There are a handful of apps in the Calais gallery that use the service. One of my early favorites is LinkedFacts.com. That said, after a bit of investigating I am left with a 'so what' feeling. OK the LinkedFacts service has highlighted companies, people, dates/events and enables me to find more information on the web about those 'things' via its overlay UI. So what? I can't really tell how or why the additional 'Yahoo News', for example, was suggested to me. Would the same information be suggested to my mom, wife or kids if they were to use the service? I've rambled on about "what's right for me? or What should somebody like me do in situation xyz" here.
This cool yet flat experience with LinkedFacts reaffirms my thesis that discovering, scoring, and providing access to experiences and justified beliefs (i.e. knowledge) is where money may be made on the web. Needless to say, it will be interesting to watch the apps evolve in the Calais gallery. My hunch is that until beliefs/experiences join the named entities, facts, and events in the Calais identification engine the apps will continue to be cool but a bit flat.
Freebase (summarized here ) is an ambitious open database project from Metaweb that is IMHO desperately in need of an application (among other things*). A tool like Endeca shields the user of the app from sparsity in the data attributes/content while encouraging further exploration. I'm not certain what 'the app' is but snapping a tool like Endeca ontop of Freebase would enable less technical folks to create apps. Some of those are bound to be interesting.
* Freebase should enforce a 'true' entry approach a la wikipedia i.e. there is only one entry per topic in Wikipedia. If you don't like that entry you modify it or fight to have your modifications accepted. In Freebase you may find several domains all varying slightly from each other but essentialy all about the same thing. There is nothing stopping a person from creating their own domain even if it is identical to an other domain.
UPDATE - Steve Papa, CEO of Endeca, just dropped me a note mentioning the just released 'real-time' version of their offering. I'll paraphrase his comments here: imagine a new Freebase object is added and it is automatically instantiated in Endeca in real-time.
Really cool especially considering the * above! Now we need a connector for the Freebase API from Endeca and it's party time.
Enjoy
Fascinating! Who would have thought that parents would know how to SEO better than the kids!
UPDATE: ReadWriteWeb just covered this phenomenon from a different more evil perspective
I found this post dissecting site 'users' at adblockplus.org to be rather validating. I use AWStats and Google Analytic at the same time and in the past had come close to reconciling the two visitor figures (GA is always lower). I know a lot of these 'missing visits and visitors' were from search engine crawlers....but could it be that nearly 10% of a website's 'traffic' is from from crawlers?
I've often thought there is a market for a company that 'caches' the web and offers cloud computing capacity at a premium in order to access the cache. Yahoo has started to do something similar with the introduction of BOSS but as far as I can tell you still need to 'move' the search results onto your computing capacity (think Amazon's EC2) and you are limited to what is in Yahoo's index (vs the entire HTML). Amazon has a service that can get you pretty close but it too doesn't provide access to to the entire page. It's called Grep the Web and is an option on the Alexa web service.
I'm waiting for a company like Cuill.com of maybe Metaweb to create this offer...or a variation that is 'free' if your output is placed into the public domain accessible on the cache provider's site.
Makes me wonder....how big is this market? Are there only 100 companies willing to pay for such a cache or would others come out of the ether.
Kontagent is a type of web analytics that provides creators of social apps insight into how the app is being used, spread, and by what type of people. The company isn't currently positioned as an advertiser analytics offering but I'd bet that's a logical next step. Analytics for social apps certainly isn't new and the competitive landscape reminds me of the ETL lanscape back in the mid 90s. So what sets Kontagent apart from the field? It's the insight into who is using and spreading the apps not just how the apps are being used. IMHO the cohort and corrolation analysis create a type of influence analytics which app creators will find handy in justifing their ad rate cards.
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The BI accelerator is a recent addition to SAP BI and has shown some impressive performance gains with typical improvement factors between 10 and 100. It is based upon the TREX search engine for unstructured data applying its technology to structured data store in the InfoCubes of an SAP BI system, Figure 12.14 shows the architecture of the BI accelerator in the context of the overall SAP BW architecture. The BI accelerator replaces the MOLAP aggregate option available in previous releases of SAP BW.
Figure 12.14 BI accelerator architecture
The BI accelerator achieves these performance gains by creating special types of additional indexes — sometimes also called BI accelerator indexes — and using these indexes as the basis for massively parallel query execution. Both indexing and query execution run on a separate, highly parallel blade server.
The underlying technology is actually not new but has been part of the TREX search engine, which itself is part of the SAP NetWeaver suite. SAP BW just uses a special instance of the TREX search engine enhanced to better support searches in structured data of a business intelligence system. Creating a BI accelerator index involves three steps:
1. Vertical decomposition decomposes the data to be indexed by attribute (or database column) instead of by record (or table row), as it is done in traditional database indexes. This approach has been known from other search engines for non-structured data — only now applied to structured information stored in SAP BW
2. Smart compression involves recoding the attribute values found in the indexed data to smaller integer values using a directory, which is generated on-the-fly. Typical reductions in the size of the indexed data reach a factor of 10-20.
3. Horizontal partitioning divides the generated index into multiple partitions in such a way that query execution using these indexes can be run in parallel without having to share data.
BI accelerator indexes are not stored in a database, but are stored in flat files residing on the BI accelerator server. Query execution using the BI accelerator again involves up to three steps:
1. Load index to memory. When starting the execution of a query the system checks if the corresponding index is already (or still) available in the main memory of the BI accelerator server. If the index is not available, it is loaded into main memory. Alternatively, loading critical indexes can be triggered by external processes to ensure that these are instantly available at all times.
2. Aggregation. BI accelerator indexes also contain key figure values corresponding to the requested characteristic values. These key figure values are used for highly parallel on-the-fly aggregations.
3. Merge and return results. The final step before returning query results to the BI server is to retrieve and merge all subresults from the different parallelized query execution processes.
Using the BI accelerator has a couple of advantages over other traditional performance optimization methods. First of all, using it does not require any changes to existing information models or queries. Considerations around logical partitioning, line item dimensions or additional database indexes could even be ignored in the development of information models for use with the BI accelerator — although you should still aim for the best possible information model. As opposed to traditional aggregates, one BI accelerator index servers all queries of an InfoCube, regardless of the granularity of the result set or the actual filters used. Therefore, the manual effort for tuning aggregates is kept to an absolute minimum while the systems still provides stable, predictable query response times. Because BI accelerator indexes are not stored in a database, there’s also no need to optimize database queries or parameter sets.
On the other hand, hardware resource requirements (especially for main memory) are too demanding to allow for using the BI accelerator for all InfoCubes of the overall information model. In many applications, classic aggregates will be sufficient to provide good performance for query execution at much lower hardware requirements. Typically the BI accelerator will be deployed in scenarios with very high data volumes (hundreds of millions, or even billions of records in the fact tables), incalculable query requirements which are hard to optimize for or very high expectations (for example, with hard service level agreements) regarding average or maximum query run times. The BI accelerator does not optimize the analytic engine itself, nor does it help to cut down network transmission times. It is “only” useful to optimize database intensive query execution.
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Here are a couple of my favorite sites or figuring out SEO and SEM strategies.
While we at Circos haven't yet automated our keyword processes, keyword information services like these are used (embedded?) in our manual process.
SpyFu is my current favorite.
UPDATE - This site is also very useful for determining how your sitemap is being viewed by major search engine. It also has a handy tool for checking whether or not your site is in the Google sandbox.
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Last week we launched www.circos.com at DEMO08. You can catch our 6 minute live demo here. Stop by the site and let us know what you think. We are currently in 'preview' mode and will be adding additional content categories shortly. Likewise if your site has a lot of user generated content or you'd like to have a more colorful search experience drop us a line.
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California January 28th.
As you may imagine we are grateful for the opportunity to launch Circos at what many consider the premier technology launch event. We are told more than 700 companies applied for a chance to launch at this conference; we are one of 70 companies selected to present.
Stay tuned for more on information about Circos and our participation at DEMO08!
]]>Aggregate optimization strategies for end-user performance are a subject for the section on “Aggregates” in Chapter 12. From an administrator’s perspective, optimizing aggregates might consist of a roll-up hierarchy plan (filling aggregates from aggregates). From a modeler’s perspective, aggregate optimization may consist of aggregating data into separate data targets to avoid the performance impacts attribute change runs have on aggregates.
From a data-maintenance perspective, the BI accelerator is very similar to an aggregate roll-up. There is the build and fill of the index, as well as roll-ups. However, there are differences to be noted.
From a performance tuning perspective, the differences are a lot more apparent. While aggregates must be manually optimized based on end-user behavior, the plan is for BI Accelerator to automatically adjust it, and index accordingly (that is, zero administration from this perspective). The goal of BI Accelerator is to deliver automatic monitoring, configuration, optimization, and self-repair of the index and TREX-based BI Accelerator engine.The build and fill of a BI Accelerator index is done manually via the data target context menu, while the roll-ups can be scheduled as process chain variants (the same exact one used for aggregates). Like aggregates, BI Accelerator indexes can be toggled active and inactive manually, and need an attribute change run scheduled after navigational attributes are changed via a master data load.
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e-Storm recently came onto my radar. While surfing their website this passage struck me as interesting:
Analytics can be a similar to philosophy. For instance, go ahead and hunker down with some Socrates and Lao-Tzu. Sure, they're interesting, but what can you actually do with their teachings? We may not be able to explain how to attain peace of mind. Fortunately, we can point out how analytics will improve your bottom line. ...."
Ahhh, all roads lead to prescriptive analytics.
]]>Check this out. It seams some of the more progressive BI vendors are finding applications on e-commerce sites. Endeca has teamed with Powereviews to create Buzzillions.com. The user experience on this site is Endeca! Endeca is an interesting company although I have to say their market positioning is a bit unclear to me ("social navigation"). I thought of them as information retrieval company (think FAST, Google appliance, etc) but the more I dive in it seams they have aspirations or history in the traditional BI space. I haven't dove in enough to figure out if they are transitioning from to towards BI but my hunch is 'towards'. Google is a real threat to any company in the in the enterprise info retrieval space- including SAP. Either way, Endeca is worth a look if you have an e-commerce site.
QlikTech is on fire. They seem to be filling the white space between difficult to learn analytics tools and brain-dead dashboards. Hat tip to the founders and leaders of this company. The fact that their technology is finding applications on consumer focused websites is truly a testament to the ease of use. That said, While I have lifted the hood to check out the engine and I haven't figured out how often it needs an oil change or tune-up.
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Yes, an other buzzword, TLA, skill to add the resume: Semantic Web Analytics. It occurs to me as I reread this that you may not be certain if I mean "Semantic Web" Analytics or Semantic "Web Analytics. While the former will eventually be hot IMHO the later is hot now.
Natural Language Processing (NLP) + Predictive Analytics = Marketer's Dream Come True
Analyze the footprints people leave on the web (blogs, comments, reviews), discover their preferences for the attributes of products/services they consume, and give the consumers what they want.
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