Enterprise search
Analysis of enterprise-specific search technology (as opposed to general web search). Related subjects include:
What’s interesting about the FAST venture in BI
FAST is annoying me a bit these days. It’s nothing serious, but travel schedule screw-up’s, an annoying embargo, and a screw-up in the annoying embargo have all hit at once. So I’ll keep this telegraphic and move on to other subjects.
- They’re doing fast queries without using a lot of RAM.
- They’re doing the usual text search thing of indexing across multiple “databases,” only now it’s applied to, well, databases. (Not that there’s much new about that particular aspect. Actually, there seems to be a bit of kludge in that they export the databases to some kind of simple text files.)
- They’re doing some level of concept identification ala the text mining guys. (They don’t call it “entity extraction” because the results aren’t dumped into a database anywhere, but instead are just used on the fly.) Of course, the text mining/search convergence goes both ways.
- They bought a BI/dashboard tool and are using it both to analyze query logs and also to do normal BI/dashboard kinds of things.
- They have big references for this stuff, at least the single-web-site query aspect. Well, actually, the customer names are confidential. Oh well.
And as another example of how this wasn’t the smoothest PR month for FAST, Steve Arnold somehow got the false idea that they were getting out of true text search altogether.
| Categories: BI integration, Enterprise search, FAST, Search engines, Text mining | 3 Comments |
FAST said to be pursuing BI
Dave Kellogg thinks FAST will be ineffective and defocused because of its efforts in business intelligence. I can’t comment on whether that analysis is brilliant, self-serving, or both, because anything I’ve been told on the subject is under embargo.
Embargos were a crucial PR tactic when Regis McKenna exploited them for the original rollout of the Macintosh in 1984. But I suspect that in many cases they’ve quite outlived their usefulness. If I wait between the time I’m briefed and the time the embargo is up to write something, my thoughts about it get fuzzy. If I write something at the time and put it on ice, it may be obsolete because of what other people write in the mean time.
More and more, if something is embargoed, I wind up not writing about it at all.
EDIT: Point #4 of my post on the mismatch between relational databases and text search is pretty relevant here.
| Categories: BI integration, Enterprise search, FAST | 1 Comment |
41 differences between web and enterprise search
Based on a patent application, SEOmoz has discerned 65 aspects of the Google ranking algorithm.* I counted only 24 that really had much at all to do with enterprise search. This leaves 41 or so focused on spam/SEO-fighting and/or on-page linking issues that have no enterprise parallel. And for more depth, here’s a long article from another SEO site, on a specific phrase-concurrence spam-fighting technique that has no apparent applicability to trusted corpuses.
*I highly recommend this link. It is by far the best single-page overview of web search algorithmic issues I’ve ever seen.
I’ve said it before, but it bears repeating — web search and enterprise search (or search of a constrained corpus) are very different technical problems.
| Categories: Enterprise search, Google, Search engines | 5 Comments |
Text mining and search, joined at the hip
Most people in the text analytics market realize that text mining and search are somewhat related. But I don’t think they often stop to contemplate just how close the relationship is, could be, or someday probably will become. Here’s part of what I mean:
- Text mining powers search. The biggest text mining outfits in the world, possibly excepting the US intelligence community, are surely Google, Yahoo, and perhaps Microsoft.
- Search powers text mining. Restricting the corpus of documents to mine, even via a keyword search, makes tons of sense. That’s one of the good ideas in Attensity 4.
- Text mining and search are powered by the same underlying technologies. For starters, there’s all the tokenization, extraction, etc. that vendors in both areas license from Inxight and its competitors. Beyond that, I think there’s a future play in integrated taxonomy management that will rearrange the text analytics market landscape.
| Categories: Attensity, Business Objects and Inxight, Enterprise search, FAST, Google, IBM and UIMA, Ontologies, Open source text analytics, Search engines, Text mining | 3 Comments |
Enterprise-specific web search: High-end web search/mining appliances?
OK. I have a vision of one way search could evolve, which I think deserves consideration on at least a “concept-car” basis. This is all speculative; I haven’t discussed it at length with the vendors who’d need to make it happen, nor checked the technical assumptions carefully myself. So I could well be wrong. Indeed, I’ve at least half-changed my mind multiple times this weekend, just in the drafting of this post. Oh yeah, I’m also mixing several subjects together here too. All-in-all, this is not my crispest post …
Anyhow, the core idea is that large enterprises spider and index a subset of the Web, and use that for most of their employees’ web search needs. Key benefits would include:
- Filtering out spam hits. This is obviously important for search, and in some cases could help with public-web text mining as well. It should be OK to be more aggressive on spam-site filtering in an enterprise-specific index than it is in general web search.
- Filtering out malicious/undesirable downloads of various sorts. I’m thinking mainly of malware/spyware here, but of course it can also be used for netnannying porn-prevention and the like as well. Again, this is more easily done for the enterprise market than for the search world at large. (I anyway think that Google could blow Websense out of the water any time they wanted to – except, of course, for the not-so-small matter of not being seen as participating in the censorship business — but that’s a separate discussion.)
- Capturing employees’ search strings. This could be useful for various purposes, including discerning their interests, and building the corporate ontology for internal web search.
- Freshness control. If there’s a site you really care about, you can make sure it’s re-indexed frequently.
| Categories: Categorization and filtering, Convera, Enterprise search, FAST, Google, IBM and UIMA, Search engines, Spam and antispam, Specialized search, Text mining, Website filtering | 1 Comment |
Two own-dogfood text-based bug-tracking applications
Last July I wrote about Google’s text-based project management system. Dave Kellogg of Mark Logic offers links to discussion of a related Google project, and adds news of his own — Mark Logic built a text-based bug tracking system in its own MarkLogic technology.
| Categories: Enterprise search, Google, Mark Logic, Search engines, Specialized search | Leave a Comment |
Why the BI vendors are integrating with Google OneBox
I’m hearing the same thing from multiple BI vendors, with SAS being the most recent and freshest in my mind — customers want them to “integrate” with Google OneBox. Why Google rather than a better enterprise search technology, such as FAST’s? So far as I’ve figured out, these are the reasons, in no particular order:
- Price.
- Ease of installation (real or imagined).
- The familiar Google brand name.
- The familiar Google UI.
- Google OneBox’s ability to search relational records, reports, etc. along with more tradtional record types.
The last point, I think, is the most interesting. Lots of people think text search is and/or should be the dominant UI of the future. Now, I’ve been a big fan of natural language command line interfaces ever since the days of Intellect and Lotus HAL. But judging by the market success of those products — or for that matter of voice command/control — I was in a very small minority. Maybe the even simpler search interface — words jumbled together without grammatical structure — will win out instead.
Who knows? Progress is a funny thing. Maybe the ultimate UI will be one that responds well to grunts, hand gestures, and stick-figure drawings. We could call it NeanderHAL, but that would wrong …
| Categories: BI integration, Enterprise search, FAST, Google, Natural language processing (NLP), SAS, Search engines | 1 Comment |
Principles of enterprise text technology architecture
My August Computerworld column starts where July’s left off, and suggests principles for enterprise text technology architecture. This will not run Monday, August 7, as I was originally led to believe, but rather in my usual second-Monday slot, namely August 14. Thus, I finished it a week earlier than necessary, and I apologize to those of you I inconvenienced with the unnecessary rush to meet that deadline.
The principles I came up with are:
- Deploy search widely across the enterprise.
- It’s OK for your text data to be distributed across a range of silos.
- Integrate fact extraction/text mining aggressively into your predictive analytics and dashboards.
- Having a preferred enterprise text technology tool suite is nice, but accept that there will probably be lots of departmental exceptions.
- Reinvent your customer communication (and other) processes to exploit text technologies.
- Integrate your taxonomies.
I’ll provide a link when the column is actually posted.
| Categories: Enterprise search, Ontologies, Search engines, Text mining | 1 Comment |
Introduction to FAST
FAST, aka Fast Search & Transfer (www.fastsearch.com) is a pretty interesting and important company. They have 3500 enterprise customers, a rapidly growing $100 million revenue run rate, and a quarter billion dollars in the bank. Their core business is of course enterprise search, where they boast great scalability, based on a Google-like grid architecture, which they fondly think is actually more efficient than Google’s. Beyond that, they’ve verticalized search, exploiting the modularity of their product line to better serve a variety of niche markets. And they’re active in elementary fact/entity extraction as well. Oh yes – they also have forms of guided navigation, taxonomy-awareness, and probably everything else one might think of as a checkmark item for a search or search-like product.
| Categories: Enterprise search, FAST, Google, Search engines | 1 Comment |
Analyst reports about enterprise search
Gartner and Forrester have high opinions of FAST. Not coincidentally, you can download both those firms’ recent search industry survey reports from almost any page of www.fastsearch.com. Of the two, Forrester’s is both better and more recent.
Summarizing brutally, the big firms’ consensus seems to be:
- FAST and Autonomy are the clear leaders.
- Endeca has great technology and is coming on strong.
- Everybody else is a niche player, at least for now.
- Convera is in deep yogurt.
Forrester is particularly harsh on Convera. Presumably this has much to do with the fact that Convera did not cooperate well with the survey process. I shall not speculate as to which way the causality runs there – but I should note that Convera was quite cooperative with my research last week.
| Categories: Autonomy, Convera, Enterprise search, FAST, Search engines | Leave a Comment |