Connecting the Dots

Over the weekend I read Kevin Rivette's book “Rembrandts in the Attic,” which outlines the lost value buried in distributed documents, and what this underutilized intellectual property costs companies. A subject near and dear to my heart. But it wasn't until Sunday, when my 6th grade son asked me to review his paper on Francis Drake's journey in the south seas, that I connected the dots. As I read how they explored and discovered new islands and peoples. How they charted, documented, and mapped not only everything they found, but everywhere the went. That all their charts and maps got me thinking.

Why don't we do this for our information? We document the output of a hypothesis or experiment, capture the data, and if the project is abandoned or failed we file it away. Often forgotten. Explorers make maps to capture what they learn so that the next visitor can find where they have been and go a little farther, learn a little more, avoid the same mistakes. Why can't we see information the same way? Apply a few tags to provide the "lay of the land" as it were to an information asset. Capture the context, meaning and value of it. A simple step, yet one that can make all the difference in discovering and leveraging our forgotten assets.

We are drowning in data. Every year, Berkeley researchers tell us, we generate 30% more information every year. The sequencing of the human genome over the past decade has led research centers in both the private and public sectors to place huge orders for thousands of servers and storage systems capable of handling terabytes of the new genomic, proteomic, drug, and health care data generated hourly.

Privately, we all struggle with this issue each day. Finding the information we're looking for. Few industries suffer more from this data deluge than pharmaceuticals. Many gifted and well-paid scientists and engineers spend 15% of their time trolling through federated storage or file servers for the data or documents they need. Sometimes they never find them, triggering rework, redundant tests, and the loss of untold millions of dollars each year. Despite significant investments in information technology, knowledge-based pharma remains “knowledge poor” in its day-to-day
operations, at every step of the value chain, from discovery through distribution.

Big data has led to flexible storage solutions that scale massively, easily, and
relatively cheaply, if you call pay as you go cheap. However, while the storage
industry has met the challenge, pharmaceutical companies are realizing they are not making as much progress as they thought investing in genomics, proteomics, and
informatics research. They're not getting the returns on investment. It is the tumultuous world of bioinformatics that has not fully met the challenge of the genomics revolution-in-waiting.

But why are we still struggling to connect the dots?

The real challenge is that the research process itself still remains personally competitive, often isolated, and widely distributed. Information exists, but unconnected. At a surprising number of firms, R&D teams are literally re-inventing the wheel, duplicating research that the company has already done, whose lessons are buried in some obscure and forgotten file. Knowledge is generated and then abandoned when research leads in a different direction.

These assets, both the data and the knowledge remain just as isolated, distributed and unconnected. Dumped into bench-side databases or file servers. Even if they are effectively consolidated in a warehouse or content system they remain unconnected and without context. And the sheer growing volume of the data, papers, and images makes it increasingly difficult to find and discover a specific resource when you need it most. How we manage this information must change. And it must change before it is too late. We must change before it becomes impossible and costly to retroactively fix the error of our ways.

The knowledge exists about all of this information. These small “Rembrandts” exist everywhere. The day it is stored in a database or filed away in a digital landfill the person that created it, the project team that worked on it, and the admins that manage it have that knowledge. They know what it is, why it was created, how it was created and what was learned from it. Yet that knowledge quickly evaporates. People move on to other projects, get excited about something else, or leave the company. What we know about the informational context and value begins to fade - like all memory. And every day more and more of this knowledge is lost. These small “Rembrandts”, that the organization paid dearly for, are being lost every day because no one can find them. Even if someone was lucky enough to stumble upon the data or the file in a year or two they often cannot interpret it correctly, or put it in the right context necessary to maximize its value.

Think of how easy it would be to apply just a few tags to that data table to make it more findable. A small description, a little provenance information, a link to a few seemingly unrelated papers to provide the missing “context”. Informational threads, human insight and experience, provided by another scientist can make all the difference in the world. But this demands that we change the way we think about
information. We must view it not as an output of a project or hypothesis that was abandoned, but for what it really is... a learning process. Explorers make maps. Why don't researchers?

The visible world may be known, but the unseen world is just begining to be explored. Why don't we see information for what it really is? An output of the exploration. Applying just a few tags to capture the context, meaning and value of your work will make all the difference. And while that benefit may at first appear to be for someone else, like karma, it may perhaps one day benefit you.

What is this thing called Personal Search

On a recent visit to a very large storage vendor I had a discussion about a social portal that they had developed. It was struggling to get user engagement and they were puzzled as to why.

My response seemed a surprise to them. Social software is personal. Users think of it as a personal tool. If you look at the most popular web 2.0 platforms like Facebook, Twitter, or even Delicious they are tools that provide a benefit to users on a personal level.

You cannot force user engagement. The tools either help them (so they use it), or they really don't provide much benefit (they don't use them). Facebook is about personal expression, Twitter about having a voice, and Delicious is about sharing your own interests. If tools don't deliver this personal benefit then users will not use them.

In a round about way this gets us back to the companies social portal. Enterprise 2.0 software suffers from this lack of personal, intimate interaction. It has a corporate aura about it, residing on the corporate portal, and workers don't feel the same personal connection with it. Does it really help them get their work done or just create more work for them? One more application they have to use.

As we discussed Jumper and how it might help I told them that if they deployed Jumper on the same corporate portal that it would likely suffer the same fate. The users would not feel any personal connection with it.

Jumper works best when it is deployed directly into a community of users. Smaller deployments that can be customized even personalized to users interests. I asked what groups had heavy information requirements and they mentioned the project managers, research lab teams, product development groups, etc. and I discussed that a customized Jumper deployed onto a small VM with minimal system resource requirements should be deployed for each of these groups. Users feel a more personal connection with a bookmarking engine when it is focused in this way. Search returns only the results relevant to them, not the whole company. Resources have been tagged by colleagues that they know and trust, basically their friends at work who they can holler over the cube wall at.

You have to change the way you think about applications. From the web to mobile phones applications are more specialized, more organic in their user communities. And large organizations need to understand and adapt to this expectation from their users. Search is no different.

Personal search or a point solution approach really means a more customized or tailored approach to search that meets the unique needs of its users. It is precisely because Jumper is an open tool that you can change to meet your own
unique requirements that this works so well. Precisely because it is license free and light-weight that it can easily be deployed in this way. The ability to reflect personal or group interests includes greater flexibility in the terminology or data dictionary to include a hybrid of corporate taxonomy and group based folksonomy. Specialist users have very specific and often highly technical terms that never make it in a formalized corporate taxonomy. Yet these terms matter to these specific users and make it easier to search and find things. Another critical factor is that the tag fields can be customized to meet the unique needs of users. For instance, with structured knowledge tags a materials engineer or biologist will have very different tagging needs than a SAN storage architect or a chemist. One might require a tag to identify the protein the other a tag to identify the compound, etc. A point solution approach allows the local bookmarking engine to be highly customized to meet these unique needs in a way a centralized system never could.

Personal search is not enterprise search. We understand that this tool must be simple. Easy to use, easy to navigate and intuitive. It must also provide a direct and immediate benefit to users. There must be something in it for them, it must not be a generalized tool, it must be very specific, even personal for each user to see and feel the value. It is more like a cube conversation. In this sense it must be localized. Sharing a common skill-set or job description, just as most users with similar skills are sitting together on the same floor and their conversations are based around this shared understanding so the tool must have the same level of intimacy.

This is what we mean by personal search and it requires an entirely new way of thinking about enterprise search. And it is often that thought process that is the hardest thing to change.

Jumper in China?

I was browsing some web stats recently and happened to find a Jumper installation in China. Normally that would not be unusual. China is, after all, our second largest volume of traffic after the US. In fact, this was the third one that I have found in China this month. What was unusual is that it was not in some Chinese company I had never heard of, no, this one had a public IP address. It was on the public web in China!

This has been an increasing phenomenon over the last several months with public sites literally popping up all over the world (India, Poland, Estonia, Russia, Germany just this month). However, no one had yet posted one online in China. But yet there it was, a Jumper search engine in what I think is Mandarin, on the Internet inside China. Wow.

What did it mean? Was someone bypassing the government? It is light-weight and portable so that users could easily move it to another address when needed. Or was it simply small enough to fall under the governments radar? My head was spinning for a second...

It is really quite astonishing to me. This little software program has been nothing short of amazing since I first created it. Jumper started as a simple tagging engine to enrich metadata in a small project with a very limited budget. After the project I added a search page to it and posted it on Sourceforge thinking that was it.

I returned to the same life sciences company a few months later (on another consulting engagement) and was pleased to see the tagging engine was still integrated into their Intranet search. When I reached out to the original project team several told me, to my surprise, that they had since deployed the full Jumper 2.0 software in their department. When I asked why the answer surprised me. “If I know where to look I can usually find what I’m looking for - the problem is when I have no idea where to look, then it is almost impossible.” OK, so I paraphrased a little. The point being it was the discovery aspect of the software that they loved. Enterprise information is distributed. You need to know where to look. With Jumper they could find all kinds of information that they never knew existed. Tagging was merely a means to an end.

And now Jumper could bring down governments? OK so my imagination got a little carried away with the possibilities… But this I certainly never saw coming. Jumper has always been an enterprise search engine. I was fascinated at this new use of the software. When I inquired with one of these deployments what I found were users alienated from the traditional search model. Jumper gave them the tool to create a culturally friendly search engine. Created by users like themselves. One that met their unique interests. Lawyers in Estonia could create a search engine that met their culturally unique and local legal needs in a way no vertical or general search engine ever could. Scientists at a University in Germany could do the same, so could programmers in Russia, developers in India. The potential seems unlimited.

A new global economic and technical infrastructure is emerging, built on networked, social computing. In the next ten years a billion new people around the globe will gain a productive foothold in this economy and become an increasingly significant online force. They will be young and will look to do things differently. The old model of monolithic search provided by a few companies will no longer meet all of their needs. They will be culturally splintered, with vastly diverging interests, and will look for a more flexible search model that will better meet their unique needs. They will shatter the current search model into millions of pieces; culturally unique, community based, and socially oriented pieces.

From a simple project two years ago too an emerging global phenomenon? Well, perhaps not yet. We still have a long way to go, but things are starting to get very interesting.

Building Social into Solr

We have had a number of customers inquire about customizing specific aspects of Solr search with Jumper.

There are really two approaches: one is to build Jumper tagging into your search engine interface allowing users to tag documents or content when it is stored. The second is to import Jumper tagging fields into solr using the DataImportHandler. This is done using basic JDBC connectivity. Tags stored in the Jumper search engine then are imported into the Solr index and attached to a document and returned when searched. Using faceted_fields you can allow users to filter search based on the knowledge tags applied by other users.

This is perhaps the easiest method. The two services can be bundled in a single web interface. In this way you are removing the Jumper search engine and replacing it with Solr. This gives you the benefit of both worlds – full text searching and user tagging – to deliver better more detailed search results.

If you prefer to embed custom search paths into Solr the primary method is using facet-fields. A Jumper tagging interface can be added when storing documents. The Jumper tag fields are then stored as facet_fields that Solr will search in addition to its full text parsing of the document. This is done on indexed rather than stored values.

This requires that we add a number of Jumper tags to the Solr index separately and add a custom sort to Solr search. Adding a new Jumper tag field to the search results requires two very small hook implementations: hook_apachesolr_update_index() and hook_apachesolr_modify_query(). To start, let’s just add the keyword tag field to the Solr index.

/**
* Implementation of hook_apachesolr_update_index()
*/
function mymodule_apachesolr_update_index(&$document, $node) {
// Index field_keyword_tag as a separate field
if ($node->type == 'profile') {
$user = user_load(array('uid' => $node->uid));
$document->setMultiValue('sm_field_keyword_tag', $user->tags);
}
elseif (count($node->field_keyword_tag)) {
foreach ($node->field_keyword_tag AS $keyword) {
$document->setMultiValue('sm_field_keyword_tag', $keyword['filepath']);
}
}
}

All we do is add the data to the index by adding it to the $document object, which is passed by reference. We used the setMultiValue method since the tag field can have multiple values, but if we were just adding one field, we would just use the addField method. The field name is simply the 'sm_' dynamic field name pattern with field_keyword_tag appended, since the field contains a keyword string, and the sm_ field type represents a small string.

Now that the data has been added to the index, we also need to add it to the query so it can be returned with the search results:
function mymodule_apachesolr_modify_query(&$query, &$params, $caller) {
$params['fl'] .= ',sm_field_keyword_tag';
}

And that's all there is to it… This can be repeated for each of the Jumper knowledge tags that you want to add. All you're doing is some basic PHP string concatenation and appending your newly indexed field to the fields to return array (['fl'])of the $params object. Although, we are simplifying the detail a little bit on the format of $params for the sake of brevity in this post.

In general, adding Jumper social tagging features into your Solr search is pretty easy, and can deliver some very powerful capabilities to your search functionality.

Building Social into Drupal Search

A customer had an existing Drupal powered Intranet site. They were excited to tell us about a project they were building for a collaborative workspace for a group of researchers that was customized to manage Karyotyping images. The collaborative workspace would tie together a number of collaboration tools they already had in use - Drupal, Jumper 2.0, and DSpace.

A researcher would create an image that needed to be stored in Dspace. The image would be uploaded to the DSpace repository. The workflow they were building would manage this process via scripts. The Drupal search module was customized to let users search for specific image thumbnails on the Intranet site. Drupal is chiefly focused on handling HTML content, so Gallery2 was installed to generate thumbnails on a preview page and extract embedded metadata.

Drupal would push the image to the DSpace repository and store the metadata and thumbnails in the Gallery. When images are recorded, Drupal offers users the option to include additional related tag objects created in Jumper 2.0. When users record a new image they have an option to tag the document or image using the Jumper 2.0 tag engine. The image is then assigned a unique identifier in the repository and published in a news feed.

The new image is picked up via the published feed and stored in the Gallery module of the research project's Drupal site. Included in the feed are the image, metadata (including the unique identifier), tag profile with pointers to any related objects. Another researcher learns that the new image is available to examine. A module was written to provide a workflow for the feeds so that the new items showed up in a block in "my workspace," and an was email generated by the notify module. Other researchers could click on a reference link to view the full res image, compare the image in the Gallery module to other images, they could click on reference links to related papers published in Connotea, they could read and edit the tag profile with new annotations, comments, or notes to build more knowledge around the image.

Using Jumper 2.0 tagging there is now a rich set of metadata associated with the image. The new metadata was published as feeds and picked up for inclusion in the DSpace repository housing the original archived image. This publishing cycle was ongoing for the life of a specific research project. The rich metadata that Jumper delivers provided the context, meaning and value to the image that was missing in traditional metadata. Critical provenance information was provided as well as links to related images or published papers. All of this made the search results more relevant.

Proposing an Open Index Initiative

Many of the customers who download Jumper are using it for personal search. They are using it themselves, or in a small company, or between a few colleagues. Jumper has many benefits that make it an ideal personal search engine, however, if you are using Jumper in this way you are missing one of its big benefits – collaborative sharing of resources. In a larger community of uses with a shared interest or profession the number of searchable resources grows rapidly and the inherent value of Jumper search increases accordingly.

In an effort to facilitate resource sharing among the many customers who use Jumper for personal search we are proposing an Open Index initiative.

Jumper will make a shared index database available for download on Sourceforge under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license. This Open Index will be built by users like you, who share your tag profiles with other users, who in turn share their tag profiles with you. This collaborative initiative will be aggregated by Jumper in a central Open Index that will be available for download on Sourceforge.

This means that you can share and change the index database as you choose.
• to Share — to copy, distribute and transmit the work
• to Remix — to adapt the work

Under the following conditions:
• Attribution — You must attribute the work in the manner specified by the author or licensor (but not in any way that suggests that they endorse you or your use of the work).
• Noncommercial — You may not use this data directly for commercial purposes. You cannot resell or repackage this data for commercial benefit. You cannot commercially benefit by providing access to this data. The intent for use is that any benefit will only be derived indirectly by using this data to search for resources that may provide a commercial benefit as a result.
• Share Alike — If you alter, transform, or build upon this work, you may distribute the resulting work only under the same license to this one.
It is important to understand that when you contribute your resources to the common index for the general good of all that it can only be used under these license guidelines.

There are two ways you can easily contribute.

You can export your Jumper index database and send the flat file to us via Email at Open Index

Or you can add individual resources to the open index via the Open Index form.

Jumper and Sphinx - integrating the best of both worlds

We had a customer who requested some direction on integrating the Jumper 2.0 tagging feature into a number of very large distributed databases. The customer had just implemented the Sphinx search engine on each of these databases.

Many of these SQL databases contained very large numbers of tables. The primary challenge was that many of these tables contained cryptic table and column names that made it very difficult to interpret exactly what the data was in the tables.

They wanted to integrate the Jumper 2.0 bookmarking engine into the Sphinx search engines so that users could apply knowledge tags to the legacy database tables. In this way users could search locally for data and then tag the data they had searched with relevant knowledge tags. In the initial design meeting it was determined that integrating the Jumper tagging fields directly into the Sphinx search interface would be the best approach and storing the tags to a central Jumper mySQL index. Provided below is a quick example of this integration.

The first thing created was a simple form in HTML.

<-html->
<-head->
<-title->Jumper Tagging Fields<-/title->
<-/head>

<-body->
<-form method="post" action="jumper_update.php">

User Name:<-br />

<-input type="text" name="creator_id" size="10" /><-br />

Table Name:<-br />

<-input type="text" name="title" size="40" /><-br />

Description:<-br />

<-input type="text" name="body" size="300" /><-br />

Database Hostname:<-br />

<-input type="text" name="url_title" size="255" /><-br />

Keywords:<-br />

<-input type="text" name="meta_keywords" size="200" /><-br />

Database Location & Access:<-br />

<-input type="text" name="meta_location" size="200" /><-br />

Realted Data:<-br />

<-input type="text" name="meta_link" size="200" /><-br />

Type of Data:<-br />

<-input type="text" name="data_type" size="30" /><-br />

Date:<-br />

<-input type="text" name="date_posted" size="30" /><-br />

// Next we need to add the submit button to the web page. //

<-input type="submit" value="Update Database"

<-/form>

<-/body>
<-/html>


The next step is to create jumper_update.php file. This will update the database with the new knowledge tags that have been applied to the database tables. Create a new file called jumper_update.php

$creator_id = $_POST['creator_id'];
$title = $POST['title];
$body = $POST['body'];
$url_title = $POST['url_title'];
$meta_keywords $POST['meta_keywords'];
$meta_location $POST['meta_location'];
$meta_link $POST['meta_link'];
$meta_datatype $POST['meta_datatype'];
$date_posted $POST['date_posted'];

mysql_connect("localhost", "username", "password") or die ('Error: ' . mysql_error());
mysql_select_db("s_jmp_entry");

$query="INSERT INTO Table (id, creator_id, title, body, url_title, meta_keywords, meta_location, meta_link, meta_datatype, date_posted)VALUES ('NOT NULL','"$creator_id."','"$title."','"$body."','"$url_title."','"$meta_keywords."','"$meta_location."','"$meta_link."','"$meta_datatype."','"$date_posted."')";

mysql_query($query) or die ('Error updating database');

echo "Database Updated With: " .$creator_id. " ".$title." "$body." "$url_title." "$meta_keywords." "$meta_location." "$meta_link." "$meta_datatype." "$date_posted ;

?>

We are working on inserting the knowledge tags directly into the Sphinx index so that users can search data by both full-text and knowledge tags to improve discoverability of the legacy data. We will keep you posted.

-apology for the all the hyphens it was the only way to get the blog to accept the HTML.

A new model of a Trusted Web and Decentralized Search

Ask most users and they will tell you in no uncertain terms that enterprise search sucks. Why is it generally so frustrating? Why does it fail to meet most user needs? Why can’t it find the information you are looking for?

Because it is a general search tool when what you are often looking for is something highly specialized. The result of a general tool and a specialized need is an extremely frustrating user experience. Finding the right information is exactly like a finding a needle in a haystack. You need to get lucky.

But good search should not be about getting lucky. Although we could all use a little luck. It should be about delivering on your unique needs. When you have a specialized need you reach for a specialized tool. One that is perfectly adapted to the job at hand. That is exactly what a personalized search engine delivers.

A Personalized Search Revolution

Jumper is a revolution in Enterprise Search precisely because it is a personalized, specialized, and trusted engine. It contains an index of searchable information that has been provided by trusted colleagues, who share a common interest, and are working toward a common goal.

At Jumper we believe that each person’s ability to drive trust into every search is the animating force that moves us from centralized search paradigms to a new, decentralized one. In this new model, we will be able to search better because trusted communities are doing search for you. They collectively share this highly specialized information and knowledge to build a better personalized search engine.

We can better trust search results because people we know had good experiences with specific information resources and then share that experience with others. We get better search results because the community of users that built the search index is like us; they share our interests, our passions, and speak our language.

Personalized search is discipline specific, much like vertical web search engines, it is focused on your specific industry or research. However, it delivers universal search across all information sources allowing you to learn about new things and ideas based on personal recommendations from a community of people who are similar to you.

The New Model of Trusted Search

Traditional monolithic enterprise search is limited by its very generality, its one-size-fits-all indexing approach, its rigid global taxonomies, its ambiguous metadata. The biggest problem is changing semantics and antiquated metadata. The differences between one word or acronym, and the other, can vary widely by user, profession, location, or industry. The word web, for instance, can have entirely different meanings in different contexts. Even if, across all different forms, locations and languages, you are looking for the word web, what is the context you want to place it in? By personalizing search the context is always relevant and decided by the community of users.

This new proactive model of creating trust is not some future, far off concept. It is happening right now with Jumper 2.0. It delivers personalized, specialized, and trusted search precisely because it is easily deployed to smaller groups of users. It is light-weight, portable, web-based, and license-free. It is easily customized and adaptable to meet the unique needs of departments and divisions, research groups and project teams. It is a point solution search approach that is highly flexible and very dynamic.

Jumper leverages social knowledge tagging that makes it easy and intuitive for users to apply tags, descriptions, definitions, classifications, almost anything you want to apply to make information easier to find and more usable. This reputation system is based on user input and expertise that allows us to better trust verified content, media, or data and to rely on the knowledge attached to that information resource. We use trust based rating systems to determine what information is considered the best and what is not worth your time.

The explosive growth of Jumper community search demonstrates how people are frustrated with traditional enterprise search. All they really want is an effective search tool. One that works for them. They are proactively creating trusted search through shared interests.

Happy Birthday Jumper 2.0

Today marks the one year anniversary of Jumper’s first production release. We are celebrating the open source release of Jumper 2.0 on Sourceforge (March 29th) and look forward to another exciting year.

We have achieved unexpected growth in this short span and the credit goes to all of our dedicated community members. Lots of smart people, from smart companies, are driving Jumper 2.0 forward. Since our initial launch one year ago we have over 5000 enterprise downloads. Our software is installed in some of the largest Life Sciences and Pharmaceutical organizations, Automobile and Construction equipment manufacturers, Food Product and Beverage companies, leading Universities around the globe, and several US Government agencies. Our open source software has been downloaded in over 76 countries with more downloads to China than any other country. We get more hits, downloads, and requests from China than any other country outside the US.

Jumper 2.0 is just beginning to gain recognition as a game changing software platform. Jumper has grown rapidly from department and project level deployments by rogue groups of employees frustrated with existing corporate IT solutions, to development level beta and sandbox platforms that opened peoples eyes to the power of social knowledge tagging, to full blown enterprise platforms that supplement existing Search and KM tools. Jumper wikis are popping up to provide users with support and advice and corporate IT, instead of trying to police or squash this self-help behavior, has begun to embrace it.

We look forward to becoming a significant driver of innovation in the enterprise, empowering users everywhere behind the firewall, and planning new developments to the Jumper platform in the coming year. Our community of users, supporters, and developers grows stronger every day and we anticipate the suggestions and contributions of each new member. It is your feedback that will result in new features and tools that benefit the broader community.

From all of us at Jumper thank you to the community of users who support us. Your comments, suggestions, complaints, bug fixes, and continued deployments help make for better software.

How Jumper survives early E2.0 market and uncertain economic times – the secret sauce is open source

A customer recently asked me how Jumper was going to survive as a start-up in tough economic times. A very good question considering…

Cogenz has folded, Connectbeam has an uncertain future (the rumor is they are out of money), and Jive recently layed off 1/3 of their staff and “reassigned” the CEO and co-founder. How will Jumper survive?

The power of Jumper is in open source. Unlike the closed enterprise platforms of our struggling competitors the Jumper 2.0 software and source code will continue on with or without Jumper Networks. The ideas and creativity of Jumper 2.0 will live on in the community even if Jumper Networks is not the community leader. A new community will emerge to drive development of the software, continue improvements, and bring new ideas and insights into the development process. Our software isn’t proprietary, it belongs to everyone.

The opportunity of open source is that you can try some truly new ideas and see if they work. Jumper has pioneered a number of ideas that are growing rapidly. Knowledge tagging (see Wikipedia definition) was a Jumper idea that has taken on a life of its own and is being openly debated in the KM community. Rating mechanisms for search results was a Jumper idea first and is now being experimented with by the biggest of search companies. Although giving power to the people when you have customers paying for search placement will be an interesting balancing act for them to pull off.

The last great benefit of open source is that we have not had to take VC money and sell our soul to the bottom line. We can still experiment, still innovate, still create new ways of doing things and let you the users decide what you like and what you don’t. At Jumper there is no bottom line, at least not yet. There is no time table to profitability, and given the current economic environment that is a good thing. It takes time for new ideas, for revolutionary software platforms to take hold. It takes time for people to begin to grasp the benefits, opportunities, and real power of groundbreaking software. In that sense Jumper has only just begun.

Jumper versus Enterprise Search

A customer recently asked us at a show what the difference was between cluster search with Vivisimo and Jumper search? We hear this and similar questions quite often. Our response is always the same...

They are different tools to solve different problems.

Universal Search
Whether cluster search, keyword search, full-text search, page rank search, or federated search they all represent specific point solutions. Jumper provides a more global or universal view of all your information assets.

Federated search for structured data, page rank search for web pages, cluster search and keyword search for content and media, they each search across a specific silo. Each can be very effective at finding information within the silo. But they do not search across silos (either different formats, schemas, locations or vendors). Jumper provides search across all your silos and it connects disparate information together across these silos.

20% Rule
Traditional enterprise search only allows you to search storage that you already know about. The assets in centralized storage represent on average only 20% of all your information assets. The other 80% of information assets are hidden from traditional enterprise search, but are visible with Jumper.

In a large organization there can be thousands of databases, hundreds of file servers, dozens of content systems that are often distributed by division, department, geography, project, etc. Searching across all of these is, to be polite, extremely difficult with traditional search. Jumper gives you high-level visibility of all these assets regardless of format or location. It allows users who know, use, or create the information to share it with the organization.

Smarter than People?
All of the search solutions we have discussed are coded algorithms. Automated software programs that represent a one-size fits all approach to search. They lack the dexterity and the intelligence that Jumper delivers to search results.

A cluster or keyword search algorithm pulls out and indexes words that appear to be significant. However, it cannot provide the context, meaning, and value that we crave in search results. Even the best algorithm is not as good as a human being at interpreting the content or data. You have to click through each search result and analyze it for relevance. Jumper delivers the who, what, when, where, how, and why details with the search results so you can quickly determine relevance and value without inquiring each source.

Final Analysis
Your organization most likely uses both cluster search as well as federated search because they each provide value in their own way. Jumper can be included in this equation.

We always tell our customers that its not one solution or the other.

Organizations should use Jumper along side these other tools for the value it provides delivering true enterprise wide (even global) visibility as well as for its next-gen knowledge management capabilities.

Issues with loading SQL tables on installation

The recent issues with loading the Jumper SQL tables on installation have been fixed.

They were the result of a bug with the auto-loading script. Thanks Dominic for helping us get this resolved!

To get the fix please download the latest Jumper 2.0.1.5 release available on Sourceforge.

Any additional issues please email support@jumpernetworks.com