May 20, 2016
I try to avoid reading more than one write up a day about alleged revolutions in content processing and information analytics. My addled goose brain cannot cope with the endlessly recycled algorithms dressed up in Project Runway finery.
I read “Ryft: Bringing High Performance Analytics to Every Enterprise,” and I was pleased to see a couple of statements which resonated with my dim view of information access systems. There is an accompanying video in the write up. I, as you may know, gentle reader, am not into video. I prefer reading, which is the old fashioned way to suck up useful factoids.
Here’s the first passage I highlighted:
Any search tool can match an exact query to structured data—but only after all of the data is indexed. What happens when there are variations? What if the data is unstructured and there’s no time for indexing? [Emphasis added]
The answer to the question is increasing costs for sales and marketing. The early warning for amped up baloney are the presentations given at conferences and pumped out via public relations firms. (No, Buffy, no, Trent, I am not interested in speaking with the visionary CEO who hired you.)
I also highlighted:
With the power to complete fuzzy search 600X faster at scale, Ryft has opened up tremendous new possibilities for data-driven advances in every industry.”
I circled the 600X. Gentle reader, I struggle to comprehend a 600X increase in content processing. Dear Mother Google has invested to create a new chip to get around the limitations of our friend Von Neumann’s approach to executing instructions. I am not sure Mother Google has this nailed because Mother Google, like IBM, announces innovations without too much real world demonstration of the nifty “new” things.
I noted this statement too:
For the first time, you can conduct the most accurate fuzzy search and matching at the same speed as exact search without spending days or weeks indexing data.
Okay, this strikes me as a capability I would embrace if I could get over or around my skepticism. I was able to take a look at the “solution” which delivers the astounding performance and information access capability. Here’s an image from Ryft’s engineering professionals:
Notice that we have Spark and pre built components. I assume there are myriad other innovations at work.
The hitch in the git along is that in order to deal with certain real world information processing challenges, the inputs come from disparate systems, each generating substantial data flows in real time.
Here’s an example of a real world information access and understanding challenge, which, as far as I know, has not been solved in a cost effective, reliable, or usable manner.
Image source: Plugfest 2016 Unclassified.
This unclassified illustration makes clear that the little things in the sky pump out lots of data into operational theaters. Each stream of data must be normalized and then converted to actionable intelligence.
The assertion about 600X sounds tempting, but my hunch is that the latency in normalizing, transferring, and processing will not meet the need for real time, actionable, accurate outputs when someone is shooting at a person with a hardened laptop in a threat environment.
In short, perhaps the spark will ignite a fire of performance. But I have my doubts. Hey, that’s why I spend my time in rural Kentucky where reasonable people shoot squirrels with high power surplus military equipment.
Stephen E Arnold, May 20, 2016
May 19, 2016
I read “The Real Lesson for Data Science That is Demonstrated by Palantir’s Struggles · Simply Statistics.” I love write ups that plunk the word statistics near simple.
Here’s the passage I highlighted in money green:
… What is the value of data analysis?, and secondarily, how do you communicate that value?
I want to step away from the Palantir Technologies’ example and consider a broader spectrum of outfits tossing around the jargon “big data,” “analytics,” and synonyms for smart software. One doesn’t communicate value. One finds a person who needs a solution and crafts the message to close the deal.
When a company and its perceived technology catches the attention of allegedly informed buyers, a bandwagon effort kicks in. Talks inside an organization leads to mentions in internal meetings. The vendor whose products and services are the subject of these comments begins to hint at bigger and better things at conferences. Then a real journalist may catch a scent of “something happening” and writes an article. Technical talks at niche conferences generate wonky articles usually without dates or footnotes which make sense to someone without access to commercial databases. If a social media breeze whips up the smoldering interest, then a fire breaks out.
A start up should be so clever, lucky, or tactically gifted to pull off this type of wildfire. But when it happens, big money chases the outfit. Once money flows, the company and its products and services become real.
The problem with companies processing a range of data is that there are some friction inducing processes that are tough to coat with Teflon. These include:
- Taking different types of data, normalizing it, indexing it in a meaningful manner, and creating metadata which is accurate and timely
- Converting numerical recipes, many with built in threshold settings and chains of calculations, into marching band order able to produce recognizable outputs.
- Figuring out how to provide an infrastructure that can sort of keep pace with the flows of new data and the updates/corrections to the already processed data.
- Generating outputs that people in a hurry or in a hot zone can use to positive effect; for example, in a war zone, not get killed when the visualization is not spot on.
The write up focuses on a single company and its alleged problems. That’s okay, but it understates the problem. Most content processing companies run out of revenue steam. The reason is that the licensees or customers want the systems to work better, faster, and more cheaply than predecessor or incumbent systems.
The vast majority of search and content processing systems are flawed, expensive to set up and maintain, and really difficult to use in a way that produces high reliability outputs over time. I would suggest that the problem bedevils a number of companies.
Some of those struggling with these issues are big names. Others are much smaller firms. What’s interesting to me is that the trajectory content processing companies follow is a well worn path. One can read about Autonomy, Convera, Endeca, Fast Search & Transfer, Verity, and dozens of other outfits and discern what’s going to happen. Here’s a summary for those who don’t want to work through the case studies on my Xenky intel site:
Stage 1: Early struggles and wild and crazy efforts to get big name clients
Stage 2: Making promises that are difficult to implement but which are essential to capture customers looking actively for a silver bullet
Stage 3: Frantic building and deployment accompanied with heroic exertions to keep the customers happy
Stage 4: Closing as many deals as possible either for additional financing or for licensing/consulting deals
Stage 5: The early customers start grousing and the momentum slows
Stage 6: Sell off the company or shut down like Delphes, Entopia, Siderean Software and dozens of others.
The problem is not technology, math, or Big Data. The force which undermines these types of outfits is the difficulty of making sense out of words and numbers. In my experience, the task is a very difficult one for humans and for software. Humans want to golf, cruise Facebook, emulate Amazon Echo, or like water find the path of least resistance.
Making sense out of information when someone is lobbing mortars at one is a problem which technology can only solve in a haphazard manner. Hope springs eternal and managers are known to buy or license a solution in the hopes that my view of the content processing world is dead wrong.
So far I am on the beam. Content processing requires time, humans, and a range of flawed tools which must be used by a person with old fashioned human thought processes and procedures.
Value is in the eye of the beholder, not in zeros and ones.
Stephen E Arnold, May 19, 2016
May 17, 2016
I read “Understanding the Cultural Differences Between NASCAR and Formula One Fans [Analysis].” The write up is in a blog post from Affinio. The company describes itself in this way:
Marketing Intelligence that leverages the social graph to understand today’s customer.
The information in the write up presents clusters of interest between the two fan bases for each of these motor sports. F1 consists of clusters labeled this way:
To illustrate the differences, Affinio presents a visualization of the Nascar audience:
The labels strike me as unhelpful; for example, Cluster 14, Cluster 6, etc.
The top interests of the two audiences consist of a collage of small images. I am not sure what each image represents.
Equally unhelpful is the word clouds for each of the audiences; for example:
The map showing the geographic area where F1 is popular focuses on a global scale with a centroid in Western Europe. The absence of a hot spot in the Middle East was puzzling. Is Australia as large an F1 market as the UAE in terms of money spent on F1 activities?
The map for the Nascar market depicts only the US of A. My question, “Why not show a global map?”
Thinking about this analysis, I have several questions:
- A list of dot points would get the message across in a more efficient, possibly less confusing way would it not?
- What is analyzed? It seems that the single actionable fact is that the F1 market is global and the Nascar market is local.
- What are the data sets used for the analysis?
- Why are terms like “Cluster 14” used instead of words?
The most important data from my uninformed vantage point is the money generated by the two types of motor racing.
My hunch is that the Affino write up wanted to show off visualizations, not substantive and actionable data analysis. In short, is this marketing or is it substance? I will leave the answer to you, gentle reader.
Stephen E Arnold, May 17, 2016
May 2, 2016
The article on The Verge titled The Most Dangerous Writing App Lets You Delete All of Your Work For Free speculates on the difficulties and hubris of charging money for technology that someone can clone and offer for free. Manuel Ebert’s The Most Dangerous Writing App offers a self-detonating notebook that you trigger if you stop typing. The article explains,
“Ebert’s service appears to be a repackaging of Flowstate, a $15 Mac app released back in January that functions in a nearly identical way. He even calls it The Most Dangerous Writing App, which is a direct reference to the words displayed on Flowstate creator Overman’s website. The difference: Ebert’s app is free, which could help it take off among the admittedly niche community of writers looking for self-deleting online notebooks.”
One such community that comes to mind is that of the creative writers. Many writers, and poets in particular, rely on exercises akin to the philosophy of The Most Dangerous Writing App: don’t let your pen leave the page, even if you are just writing nonsense. Adding higher stakes to the process might be an interesting twist, especially for those writers who believe that just as the nonsense begins, truth and significance are unlocked.
Chelsea Kerwin, May 2, 2016
April 27, 2016
Here’s a passage I highlighted:
It’s clear the “Google way” of indexing data to enable fuzzy search isn’t always the best way. It’s also clear that limiting the fuzzy search to an edit distance of two won’t give you the answers you need or the most comprehensive view of your data. To get real-time fuzzy searches that return all relevant results you must use a data analytics platform that is not constrained by the underlying sequential processing architectures that make up software parallelism. The key is hardware parallelism, not software parallelism, made possible by the hybrid FPGA/x86 compute engine at the heart of the Ryft ONE.
I also circled:
By combining massively parallel FPGA processing with an x86-powered Linux front-end, 48 TB of storage, a library of algorithmic components and open APIs in a small 1U device, Ryft has created the first easy-to-use appliance to accelerate fuzzy search to match exact search speeds without indexing.
An outfit called InsideBigData published “Ryft Makes Real-time Fuzzy Search a Reality.” Alas, that link is now dead.
Perhaps a real time fuzzy search will reveal the quickly deleted content?
Sounds promising. How does one retrieve information within videos, audio streams, and images? How does one hook together or link a reference to an entity (discovered without controlled term lists) with a phone number?
My hunch is that the methods disclosed in the article have promise, the future of search seems to be lurching toward applications that solve real world, real time problems. Ryft may be heading in that direction in a search climate which presents formidable headwinds.
Stephen E Arnold, April 27, 2016
April 21, 2016
I read “Big Data’s Biggest Problem: It’s Too Hard to Get the Data In.” Here’s a quote I noted:
According to a study by data integration specialist Xplenty, a third of business intelligence professionals spend 50% to 90% of their time cleaning up raw data and preparing to input it into the company’s data platforms. That probably has a lot to do with why only 28% of companies think they are generating strategic value from their data.
My hunch is that with the exciting hyperbole about Big Data, the problem of normalizing, cleaning, and importing data is ignored. The challenge of taking file A in a particular file format and converting to another file type is indeed a hassle. A number of companies offer expensive filters to perform this task. The one I remember is Outside In, which sort of worked. I recall that when odd ball characters appeared in the file, there would be some issues. (Does anyone remember XyWrite?) Stellent purchased Outside In in order to move content into that firm’s content management system. Oracle purchased Stellent in 2006. Then Kapow “popped” on the scene. The firm promoted lots of functionality, but I remember it as a vendor who offered software which could take a file in one format and convert it into another format. Kofax (yep, the scanner oriented outfit) bought Kofax to move content from one format into one that Kofax systems could process. Then Lexmark bought Kofax and ended up with Kapow. With that deal, Palantir and other users of the Kapow technology probably had a nervous moment or are now having a nervous moment as Lexmark marches toward a new owner. Entropy, a French outfit, was a file conversion outfit. It sold out to Salesforce. Once again, converting files from Type A to another desired format seems to have been the motivating factor.
Let us not forget the wonderful file conversion tools baked into software. I can save a Word file as an RTF file. I can import a comma separated file into Excel. I can even fire up Framemaker and save a Dot fm file as RTF. In fact, many programs offer these import and export options. The idea is to lessen the pain of have a file in one format which another system cannot handle. Hey, for fun, try opening a macro filled XyWrite file in Framemaker or Indesign. Just change the file extension to one the system thinks it recognizes. This is indeed entertaining.
The write up is not interested in the companies which have sold for big bucks because their technology could make file conversion a walk in the Hounz Lane Park. (Watch out for the rats, gentle reader.) The write up points out three developments which will make the file intake issues go away:
- The software performing file conversion “gets better.” Okay, I have been waiting for decades for this happy time to arrive. No joy at the moment.
- “Data preparers become the paralegals of data science.” Now that’s a special idea. I am not clear on what a “data preparer” is, but it sounds like a task that will be outsourced pretty quickly to some country far from the home of NASCAR.
- Artificial intelligence” will help cleanse data. Excuse me, but smart software has been operative in file conversion methods for quite a while. In my experience, the exception files keep on piling up.
What is the problem with file conversion? I don’t want to convert this free blog post into a lengthy explanation. I can highlight five issues which have plagued me and my work in file conversion for many years:
First, file types change over time. Some of the changes are not announced. Others like the Microsoft Word XML thing are the subject of months long marketing., The problem is that unless the outfit responsible for the file conversion system creates a fix, the exception files can overrun a system’s capacity to keep track of problems. If someone is asleep at the switch, data in the exception folder can have an adverse impact on some production systems. Loss of data is interesting but trashing the file structure is a carnival. Who does not pay attention? In my experience, vendors, licensees, third parties, and probably most of the people responsible for a routine file conversion task.
Second, the thrill of XML is that it is not particularly consistent. Somewhere along the line, creativity takes precedence over for well formed. How does one deal with a couple hundred thousand XML files in an exception folder? What do you think about deleting them?
Third, the file conversion software works as long as the person creating a document does not use Fancy Dan “inserts” in the source document. Problems arise from videos, certain links, macros, and odd ball formatting of the source document. Yep, some folks create text in Excel and wonder why the resulting text is a bit of a mess.
Fourth, workflows get screwed up. A file conversion system is semi smart. If a process creates a file with an unrecognized extension, the file conversion system fills the exception folder. But what if one valid extension is changed to a supported but incorrect extension. Yep, XML users be aware that there are proprietary XML formats. The files converted and made available to a system are “sort of right.” Unfortunately sort of right in mission critical applications can have some interesting consequences.
Fifth, attention to detail is often less popular than fiddling with one’s mobile phone or reading Facebook posts. Human inattention can make large scale data conversion fail. I have watched as a person of my acquaintance deleted the folder of exception files. Yo, it is time for lunch.
So what? Smart software makes certain assumptions. At this time, file intake is perceived as a problem which has been solved. My view is that file intake is a core function which needs a little bit more attention. I do not need to be told that smart software will make file intake pain go away.
Stephen E Arnold, April 21, 2016
April 19, 2016
I read an article in Jeff Bezos’ newspaper. The title was “We Analyzed the Names of Almost Every Chinese Restaurant in America. This Is What We Learned.” The almost is a nifty way of slip sliding around the sampling method which used restaurants listed in Yelp. Close enough for “real” journalism.
Using the notion of a frequency count, the write up revealed:
- The word appearing most frequently in the names of the sample was “restaurant.”
- The words “China” and “Chinese” appear in about 15,000 of the sample’s restaurant names
- “Express” is a popular word, not far ahead of “panda”.
The word list and their frequencies were used to generate a word cloud:
To answer the question where Chinese food is most popular in the US, the intrepid data wranglers at Jeff Bezos’ newspaper output a map:
Amazing. I wonder if law enforcement and intelligence entities know that one can map data to discover things like the fact that the word “restaurant” is the most used word in a restaurant’s name.
Stephen E Arnold, April 19, 2016
April 19, 2016
I read “Goldman Sachs Leads a $30 million Round for Persado’s AI-Based, Automated Copywriting Service.” My first reactions:
- Search engine optimization wizards will have a tool to increase the flow of baloney search and content marketing to people who write blogs
- Journalists, who have been subject to reduction in force actions, may face fierce competition from a smart software
- Teachers of college composition will have a tough time figuring out if the student essays are coming from fraternity and sorority reference files or from a cloud based writing service.
According to the write up, the service is a “cognitive one.” Poor IBM. The company wants Watson to be the cognitive champion. Now an outfit which uses software to create articles has embraced the concept. I noted:
The company [Persado] has cataloged 1 million words and phrases that marketers use in their copy, and scored those words based on sentiment analysis and the structure of marketing pitches defined by a message’s format, linguistic structure, description, emotional language, and its actual call to action. The software can create a message, optimize its language, and then translate that message into any of 23 language…
There is a bright side. IBM could purchase Persado and then use the system to flog its confection of Lucene, acquired technology, and home brew code into a system which tirelessly promotes IBM.
Stephen E Arnold, April 19, 2016
April 11, 2016
Short honk. I read “How to Hack an Election.” The write up reports that a person was able to rig elections. According to the story:
For $12,000 a month, a customer hired a crew that could hack smartphones, spoof and clone Web pages, and send mass e-mails and texts. The premium package, at $20,000 a month, also included a full range of digital interception, attack, decryption, and defense. The jobs were carefully laundered through layers of middlemen and consultants.
Worth reading and then considering this question:
What are the implications of weaponized information?
Are pundits, mavens, self appointed experts, and real journalists on the job and helping to ensure that information online is “accurate”?
Stephen E Arnold, April 11, 2016
April 8, 2016
The chatter about smart is loud. I cannot hear the mixes on my Creamfields 2014 CD. Mozart, you are a goner.
If you want to cook up some smart algorithms to pick music or drive your autonomous vehicle without crashing into a passenger carrying bus, navigate to “Top 10 Machine Learning Algorithms.”
The write up points out that just like pop music, there is a top 10 list. More important in my opinion is the concomitant observation that smart software may be based on a limited number of procedures. Hey, this stuff is taught in many universities. Go with what you know maybe?
What are the top 10? The write up asserts:
- Linear regression
- Logistic regression
- Linear discriminant analysis
- Classification and regression trees
- Naive Bayes
- K nearest neighbors
- Learning vector quantization
- Support vector machines
- Bagged decision trees and random forest
- Boosting and AdaBoost.
The article tosses in a bonus too: Gradient descent.
What is interesting is that there is considerable overlap with the list I developed for my lecture on manipulating content processing using shaped or weaponized text strings. How’s that, Ms. Null?
The point is that when systems use the same basic methods, are those systems sufficiently different? If so, in what ways? How are systems using standard procedures configured? What if those configurations or “settings” are incorrect?
Stephen E Arnold, April 8, 2016