is indexing the entire Internet in Yippy’s future? Cizmar smiled. He told me:
Yippy has a clear blueprint for becoming a leader in cloud computing technology.
For the full text of the interview with Yippy’s head of enterprise search, Michael Cizmar, navigate to the complete Search Wizards Speak interview. Information about Yippy is available at http://yippyinc.com/.
Stephen E Arnold, August 16, 2016
August 4, 2016
Data-management firm Semantify has secured more funding, we learn from “KGC Capital Invests in Semantify, Leaders in Cognitive Discovery and Analytics” at Benzinga. The write-up tells us primary investor KGC Capital was joined by KDWC Venture Fund and Bridge Investments in making the investment, as well as by existing investors (including its founder, Vishy Dasari.) The funds from this Series A funding round will be used to address increased delivery, distribution, and packaging needs.
The press release describes Semantify’s platform:
“Semantify automates connecting information in real time from multiple silos, and empowers non-technical users to independently gain relevant, contextual, and actionable insights using a free form and friction-free query interface, across both structured and unstructured content. With Semantify, there would be no need to depend on data experts to code queries and blend, curate, index and prepare data or to replicate data in a new database. A new generation self-service enterprise Ad-hoc discovery and analytics platform, it combines natural language processing (NLP), machine learning and advanced semantic modeling capabilities, in a single seamless proprietary platform. This makes it a pioneer in democratization of independent, on demand information access to potentially hundreds of millions of users in the enterprise and e-commerce world.”
Semantify cites their “fundamentally unique” approach to developing data-management technology as the force behind their rapid deployment cycles, low maintenance needs, and lowered costs. Formerly based in Delaware, the company is moving their headquarters to Chicago (where their investors are based). Semantify was founded in 2008. The company is also hiring; their About page declares, toward the bottom: “Growing fast. We need people;” as of this writing, they are seeking database/ BI experts, QA specialists, data scientists & knowledge modelers, business analysts, program & project managers, and team leads.
Cynthia Murrell, August 4, 2016
July 29, 2016
Are you a struggling search engine optimization “expert”? Do you know how to use Google to look up information? Can you say “semantics” five times without slurring your words?
If you answered “yes” to two of these questions, you can apply for the flurry of job openings for “semantic experts.” Incredible, I know. Just think. Unemployed SEO mavens, failed middle school teachers, and clueless webmasters can join the many folks with PhDs in the booming semantic technology sector.
Just think. No more Uber driving on Friday and Saturday nights. No more shame at a conference when someone asks, “What is it you do exactly?”
Navigate to “Semantic Technology Experts In Demand.” Get the truth. Don’t worry to much about:
- A definition of semantics
- A knowledge of semantic methods which actually work
- How semantic methods are implemented
- Which numerical recipes are most likely to generate accurate outputs.
Cash in now. Embrace this truth:
If you’re not heavily involved in the data world, you may not have heard of semantic technology, but it might be time to give the category some attention. It’s one of those areas of tech that’s becoming more important as organizations of all kinds contend with streams of information that contain multiple data structures (or no structures) and move at speeds that approach the threshold of mind-boggling. If you follow the news, you can watch the technology’s spread through a variety of industries and products. Ford, for example, recently acquired California startup Civil Maps, which develops and maintains live semantic maps of all the roads in the United States. And health IT experts say the day is coming when “data silos and lack of semantic interoperability will not be tolerated.”
If you spent months or years learning about Big Data, the cloud, and natural language processing, you can repurpose your expertise. Just say, “I am an expert in semantics.” Easy, right?
Stephen E Arnold, July 29, 2016
May 9, 2016
For fans of semantic technology, Ontotext has a late spring delight for you. The semantic platform vendor Ontotext has released GraphDB 7. I read “Ontotext Releases New Version of Semantic Graph Database.” According to the announcement, set up and data access are easier. I learned:
The new release offers new tools to access and explore data, eliminating the need to know everything about the dataset before start working with it. GraphDB 7 enables users to navigate their way through third-party and any other dataset regardless of data volumes, which makes it a powerful Big Data analytics tool. Ver.7 offers visual exploration of the loaded data schema – ontology, interactive query builder for better entity retrieval, and full support for RDF 1.1 allowing smooth import of a huge number of public Open Data as well as proprietary Linked Datasets.
If you want to have a Palantir-type system, check out Ontotext. The company is confident that semantic technology will yield benefits, a claim made by other semantic technology vendors. But the complexity challenges associated with conversion and normalization of content is likely to be a pebble in the semantic sneaker.
Stephen E Arnold, May 9, 2016
May 8, 2016
I know zero about semantics as practiced at big time universities. I know about the same when it comes to semantic search. With my background as a tabula rasa, I read “A Semantic Map for Evaluating Creativity.” According to the write up:
We present a semantic map of words related with creativity. The aim is to empirically derive terms which can be used to rate processes or products of computational creativity. The words in the map are based on association studies per for med by human subjects and augmented with words derived from the literature (based on human raters).
After considerable text processing and a dose of analytics, the paper states:
… There is an overlap in the set of words formed by the two methods, but there are also some differences. Further investigations could reveal how these methods are related and if they are both needed (as complements) to arrive at more objective procedures for the evaluation of computational (and human) creativity.
I await a mid tier consulting firm’s for fee study about the applications of this technology in determining which companies are creative. And what about government use cases; for example, which entry lever professional is most creative. Then there are academic applications; for instance, which professors are their most creative. Creative folks can create creative ways to understand creativity. Stay tuned.
Stephen E Arnold, May 8, 2016
May 6, 2016
A martial artist once told me that an individual’s fighting style, if defined enough, was like a set of fingerprints. The same can be said for painting style, book preferences, and even Netflix selections, but what about something as anonymous as a computer mouse’s movement? Here is a new scary thought from PC & Tech Authority: “Researcher Can Indentify Tor Users By Their Mouse Movements.”
It seems far-fetched, especially when one considers how random this data is, but
This is the age of big data, but looking Norte’s claim from a logical standpoint one needs to consider that not all computer mice are made the same, some use lasers, others prefer trackballs, and what about a laptop’s track pad? As diverse as computer users are, there are similarities within the population and random mouse movement is not individualistic enough to ID a person. Fear not Tor users, move and click away in peace.
May 1, 2016
Apache Lucene receives the most headlines when it comes to discussion about open source search software. My RSS feed pulled up another open source search engine that shows promise in being a decent piece of software. Open Semantic Search is free software that cane be uses for text mining, analytics, a search engine, data explorer, and other research tools. It is based on Elasticsearch/Apache Solrs’ open source enterprise search. It was designed with open standards and with a robust semantic search.
As with any open source search, it can be programmed with numerous features based on the user’s preference. These include, tagging, annotation, varying file format support, multiple data sources support, data visualization, newsfeeds, automatic text recognition, faceted search, interactive filters, and more. It has the benefit that it can be programmed for mobile platforms, metadata management, and file system monitoring.
Open Semantic Search is described as
“Research tools for easier searching, analytics, data enrichment & text mining of heterogeneous and large document sets with free software on your own computer or server.”
While its base code is derived from Apache Lucene, it takes the original product and builds something better. Proprietary software is an expense dubbed a necessary evil if you work in a large company. If, however, you are a programmer and have the time to develop your own search engine and analytics software, do it. It could be even turn out better than the proprietary stuff.
April 23, 2016
Years ago I listened to Endeca (now owned by Oracle) extol the virtues of its various tools. The idea was that the tools made it somewhat easier to get Endeca up and running. The original patents for Endeca reveal the computational blender which the Endeca method required. Endeca shifted from licensing software to bundling consulting with a software license. Setting up Endeca required MBAs, patience, and money. Endeca rose to generate more than $120 million in revenues before its sale to Oracle. Today Endeca is still available, and the Endeca patents—particularly 7035864—reveal how Endeca pulled off its facets. Today Endeca has lost a bit of its spit and polish, a process that began when Autonomy blasted past the firm in the early 2000s.
Endeca rolled out its “studio” a decade ago. I recall that Business Objects had a “studio.” The idea behind a studio was to make the complex task of creating something an end user could use without much training. But the studio was not aimed at an end user. The studio was a product for a developer, who found the tortuous, proprietary methods complex and difficult to learn. A studio would unleash the developers and, of course, propel the vendors with studios to new revenue heights.
Studio is back. This time, if the information in “Expert System Releases Cogito Studio for Combining the Advantages of Semantic Technology with Deep Learning,” is accurate. The spin is that semantic technology and deep learning—two buzzwords near and dear to the heart of those in search of the next big thing—will be a boon. Who is the intended user? Well, developers. These folks are learning that the marketing talk is a heck of a lot easier than designing, coding, debugging, stabilizing, and then generating useful outputs is quite difficult work.
According to the Expert System announcement:
The new release of Cogito Studio is the result of the hard work and dedication of our labs, which are focused on developing products that are both powerful and easy to use,” said Marco Varone, President and CTO, Expert System. “We believe that we can make significant contributions to the field of artificial intelligence. In our vision of AI, typical deep learning algorithms for automatic learning and knowledge extraction can be made more effective when combined with algorithms based on a comprehension of text and on knowledge structured in a manner similar to that of humans.”
Does this strike you as vague?
Expert System is an Italian, high tech outfit, which was founded in 1989. That’s almost a decade before the Endeca system poked its moist nose into the world of search. Fellow travelers from this era include Fulcrum Technologies and ISYS Search Software. Both of these companies’ technology are still available today.
Thus, it makes sense that the idea of a “studio” becomes a way to chop away at the complexity of Expert System-type systems.
According to Google Finance, Expert System’s stock is trending upwards.
That’s a good sign. My hunch is that announcements about “studios” wrapped in lingo like semantics and Big Data are a good thing.
Stephen E Arnold, April 23, 2016
April 14, 2016
I saw a reference to a 2015 book, Semantic Mining of Social Networks by Jie Tang and Juanzi Li. This volume consists of essays about things semantic. Published by Morgan & Claypool publishers, the link I clicked did not return a bibliographic citation nor a review. The link displayed the book which appeared to be downloadable. If your engines are revved with the notion of semantic analysis, you may want to explore the volume yourself. I advocate purchasing monographs. Here’s the link I followed. Keep in mind that if the link 404s you, the fault is not mine.
Stephen E Arnold, April 14, 2016
April 3, 2016
How is that for a statement? Search is getting hard. No, search is becoming impossible.
For evidence, I point to the Search Today and Beyond: Optimizing for the Semantic Web Wired Magazine article “Search Today and Beyond: Optimizing for the Semantic Web.”
Here’s a passage I noted:
Despite the billions and billions of searches, Google reports that 20 percent of all searches in 2012 were new. It seems quite staggering, but it’s a product of the semantic search rather than the simple keyword search.
Wow, unique queries. How annoying? Isn’t it better for people to just run queries which Google has seen and cached the results?
I have been poking around for information about a US government program called “DCGS.” Enter the query and what do you get? A number of results unrelated to the terms in my query; for example, US Army. Toss in quotes to “tell” Google to focus only on the string DCGS. Nah, does not do the job. Add the filetype:ppt operator and what do you get, documents in other formats too.
Semantic search is now a buzzword which is designed to obfuscate one important point: Methods for providing on point information are less important than assertions about what jargon can deliver.
For me, when I enter a query, I want the search system to deliver documents in which the key words appear. I want an option to see related documents. I do not want the search system doing the ad thing, the cheapest and most economical query, and I don’t want unexpected behaviors from a search and retrieval system.
Unfortunately lots of folks, including Wired Magazine, this that semantic search optimizes. Wonderful. With baloney like this I am not sure about the future of search; to wit:
…the future possibilities are endless for those who are studious enough to keep pace and agile enough to adjust.
Yeah, agile. What happened to the craziness that search is the new interface to Big Data? Right, agile.
Stephen E Arnold, April 3, 2016