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cezarovidiu

Google Reader (250) - 0 views

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OBIEE BI performance

shared by cezarovidiu on 17 Mar 13 - No Cached
  • What this means in practice is that when the BI Server component starts up, it creates and reserves a number of threads in advance, determined by a number of parameters including SERVER_THREAD_RANGE.
  • You can see these threads running and ready to perform tasks for the BI Server component by using a tool such as Process Explorer for Windows
  • Thinking it through a bit, any given single query is, to a certain extent, only really going to use a small part of the total amount of CPUs available on a server, because it’s not the BI Server that runs queries in parallel, it’s the underlying database. For example, a single analysis against a single Oracle Database datasource would only really need a single BI Server thread to handle the query request, but when the underlying database receives the query, it might use a large number of its CPUs to process the query, returning results back to the BI Server to then pass back to the Presentation Server for display to the user.
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  • The BI Server wouldn’t have any use for any more query threads, as it can’t really do anything with them – the exception to this being queries that generate multiple physical SQLs, for example to join data from multiple sources together and return a single set of data to the user, for which the BI Server could benefit from a higher CPU count if each of these queries in turn led to lots of threads being used – but two queries, in themselves, don’t neccessarily require two CPUs, because of course the BI Server, and the underlying CPUs, are themselves multi-threaded.
  • To conclude then – all things begin equal, the BI Server should make use of all of the CPUs that the underlying operating system presents to it, with the OS itself deciding what threads are scheduled against which CPUs. In-theory, all CPUs on the server are available to each BI Server component, but each OS is different and it might be worth experimenting if you’re sure that certain CPUs aren’t being used – but this is most probably unlikely and the main reason you’d really consider vertical scale-out of BI Server components is for fault-tolerance, or if you’re using a 32-bit OS and each process can only see a subset of the total overall memory. And, bear in mind that however many CPUs the BI Server has available to it, for queries that send just a single SQL statement down to the underlying database server, adding more CPUs or faster CPUs isn’t going to help as only a single (or so) thread will be needed to send the query from the BI Server to the database, and it’s the database that’s doing all of the work – all that this would help with is compilation and post-aggregation work, and enabling the server to handle a higher number of concurrent users. Invest in a better underlying database instead, sort out your data model, and make sure your data source back-end is as optimised as possible.
cezarovidiu

Analyzing Human Data: Take a Dive to Find Out What Your Customers Really Feel - Content... - 0 views

contentmarketinginstitute.com/...man-data-customers-really-feel

machine learning blog sentiment analysis customers marketing

shared by cezarovidiu on 30 Oct 14 - No Cached
  • What really interests me, and what I think should interest marketers, is what I’ll call signals – one of which is intent. Intent is critical because it can predict action. For example, “Is this person shopping to buy a product like my product?” “Is this person unhappy and needing some form of attention?” “Is this person about to return the product for a reason that is addressable?”
  • Sentiment is one ingredient of intent. If someone is happy, sad, angry … that can be determined via sentiment analysis technologies.
  • Many tools struggle with context.
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  • An example I hear over and over again is “thin” – good when you’re talking about electronics, but bad if you’re talking about hotel walls or the feel of hotel sheets. To do sentiment analysis correctly, you need refinement. You need customization for particular industries and business functions.
  • The market, unfortunately, is polluted with tools that claim to have sentiment abilities, but are too crude to be usable. Even with refinement (e.g., the ability to handle negators and contextual sentiment), approaches that deliver only positive and negative ratings don’t take you very far.
  • There are definitely easy, inexpensive entry points that can meet basic, just-getting-started needs: tools for social listening, survey analysis, customer service (handling contact-center notes, for instance), customer experience (via analysis of online reviews and forums), automated email processing, and other needs. These technologies are user friendly, available on demand, as a service.
  • Text mining:
  • Digital Reasoning, Luminoso and AlchemyAPI.
  • Image recognition and analysis: Image analysis now automatically identifies brand labels in pictures.
  • VisualGraph (now owned by Pinterest), Curalate, Piqora (nee Pinfluencer), and gazeMetrix.
  • Emotional analysis in images, audio, and video: These companies promote analysis of speech and facial expression primarily for structured studies
  • • Affectiva conducts webcam emotional analysis for media and ad research, including development tools to integrate emotional study in mobile apps. • Emotient performs emotional analyses in retail environments, evaluating signage, displays, and customer service. • EmoVu by Eyeris tests the engagement level of both short- and long-form video content. • Beyond Verbal studies emotion based on a person’s voice in real time.
cezarovidiu

Why Soft Skills Matter in Data Science - 0 views

data-informed.com/oft-skills-matter-data-science

bigdata data science

shared by cezarovidiu on 24 Jan 14 - No Cached
  • You cannot accept problems as handed to you in the business environment. Never allow yourself to be the analyst to whom problems are “thrown over the fence.” Engage with the people whose challenges you’re tackling to make sure you’re solving the right problem. Learn the business’s processes and the data that’s generated and saved. Learn how folks are handling the problem now, and what metrics they use (or ignore) to gauge success.
  • Solve the correct, yet often misrepresented, problem. This is something no mathematical model will ever say to you. No mathematical model can ever say, “Hey, good job formulating this optimization model, but I think you should take a step back and change your business a little instead.” And that leads me to my next point: Learn how to communicate.
  • In today’s business environment, it is often unacceptable to be skilled at only one thing. Data scientists are expected to be polyglots who understand math, code, and the plain-speak (or sports analogy-ridden speak . . . ugh) of business. And the only way to get good at speaking to other folks, just like the only way to get good at math, is through practice.
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  • Beware the Three-Headed Geek-Monster: Tools, Performance, and Mathematical Perfection Many things can sabotage the use of analytics within the workplace. Politics and infighting perhaps; a bad experience from a previous “enterprise, business intelligence, cloud dashboard” project; or peers who don’t want their “dark art” optimized or automated for fear that their jobs will become redundant.
  • Not all hurdles are within your control as an analytics professional. But some are. There are three primary ways I see analytics folks sabotage their own work: overly complex modeling, tool obsession, and fixation on performance.
  • In other words, work with the rest of your organization to do better business, not to do data science for its own sake.
  • Data Smart: Using Data Science to Transform Information into Insight by John W. Foreman. Copyright © 2013.
cezarovidiu

Is Big Data Really Working for Marketers? | ClickZ - 0 views

www.clickz.com/...a-really-working-for-marketers

shared by cezarovidiu on 09 Apr 14 - No Cached
  • Channel Optimization. Many marketers struggle to optimize each individual channel, let alone optimizing at a customer level across many channels. To the extent that Big Data can help marketers understand what is important in the moment and across touch points, that could be valuable, but it seems more of us need stronger attribution models and analytics methodologies more than access to data. Big Data does seem to be valuable if you want to understand which customers are highest value within each channel and across channels, because the platforms that manage Big Data can handle both structured and unstructured data - which is what you need to truly include Web/clickstream and social data in your analysis.
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