The Gilbane Advisor

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Category: Enterprise search & search technology (Page 21 of 60)

Research, analysis, and news about enterprise search and search markets, technologies, practices, and strategies, such as semantic search, intranet collaboration and workplace, ecommerce and other applications.

Before we consolidated our blogs, industry veteran Lynda Moulton authored our popular enterprise search blog. This category includes all her posts and other enterprise search news and analysis. Lynda’s loyal readers can find all of Lynda’s posts collected here.

For older, long form reports, papers, and research on these topics see our Resources page.

ETL and Building Intelligence Behind Semantic Search

March 11, 2011 / / 0 Comments

A recent inquiry about a position requiring ETL (Extraction/Transformation/Loading) experience prompted me to survey the job market in this area. It was quite a surprise to see that there are many technical positions seeking this expertise, plus experience with SQL databases, and XML, mostly in healthcare, finance or with data warehouses. I am also observing an uptick in contract positions for metadata and taxonomy development.

My research on Semantic Software Technologies placed me on a path for reporters and bloggers to seek my thoughts on the Watson-Jeopardy story. Much has been written on the story but I wanted to try a fresh take on the meaning of it all. There is a connection to be made between the ETL field and building a knowledgebase with the smarts of Watson. Inspiration for innovation can be drawn from the Watson technology but there is a caveat; it involves the expenditure of serious mental and computing perspiration.

Besides baked-in intelligence for answering human questions using natural language processing (NLP) to search, an answer-platform like Watson requires tons of data. Also, data must be assembled in conceptually and contextually relevant databases for good answers to occur. When documents and other forms of electronic content are fed to a knowledgebase for semantic retrieval, finely crafted metadata (data describing the content) and excellent vocabulary control add enormous value. These two content enhancers, metadata and controlled vocabularies, can transform good search into excellent search.

The irony of current enterprise search is that information is in such abundance that it overwhelms rather than helps findability. Content and knowledge managers can’t possibly contribute the human resources needed to generate high quality metadata for everything in sight. But there are numerous techniques and technologies to supplement their work by explicitly exploiting the mountain of information.

Good content and knowledge managers know where to find top quality content but may not know that, for all common content formats, there are tools to extract key metadata embedded (but hidden) in it. Some of these tools can also text mine and analyze the content for additional intelligent descriptive data. When content collections are very large but too small to justify (under a million documents) the most sophisticated and complex semantic search engines, ETL tools can relieve pressure on metadata managers by automating a lot of mining, extracting entities and concepts needed for good categorization.

The ETL tool array is large and varied. Platform tools from Microsoft (SSIS) and IBM (DataStage) may be employed to extract, transform and load existing metadata. Other independent products such as those from Pervasive and SEAL may contribute value across a variety of platforms or functional areas from which content can be dramatically enhanced for better tagging and indexing. The call for ETL experts is usually expressed in terms of engineering functions who would be selecting, installing and implementing these products. However, it has to be stressed that subject and content experts are required to work with engineers. The role of the latter is to help tune and validate the extraction and transformation outcomes, making sure terminology fits function.

Entity extraction is one major outcome of text mining to support business analytics, but tools can do a lot more to put intelligence into play for semantic applications. Tools that act as filters and statistical analyzers of text data warehouses will help reveal terminology for use in building specialized controlled vocabularies for use in auto-categorization. A few vendors that are currently on my radar to help enterprises understand and leverage their content landscape include EntropySoft Content ETL, Information Extraction Systems, Intelligenx, ISYS Document Filters, RAMP, and XBS, something here for everyone.

The diversity of emerging applications is a leading indicator that there is a lot of innovation to come with all aspects of ETL. While RAMP is making headway with video, another firm with a local connection is Inforbix. I spoke with a co-founder, Oleg Shilovitsky for my semantic technology research last year before they launched. As he then asserted, it is critical to preserve, mine and leverage the data associated with design and manufacturing operations. This area has huge growth potential and Inforbix is now ready to address that market.

Readers who seek to leverage ETL and text mining will gain know-how from the cases presented at the 2011 Text Analytics Summit, May 18-19 in Boston. As well, the exhibits will feature products to consider for making piles of data a valuable knowledge asset. I’ll be interviewing experts who are speaking and exhibiting at that conference for a future piece. I hope readers will attend and seek me out to talk about your metadata management and text mining challenges. This will feed ideas for future posts.

Finally, I’m not the only one thinking along these lines. You will find other ideas and a nudge to action in these articles.

Boeri, Bob. Improving Findability Behind the Firewall, 28 slides. Enterprise Search Summit 2010, NY, 05/2010.
Farrell, Vickie. The Need for Active Metadata Integration: The Hard Boiled Truth. DM Direct Newsletter, 09/09/2005, 3p
McCreary, Dan. Entity Extraction and the Semantic Web, Semantic Universe, 01/01/2009
White, David. BI or bust? KMWorld, 10/28/2009, 3p.

How Far Does Semantic Software Really Go?

February 3, 2011 / / 0 Comments

A discussion that began with a graduate scholar at George Washington University in November, 2010 about semantic software technologies prompted him to follow up with some questions for clarification from me. With his permission, I am sharing three questions from Evan Faber and the gist of my comments to him. At the heart of the conversation we all need to keep having is, how far does this technology go and does it really bring us any gains in retrieving information?

1. Have AI or semantic software demonstrated any capability to ask new and interesting questions about the relationships among information that they process?

In several recent presentations and the Gilbane Group study on Semantic Software Technologies, I share a simple diagram of the nominal setup for the relationship of content to search and the semantic core, namely a set of terminology rules or terminology with relationships. Semantic search operates best when it focuses on a topical domain of knowledge. The language that defines that domain may range from simple to complex, broad or narrow, deep or shallow. The language may be applied to the task of semantic search from a taxonomy (usually shallow and simple), a set of language rules (numbering thousands to millions) or from an ontology of concepts to a semantic net with millions of terms and relationships among concepts.

The question Evan asks is a good one with a simple answer, “Not without configuration.” The configuration needs human work in two regions:

  • Management of the linguistic rules or ontology
  • Design of search engine indexing and retrieval mechanisms

When a semantic search engine indexes content for natural language retrieval, it looks to the rules or semantic nets to find concepts that match those in the content. When it finds concepts in the content with no equivalent language in the semantic net, it must find a way to understand where the concepts belong in the ontological framework. This discovery process for clarification, disambiguation, contextual relevance, perspective, meaning or tone is best accompanied with an interface making it easy for a human curator or editor to update or expand the ontology. A subject matter expert is required for specialized topics. Through a process of automated indexing that both categorizes and exposes problem areas, the semantic engine becomes a search engine and a questioning engine.

The entire process is highly iterative. In a sense, the software is asking the questions: “What is this?”, “How does it relate to the things we already know about?”, “How is the language being used in this context?” and so on.

2. In other words, once they [the software] have established relationships among data, can they use that finding to proceed – without human intervention- to seek new relationships?

Yes, in the manner described for the previous question. It is important to recognize that the original set of rules, ontologies, or semantic nets that are being applied were crafted by human beings with subject matter expertise. It is unrealistic to think that any team of experts would be able to know or anticipate every use of the human language to codify it in advance for total accuracy. The term AI is, for this reason, a misnomer because the algorithms are not thinking; they are only looking up “known-knowns” and applying them. The art of the software is in recognizing when something cannot be discerned or clearly understood; then the concept (in context) is presented for the expert to “teach” the software what to do with the information.

State-of-the-art software will have a back-end process for enabling implementer/administrators to use the results of search (direct commentary from users or indirectly by analyzing search logs) to discover where language has been misunderstood as evidenced by invalid results. Over time, more passes to update linguistic definitions, grammar rules, and concept relationships will continue to refine and improve the accuracy and comprehensiveness of search results.

3. It occurs to me that the key value added of semantic technologies to decision-making is their capacity to link sources by context and meaning, which increases situational awareness and decision space. But can they probe further on their own?

Good point on the value and in a sense, yes, they can. Through extensive algorithmic operations, instructions can be embedded (and probably are for high-value situations like intelligence work), instructing the software what to do with newly discovered concepts. Instructions might then place these new discoveries into categories of relevance, importance, or associations. It would not be unreasonable to then pass documents with confounding information off to other semantic tools for further examination. Again, without human analysis along the continuum and at the end point, no certainty about the validity of the software’s decision-making can be asserted.

I can hypothesize a case in which a corpus of content contains random documents in foreign languages. From my research, I know that some of the semantic packages have semantic nets in multiple languages. If the corpus contains material in English, French, German and Arabic, these materials might be sorted and routed off to four different software applications. Each batch would be subject to further linguistic analysis, followed by indexing with some middleware applied to the returned results for normalization, and final consolidation into a unified index. Does this exist in the real world now? Probably there are variants but it would take more research to find the cases, and they may be subject to restrictions that would require the correct clearances.

Discussions with experts who have actually employed enterprise specific semantic software, underscores the need for subject expertise, and some computational linguistics training coupled with an aptitude for creative inquiry. These scientists informed me that individuals, who are highly multi-disciplinary and facile with electronic games and tools, did the best job of interacting with the software and getting excellent results. Tuning and configuration over time by the right human players is still a fundamental requirement.

Enterprise Trends: Contrarians and Other Wise Forecasters

January 13, 2011 / / 0 Comments

The gradual upturn from the worst economic conditions in decades is reason for hope. A growing economy coupled with continued adoption of enterprise software, in spite of the tough economic climate, keep me tuned to what is transpiring in this industry. Rather than being cajoled into believing that “search” has become commodity software, which it hasn’t, I want to comment on the wisdom of Jill Dyché and her Anti-predictions for 2011 in a recent Information Management Blog. There are important lessons here for enterprise search professionals, whether you have already implemented or plan to soon.

Taking her points out of order, I offer a bit of commentary on those that have a direct relationship to enterprise search. Based on past experience, Ms. Dyché predicts some negative outcomes but with a clear challenge for readers to prove her wrong. As noted, enterprise search offers some solutions to meet the challenges:

  1. No one will be willing to shine a bright light on the fact that the data on their enterprise data warehouse isn’t integrated. It isn’t just the data warehouse that lacks integration among assets, but among all applications housing critical structured and unstructured content. This does not have to be the case. Several state-of-the-art enterprise search products that are not tied to a specific platform or suite of products do a fine job of federating indexing of disparate content repositories. In a matter of weeks or few months, a search solution can be deployed to crawl, index and search multiple sources of content. Furthermore, newer search applications are being offered for pre-purchase testing for out-of-the-box suitability in pilot or proof-of-concept (POC) projects. Organizations that are serious about integrating content silos have no excuse for not taking advantage of easier to deploy search products.
  2. Even if they are presented with proof of value, management will be reluctant to invest in data governance. Combat this entrenched bias with a strategy to overcome lack of governance; a cost cutting argument is unlikely to change minds. However, risk is an argument that will resonate, particularly when bolstered with examples. Include instances when customers were lost due to poor performance or failure to deliver adequate support services, sales were lost because answers to qualifying questions could not be answered or were not timely, legal or contract issues could not be defended due to inaccessibility of critical supporting documents, or when maintenance revenue was lost due to incomplete, inaccurate or late renewal information getting out to clients. One simple example is the consequences of not sustaining a concordance of customer name, contact, and address changes. The inability of content repositories to talk to each other or aggregate related information in a search because a Customer labeled as Marion University at one address is the same as the Customer labeled University of Marion at another address will be embarrassing in communications and, even worse, costly. Governance of processes like naming conventions and standardized labeling enhances the value and performance of every enterprise system including search.
  3. Executives won’t approve new master data management or business intelligence funding without an ROI analysis. This ties in with the first item because many enterprise search applications include excellent tools for performing business intelligence, analytics, and advanced functions to track and evaluate content resource use. The latter is an excellent way to understand who is searching, for what types of data, and the language used to search. These supporting functions are being built into applications for enterprise search and do not add additional cost to product licenses or implementation. Look for enterprise search applications that are delivered with tools that can be employed on an ad hoc basis by any business manager.
  4. Developers won’t track their time in any meaningful way. This is probably true because many managers are poorly equipped to evaluate what goes into software development. However, in this era of adoption of open source, particularly for enterprise search, organizations that commit to using Lucene or Solr (open source search) must be clear on the cost of building these tools into functioning systems for their specialized purposes. Whether development will be done internally or by a third party, it is essential to place strong boundaries around each project and deployment, with specifications that stage development, milestones and change orders. “Free” open source software is not free or even cost effective when an open meter for “time and materials” exists.
  5. Companies that don’t characteristically invest in IT infrastructure won’t change any time soon. So, the silo-ed projects will beget more silo-ed data…Because the adoption rate for new content management applications is so high, and the ease for deploying them encourages replication like rabbits, it is probably futile to try to staunch their proliferation. This is an important area for governance to be employed, to detect redundancy, perform analytics across silos, and call attention to obvious waste and duplication of content and effort. Newer search applications that can crawl and index a multitude of formats and repositories will easily support efforts to monitor and evaluate what is being discovered in search results. Given a little encouragement to report redundancy and replicated content, every user becomes a governor over waste. Play on the natural inclination for people to complain when they feel overwhelmed by messy search results, by setting up a simple (click a button) reporting mechanism to automatically issue a report or set a flag in a log file when a search reveals a problem.

It is time to stop treating enterprise search like a failed experiment and instead, leverage it to address some long-standing technology elephants roaming around our enterprises.

To follow other search trends for the coming year, you may want to attend a forthcoming webinar, 11 Trends in Enterprise Search for 2011, which I will be moderating on January 25th. These two blogs also have interesting perspectives on what is in store for enterprise applications: CSI Info-Mgmt: Profiling Predictors 2011, by Jim Ericson and The Hottest BPM Trends You Must Embrace In 2011!, by Clay Richardson. Also, some of Ms. Dyché’s commentary aligns nicely with “best practices” offered in this recent beacon, Establishing a Successful Enterprise Search Program: Five Best Practices

Focused on Unifying Content to Reduce Information Overload

December 9, 2010 / / 2 Comments

A theme running through the sessions I attended at Enterprise Search Summit and KMWorld 2010 in Washington, DC last month was the diversity of ways in which organizations are focused on getting answers to stakeholders more quickly. Enterprises deploying content technologies, all with enterprise search as the end game, seek to narrow search results accurately to retrieve and display the best and most relevant content.

Whether the process is referred to as unified indexing, federating content or information integration, each constitutes a similar focus among the vendors I took time to engage with at the conference. Each is positioned to solve different information retrieval problems, and were selected to underscore what I have tried to express in my recent Gilbane Beacon, Establishing a Successful Enterprise Search Program: Five Best Practices, namely the need to first establish a strategic business need. The best practices include the need for understanding how existing technologies and content structures function is the enterprise before settling on any one product or strategy. The essential activity of conducting a proof of concept (POC) or pilot project to confirm product suitability for the targeted business challenge is clearly mandated.

These products, in alphabetic order, are all notable for their unique solutions tailored to different audiences of users and business requirements. All embody an approach to unifying enterprise content for a particular business function:

Access Innovations (AI) was at KMWorld to demonstrate the aptly named product suite, Data Harmony. AI products cover a continuum of tools to build and maintain controlled vocabularies (AKA taxonomies and thesauri), add content metadata through processes tightly integrated with the corresponding vocabularies, search and navigation. Its vocabulary and content management tools can be layered to integrate with existing CMS and enterprise search systems.

Attivio, a company providing a platform solution known as Active Intelligence Engine (AIE), has developers specializing in open source tools for content retrieval solutions with excellent retrieval as the end point. AIE is a platform for enterprises seeking to unify structured and unstructured content across the enterprise, and from the web. By leveraging open source components they provide their customers with a platform that can be developed to enhance search for a particular solution, including bringing Web 2.0 social content into unity with enterprise content for further business intelligence analysis.

Coveo has steadily marched into a dominant position across all vertical industries with its efficiently packaged and reasonably priced enterprise search solutions, since I was first introduced to them in 2007. Their customers are always enthusiastic presenters at KMWorld, representing a population of implementers who seek to make enterprise search available to users quickly, and with a minimum of fuss. This year, Shelley Norton from Children’s Hospital Boston did not disappoint. She ticked off steps in an efficient selection, implementation and deployment process for getting enterprise search up and running smoothly to deliver trustworthy and accurate results to the hospital’s constituents. I always value and respect customer story-telling.

Darwin Awareness Engine was named the KMWorld Promise Award Winner for 2010. Since their founder is local to our home-base and a frequent participant in the Boston KM Forum (KMF) meetings, we are pretty happy for their official arrival on the scene and the recognition. It was just a year ago that they presented the prototype at the KMF. Our members were excited to see the tool exposing layers of news feeds to hone in on topics of interest to see what was aggregated and connected in really “real-time.” Darwin content presentation is unique in that the display reveals relationships and patterns among topics in the Web 2.0 sphere that are suddenly apparent due to their visual connections in the display architecture. The public views are only an example of what a very large enterprise might reveal about its own internal communications through social tools within the organization.

The newest newcomer, RAMP, was introduced to me by Nate Treloar in the closing hours of KMWorld. Nate came to this start-up from Microsoft and the FAST group and is excited about this new venture. Neither exhibiting, nor presenting, Nate was anxious to reach out to analysts and potential partners to share the RAMP vision for converting speech from audio and video feeds to reliable searchable text. This would enable the unification of audio, video and other content to finally be searched from its “full text” on the Web in a single pass. Now, we depend on the contribution of explicit metadata by contributors of non-text content. Long awaiting excellence in speech to indexing for search, I was “all ears” during our conversation and look forward to seeing more of RAMP at future meetings.

Whatever the strategic business need, the ability to deliver a view of information that is unified, cohesive and contextually understandable will be a winning outcome. With the Beacon as a checklist for your decision process, information integration is attainable by making the right software selection for your enterprise application.

Coherence and Augmentation: KM-Search Connection

November 29, 2010 / / 0 Comments

This space is not normally used to comment on knowledge management (KM), one of my areas of consulting, but a recent conference gives me an opening to connect the dots between KM and search. Dave Snowden and Tom Stewart always have worthy commentary on KM and as keynote speakers they did not disappoint at KMWorld. It may seem a stretch but by taking a few of their thoughts out of context, I can synthesize a relationship between KM and search.

KMWorld, Enterprise Search Summit, SharePoint Symposium and Taxonomy Boot Camp moved to Washington D.C. for the 2010 Fall Conference earlier this month. I attended to teach a workshop on building a semantic platform, and to participate in a panel discussion to wrap up the conference with two other analysts, Leslie Owen and Tony Byrne with Jane Dysart moderating.

Comments from the first and last keynote speakers of the conference inspired my final panel comments, counseling attendees to lead by thoughtfully leveraging technology only to enhance knowledge. But there were other snippets that prompt me to link search and KM.

Tom Stewart’s talk was entitled, Knowledge Driven Enterprises: Strategies & Future Focus, which he couched in the context of achieving a “coherent” winning organization. He explained that to reach the coherence destination requires understanding of different types of knowledge and how we need to behave for attaining each type (e.g. “knowable complicated “knowledge calls for experts and research; “emergent complex” knowledge calls for leadership and “sense-making.”).

Stewart describes successful organizations as those in which “the opportunities outside line up with the capabilities inside.” He explains that those “companies who do manage to reestablish focus around an aligned set of key capabilities” use their “intellectual capital” to identify their intangible assets,” human capability, structural capital, and customer capital. They build relationship capital from among these capabilities to create a coherent company. Although Stewart does not mention “search,” it is important to note that one means to identify intangible assets is well-executed enterprise search with associated analytical tools.

Dave Snowden also referenced “coherence,” (messy coherence), even as he spoke about how failures tend to be more teachable (memorable) than successes. If you follow Snowden, you know that he founded the Cognitive Edge and has developed a model for applying cognitive learning to help build resilient organizations. He has taught complexity analysis and sense-making for many years and his interest in human learning behaviors is deep.

To follow the entire thread of Snowden’s presentation on the “The Resilient Organization” follow this link. I was particularly impressed with his statement about the talk, “one of the most heart-felt I have given in recent years.” It was one of his best but two particular comments bring me to the connection between KM and search.

Dave talked about technology as “cognitive augmentation,” its only truly useful function. He also puts forth what he calls the “three Golden rules: Use of distributed cognition, wisdom but not foolishness of crowds; finely grained objects, information and organizational; and disintermediation, putting decision makers in direct contact with raw data.”

Taking these fragments of Snowden’s talk, a technique he seems to encourage, I put forth a synthesized view of how knowledge and search technologies need to be married for consequential gain.

We live and work in a highly chaotic information soup, one in which we are fed a steady diet of fragments (links, tweets, analyzed content) from which we are challenged as thinkers to derive coherence. The best knowledge practitioners will leverage this messiness by detecting weak signals and seek out more fragments, coupling them thoughtfully with “raw data” to synthesize new innovations, whether they be practices, inventions or policies. Managing shifting technologies, changing information inputs, and learning from failures (our own, our institution’s and others) contributes to building a resilient organization.

So where does “search” come in? Search is a human operation and begins with the workforce. Going back to Stewart who commented on the need to recognize different kinds of knowledge, I posit that different kinds of knowledge demand different kinds of search. This is precisely what so many “enterprise search” initiatives fail to deliver. Implementers fail to account for all the different kinds of search, search for facts, search for expertise, search for specific artifacts, search for trends, search for missing data, etc.

When Dave Snowden states that “all of your workforce is a human scanner,” this could also imply the need for multiple, co-occurring search initiatives. Just as each workforce member brings a different perspective and capability to sensory information gathering, so too must enterprise search be set up to accommodate all the different kinds of knowledge gathering. And when Snowden notes that “There are limits to semantic technologies: Language is constantly changing so there is a requirement for constant tuning to sustain the same level of good results,” he is reminding us that technology is only good for cognitive augmentation. Technology is not a “plug ‘n play,” install and reap magical cognitive insights. It requires constant tuning to adapt to new kinds of knowledge.

The point is one I have made before; it is the human connection, human scanner and human understanding of all the kinds of knowledge we need in order to bring coherence to an organization. The better we balance these human capabilities, the more resilient we’ll be and the better skilled at figuring out what kinds of search technologies really make sense for today, and tomorrow we had better be ready for another tool for new fragments and new knowledge synthesis.

Lucene Open Source Community Commits to a Future in Search

October 27, 2010 / / 2 Comments

It has been nearly two years since I commented on an article in Information Week, Open Source, Its Time has Come, Nov. 2008. My main point was the need for deep expertise to execute enterprise search really well. I predicted the growth of service companies with that expertise, particularly for open source search. Not long after I announced that, Lucid Imagination was launched, with its focus on building and supporting solutions based on Lucene and, its more turnkey version, Solr.

It has not taken long for Lucid Imagination (LI) to take charge of the Lucene/Solr community of practice (CoP), and to launch its own platform built on Solr, Lucidworks Enterprise. Open source depends on deep and sustained collaboration; LI stepped into the breach to ensure that the hundreds of contributors, users and committers have a forum. I am pretty committed to CoPs myself and know that nurturing a community for the long haul takes dedicated leadership. In this case it is undoubtedly enlightened self-interest that is driving LI. They are poised to become the strongest presence for driving continuous improvements to open source search, with Apache Lucene as the foundation.

Two weeks ago LI hosted Lucene Revolution, the first such conference in the US. It was attended by over 300 in Boston, October 7-8 and I can report that this CoP is vibrant, enthusiastic. Moderated by Steve Arnold, the program ran smoothly and with excellent sessions. Those I attended reflected a respectful exchange of opinions and ideas about tools, methods, practices and priorities. While there were allusions to vigorous debate among committers about priorities for code changes and upgrades, the mood was collaborative in spirit and tinged with humor, always a good way to operate when emotions and convictions are on stage.

From my 12 pages of notes come observations about the three principal categories of sessions:

  1. Discussions, debates and show-cases for significant changes or calls for changes to the code
  2. Case studies based on enterprise search applications and experiences
  3. Case studies based on the use of Lucene and Solr embedded in commercial applications

Since the first category was more technical in nature, I leave the reader with my simplistic conclusions: core Apache Lucene and Solr will continue to evolve in a robust and aggressive progression. There are sufficient committers to make a serious contribution. Many who have decades of search experience are driving the charge and they have cut their teeth on the more difficult problems of implementing enterprise solutions. In announcing Lucidworks Enterprise, LI is clearly bidding to become a new force in the enterprise search market.

New and sustained build-outs of Lucene/Solr will be challenged by developers with ideas for diverging architectures, or “forking” code, on which Eric Gries, LI CEO, commented in the final panel. He predicted that forking will probably be driven by the need to solve specific search problems that current code does not accommodate. This will probably be more of a challenge for the spinoffs than the core Lucene developers, and the difficulty of sustaining separate versions will ultimately fail.

Enterprise search cases reflected those for whom commercial turnkey applications will not or cannot easily be selected; for them open source will make sense. Coming from LI’s counterpart in the Linux world, Red Hat, are these earlier observations about why enterprises should seek to embrace open source solutions, in short the sorry state of quality assurance and code control in commercial products. Add to that the cost of services to install, implement and customize commercial search products. The argument would be to go with open source for many institutions when there is an imperative or call for major customization.

This appears to be the case for two types of enterprises that were featured on the program: educational institutions and government agencies. Both have procurement issues when it comes to making large capital expenditures. For them it is easier to begin with something free, like open source software, then make incremental improvements and customize over time. Labor and services are cost variables that can be distributed more creatively using multiple funding options. Featured on the program were the Smithsonian, Adhere Solutions doing systems integration work for a number of government agencies, MITRE (a federally funded research laboratory), U. of Michigan, and Yale. CISCO also presented, a noteworthy commercial enterprise putting Lucene/Solr to work.

The third category of presenters was, by far, the largest contingent of open source search adopters, producers of applications that leverage Lucene and Solr (and other open source software) into their offerings. They are solidly entrenched because they are diligent committers, and share in this community of like-minded practitioners who serve as an extended enterprise of technical resources that keeps their overhead low. I can imagine the attractiveness of a lean business that can run with an open source foundation, and operates in a highly agile mode. This must be enticing and exciting for developers who wilt at the idea of working in a constrained environment with layers of management and political maneuvering.

Among the companies building applications on Lucene that presented were: Access Innovations, Twitter, LinkedIn, Acquia, RivetLogic and Salesforce.com. These stand out as relatively mature adopters with traction in the marketplace. There were also companies present that contribute their value through Lucene/Solr partnerships in which their products or tools are complementary including: Basis Technology, Documill, and Loggly.

Links to presentations by organizations mentioned above will take you to conference highlights. Some will appeal to the technical reader for there was a lot of code sharing and technical tips in the slides. The diversity and scale of applications that are being supported by Lucene and Solr was impressive. Lucid Imagination and the speakers did a great job of illustrating why and how open source has a serious future in enterprise search. This was a confidence building exercise for the community.

Two sentiments at the end summed it up for me. On the technical front Eric Gries observed that it is usually clear what needs to be core (to the code) and what does not belong. Then there is a lot of gray area, and that will contribute to constant debate in the community. For the user community, Charlie Hull, of flax opined that customers don’t care whether (the code) is in the open source core or in the special “secret sauce” application, as long as the product does what they want.

What an Analyst Needs to Do What We Do

September 27, 2010 / / 0 Comments

Semantic Software Technologies: Landscape of High Value Applications for the Enterprise is now posted for you to download for free; please do so. The topic is one I’ve followed for many years and was convinced that the information about it needed to be captured in a single study as the number of players and technologies had expanded beyond my capacity for mental organization.

As a librarian, it was useful to employ a genre of publications known as “bibliography of bibliographies” on any given topic when starting a research project. As an analyst, gathering the baskets of emails, reports, and publications on the industry I follow, serves a similar purpose. Without a filtering and sifting of all this content, it had become overwhelming to understand and comment on the individual components in the semantic landscape.

Relating to the process of report development, it is important for readers to understand how analysts do research and review products and companies. Our first goal is to avoid bias toward one vendor or another. Finding users of products and understanding the basis for their use and experiences is paramount in the research and discovery process. With software as complex as semantic applications, we do not have the luxury of routine hands-on experience, testing real applications of dozens of products for comparison.

The most desirable contacts for learning about any product are customers with direct experience using the application. Sometimes we gain access to customers through vendor introductions but we also try very hard to get users to speak to us through surveys and interviews, often anonymously so that they do not jeopardize their relationship with a vendor. We want these discussions to be frank.

To get a complete picture of any product, I go through numerous iterations of looking at a company through its own printed and online information, published independent reviews and analysis, customer comments and direct interviews with employees, users, former users, etc. Finally, I like to share what I have learned with vendors themselves to validate conclusions and give them an opportunity to correct facts or clarify product usage and market positioning.

One of the most rewarding, interesting and productive aspects of research in a relatively young industry like semantic technologies is having direct access to innovators and seminal thinkers. Communicating with pioneers of new software who are seeking the best way to package, deploy and commercialize their offerings is exciting. There are many more potential products than those that actually find commercial success, but the process for getting from idea to buyer adoption is always a story worth hearing and from which to learn.

I receive direct and indirect comments from readers about this blog. What I don’t see enough of is posted commentary about the content. Perhaps you don’t want to share your thoughts publicly but any experiences or ideas that you want to share with me are welcomed. You’ll find my direct email contact information through Gilbane.com and you can reach me on Twitter at lwmtech. My research depends on getting input from all types of users and developers of content software applications, so, please raise your hand and comment or volunteer to talk.

Sophia Launches Sophia Search for Intelligent Enterprise Search and Contextual Discovery

September 22, 2010 /

Sophia, the provider of contextually aware enterprise search solutions, announced Sophia Search, a new search solution which uses a Semiotic-based linguistic model to identify intrinsic terms, phrases and relationships within unstructured content so that it can be recovered, consolidated and leveraged. Use of Sophia Search is designed to minimize compliance risk and reduce the cost of storing and managing enterprise information. Sophia Search is able to deliver a “three-dimensional” solution to discover, consolidate and optimize enterprise data, regardless of its data type or domain. Sophia Search helps organizations manage and analyze critical information by discovering the themes and intrinsic relationships behind their information, without taxonomies or ontologies, so that more relevant information may be discovered. By identifying both duplicates and near duplicates, Sophia Search allows organizations to effectively consolidate information and minimizing storage and management costs. Sophia Search features a patented Contextual Discovery Engine (CDE) which is based on the linguistic model of Semiotics, the science behind how humans understand the meaning of information in context. Sophia Search is available now to both customers and partners. Pricing starts at $30,000. http://www.sophiasearch.com/

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