The Current State of Enterprise Search, by the Numbers

February 17, 2017

The article and delightful Infographic on BA Insight titled Stats Show Enterprise Search is Still a Challenge builds an interesting picture of the present challenges and opportunities surrounding enterprise search, or at least alludes to them with the numbers offered. The article states,

As referenced by AIIM in an Industry Watch whitepaper on search and discovery, three out of four people agree that information is easier to find outside of their organizations than within. That is startling! With a more effective enterprise search implementation, these users feel that better decision-making and faster customer service are some of the top benefits that could be immediately realized.

What follows is a collection of random statistics about enterprise search. We would like to highlight one stat in particular: 58% of those investing in enterprise search get no payback after one year. In spite of the clear need for improvements, it is difficult to argue for a technology that is so long-term in its ROI, and so shaky where it is in place. However, there is a massive impact on efficiency when employees waste time looking for the information they need to do their jobs. In sum: you can’t live with it, and you can’t live (productively) without it.

Chelsea Kerwin, February 17, 2017

Investment Group Acquires Lexmark

February 15, 2017

We read with some trepidation the Kansas City Business Journal’s article, “Former Perceptive’s Parent Gets Acquired for $3.6B in Cash.”  The parent company referred to here is Lexmark, which bought up one of our favorite search systems, ISYS Search, in 2012 and placed it under its Perceptive subsidiary, based in Lenexa, Kentucky. We do hope this valuable tool is not lost in the shuffle.

Reporter Dora Grote specifies:

A few months after announcing that it was exploring ‘strategic alternatives,’ Lexmark International Inc. has agreed to be acquired by a consortium of investors led by Apex Technology Co. Ltd. and PAG Asia Capital for $3.6 billion cash, or $40.50 a share. Legend Capital Management Co. Ltd. is also a member of the consortium.

Lexmark Enterprise Software in Lenexa, formerly known as Perceptive Software, is expected to ‘continue unaffected and benefit strategically and financially from the transaction’ the company wrote in a release. The Lenexa operation — which makes enterprise content management software that helps digitize paper records — dropped the Perceptive Software name for the parent’s brand in 2014. Lexmark, which acquired Perceptive for $280 million in cash in 2010, is a $3.7 billion global technology company.

If the Lexmark Enterprise Software (formerly known as Perceptive) division will be unaffected, it seems they will be the lucky ones. Grote notes that Lexmark has announced that more than a thousand jobs are to be cut amid restructuring. She also observes that the company’s buildings in Lenexa have considerable space up for rent. Lexmark CEO Paul Rooke is expected to keep his job, and headquarters should remain in Lexington, Kentucky.

Cynthia Murrell, February 15, 2017

IDOL Is Back and with NLP

December 11, 2016

I must admit that I am confused. Hewlett Packard bought Autonomy, wrote off billions, and seemed to sell the Autonomy software (IDOL and DRE) to an outfit in England. Oh, HPE, the part of the Sillycon Valley icon, which sells “enterprise” products and services owns part of the UK outfit which owns Autonomy. Got that? I am not sure I have the intricacies of this stunning series of management moves straight in my addled goose brain.

Close enough for horseshoes, however.

I read what looks like a content marketing flufferoo called “HPE Boosts IDOL Data Analytics Engine with Natural Language Processing Tools.”

Image result for fluff

I thought that IDOL had NLP functions, but obviously I am wildly off base. To get my view of the Autonomy IDOL system, check out the free analysis at this link. (Nota bene: I have done a tiny bit of work for Autonomy and have had to wrestle with the system when I labored with the system as a contractor when I worked on US government projects. I know that this is no substitute for the whizzy analysis included in the aforementioned write up. But, hey, it is what it is.)

The write up states in reasonably clear marketing lingo:

HPE has added natural language processing capabilities to its HPE IDOL data analytics engine, which could improve how humans interact with computers and data, the company announced Tuesday. By using machine learning technology, HPE IDOL will be able to improve the context around data insights, the company said.

A minor point: IDOL is based on machine learning processes. That’s the guts of the black box comprising the Bayesian, LaPlacian, and Markovian methods in the guts of the Digital Reasoning Engine which underpins the Integrated Data Operating Layer of the Autonomy system.

Here’s the killer statement:

… the company [I presume this outfit is Hewlett Packard Enterprise and not MicroFocus] has introduced HPE Natural Language Question Answering to its IDOL platform to help solve the problem. According to the release, the technology seeks to determine the original intent of the question and then “provides an answer or initiates an action drawing from an organization’s own structured and unstructured data assets in addition to available public data sources to provide actionable, trusted answers and business critical responses.


I love the actionable, trusted bit. Autonomy’s core approach is based on probabilities. Trust is okay, but it is helpful to understand that probabilities are — well — probable. The notion of “trusted answers” is a quaint one to those who drink deep from the statistical springs of data.

I highlighted this quotation, presumably from the wizards at HPE:

“IDOL Natural Language Question Answering is the industry’s first comprehensive approach to delivering enterprise class answers,” Sean Blanchflower, vice president of engineering for big data platforms at HPE, said in the release. “Designed to meet the demanding needs of data-driven enterprises, this new, language-independent capability can enhance applications with machine learning powered natural language exchange.”

My hunch is that HPE or MicroFocus or an elf has wrapped a query system around the IDOL technology. The write up does not provide too many juicy details about the plumbing. I did note these features, however:

  1. An IDOL Answer Bank. Ah, ha. Ask Jeeves style canned questions. There is no better way to extract information than the use of carefully crafted queries. None of the variable, real life stuff that system users throw at search and retrieval systems. My experience is that maintaining canned queries can become a bit tedious and also expensive.
  2. IDOL Fact Bank. Ah, ha. A query that processes content to present “factoids.” Much better than a laundry list of results. What happens when the source data return factoids which are [a] not current, [b] not accurate, or [c] without context? Hey, don’t worry about the details. Take your facts and decide, folks.
  3. IDOL Passage Extract. Ah, ha. A snippet or key words in context! Not exactly new, but a time proven way to provide some context to the factoid. Now wasn’t that an IDOL function in 2001? Guess not.
  4. IDOL Answer Server. Ah, ha. A Google style wrapper; that is, leave the plumbing alone and provide a modernized paint job.

If you match these breakthroughs with the diagram in the HP IDOL write up’s diagrams, you will note that these capabilities appear in the IDOL/DRE system diagram and features.

Image result for fluff

What’s important in this content marketing piece. The write up provides a takeaway section to help out those who are unaware of the history of IDOL, which dates from the late 1990s. Here you go. Revel in new features, enjoy NLP, and recognize that HPE is competing with IBM Watson.

There you go. Factual content in action. Isn’t modern technology analysis satisfying? IBM Watson, your play.

Stephen E Arnold, December 11, 2017

MC+A Is Again Independent: Search, Discovery, and Engineering Services

December 7, 2016

Beyond Search learned that MC+A has added a turbo-charger to its impressive search, content  processing, and content management credentials. The company, based in Chicago, earned a  gold star from Google for MC+A’s support and integration services for the now-discontinued Google Search Appliance. After working with the Yippy implementation of Watson Explorer, MC+A retains its search and retrieval capabilities, but expanded its scope. Michael Cizmar, the company’s president told Beyond Search, “Search is incredibly important, but customers require more multi-faceted solutions.” MC+A provides the engineering and technical capabilities to cope with Big Data, disparate content, cloud and mixed-environment platforms, and the type of information processing needed to generate actionable reports. [For more information about Cizmar’s views about search and retrieval, see “An Interview with Michael Cizmar.”

Cizmar added:

We solve organizational problems rooted in the lack of insight and accessibility to data that promotes operational inefficiency. Think of a support rep who has to look through five systems to find an answer for a customer on the phone. We are changing the way these users get to answers by providing them better insights from existing data securely. At a higher level we provide strategy support for executives looking for guidance on organizational change.


Alphabet Google’s decision to withdraw the  Google Search Appliance has left more than 60,000 licensees looking for an alternative. Since the début of the GSA in 2002, Google trimmed the product line and did not move the search system to the cloud. Cizmar’s view of the GSA’s 12 year journey reveals that:

The Google Search Appliance was definitely not a failure. The idea that organizations wanted an easy-to-use, reliable Google-style search system was ahead of its time. Current GSA customers need some guidance on planning and recommendations on available options. Our point of view is that it’s not the time to simply swap out one piece of metal for another even if vendors claim “OEM” equivalency. The options available for data processing and search today all provide tremendous capabilities, including cognitive solutions which provide amazing capabilities to assist users beyond the keyword search use case.

Cizmar sees an opportunity to provide GSA customers with guidance on planning and recommendations on available options. MC+A understands the options available for data processing and information access today. The company is deeply involved in solutions which tap “smart software” to deliver actionable information.

Cizmar said:

Keyword search is a commodity at this point, and we helping our customers put search where the user is without breaking an established workflow. Answers, not laundry lists of documents to read, is paramount today. Customers want to solve specific problems; for example, reducing average call time customer support using smart software or adaptive, self service solutions. This is where MC+A’s capabilities deliver value.

MC+A is cloud savvy. The company realized that cloud and hybrid or cloud-on premises solutions were ways to reduce costs and improve system payoff. Cizmar was one of the technologists recognized by Google for innovation in cloud applications of the GSA. MC+A builds on that engineering expertise. Today, MC+A supports Google, Amazon, and other cloud infrastructures.

Cizmar revealed:

Amazon Elastic Cloud Search is probably doing as much business as Google did with the GSA but in a much different way. Many of these cloud-based offerings are generally solving the problem with the deployment complexities that go into standing up Elasticsearch, the open source version of Elastic’s information access system.

MC+A does not offer a one size fits all solution. He said:

The problem still remains of what should go into the cloud, how to get a solution deployed, and how to ensure usability of the cloud-centric system. The cloud offers tremendous capabilities in running and scaling a search cluster. However, with the API consumption model that we have to operate in, getting your data out of other systems into your search clusters remains a challenge. MC+A does not make security an afterthought. Access controls and system integrity have high priority in our solutions.

MC+A takes a business approach to what many engineering firms view as a technical problem. The company’s engineers examine the business use case. Only then does MC+A determine if the cloud is an option. If so, which product or projects capabilities meet the general requirements. After that process, MC+A implements its carefully crafted, standard deployment process.

Cizmar noted:

If you are a customer with all of your data on premises or have a unique edge case, it may not make sense to use a cloud-based system. The search system needs to be near to the content most of the time.

MC+A offers its white-labeled search “Practice in a Box” to former Google partners and other integrators. High-profile specialist vendors like Onix in Ohio are be able to resell our technology backed by the MC+A engineering team.

In 2017, MC+A will roll out a search solution which is, at this time, shrouded in secrecy. This new offering will go “beyond the GSA” and offer expanded information access functionality. To support this new product, MC+A will announce a specialized search practice.

He said:

This international practice will offer depth and breadth in selling and implementing solutions around cognitive search, assist, and analytics with products other than Google throughout the Americas. I see this as beneficial to other Google and non-Google resellers because, it allows other them to utilize our award winning team, our content filters, and a wealth of social proofs on a just in time basis.

For 2017, MC+A offers a range of products and services. Based on the limited information provided by the secrecy-conscious Michael Ciznar, Beyond Search believes that the company will offer implementation and support services for Lucene and Solr, IBM Watson, and Microsoft SharePoint. The SharePoint support will embrace some vendors supplying SharePoint centric like Coveo. Plus, MC+A will continue to offer software to acquire content and perform extract-transform-load functions on premises, in the cloud, or in hybrid configurations.,

MC+A’s approach offers a business-technology approach to information access.

For more information about MC+A, contact 312-585-6396.

Stephen E Arnold, December 7, 2016

Search Competition Is Fiercer Than You Expect

December 5, 2016

In the United States, Google dominates the Internet search market.  Bing has gained some traction, but the results are still muddy.  In Russia, Yandex chases Google around in circles, but what about the enterprise search market?  The enterprise search market has more competition than one would think.  We recently received an email from Searchblox, a cognitive platform that developed to help organizations embed information in applications using artificial intelligence and deep learning models.  SearchBlox is also a player in the enterprise software market as well as text analytics and sentiment analysis tool.

Their email explained, “3 Reasons To Choose SearchBlox Cognitive Platform” and here they are:

1. EPISTEMOLOGY-BASED. Go beyond just question and answers. SearchBlox uses artificial intelligence (AI) and deep learning models to learn and distill knowledge that is unique to your data. These models encapsulate knowledge far more accurately than any rules based model can create.

2. SMART OPERATION Building a model is half the challenge. Deploying a model to process big data can be even for challenging. SearchBlox is built on open source technologies like Elasticsearch and Apache Storm and is designed to use its custom models for processing high volumes of data.

3. SIMPLIFIED INTEGRATION SearchBlox is bundled with over 75 data connectors supporting over 40 file formats. This dramatically reduces the time required to get your data into SearchBlox. The REST API and the security capabilities allow external applications to easily embed the cognitive processing.

To us, this sounds like what enterprise search has been offering even before big data and artificial intelligence became buzzwords.  Not to mention, SearchBlox’s competitors have said the same thing.  What makes Searchblox different?  The company claims to be more inexpensive and they have won several accolades.  SearchBlox is made on open source technology, which allows it to lower the price.  Elasticsearch is the most popular open source search software, but what is funny is that Searchblox is like a repackaged version of said Elasticsearch.  Mind you are paying for a program that is already developed, but Searchblox is trying to compete with other outfits like Yippy.

Whitney Grace, December 5, 2016

BA Insight and Its Ideas for Enterprise Search Success

October 25, 2016

I read “Success Factors for Enterprise Search.” The write up spells out a checklist to make certain that an enterprise search system delivers what the users want—on point answers to their business information needs. The reason a checklist is necessary after more than 50 years of enterprise search adventures is a disconnect between what software can deliver and what the licensee and the users expect. Imagine figuring out how to get across the Grand Canyon only to encounter the Iguazu Falls.

The preamble states:

I’ll start with what absolutely does not work. The “dump it in the index and hope for the best” approach that I’ve seen some companies try, which just makes the problem worse. Increasing the size of the haystack won’t help you find a needle.

I think I agree, but the challenge is multiple piles of data. Some data are in haystacks; some are in odd ball piles from the AS/400 that the old guy in accounting uses for an inventory report.

Now the check list items:

  1. Metadata. To me, that’s indexing. Lousy indexing produces lousy search results in many cases. But “good indexing” like the best pie at the state fair is a matter of opinion. When the licensee, users, and the search vendor talk about indexing, some parties in the conversation don’t know indexing from oatmeal. The cost of indexing can be high. Improving the indexing requires more money. The magic of metadata often leads back to a discussion of why the system delivers off point results. Then there is talk about improving the indexing and its cost. The cycle can be more repetitive than a Kenmore 28132’s.
  2. Provide the content the user requires. Yep, that’s easy to say. Yep, if its on a distributed network, content disappears or does not get input into the search system. Putting the content into a repository creates another opportunity for spending money. Enterprise search which “federates” is easy to say, but the users quickly discover what is missing from the index or stale.
  3. Deliver off point results. The results create work by not answering the user’s question. From the days of STAIRS III to the latest whiz kid solution from Sillycon Valley, users find that search and retrieval systems provide an opportunity to go back to traditional research tools such as asking the person in the next cube, calling a self-appointed expert, guessing, digging through paper documents, or hiring an information or intelligence professional to gather the needed information.

The check list concludes with a good question, “Why is this happening?” The answer does not reside in the check list. The answer does not reside in my Enterprise Search Report, The Landscape of Search, or any of the journal and news articles I have written in the last 35 years.

The answer is that vendors directly or indirectly reassure that their software will provide the information a user needs. That’s an easy hook to plant in the customer who behaves like a tuna. The customer has a search system or experience with a search system that does not work. Pitching a better, faster, cheaper solution can close the deal.

The reality is that even the most sophisticated search and content processing systems end up in trouble. Search remains a very difficult problem. Today’s solutions do a few things better than STAIRS III did. But in the end, search software crashes and burns when it has to:

  • Work within a budget
  • Deal with structured and unstructured data
  • Meet user expectations for timeliness, precision, recall, and accuracy
  • Does not require specialized training to use
  • Delivers zippy response time
  • Does not crash or experience downtime due to maintenance
  • Outputs usable, actionable reports without having to involve a programmer
  • Provides an answer to a question.

Smart software can solve some of these problems for specific types of queries. Enterprise search will benefit incrementally. For now, baloney about enterprise search continues to create churn. The incumbent loses the contract, and a new search vendors inks a deal. Months later, the incumbent loses the contract, and the next round of vendors compete for the contract. This cycle has eroded the credibility of search and content processing vendors.

A check list with three items won’t do much to change the credibility gap between what vendors say, what licensees hope will occur, and what users expect. The Grand Canyon is a big hole to fill. The Iguazu Falls can be tough to cross. Same with enterprise search.

Stephen E Arnold, October 25, 2016

Attivio: Search and Almost Everything Else

October 24, 2016

I spent a few minutes catching up with the news on the Attivio blog. You can find the information at this link. As I worked through the write ups over the past five weeks, I was struck by the diversity of Attivio’s marketing messages. Here are the ones which I noted:

  • Attivio is a cognitive computing company, not a search or database company
  • Attivio has an interest in governance and risk / compliance
  • Attivio is involved in Big Data management
  • Attivio is active in anti fraud solutions
  • Attivio embraces NoSQL
  • Attivio knows about modernizing an organization’s data architecture
  • Attivio is a business intelligence solution.

My reaction to these capabilities is two fold:

First, for a company which has its roots in Fast Search & Transfer type of software, Attivio has added a number of applications to basic content processing and information access. Attivio embodies the vision Fast Search articulated before the company ran into some “challenges” and sold to Microsoft in 2008. Fast Search, as I understood the vision, was a platform upon which information applications could be built. Attivio appears to be heading in that direction.

The second reaction is that Attivio is churning out capabilities which embody buzzwords, jargon, and trends. Like a fisherman in a bass boat, the Attivio approach is to use different lures in order to snag a decent sized bass. I find it difficult to accept the assertion that a company rooted in search can deliver in the array of technical niches the blog posts reference.

The major takeaway for me was that Attivio has hired a new Chief Revenue Officer whose job is to generate revenue from the company’s “data catalog” business.  I learned from “Attivio Names New Chief Revenue Officer”:

Connon [the insider who took over the revenue job] sees his new role as a reflection of the growing demand for technology that can break down data silos and help successful companies answer, not just the question of “what” the data is reporting, but identify correlation and patterns to answer critical “why” questions. Connon is passionate when he talks about the value of Attivio’s newest technology solution—the Semantic Data Catalog–and its ability to unify a wide array of data for a diverse customer base. “The Semantic Data Catalog is not just for financial service industries. It’s truly a horizontal technology solution that can benefit companies in any industry with data—in other words, with any company, in any industry,” explains Connon. “Our established Cognitive Search and Insight technology provides the foundation for our Semantic Data Catalog to provide companies with a self-service, permission-based ability to locate, sort, and analyze key information across an unlimited number of data applications,” adds Connon.

For me, Attivio’s “momentum” in marketing has to be converted to sustainable revenue. My assumption is that almost every professional at a software / services company sells and generates revenue. When a company lags in revenue, will one person be able to generate revenue?

I don’t have an answer. Worth monitoring to learn if the Chief Revenue Officer can deliver the money.

Stephen E Arnold, October 24, 2016

Quote to Note: Enterprise Search As a Black Hole

October 19, 2016

Here’s a quote to note from “Slack CEO Describes Holy Grail of Virtual Assistants.” Slack seeks to create smart software capable of correlating information from enterprise applications. Good idea. The write up says:

Slack CEO Stewart Butterfield has an audacious goal: Turning his messaging and collaboration platform into an uber virtual assistant capable of searching every enterprise application to deliver employees pertinent information.

Got it. Employees cannot locate information needed for their job. Let me sidestep the issue of hiring people incapable of locating information in the first place.

Here’s the quote I noted:

And if Slack succeeds, it could seal the timeless black hole of wasted productivity enterprise search and other tools have failed to close.

I love the “timeless black hole of wasted productivity of enterprise search.” Great stuff, particularly because outfits like Wolters Kluwer continue to oscillate between proprietary search investments like and open source solutions like Lucene/Solr.

Do organizations create these black holes or is software to blame? Information is a slippery fish, which often find “timeless black holes” inhospitable.

Stephen E Arnold, October 19, 2016

Definitions of Search to Die For. Maybe With?

October 13, 2016

I read “Search Terminology. Web Search, Enterprise Search, Real Time Search, Semantic Search.” I have included glossaries in some of my books about search. I did not realize that I could pluck out four definitions and present them as a stand alone article. Ah, the wonders of content marketing.

If you want to read the definition with which one can die, either for or with, have at it. May I suggest that you consider these questions prior to your perusing the content marketing write up thing:

Web search

  • What’s the method for password protected sites and encrypted sites which exist under current Web technology?
  • What Web search systems build their own indexes and which send a query to multiple search systems and aggregate the results? Does the approach matter?
  • What is the freshness or staleness of Web indexes? Does it matter that one index may be a few minutes “old” and another index several weeks “old”?

Enterprise search

  • How does an enterprise search system deliver internal content points and external content pointers?
  • What is the consequence of an enterprise search user who accesses content which is incomplete or stale?
  • What does the enterprise search system do with third party content such as consultants’ reports which someone in the organization has purchased? Ignore? Re-license? Index the content and worry later?
  • What is the refresh cycle for changed and new content?
  • What is the search function for locating database content or rich media residing on the organization’s systems?

Real time search

  • What is real time? The indexing of content in the millisecond world of Wall Street? Indexing content when machine resources and network bandwidth permit?
  • How does a user determine the latency in the search system because marketers can write “real time” while programmers implement index update options which the search administrator selects?
  • What search system indexes videos in real time? YouTube struggles with 10 minute or longer latency with some videos requiring hours before the index points to those videos?

Semantic search

  • What is the role of human subject matter experts in semantic search?
  • What is the benefit of human-intermediated systems versus person-machine or automated smart indexing?
  • How does one address concept drift as a system “learns” from its indexing of information?
  • What happens to taxonomies, dictionary lists of entities, and other artifacts of concept indexing?
  • What does a system do when encountering documents, audio, and videos in a language different from the language of the majority of a system’s users?

Get the idea that zippy, brief definitions cannot deliver Gatorade to the college football players studying in the dorm the night before a big game?

Stephen E Arnold, October 13, 2016

Structured Search: New York Style

October 10, 2016

An interesting and brief search related content marketing white paper “InnovationQ Plus Search Engine Technology” attracted my attention. What’s interesting is that the IEEE is apparently in the search engine content marketing game. The example I have in front of me is from a company doing business as

What does InnovationQ Plus do to deliver on point results? The write up says:

This engine is powered by’s patented neural network machine learning technology that improves searcher productivity and alleviates the difficult task of identifying and selecting countless keywords/synonyms to combine into Boolean syntax. Simply cut and paste abstracts, summaries, claims, etc. and this state-of-the art system matches queries to documents based on meaning rather than keywords. The result is a search that delivers a complete result set with less noise and fewer false positives. Ensure you don’t miss critical documents in your search and analysis by using a semantic engine that finds documents that other tools do not.

The use of snippets of text as the raw material for a behind-the-scenes query generator reminds me of the original DR-LINK method, among others. Perhaps there is some Syracuse University “old school” search DNA in the InnovationQ Plus approach? Perhaps the TextWise system has manifested itself as a “new” approach to patent and STEM (scientific, technology, engineering, and medical)  online searching? Perhaps Manning & Napier’s interest in information access has inspired a new generation of search capabilities?

My hunch is, “Yep.”

If you don’t have a handy snippet encapsulating your search topic, just fill in the query form. Google offers a similar “fill in the blanks” approach even thought a tiny percentage of those looking for information on Google use advanced search. You can locate the Google advanced search form at this link.

Part of the “innovation” is the use of fielded search. Fielded search is useful. It was the go to method for locating information in the late 1960s. The method fell out of favor with the Sillycon Valley crowd when the idea of talking to one’s mobile phone became the synonym for good enough search.

To access the white paper, navigate the IEEE registration page and fill out the form at this link.

From my vantage point, structured search with “more like this” functions is a good way to search for information. There is a caveat. The person doing the looking has to know what he or she needs to know.

Good enough search takes a different approach. The systems try to figure out what the searcher needs to know and then deliver it. The person looking for information is not required to do much thinking.

The InnovationQ Plus approach shifts the burden from smart software to smart searchers.

Good enough search is winning the battle. In fact, some Sillycon Valley folks, far from upstate New York, have embraced good enough search with both hands. Why use words at all? There are emojis, smart software systems predicting what the use wants to know, and Snapchat infused image based methods.

The challenge will be to find a way to bridge the gap between the Sillycon Valley good enough methods and the more traditional structured search methods.

IEEE seems to agree as long as the vendor “participates” in a suitable IEEE publishing program.

Stephen E Arnold, October 10, 2016

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