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Wednesday, May 2, 2012

Federated Search & Big Data gets bigger

The world of independent Federated Search is diminishing; last week IBM announced that they will be acquiring Vivisimo.[1]  There are a number of interesting aspects to this, and the analysts have covered some of them [2],[3], but some particular quotes from IBM itself and the analysts piqued my interest:

“The combination of IBM's big data analytics capabilities with Vivisimo software will further IBM's efforts to automate the flow of data into business analytics applications …” [IBM]
IBM also intends to use Vivisimo's technology to help fuel the learning process for their Watson
applications.” [IDC]
Overall, this is a very smart move for IBM, and it indicates that unstructured information is going to play an increasingly large role in the Big Data story…” [IDC]

All this shows the handling of structured and unstructured information growing in importance.

What does IBM want Vivisimo for? It seems to all stem round Big Data and the analytics that it can produce to enable better corporate decisions.  Of course, there’s also the lovely teaser of a better performing Watson! Both Watson and Analytics massage vast amounts of data and information to draw conclusions, assign values, and create relationships. But, like all such endeavors, the quality of the result depends critically on the quality of the incoming data. GIGO says it all!

Big Data analytics work very well with structured data, where the “meaning” of each number or term is exactly known and can be algorithmically combined with its peers, parents, siblings, and opposites to give a visualization of the state of play at the moment or over time. Gathering such data is a tedious process (hooray for computers!), but is not intrinsically difficult. All that needs to happen is to set up a mapping from each data Source to the master and let it run. The mappings are precise and the process effective, but the volumes are vast and the time-to-repeat rather slow for today’s fast paced world.

However, now add the fact that not everything you want to know is held in those nice regular relational database tables, and the picture looks far less rosy. Product reviews are unstructured, press releases are vague, social comments are fleeting, and technical and legal documents tend to be obtuse. But all these are vital if you want to make a really informed decision. So bring in Federated Search to the rescue.

Federated Search is a real time activity. It is focused on just what data or information is needed now. And it provides quality data. It is directed to just those Sources needed for “this report”, and it analyzes them in terms of known semantics so that the reviews, blogs, etc. mesh with the numerical analytics, and then provide the essential “external view” of the situation. And this is done right now, in real time. For the knowledge based systems (like Watson) the FS Sources provide in-depth data pertinent to the current problem. And if the Sources don’t have it, FS goes and finds it, thus allowing  Watson (as an example)  to add it to its knowledge base, and provide a more informed opinion.

So that is why IBM is adding Federated Search to its armory. What are the issues? In a word (or two): coverage and completeness.

All the Big Data systems use standardized access to the massive databases of the corporation’s transaction and repository systems. Most of these understand SQL or some other standard access language, and the customization is a matter of reading a schema mapping table. That mapping table is the same for every SharePoint or Exchange system (or similar), so once created, it is easily deployed. These types of standardized accesses are often referred to as “Indexing Connectors” because they extract enough data to enable the content to be indexed and searched. (For more on this see a future post on the deep differences between Connectors and Crawlers.)

Now, move to the world of web data and the complexity and difficulty escalates enormously.  The number of formats and access methods multiplies almost to the point of one-to-one for each Source. As an example look at the two press releases for this acquisition: IBM’s is a press release, with an initial dateline, and no tags, Vivisimo’s [4] is a blog post with tags and an author. The same Connector will not make sense of both at the level of detail needed for a decision making analysis.

Add in the velocity of the data in the social media (“velocity”, as you will recall, is one of the 3 “v”s that define Big Data – Volume, Variety, Velocity) and the relatively slow to aggregate times of conventional databases become a problem. Timing is an issue because of volume, but also because applications have to analyze input data from users and other sources, store it in their transactional database, and then the ETL function has to extract from that database and move the data to the analytics database or storage area. These are two stages, both relatively slow, that must be batched together.

So, once moving from structured data to unstructured data, and from the sheltered waters of the corporation to the rough seas of the Web, a very different set of techniques is needed. And that is where Federated Search (FS) comes in.   This is the truly hard, difficult part, and it’s where MuseGlobal shines.   But first, some more information on what FS is, and what it needs to do.

FS is immediate, which involves many synchronization and “freshness” issues, but essentially solves the “velocity” problem by obtaining data as it is needed. That is because FS is a “on demand” service. It is brought into play just-in-time to get the data when needed, not in batch mode to store it away just-in-case. Since it is used when needed it needs to be able to target the Sources of interest right now. That means it is flexible and dynamically configured, not painstakingly set up ahead of time and left alone.

Since it is a focused operation, targeting only the data needed, it must be able to get the maximum out of each Source. This requires two levels of complexity not common in other types of connectors or crawlers. These Sources have specific protocols and search languages and often security requirements. All these must be handled by the FS Connector so that the search is faithfully translated to the language of the Source, and the results are accurately retrieved. Second is getting the retrieved data into a useable form (and format). This involves a “deep extract” involving record formats, field/tag/schema semantics, content semantics, data normalization and cleansing, reference to ontologies, field splitting, field combination, entity extraction on rules and vocabularies, conversion to standard forms, enhancement with data from third Sources, and other manipulations. None of this is off-the-shelf processing where a single connector can be parameterized to work with all Sources. So FS has started at the “single, deep” end of the spectrum (crawlers are the epitome of the “broad, shallow” end) and builds Connectors to the characteristics of each Source.

These Connectors bring focused, quality data, but they come at a price. Vivisimo and MuseGlobal, and the other FS vendors build a very special type of software – something that we know will eventually fail, when the characteristics of the Source change. This needs a special dynamic architecture to accommodate it. It needs very powerful ways to build Connectors which can involve data analysts and programmers, as well as highly sophisticated tools, such as the Muse Connector Builder. It needs a robust and automated way to check for end-of-life situations, such as the Muse Source Checker, and a highly automated build and deploy process – the Muse Source Factory has been delivering automated software updates for 11 years now. Source Connectors *will* stop working, and a big part of a viable FS ecosystem is being able to get them back on line quickly and reliably.   MuseGlobal has put together a data virtualization platform with thousands of Connectors, because we know there’s a one-on-one relationship with each data source if you want to connect to the world out there.   Figuring out the unstructured data problem was one of our main goals at Muse from the very beginning, some 11 years ago.

Of course, building Connectors in the first place is an equal challenge, including the human element of dealing with a multitude of companies publishing information and data. This is something all FS vendors have to handle, and MuseGlobal chose to create a Content Partner Program about 10 years ago where we talk regularly to hundreds of major Sources and content vendors. Breadth of coverage of the Connector library is a major factor in “getting up and running” time, and a major investment for the FS vendors. We believe that Muse has one of the largest libraries with over 6,000 Source specific Connectors, as well as all the standard API and protocol and search languages ones for access where that is appropriate – but still with the “deep extraction” which is the hallmark of Federated Search.

It is not an easy task to get right at a quality and sustainable level, but a few vendors have produced the technology. MuseGlobal is one – and Vivisimo is another.

IBM Analytics and Watson are set for a real quality revolution!

Another analyst 's comments can be found on enterprise search blog at [6].


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