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 If that information is out of date or statistically flawed, how does Endeca ameliorate risk?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

Faceted Search Is Now A Common Feature

March 2, 2014

SearchBlox is “Moving From Simple Search To Faceted Search,” says a new press release. The article says that as the amount of data increased, it completed search and users needed a more complex and robust feature. Ecommerce Web sites are touted as popularizing faceted search that returned results based on information attributes or facets on content. Faceted search makes searching smarter and gives users better control of displayed search results.

There are many products that offer faceted search. SearchBlox’s offers some familiar and different features:

“Customers often pose the question around what types of facets are available for display within SearchBlox and what UI is best suited to offer the best facet selection for the end users. SearchBlox, out-of-the-box provides keywords, date, content type and content size facets for display but provides the framework to create a facet without any coding. You can edit our html plug-in within SearchBlox to add or remove facets or even set a different display name for a facet field. You can create term, date or number range faceting on the fly without touching a schema file or any backend. The facets can be specified on the query string and created on the fly to return the right number of values.”

Faceted search is already a common feature on most search engines and has been for over twenty years. Why has it taken so long for a company like SearchBlox to finally make it standard? As a side note, it is also available on Google’s uber expensive Google Search Appliance.

Whitney Grace, March 02, 2014
Sponsored by ArnoldIT.com, developer of Augmentext

Endeca Explanation

November 19, 2012

We’ve turned up a useful summary of Endeca’s Information Discovery system; the description occurs within a post about using integration platform CloverETL with the Endeca product. “Oracle Endeca Information Discovery—CloverETL” is posted at Saichand Varanasi’s OBIEE, Endeca and ODI Blog. After referring readers to his Endeca overview, the blogger dives into the Clover. He writes:

“Today we will see how to create Clover ETL graph and populating data which will be used by MDEX engine for reporting (Studio). Endeca Information discovery helps organization to answer quickly on relevant data of both structured and Un structured. It helps to search and discover and analysis. Information is loaded from multiple data source systems and stored in a faceted data model that dynamically supports changing data. Information discovery enables an iterative approach. Integration features a new ETL tool, The integrator (Clover ETL) that lets you extract source records from a variety of source types flat files to databases.”

Next, Varanasi walks us through an example project. Along the way, he also explains how Endeca Information Discovery functions. A happy side effect, if you will. See the post for details.

Founded in 1999 and based in Cambridge, MA, Endeca was acquired by Oracle just over a year ago. The company has been at the forefront of faceted search technology, particularly for large e-commerce and online library systems.

Cynthia Murrell, November 19, 2012

Sponsored by ArnoldIT.com, developer of Augmentext

Enterprise Search Has a Back Seat Driver

July 3, 2012

Once again, the technology road behind enterprise search is being questioned and some are mapping out a new route for a company road trip. According to Norm.al’s article, ‘Search vs. Findability vs. Information Retrieval’ findability is the new buzz word of today, but utilizing a back seat driver seems questionable.

The self-appointed tour guides have determined:

“What Findability should be, and what the Semantic Web promises is a new approach. Order first and then the rest will be easy. By using Faceted Search or other Information Retrieval interfaces findability is achieved. Computer Search is based on indexing a junk of data, while Findability should be a process defined at the moment when the data are created.”

“If we could note the order, is Junk of Data, to Order by a third party who analyzes your content based on keywords, NLP and some other great metrics.”

No one really likes a back seat driver and now they are trying to hop in and bark out directions. Sometimes the search engine road may get a little bumpy, but utilizing the right landmarks will get you where you need to go without the interference of detours.

The pavement on this new road seems to still be a bit wet, so one might yet find themselves spattered with debris. Will these distinctions stick? We think not. Search is dead. Long live the next set of buzzwords from self-appointed experts, “real” analysts, and failed Webmasters.

Jennifer Shockley, July 3, 2012

Sponsored by Polyspot

Exogenous Complexity 1: Search

January 31, 2012

I am now using the phrase “exogenous complexity” to describe systems, methods, processes, and procedures which are likely to fail due to outside factors. This initial post focuses on indexing, but I will extend the concept to other content centric applications in the future. Disagree with me? Use the comments section of this blog, please.

What is an outside factor?

Let’s think about value adding indexing, content enrichment, or metatagging. The idea is that unstructured text contains entities, facts, bound phrases, and other identifiable entities. A key word search system is mostly blind to the meaning of a number in the form nnn nn nnnn, which in the United States is the pattern for a Social Security Number. There are similar patterns in Federal Express, financial, and other types of sequences. The idea is that a system will recognize these strings and tag them appropriately; for example:

nnn nn nnn Social Security Number

Thus, a query for Social Security Numbers will return a string of digits matching the pattern. The same logic can be applied to certain entities and with the help of a knowledge base, Bayesian numerical recipes, and other techniques such as synonym expansion determine that a query for Obama residence will return White House or a query for the White House will return links to the Obama residence.

One wishes that value added indexing systems were as predictable as a kabuki drama. What vendors of next generation content processing systems participate in is a kabuki which leads to failure two thirds of the time. A tragedy? It depends on whom one asks.

The problem is that companies offering automated solutions to value adding indexing, content enrichment, or metatagging are likely to fail for three reasons:

First, there is the issue of humans who use language in unexpected or what some poets call “fresh” or “metaphoric” methods. English is synthetic in that any string of sounds can be used in quite unexpected ways. Whether it is the use of the name of the fruit “mango” as a code name for software or whether it is the conversion of a noun like information into a verb like informationize which appears in Japanese government English language documents, the automated system may miss the boat. When the boat is missed, continued iterations try to arrive at the correct linkage, but anyone who has used fully automated systems know or who paid attention in math class, the recovery from an initial error can be time consuming and sometimes difficult. Therefore, an automated system—no matter how clever—may find itself fooled by the stream of content flowing through its content processing work flow. The user pays the price because false drops mean more work and suggestions which are not just off the mark, the suggestions are difficult for a human to figure out. You can get the inside dope on why poor suggestions are an issue in Thining, Fast and Slow.

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Protected: Tips to Improve Search In SharePoint 2010

June 21, 2011

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Protected: SharePoint: Time Is Money

April 18, 2011

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Lucid Imagination Moves to the Enterprise

January 7, 2011

Has Lucid Imagination Found the Open Source Solution for Enterprise Search?” asks if, like the Star Trek Enterprise, Lucid Imagination has done what no other open source search engine has done before and created a product worth paying for.  Why not just use Apache Solr/Lucene?

The article points out that without Lucid you can’t just index and search a set of documents, you have to create each connection type, and, most importantly, there is no security.  It’s also easy to change over to Lucid since it’s built on top of the Apache engine without any significant alterations.  To sum up:

Lucid Imagination reduces the technical complexity of leveraging Solr by providing an automated installer, configurable data connectors and a web-based administration interface. In addition to the add-on to Solr/Lucene users can easily observe, multiple enhancements were made to make the solution easier to deploy to the cloud.

If you’re interested in trying it out, the annual fees are straightforward too.

Alice Wasielewski, January 7, 2011

Funnelback Feature List Slideshow

December 17, 2010

We’ve unearthed a slideshare.net document worth mentioning: the Funnelback Enterprise Search Features list.

Acquired by the open source software services company Squiz in 2009, Funnelback is an Australian-based enterprise search engine and services company with a client list including universities, government agencies and large corporations spanning three continents. In Funnelback’s own words:

“Our technology is used to search information across the breadth of an organization. We offer externally hosted search solutions as well as in-house server installed solutions and consultancy services. We search across websites, intranets, portals, databases, fileshares and many other data sources. Our feature rich, high powered, customizable, search engine allows organizations to find accurate information quickly and easily.”

For a concise overview of what Funnelback offer, visit the link above to the four page features list. Whether you are interested in the particulars of its search features, query language, results & reporting or security, amongst even more categories, it’s all organized and detailed right there.

Sarah Rogers, December 17, 2010

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Open Source Search Run Down

October 25, 2010

Open Source Search with Lucene & Solr” provides a useful overview of information similar to that presented at the Lucene Revolution in Boston, October 7 and 8, 2010. I found the information useful. Even though I poked my head into most sessions and met a number of speakers, Igvita.com has assembled a number of useful factoids. Here’s a selection of four.

First, the Salesforce.com implementation of Lucene “consists of roughly 16 machines, which in turn contain may small and sharded Lucene indexes. Currently, [Salesforce.com] handles 4,000 queries per second (qps) and provides an incremental indexing model where the new user data is searchable within ~ three minutes.”

Second, iTunes is a Lucene user “said to be handling up to 800 queries per second.” I thought Apple was drinking Google Kool-Aid or was before the friction between the two companies entered into a marital separation without counseling.

Third, I found this description of Lucene/Solr interesting:

If Lucene is a low-level IR toolkit, then Solr is the fully-featured HTTP search server which wraps the Lucene library and adds a number of additional features: additional query parsers, HTTP caching, search faceting, highlighting, and many others. Best of all, once you bring up the Solr server, you can speak to it directly via REST XML/JSON API’s. No need to write any Java code or use Java clients to access your Lucene indexes. Solr and Lucene began as independent projects, but just this past year both teams have decided to merge their efforts – all around, great news for both communities. If you haven’t already, definitely take Solr for a spin.

Finally, this passage opened my eyes to some interesting opportunities.

Instead of running Lucene or Solr in standalone mode, both are also easily integrated within other applications. For example, Lucandra is aiming to implement a distributed Lucene index directly on top of Cassandra. Jake Luciani, the lead developer of the project, has recently joined the Riptano team as a full-time developer, so do not be surprised if Cassandra will soon support a Lucene powered IR toolkit as one of its features! At the same time, Lily is aiming to transparently integrate Solr with HBase to allow for a much more flexible query and indexing model of your HBase datasets. Unlike Lucandra, Lily is not leveraging HBase as an index store (see HBasene for that), but runs standalone, albeit tightly integrated Solr servers for flexible indexing and query support.

Navigate to the Igvita Web site and get the full scoop, not a baby cup of goodness.

Stephen E Arnold, October 25, 2010

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