Alternatives to Windows Search
September 16, 2014
For some common searches, Windows’ built-in desktop search function works just fine. Other times, though, our hard-drive hunts call for something more. Reporter Martin Brinkmann at ghacks.net shares his list of “The Best Free Desktop Search Programs for Windows.” He writes:
Desktop search tools offer faster searches, better options and filters, and a better user experience as a consequence. These tools can be sorted into two main categories: programs that require indexing before they can be used, and programs that work right out of the box without it. Let’s take a look at the requirements for this top list.
Requirements
*A free version of the program needs to be available.
*Search all files and don’t limit results.
*Compatibility with all recent 32-bit and 64-bit editions and versions of the Windows operating system.
*Top list of desktop search programs
The list takes a quick look at each application so that you know what it is about. Below that is a table that you can use to compare the core functionality followed by our recommendations.
Brinkmann describes 11 services and tacks on four more suggested by readers. Curiously, absent is one of our recommendations, Sowsoft Effective File Search. For the rest, see the ghacks article for Brinkmann’s observations, and don’t forget to scroll down for his handy-dandy comparison table.
Cynthia Murrell, September 16, 2014
Sponsored by ArnoldIT.com, developer of Augmentext
BA Insight Delves into Connectors
September 12, 2014
I read “BA Insight Adds 10 New Indexing Connectors to Surface Information.” The article reports that it offers connectors for:
- SharePoint Online
- Confluence
- Salesforce.com
- Microsoft Dynamics Online
- CuadraSTAR
- Alfresco
- Scopus
- PharmaCircle
- Jive
- Box
Outside In (now Oracle) and Entropy Soft (now Salesforce) proved that connectors could be more important than the software to which folks want to connect in terms of buy out magnetism.
Is BA Insight embracing connectors as a way to accelerate its attractiveness to a potential acquirer? Will BA Insight’s play provide Microsoft Delve (when it becomes a carrier class product in a couple of years) with an easy way to support more than a handful of content types? Will Microsoft buy BA Insight? (Both companies share a vision that keyword search is not about search but about related information.)
What the move suggests to me is that BA Insight is filling in some gaps in the Delve product offering. I address the Microsoft Delve collection of functions in my forthcoming Information Today column. Connectors may be the least of Delve’s challenges. I think it would be helpful if Delve could process email attachments, a feature I understand is not supported. The packing of components around Yammer is not a revolution in search. The approach reminds me of Microsoft’s creation of SharePoint from acquired and home grown code. Do you remember
My hunch is that other Microsoft dependent services firms will “delve” into this gap as well. Me too is a time honored practice in the pond choked with search fish.
Some folks are nosing around ElasticSearch as a low cost, relatively easy solution to content aggregation. What happens if ElasticSearch community developers focus on SharePoint? Interesting question.
Stephen E Arnold, September 12, 2014
ElasticSearch Rides The Rails
September 8, 2014
If you have been reading this blog for a while, then you are aware that search is an important feature for using any computer with ease. Without search, people would be forced to scan information one piece at a time or rely on indices. For those who remember microfiche, you can understand. Search in applications has been a semi-fleeting endeavor for some developers, but SitePoint has an article, “Full-Text Search In Rails With ElasticSearch” that explains how to integrate ElasticSearch into a Rails application.
“A full-text search engine examines all of the words in every stored document as it tries to match search criteria (text specified by a user) Wikipedia. For example, if you want to find articles that talk about Rails, you might search using the term “rails”. If you don’t have a special indexing technique, it means fully scanning all records to find matches, which will be extremely inefficient. One way to solve this is an “inverted index” that maps the words in the content of all records to its location in the database.”
As applications become more versatile, they will need to be searched. The article provides one way to make your applications searchable, scan the Web with a search engine and learn about other ways to integrate search. Also make sure that it is a decent search code, otherwise it will not be worth the deployment.
Whitney Grace, September 08, 2014
Sponsored by ArnoldIT.com, developer of Augmentext
Galaxy Consulting Explains Vivisimo at IBM
September 5, 2014
The Galaxy Consulting Blog shares information on all things information. Recently, they spelled out details on one of IBM’s smarter acquisitions in the profile, “Search Applications – Vivisimo.” In our opinion, that outfit is one of the more solid search providers. The write-up begins with a brief rundown of the company’s history, including its purchase by IBM in 2012. We learn:
“Vivisimo Velocity Platform is now IBM InfoSphere Data Explorer. It stays true to its heritage of providing federated navigation, discovery and search over a broad range of enterprise content. It covers broad range of data sources and types, both inside and outside an organization.
“In addition to the core indexing, discovery, navigation and search engine the software includes a framework for developing information-rich applications that deliver a comprehensive, contextually-relevant view of any topic for business users, data scientists, and a variety of targeted business functions.”
As one should expect, InfoSphere handles many types of data from disparate sources with aplomb, and its support for mobile tech is a feature ahead of the curve. Perhaps most importantly, the platform boasts strong security while maintaining scalability. See the article for a detailed list of InfoSphere’s features.
Before IBM snapped it up in 2012, Vivisimo passed through the hands of Yippy, which had purchased it in 2010. The firm is headquartered in Pittsburgh but maintains other offices on the East Coast and in Europe.
Cynthia Murrell, September 05, 2014
Sponsored by ArnoldIT.com, developer of Augmentext
Google and Universal Search or Google Floudering with Search
August 30, 2014
There have been some experts who have noticed that Google has degraded blog search. In the good old days, it was possible to query Google’s index of Web logs. It was not comprehensive, and it was not updated with the zippiness of years past.
Search Engine Land and Web Pro News both pointed out that www.google.com/blogsearch redirects to Google’s main search page. The idea of universal search, as I understood it, was to provide a single search box for Google’s content. Well, that is not too useful when it is not possible to limit a query to a content type or a specific collection.
“Universal” to Google is similar to the telco’s use of the word “unlimited.”
According the to experts, it is possible to search blog content. Here’s the user friendly sequence that will be widely adopted by Google users:
- Navigate to the US version of Google News. Note that this can be tricky if one is accessing Google from another country
- Enter a query; for example, “universal search”
- Click on “search tools” and then click on “All news”
- Then click on “Blogs”
Several observations:
First, finding information in Google is becoming more and more difficult.
Second, obvious functions such as providing an easy way to run queries against separate Google indexes is anything but obvious. Do you know how to zip to Google’s patent index or its book index? Not too many folks do.
Third, the “logic” of making search a puzzle is no longer of interest to me. Increasing latency in indexing, Web sites that are pushed deep in the index for a reason unrelated to the site’s content, and a penchant for hiding information points to some deep troubles in Google search.
Net net: Google has lost its way in search. Too bad. As the volume of information goes up, the findability goes down. Wild stuff like Loon and Glass go up. Let’s hope Google can keep its ad revenue flowing; otherwise, there would be little demand for individuals who can perform high value research.
Stephen E Arnold, August 30, 2014
Oracle Endeca Business Intelligence Rules
August 21, 2014
Rules are good. The problem is getting people to do what the rule maker wants. Oracle wants Endeca to be a business intelligence system at the same time Oracle wants Endeca to be an ecommerce system. You can find the five rules in the white paper “The Five Rules of the Road for Enterprise Data Discovery.”
What are these rules?
I don’t want to spoil your fun. I want to encourage you to dig into Endeca’s rules and to work through the white paper to see if you are doing enterprise data discovery the Oracle way. What is “enterprise data discovery”? Beats me. I think it is 1998 style search based on Endeca’s 1998 technology disclosed in those early Endeca patents.
First, you want to get results without risk. That sounds great. How does one discover information when one does not know exactly what information will be presented? If that information is out of date or statistically flawed, how does Endeca ameliorate risk? Big job.
Second, Endeca wants you to blend data so you get deeper insights. What if the data are not normalized, consistent, or accurate? Those insights may not be deeper; they may be misleading.
Third, Endeca wants everything integrated. How does one figure out what is important in a syst3m that gives the user a search box, links to follow, and analytics? Is this discovery or just plain old 1998 style Endeca search? Where’s the discovery thing? Blind clicking?
Fourth, Endeca wants you to “have a dialog with your data”. I find this interesting but fuzzy. Does Endeca now support voice input to its ageing technology?
Finally, Endeca wants those data indexed and updated. The goal is “keep on discovering.” I wonder what the latency in Endeca’s system is for most users? I suppose the cure for latency and Endeca’s indexing method can be resolved with Oracle servers. How much does the properly configured fully resourced Endeca system cost? My hunch. More than a couple of Pebble Beach show winners.
The white paper is interesting because it contains an example of the Endeca interface and the most amazing leap from five rules to customer support. Oracle also owns RightNow and InQuira. Where do these systems fit into the five rules?
Confused? I am.
Stephen E Arnold, August 21, 2014
Does Anything Matter Other Than the Interface?
August 7, 2014
I read what I thought was a remarkable public relations story. You will want to check the write up out for two reasons. First, it demonstrates how content marketing converts an assertion into what a company believes will generate business. And, second, it exemplifies how a fix can address complex issues in information access. You may, like Archimedes, exclaim, “I have found it.”
The title and subtitle of the “news” are:
My reaction was, “What?”
The guts of the NewLane “search discovery platform” is explained this way:
Eureka! was developed from the ground up as a platform to capture all the commonalities of what a search app is and allows for the easy customization of what a company’s search app specifically needs.
I am confused. I navigated to the company’s Web site and learned:
Eureka! empowers key users to configure and automatically generate business applications for fast answers to new question that they face every day. http://bit.ly/V0E8pI
The Web site explains:
Need a solution that provides a unified view of available information housed in multiple locations and formats? Finding it hard to sort among documents, intranet and wiki pages, and available reporting data? Create a tailored view of available information that can be grouped by source, information type or other factors. Now in a unified, organized view you can search for a project name and see results for related documents from multiple libraries, wiki pages from collaboration sites, and the profiles of project team members from your company’s people directory or social platform.
“Unified information access” is a buzzword used by Attivio and PolySpot, among other search vendors. The Eureka! approach seems to be an interface tool for “key users.”
Here’s the Eureka technology block diagram:
Notice that Eureka! has connectors to access the indexes in Solr, the Google Search Appliance, Google Site Search, and a relational database. The content that these indexing and search systems can access include Documentum, Microsoft SharePoint, OpenText LiveLink, IBM FileNet, files shares, databases (presumably NoSQL and XML data management systems as well), and content in “the cloud.”
For me the diagram makes clear that NewLane’s Eureka is an interface tool. A “key user” can create an interface to access content of interest to him or her. I think there are quite a few people who do not care where data come from or what academic nit picking went on to present information. The focus is on something a harried professional like an MBA who has to make a decision “now” needs some information.
Archimedes allegedly jumped from his bath, ran into the street, and shouted “Eureka.” He reacted, I learned from a lousy math teacher, that he had a mathematical insight about displacement. The teacher did not tell me that Archimedes was killed because he was working on a math problem and ignored a Roman soldier’s command to quit calculating. Image source: http://blocs.xtec.cat/sucdecocu/category/va-de-cientifics/
I find interfaces a bit like my wife’s questions about the color of paint to use for walls. She shows me antique ivory and then parchment. For me, both are white. But for her, the distinctions are really important. She knows nothing about paint chemistry, paint cost, and application time. She is into the superficial impact the color has for her. To me, the colors colors are indistinguishable. I want to know about durability, how many preparation steps the painter must go through between brands, and the cost of getting the room painted off white.
Interfaces for “key users” work like this in my experience. The integrity of the underlying data, the freshness of the indexes, the numerical recipes used to prioritize the information in a report are niggling details of zero interest to many system users. An answer—any answer—may be good enough.
Eureka! makes it easier to create interfaces. My view is that a layer on top of connectors, on top of indexing and content processing systems, on top of wildly diverse content is interesting. However, I see the interfaces as a type of paint. The walls look good but the underlying structure may be deeply flawed. The interface my wife uses for her walls does not address the fact that the wallboard has to be replaced BEFORE she paints again. When I explain this to her when she wants to repaint the garage walls, she says, “Why can’t we just paint it again?” I don’t know about you, but I usually roll over, particularly if it is a rental property.
Now what does the content marketing-like “news” story tell me about Eureka!
I found this statement yellow highlight worthy:
Seth Earley, CEO of Earley and Associates, describes the current global search environment this way, “What many executives don’t realize is that search tools and technologies have advanced but need to be adapted to the specific information needed by the enterprise and by different types of employees accomplishing their tasks. The key is context. Doing this across the enterprise quickly and efficiently is the Holy Grail. Developing new classes of cloud-based search applications are an essential component for achieving outstanding results.”
Yep, context is important. My hunch is that the context of the underlying information is more important. Mr. Earley, who sponsored an IDC study by an “expert” named Dave Schubmehl on what I call information saucisson, is an expert on the quasi academic “knowledge quotient” jargon. He, in this quote, seems to be talking about a person in shipping or a business development professional being able to use Eureka! to get the interface that puts needed information front and center. I think that shipping departments use dedicated systems who data typically does not find their way into enterprise information access systems. I also think that business development people use Google, whatever is close at hand, and enterprise tools if there is time. When time is short, concise reports can be helpful. But what if the data on which the reports are based are incorrect, stale, incomplete, or just wrong? Well, that is not a question germane to a person focused on the “Holy Grail.”
I also noted this statement from Paul Carney, president and founder of NewLane:
The full functionality of Eureka! enables understaffed and overworked IT departments to address the immediate search requirements as their companies navigate the choppy waters of lessening their dependence on enterprise and proprietary software installations while moving critical business applications to the Cloud. Our ability to work within all their existing systems and transparently find content that is being migrated to the Cloud is saving time, reducing costs and delivering immediate business value.
The point is similar to what Google has used to sell licenses for its Google Search Appliance. Traditional information technology departments can be disintermediated.
If you want to know more about FastLane, navigate to www.fastlane.com. Keep a bathrobe handy if you review the Web site relaxing in a pool or hot tube. Like Archimedes, you may have an insight and jump from the water and run through the streets to tell others about your insight.
Stephen E Arnold, August 7, 2014
Data Augmentation: Is a Step Missing or Mislocated?
August 6, 2014
I read “Data Warehouse Augmentation, Part 4.” You can find the write up a http://ibm.co/1obWXDh. There are other sections of the write, but I want to focus on the diagrams in this fourth chapter/section.
IBM is working overtime to generate additional revenues. Some of the ideas are surprising; for example, positioning Vivisimo’s metasearch function as a Big Data solution or buying Cybertap and then making the quite valuable technology impossible to find unless one is an intelligence procurement official. Then there is Watson, and I am just not up to commenting on this natural language processing system.
To the matter at hand. There is basic information about in this write up about specific technical components of a Big Data solution. The words, for the most part, will not surprise anyone who has looked at marketing collateral from any of the Big Data vendors/integrators.
What is fascinating about the write up is the wealth of diagrams in the document. I worked through the text and the diagrams and I noticed that one task is not identified as important; specifically, the conversion of source content into a file type or form that the content processing system can process.
Here’s an example. First the IBM diagram:
Source: IBM, Data Warehouse Augmentation, 2014.
Notice that after “staging”, there is a function described in time-honored database speak, “ETL.” Now “extract, transform, and load” is a very important step. But is there a step that precedes ETL?
How can one extract from disparate content if a connector is not available or the source system cannot support file transfers, direct access, or reports that reflect in memory data?
In my experience, there will be different methods of acquiring content to process. There are internal systems. If there is an ancient AS/400, some work is required to generate outputs that provide the data required. Due to the nature of the AS/400, direct interaction with the outstanding memory system of the AS/400, some care is needed to get the data and the updates not yet written to disc without corrupting the in memory information. We have addressed this “memory fragility” by using a standalone machine that accepts an output from the AS/400 and then disconnects. The indexing system, then, connects to the standalone machine to pick up the AS/400 outputs. Clunky? You bet. But there are some upsides. To learn about the excitement of direct interaction with AS/400, just do some real time data acquisition. Let me know how this works out for you.
The same type of care is often needed with the content assembled for the data warehouse pipeline. Let me illustrate this. Assume the data warehouse will obtain data from these sources: internal legacy systems, third party providers, custom crawls with the content residing on a hosted service, and direct data acquisition from mobile devices that feed information into a collection point parked at Amazon.
Now each of these content streams has different feathers in its war bonnet. Some of the data will be well formed XML. Some will be JSON. Some will be a proprietary format unique to the source. For each file type, there will be examples of content objects that are different, due to a vendor format change or a glitch.
These disparate content objects, therefore, have to be processed before extraction can occur. So has IBM put ETL in the wrong place in this diagram or has IBM omitted the pre-processing (normalization) operation.
In our experience, content that cannot be processed is not available to the system. If big chunks of content end up in the exceptions folder, the resulting content processing may be flawed. One of the data points that must be checked is the number of content objects that can be normalized in a pre processing stream. We have encountered situations like these. Your mileage may vary:
- Entire streams of certain types of content are exceptions, so the resulting indexing does not contain the data. Example: outputs from certain intercept systems.
- Streams of content skip non processable content without writing exceptions to a file due to configuration or resource availability
- Streams of content are automatically “capped” when the processing system cannot keep pace. When the system accepts more content, it does not pull information from a cache or storage pool. The system just ignores the information it was unable to process.
There are fixes for each of these situations. What we have learned is that this pre processing function can be very expensive, have an impact on the reliability of the outputs from the data warehousing system when queried, and generate a bottleneck that affects downstream processes.
After decades of data warehousing refinement, why does this problem keep surfacing?
The answer is that recycling traditional thinking about content processing is much easier than figuring out what causes a complex system to derail itself. I think that may be part of the reason the IBM diagram may be misleading.
Pre-processing can be time consuming, hungry for machine resources, and very expensive to implement.
Stephen E Arnold, August 6, 2014
The March of IBM Watson: From Kitchen to Executive Suite
August 5, 2014
Watson, fresh from its recipe innovations at Bon Appétit, is on the move…again. From the game show to the hospital, Watson has been demonstrating its expertise in the most interesting venues.
I read “A Room Where Executives Go to Get Help from IBM’s Watson.” The subtitle is an SEO dream: “Researchers at IBM are testing a version of Watson designed to listen and contribute to business meetings.” I know IBM has loads of search and content processing capability. In addition to the gems cranked out by Dr. Jon Kleinberg and Dr. Ramanathan Guha, IBM has oodles of acquisitions in the search and content processing sector. Do you know about Clementine? Are you familiar with iPhrase? Have your explored Cybertap’s indexing and search function with your local IBM representative? What about Vivisimo? What about the search functions in DB2, FileNet, and OminFind regardless of its incarnation? Whew. That’s a lot of search and content processing horsepower. I think most of that power remains in the barn.
Watson is not in the barn. Watson is a raging bull. Watson is, I believe, something special. Based on open source technology plus home brew wizardry, Watson is a next-generation information retrieval world beater. The idea is that Watson is trained in a manner similar to the approach used by Autonomy in 1996. Then that indexed content is whipped into a question answering system. Hapless chefs, litigation wary physicians, and now risk averse MBAs can use Watson to make better decisions or answer really tough questions.
I know this to be true because Technology Review tells me so. Whatever MIT-tinged Technology Review says is pretty darned solid. Here’s a passage I noted:
Everything said in the room can be instantly transcribed, providing a detailed record of any meeting, and allowing the system to listen out for commands addressed to “Watson.” Those commands can be simple requests for information of the kind you might type into a search box. But Watson can also take a more active role in a discussion. In a live demonstration, it helped researchers role-playing as executives to generate a short list of companies to acquire.
The write up explains that a little bit of preparation is required. There’s the pesky training, which is particularly annoying when the topic of the meeting is, “The DOJ attorneys are here to discuss the depositions” or “We have a LOCA at the reactor. Everyone to my conference room now.” I suppose most business meetings are even more exciting.
Technology Review points out that the technology has a tough time converting executive speech to text. Watson uses the text as fodder for the indexing and parsing required to pass queries to the internal subsystems which then tap into Watson for answers. The natural language query and automatic query refinement functions seem to work well for game show questions and for discerning uses of tamarind. For a LOCA meeting or discussion of a deposition, Watson may need a bit more work.
I find the willingness of major “real” news outlets to describe Watson in juicy write ups an indication of the esteem in which IBM is held. My view is a bit different. I am not sure the Watson group at IBM knows how to generate substantial revenues. The folks have to make some progress toward $1 billion in revenue and then grow that revenue to a modest $10 billion in five or six years.
The fact that outfits in search and content processing have failed to hit more modest benchmarks for decades is irrelevant. The only search company that I know has generated billions is Google. Keep in mind that those billions come from online advertising. HP bought Autonomy for $11 billion in the hopes of owning a Klondike. IBM wisely went with open source technology and home grown code.
But the eventual effect of both HP’s and IBM’s approach will be more modest revenues. HP makes a name for itself via litigation and IBM is making a name for itself with demonstrations and some recipes.
Search and content processing, whether owned by a large company or a small one, faces some credibility, marketing, revenue, technology, and profit challenges. I am not sure a business triathlete can complete the course at this time. Talk is just so much easier than getting over or around the course intact.
Stephen E Arnold, August 5, 2014
Attensity Leverages Biz360 Invention
August 4, 2014
In 2010, Attensity purchased Biz360. The Beyond Search comment on this deal is at http://bit.ly/1p4were. One of the goslings reminded me that I had not instructed a writer to tackle Attensity’s July 2014 announcement “Attensity Adds to Patent Portfolio for Unstructured Data Analysis Technology.” PR-type “stories” can disappear, but for now you can find a description of “Attensity Adds to Patent Portfolio for Unstructured Data Analysis Technology” at http://reut.rs/1qU8Sre.
My researcher showed me a hard copy of 8,645,395, and I scanned the abstract and claims. The abstract, like many search and content processing inventions, seemed somewhat similar to other text parsing systems and methods. The invention was filed in April 2008, two years before Attensity purchased Biz360, a social media monitoring company. Attensity, as you may know, is a text analysis company founded by Dr. David Bean. Dr. Bean employed various “deep” analytic processes to figure out the meaning of words, phrases, and documents. My limited understanding of Attensity’s methods suggested to me that Attensity’s Bean-centric technology could process text to achieve a similar result. I had a phone call from AT&T regarding the utility of certain Attensity outputs. I assume that the Bean methods required some reinforcement to keep pace with customers’ expectations about Attensity’s Bean-centric system. Neither the goslings nor I are patent attorneys. So after you download 395, seek out a patent attorney and get him/her to explain its mysteries to you.
The abstract states:
A system for evaluating a review having unstructured text comprises a segment splitter for separating at least a portion of the unstructured text into one or more segments, each segment comprising one or more words; a segment parser coupled to the segment splitter for assigning one or more lexical categories to one or more of the one or more words of each segment; an information extractor coupled to the segment parser for identifying a feature word and an opinion word contained in the one or more segments; and a sentiment rating engine coupled to the information extractor for calculating an opinion score based upon an opinion grouping, the opinion grouping including at least the feature word and the opinion word identified by the information extractor.
This invention tackles the Mean Joe Green of content processing from the point of view of a quite specific type of content: A review. Amazon has quite a few reviews, but the notion of an “shaped” review is a thorny one. See, for example, http://bit.ly/1pz1q0V.) The invention’s approach identifies words with different roles; some words are “opinion words” and others are “feature words.” By hooking a “sentiment engine” to this indexing operation, the Biz360 invention can generate an “opinion score.” The system uses item, language, training model, feature, opinion, and rating modifier databases. These, I assume, are either maintained by subject matter experts (expensive), smart software working automatically (often evidencing “drift” so results may not be on point), or a hybrid approach (humans cost money).
The Attensity/Biz360 system relies on a number of knowledge bases. How are these updated? What is the latency between identifying new content and updating the knowledge bases to make the new content available to the user or a software process generating an alert or another type of report?
The 20 claims embrace the components working as a well oiled content analyzer. The claim I noted is that the system’s opinion score uses a positive and negative range. I worked on a sentiment system that made use of a stop light metaphor: red for negative sentiment and green for positive sentiment. When our system could not figure out whether the text was positive or negative we used a yellow light.
The approach used for a US government project a decade ago, used a very simple metaphor to communicate a situation without scores, values, and scales. Image source: http://bit.ly/1tNvkT8
Attensity said, according the news story cited above:
By splitting the unstructured text into one or more segments, lexical categories can be created and a sentiment-rating engine coupled to the information can now evaluate the opinions for products, services and entities.
Okay, but I think that the splitting of text into segment was a function of iPhrase and search vendors converting unstructured text into XML and then indexing the outputs.
Attensity’s Jonathan Schwartz, General Counsel at Attensity is quoted in the news story as asserting:
“The issuance of this patent further validates the years of research and affirms our innovative leadership. We expect additional patent issuances, which will further strengthen our broad IP portfolio.”
Okay, this sounds good but the invention took place prior to Attensity’s owning Biz360. Attensity, therefore, purchased the invention of folks who did not work at Attensity in the period prior to the filing in 2008. I understand that company’s buy other companies to get technology and people. I find it interesting that Attensity’s work “validates” Attensity’s research and “affirms” Attensity’s “innovative leadership.”
I would word what the patent delivers and Attensity’s contributions differently. I am no legal eagle or sentiment expert. I do like less marketing razzle dazzle, but I am in the minority on this point.
Net net: Attensity is an interesting company. Will it be able to deliver products that make the licensees’ sentiment score move in a direction that leads to sustaining revenue and generous profits. With the $90 million in funding the company received in 2014, the 14-year-old company will have some work to do to deliver a healthy return to its stakeholders. Expert System, Lexalytics, and others are racing down the same quarter mile drag strip. Which firm will be the winner? Which will blow an engine?
Stephen E Arnold, August 4, 2014
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