Compound Search Processing Repositioned at ConceptSearching
July 2, 2015
The article titled Metadata Matters; What’s The One Piece of Technology Microsoft Doesn’t Provide On-Premises Or in the Cloud? on ConceptSearching re-introduces Compound Search Processing, ConceptSearching’s main offering. Compound Search Processing is a technology achieved in 2003 that can identify multi-word concepts, and the relationships between words. Compound Search Processing is being repositioned, with Concept Searching apparently chasing Sharepoint Sales. The article states,
“The missing piece of technology that Microsoft and every other vendor doesn’t provide is compound term processing, auto-classification, and taxonomy that can be natively integrated with the Term Store. Take advantage of our technologies and gain business advantages and a quantifiable ROI…
Microsoft is offering free content migration for customers moving to Office 365…If your content is mismanaged, unorganized, has no value now, contains security information, or is an undeclared record, it all gets moved to your brand new shiny Office 365.”
The angle for Concept Searching is metadata and indexing, and they are quick to remind potential customers that “search is driven by metadata.” The offerings of ConceptSearching comes with the promise that it is the only platform that will work with all versions of Sharepoint while delivering their enterprise metadata repository. For more information on the technology, see the new white paper on Compoud Term Processing.
Chelsea Kerwin, July 2, 2014
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Basho Enters Ring With New Data Platform
June 18, 2015
When it comes to enterprise technology these days, it is all about making software compliant for a variety of platforms and needs. Compliancy is the name of the game for Basho, says Diginomica’s article, “Basho Aims For Enterprise Operational Simplicity With New Data Platform.” Basho’s upgrade to its Riak Data Platform makes it more integration with related tools and to make complex operational environments simpler. Data management and automation tools are another big seller for NoSQL enterprise databases, which Basho also added to the Riak upgrade. Basho is not the only company that is trying to improve NoSQL enterprise platforms, these include MongoDB and DataStax. Basho’s advantage is delivering a solution using the Riak data platform.
Basho’s data platform already offers a variety of functions that people try to get to work with a NoSQL database and they are nearly automated: Riak Search with Apache Solr, orchestration services, Apache Spark Connector, integrated caching with Redis, and simplified development using data replication and synchronization.
“CEO Adam Wray released some canned comment along with the announcement, which indicates that this is a big leap for Basho, but also is just the start of further broadening of the platform. He said:
‘This is a true turning point for the database industry, consolidating a variety of critical but previously disparate services to greatly simplify the operational requirements for IT teams working to scale applications with active workloads. The impact it will have on our users, and on the use of integrated data services more broadly, will be significant. We look forward to working closely with our community and the broader industry to further develop the Basho Data Platform.’”
The article explains that NoSQL market continues to grow and enterprises need management as well as automation to manage the growing number of tasks databases are used for. While a complete solution for all NoSQL needs has been developed, Basho comes fairly close.
Whitney Grace, June 18, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Magic May Not Come From Pre-Made Taxonomies
June 17, 2015
There are hundreds of companies that advertise they can increase information access, retrieval and accuracy for enterprise search by selling prefabricated taxonomies. These taxonomies are industry specific and are generated by using an all-or-nothing approach, rather than individualizing them for each enterprise search client. It turns out that the prefabricated taxonomies are not guaranteed to help enterprise search; in fact, they might be a waste of money. The APQC Blog posted “Make Enterprise Search Magical Without Money” that uses an infographic to explain how organizations can improve their enterprise search without spending a cent.
APQC found that “best-practice organizations don’t have significantly better search technology. Instead, they meet employees’ search needs with superior processes and approaches the content management.”
How can it be done?
The three steps are quite simple:
- Build taxonomies that reflect how people actually think and work-this can be done with focus groups and periodically reviewing taxonomies and metadata. This contributes to better and more effective content management.
- Use scope, metadata, and manual curation to ensure search returns the most relevant results-constantly the taxonomies for ways to improve and how users are actually users search.
- Clear out outdated, irrelevant, and duplicate content that’s cluttering up your search results-keep taxonomies updated so they continue to deliver accurate results.
These are really simple editing steps, but the main problem organizations might have is actually implementing the steps. Will they assign the taxonomy creation task to the IT department or information professionals? Who will be responsible for setting up focus groups and monitoring usage? Yes, it is easy to do, but it takes a lot of time.
Whitney Grace, June 17, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Progress in Image Search Tech
April 8, 2015
Anyone interested in the mechanics behind image search should check out the description of PicSeer: Search Into Images from YangSky. The product write-up goes into surprising detail about what sets their “cognitive & semantic image search engine” apart, complete with comparative illustrations. The page’s translation seems to have been done either quickly or by machine, but don’t let the awkward wording in places put you off; there’s good information here. The text describes the competition’s approach:
“Today, the image searching experiences of all major commercial image search engines are embarrassing. This is because these image search engines are
- Using non-image correlations such as the image file names and the texts in the vicinity of the images to guess what are the images all about;
- Using low-level features, such as colors, textures and primary shapes, of image to make content-based indexing/retrievals.”
With the first approach, they note, trying to narrow the search terms is inefficient because the software is looking at metadata instead of inspecting the actual image; any narrowed search excludes many relevant entries. The second approach above simply does not consider enough information about images to return the most relevant, and only most relevant, results. The write-up goes on to explain what makes their product different, using for their example an endearing image of a smiling young boy:
“How can PicSeer have this kind of understanding towards images? The Physical Linguistic Vision Technologies have can represent cognitive features into nouns and verbs called computational nouns and computational verbs, respectively. In this case, the image of the boy is represented as a computational noun ‘boy’ and the facial expression of the boy is represented by a computational verb ‘smile’. All these steps are done by the computer itself automatically.”
See the write-up for many more details, including examples of how Google handles the “boy smiles” query. (Be warned– there’s a very brief section about porn filtering that includes a couple censored screenshots and adult keyword examples.) It looks like image search technology progressing apace.
Cynthia Murrell, April 08, 2015
Stephen E Arnold, Publisher of CyberOSINT at www.xenky.com
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