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IBMSPSSMBPDM exam Dumps Source : IBM SPSS Modeler - Business(R) confederate Data Mining Associate

Test Code : IBMSPSSMBPDM
Test appellation : IBM SPSS Modeler - Business(R) confederate Data Mining Associate
Vendor appellation : IBM
: 25 existent Questions

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IBM IBM SPSS Modeler -

IBM is rushing up SPSS with Apache Spark | killexams.com existent Questions and Pass4sure dumps

IBM nowadays introduced that it is bettering several of its present software products with the Apache Spark open-source information processing engine, together with the SPSS predictive analytics utility.

IBM bought SPSS for $1.2 billion in 2009. SPSS itself begun at Stanford university in 1968 and is general in records courses at universities.

IBM is in particular incorporating Spark into SPSS Modeler and SPSS Analytic Server, a spokesman told VentureBeat in an e mail.

Spark is too being utilized to IBM’s BigInsights, Streams, and DataWorks application, according to a press release.

The Sparkification of IBM comes after huge Blue announced an gargantuan dedication to the open-source application in June. at the time, IBM announced Spark as a provider on its Bluemix cloud; that provider is now frequently attainable.

IBM has made greater than 60 contributions to Spark given that the June announcement, according to the statement.


IBM Watson Studio is Coming to Your computer | killexams.com existent Questions and Pass4sure dumps

example of a Modeler scurry operating on Watson Studio computing device

IBM Watson Studio is a really potent data science providing. in case you’re not commonplace, Watson Studio is an integrated atmosphere designed to obtain it handy to advance, teach, and manipulate fashions. It includes a wide selection of tackle used for every thing from information prep and blend to photograph cognizance. And now Watson Studio is on its approach to your home windows or Mac desktop computing device.

Overview of Watson Studio

Watson Studio contains a variety of tools outright within one built-in ambiance. the most prevalent tools of Watson Studio are:

  • SPSS Modeler for statistics prep, mix, and modeling with out coding
  • records Refinery, a spreadsheet style device, that allows fast, self-carrier data training
  • Python, R, and Scala Jupyter Notebooks to create and partake documents that comprise reside code, equations, visualizations, and explanatory textual content
  • Neural network Modeler for designing TensorFlow, Keras, PyTorch and Caffe structures devoid of coding
  • Interactive Dashboards to carry sharable live reports so the conclusion users can find their own insights
  • purchasable Deployment options

    Watson Studio will soon be attainable as three deployment alternatives:

  • Watson Studio on the IBM Cloud is the easiest way to regain begun. It presents 3 tiers from a free starter tier with pay-as-you-go compute outright the option to an commercial enterprise edition.
  • Watson Studio local is their at the back of-the-firewall solution presenting a restful manner so as to add Watson Studio capabilities to your enterprise. contains buy alternate options for a handful of clients or the entire organization.
  • And coming quickly: Watson Studio computing device
  • Introducing Watson Studio computing device

    Watson Studio desktop is their original 3rd deployment option within the Watson Studio household. It takes the most accepted portions of what is available within Watson Studio on the cloud and ports it to your home windows or Mac laptop for offline use, for evaluation of records information you don’t want to add, and for snappy performance. outright with a speedily and straightforward computing device setting up.

    Watson Studio on the desktop will scale from an individual consumer outright the means up to a huge group, and should be a superb addition to the Watson Studio family unit along with their cloud and local flavors.

    The initial leading aspects of Watson Studio laptop might be a SPSS Modeler canvas along side the facts refinery spreadsheet editor. this may encompass backwards compatibility for SPSS Modeler streams.

    benefits of Watson Studio computer
  • It’s quickly, certainly for local files. boost particular person productivity using a locally reserve in client.
  • It provides offline use. want to build a mannequin on a airplane devoid of WiFi? Watson Studio computing device will travail devoid of an internet connection for weeks at a time.
  • Adhere to safety and governance mandates along with your facts. Watson Studio desktop makes it viable for your entire information to remain to your desktop.
  • limitless modeling devoid of overages. using Watson Studio laptop, that you can extend fashions without any incremental cost. This permits you to construct fashions iteratively on your computing device, then scurry it to Watson Studio on the Cloud to installation.
  • It’s one Watson Studio, so no re-researching required. With the identical designs because the cloud and local types, switching between deployment alternatives is seamless with out a exciting practicing.
  • And just dote the relaxation of Watson Studio, any individual — coders or non-coders — may be able to prepare, blend, discover, and mannequin their data in precisely minutes.
  • Watson Studio computer’s powerful graphing expertiseJoin the Beta

    The Beta for Watson Studio is starting now with two tools already ported to the computing device: a refreshed version of SPSS Modeler & information Refinery. it will too involve tasks to organize your work, information assets company, and a facile determine for updates feature.

    be partake of the beta to provide feedback on how you dote these equipment, and imply which other tackle they deliver to the computing device. The Beta is a partake of the IBM SPSS Modeler Subscription Trial, so once you register for that you’ll hold access to each downloads.

    After you register, search the “IBM SPSS Modeler Subscription Trial” card, click on download, then you definately’ll see the Watson Studio desktop Beta downloads on the privilege hand facet of the web page.

    join the discussion on the beta neighborhood

    After registering for the beta, be a partake of in the dialogue on the Watson Studio desktop beta community. here which you could exact questions about the beta, supply recommendations for future features, regain forum aid, and participate in useful surveys to impress product route. combine the forum here via logging in with your IBMid.

    watch for future bulletins here about this entertaining addition for the Watson Studio family unit.

    Watson Studio computing device mission organizer

    IBM continues to first-rate-tune DSX in live performance with Hortonworks | killexams.com existent Questions and Pass4sure dumps

    essentially 365 days in the past, IBM Corp. introduced plans to merge its records science platform with Hortonworks Inc.’s software for information administration. What IBM dropped at the partnership became its statistics Science event, or DSX, solution, designed to let builders create analytics fashions and laptop researching tools directly.

    when you reckon that then, IBM has been quality-tuning its data science mannequin by using including tackle for DSX consumers to access tips saved within the Hortonworks records Platform information lakes.

    “DSX and Hortonworks are integrated to the point where that you can manipulate your Hadoop cluster and you can travail for your Python fashions inside DSX, and then push it remotely to be executed the plot your records is,” observed Piotr Mierzejewski (pictured), program director of development DSX, information science and laptop researching at IBM. “That’s probably the most huge charge propositions.”

    Mierzejewski spoke with James Kobielus (@jameskobielus), host of theCUBE, SiliconANGLE Media’s cellular livestreaming studio, at the DataWorks Summit in Berlin, Germany. They discussed latest and shortly-to-be introduced points for DSX and the expend of data analytics tools in quite a few commercial enterprise environments. (* Disclosure beneath.)

    The latest version of DSX contains a pair of key aspects for builders, together with IBM’s SPSS Modeler that offers predictive analytics to unearth statistics patterns, serve determination making, and profit more forecasting accuracy. “As of DSX 1.2, we've integrated an SPSS modeler, redesigned and rebranded,” Mierzejewski spoke of. “here's outstanding technology from IBM that has been round for a while.”

    within the next two months, IBM plans to add herbal language processing along with textual content and sentiment evaluation to DSX, in response to Mierzejewski. The goal is to offer purchasers full-scale flexibility, whether the records resides on-premises or in the cloud.

    “With DSX, they designed it to set up, elude and scale in outright places,” Mierzejewski said. “Now you could hold this extendable collaborative platform that can installation and elude in records facilities with out needing to access the internet. That’s crucial.”

    Watch the complete video interview under, and obtain inescapable to check out greater of SiliconANGLE’s and theCUBE’s coverage of the DataWorks zenith eu. (* Disclosure: Hortonworks Inc. sponsored this segment of theCUBE. Neither Hortonworks nor different sponsors hold editorial wield over content on theCUBE or SiliconANGLE.)

    photo: SiliconANGLE on account that you’re here … … We’d dote to inform you about their mission and the way you can aid us fulfill it. SiliconANGLE Media Inc.’s traffic model is according to the intrinsic value of the content, now not promoting. in contrast to many online publications, they don’t hold a paywall or elude banner promoting, as a result of they want to hold their journalism open, devoid of hold an effect on or the should chase site visitors.

    The journalism, reporting and commentary on SiliconANGLE — together with are living, unscripted video from their Silicon Valley studio and globe-trotting video groups at theCUBE — buy a lot of challenging work, time and funds. maintaining the exceptional elevated requires the sheperd of sponsors who are aligned with their imaginative and prescient of advert-free journalism content.

    in case you just dote the reporting, video interviews and other ad-free content material privilege here, please buy a second to buy a view at a sample of the video content supported via their sponsors, tweet your help, and retain coming back to SiliconANGLE.




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    IBMSPSSMBPDM exam Dumps Source : IBM SPSS Modeler - Business(R) confederate Data Mining Associate

    Test Code : IBMSPSSMBPDM
    Test appellation : IBM SPSS Modeler - Business(R) confederate Data Mining Associate
    Vendor appellation : IBM
    : 25 existent Questions

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    Can Government Mine Tweets to Assess Public Opinion? | killexams.com existent questions and Pass4sure dumps

    What if instead of going to a city meeting, you could scurry on Twitter, tweet your opinion, and still be heard by those in government? original research suggests this is a possibility. 

    The Urban Attitudes Lab at Tufts University has conducted research on accessing "big data" on gregarious networking sites for civic purposes, according to Justin Hollander, associate professor in the Department of Urban and Environmental Policy and Planning at Tufts.  

    About six months ago, Hollander began researching original ways of accessing how people mediate about the places they live, travail and play. "We're looking to see how tapping into gregarious media data to understand attitudes and opinions can profit both urban planning and public policy," he said.   

    Harnessing natural comments -- there are about one billion tweets per day -- could serve governments learn what people are maxim and feeling, said Hollander. And while formal types of data can be used as proxies for how jubilant people are, people openly partake their sentiments on gregarious networking sites.

    Twitter and other gregarious media sites can too provide information in an unobtrusive way. "The understanding is that they can capture a potentially more valid and reliable view [of people's] opinions about the world," he said. As an inexact science, gregarious science relies on a wide range of data sources to inform research, including surveys, interviews and focus groups; but people respond to being the topic of study, possibly affecting outcomes, Hollander said. 

    Hollander is too interested in extracting data from gregarious sites because it can be done on a 24/7 basis, which means not having to wait for government to administer surveys, dote the Decennial Census. Information from Twitter can too be connected to place; Hollander has approximated that about 10 percent of outright tweets are geotagged to location.

    In its first study earlier this year, the lab looked at using astronomical data to learn about people's sentiments and civic interests in original Bedford, Mass., comparing Twitter messages with the city's published meeting minutes. 

    To extract tweets over a six-week epoch from February to April, researchers used the lab's own software to capture 122,186 tweets geotagged within the city that too had words pertaining to the original Bedford area. Hollander said anyone can regain API information from Twitter to too mine data from an region as minuscule as a neighborhood containing a pair hundred houses. 

    For its first study, the Urban Attitudes lab at Tufts University created its own software to sequester and analyze tweets geotagged in the region of original Bedford, Mass.

    Researchers used IBM's SPSS Modeler software, comparing this to custom-designed software, to leverage a sentiment dictionary of nearly 3,000 words, assigning a sentiment score to each phrase -- ranging from -5 for needy feelings to +5 for feelings of elation. The lab did this for the Twitter messages, and found that about 7 percent were positive versus 5.5 percent negative, and correspondingly in the minutes, 1.7 percent were positive and .7 percent negative. In total, about 11,000 messages contained sentiments. 

    The lab too used NVivo qualitative software to analyze 24 key words in a one-year sample of the city's meeting minutes. By searching for the identical words in Twitter posts, the researchers found that "school," "health," "safety," "parks," "field" and "children" were used frequently across both mediums.

    Comparing tweets with the minutes, the lab's preliminary findings presented at a Chicago conference on astronomical data imply that slightly more sentiment was expressed in the Twitter realm, and that people in both the formal and informal contexts had expressed similar interests. 

    Hollander said other researchers or applications could expand the timeline for extracting data from gregarious media sites, to cast a wider net. There is too the possibility of analyzing the data for discussions, retweets, and people's positions on issues, although working with large datasets can be challenging, he said.   

    Maria Pina-Rocha, director of Management Information Systems for original Bedford, said there's a municipal expend for the study's sentiment-combing software, especially for original Bedford's tourism business. Yet Pina-Rocha said she has reservations about whether Twitter is the privilege forum to assess opinions on city-related issues, and to what extent opinions shared there are done so for other reasons, such as a way to blow off steam. 

    "I would view at the data and it would play a role, but I'm not positive what weight I would give it," Pina-Rocha said. "I mediate it is worthwhile to consider." 

    Still, Pina-Rocha said, gregarious networking sites are the future public forums. "That's the way we're going these days -- people hold their opinions and they are putting it out there." 

    Using the study's way of categorizing sentiments on Twitter, Hollander said governments can mine the data to answer specific questions, such as: What is the overall sentiment of a proposed municipal bike path? In this way, supplementing traditional information-gathering methods, he said, a city could regain a sense of people's feelings about a project. 

    Still, there are limits to this nature of data, Hollander said, because the sample is not representative of the wider population. For instance, younger users are more energetic on these sites, according to Philip Chester, who serves as town planner for Lebanon, Conn., who sticks mainly to e-mail. Even so, he acknowledged that a public hearing, which is one way he gleans public opinion, is too not a valid statistical representation of the population, and that there might be some profit to the technology. 

    "It would organize what people are maxim and it might be a tool for planners and other government officials to regain some of the pulse of what's out there," said Chester, who studied alongside Hollander in graduate school.  

    Next up for the lab is a original study contrasting Twitter posts from four Massachusetts cities with the recent election results. 

    As for using the technology to serve government, Hollander said that is partake of the lab's mission. Hollander is now engaging in discussions with viable government partners, and he is too looking at opportunities with Foursquare, a location-based gregarious media platform. He said is interested in studying other gregarious platforms, including Facebook, Tumblr, Wanelo and Yik Yak. 

    "The understanding of really mining the attitudes and opinions of people using the Internet, using gregarious media, is really the future," Hollander said. "As government professionals, they really hold to find ways to tap into this knowledge ground and not just rely on the obsolete style ways to understand how things are going." 


    Perspectives on Text Analytics in 2009 | killexams.com existent questions and Pass4sure dumps

    You’re reading this article because you’re actively interested in text analytics technology and market trends. Whether you’re a solutions provider or consumer, you want to be positive you’ve invested your time and pecuniary resources wisely.

    The picture is complex, however, as the text analytics domain is not dominated by any one algorithm or approach, vendor or traffic sector. So far, the diversity of options has benefitted potential users; awareness (and realization) of the technology’s capabilities is pile rapidly in areas such as media monitoring, publishing, customer-experience management and semantic search. Meanwhile, growth remains stout in domains that hold long applied text analytics. These areas involve life sciences, intelligence and law enforcement, and pecuniary services. While the text analytics domain isn’t unaffected by current traffic conditions, economic pressures could actually spur uptake motivated by a quest for efficiency through automation.

    Sources and Perspectives

    To remain on top of trends, I try to talk to current and prospective users as often as I can, and I too try to preserve up with researchers and fellow analysts. (You’ll find links for some of the folks I succeed at my BeyeNETWORK channel.) I too entangle up with vendors periodically.

    Because it’s helpful to understand current industry perspectives, I recently invited CEOs, CTOs and thought leaders to respond to the following query:

    What finish you see as the 3 (or fewer) most essential text analytics technology, solution or market challenges in 2009?

    Before relaying answers I received, I’ll mention two meta-replies. First, Marti Hearst, associate professor in the UC Berkeley School of Information, declined to respond, in partake because “…for me, 2009 is too close a time horizon.” This thought applies not only to academic researchers but too to anyone who’s thinking strategically, beyond this year’s crop of product releases and traffic conditions. Next, Lexalytics CEO Jeff Catlin candidly characterized his response as “somewhat self-serving thoughts.” No doubt many of the vendors quoted see the greatest challenges as residing in areas their tools address, so buy their views with a grain of salt.

    I’ll relay outright views with minimal editing, complete with vendor self-promotion because, after all, commercial products are “where the rubber hits the road” for most enterprise users.

    Text Analytics in 2009

    Let’s start with Maria Milosavljevic, CTO at Capital Markets Cooperative Research Centre, an Australian group that is conducting leading-edge R&D in real-time security-market surveillance. The top 3 for Maria are:

  • Expectations: Unfortunately I mediate that the most difficult challenges of the past are still with us, and this is the biggest. People either believe that not much at outright is viable or they believe that more is viable than reality. It is never the case where discontinuance users hold realistic expectations.
  • Portability: Text analytics systems trained on a particular nature of input data finish not typically transfer well to other types of input. Cross-domain issues (e.g., from advice to medical) are similar to transfer between intra-domain input documents (e.g., from medical advice to medical journals).
  • Data quality: Data is never clean. Garbage in = garbage out!
  • Breck Baldwin of Alias-i, author of LingPipe natural language processing software, is very to-the-point in his reply, with a sort of twist on Maria’s expectations response.

    #1 thing the sphere needs is a profound and existent success myth of text analytics.

    The reply from Sid Banerjee, CEO of Clarabridge, looks at factors related to scaling text analytics to the enterprise, “the expansion of text analytics across a few dimensions.” His reply:

  • From a functional to an enterprise imperative. In 2006-2008, text analytics was deployed to marketing, convoke center, survey groups. Over the past few years, solutions hold morphed from those that serve one group to an offering that supports multiple groups, and now customers are looking, on day one, to pick solutions that can demonstrate cross-functional value. The selling cycles are more complex, the solutions necessity to present more a priori traffic value and relevance, and at the identical time, because the solutions are not just deployed in one area, IT wants to know more how the solution is going to felicitous with "enterprise" standards. In total: selling is now more complex, more constituencies are involved in the decision, but at the identical time articulating and demonstrating traffic value is even more important.
  • From an isolated to an integrated solution. Toward the discontinuance of 2008, they started seeing more interest from companies and partners looking to seamlessly integrate the results of text analytics back into operational systems, i.e., process customer verbatims and merge the categorized, scored results back into convoke heart applications. This original expend cycle – not just for analytics, but for operational integration – provides an titillating occasion for text analytics vendors to reckon whether to be a stand-alone application or to be more tightly integrated with confederate products and offerings.
  • From a static to a scalable architecture. The astronomical myth in 2008 was scale. Data volumes are going up. Usage requirements are going up. There's no reason to hope scalability requirements won’t continue to grow in 2009 and beyond. The winners will be those who can see beyond today's data and user volumes and design for an order or two more magnitude in their offerings, predicting this inescapable future.
  • Aaron B. Brown of IBM similarly looks at architectural issues and too at traffic concerns. Aaron is Program Director, ECM Discovery, for IBM Information Management Software and has mighty market and technical insights. I interviewed him final year on “Text Analytics for Legal Compliance.” That exchange is still a advantageous read. Here are his thoughts on challenges for 2009:

  • Defining the traffic case for text analytics. In the current economic situation, organizations are clamping down on original projects and more than ever looking for arduous ROI savings to justify investment. To pass the funding bar, text analytics solutions, which typically topple in the category of original projects undertaken for traffic optimization, necessity to promote with solid traffic cases that demonstrate hard-dollar operational savings based on proven examples. Given the emerging nature of many text analytics solution areas, this will be a challenge to growth in 2009.
  • Creating fully interactive exploratory analytics. Most current text analytics approaches rely on extensive design-time configuration and customization. This has limited its applicability to expend cases where categories of extracted entities, etc. are understood up front. An emerging class of investigation-centric applications, such as eDiscovery, requires text analytics that can be rapidly and seamlessly reconfigured by the traffic analyst as they explore data sets and interactively refine their understanding of what needs to be extracted. Further, these applications add pressure to democratize text analytics, as the traffic user can no longer wait for a linguistic or data processing expert to reconfigure the analytics each time a original insight is reached. The original challenge for text analytics is to enable extraction and analysis that are near instantaneous and at the identical time are reconfigurable at interactive speeds by a traffic user without specialized text or linguistic expertise.
  • Expanding mainstream expend cases for text analytics. To date, text analytics has had stout traction in inescapable niche solution markets. However, it has yet to be widely adopted as a horizontal capability powering broad traffic optimization expend cases in content-centric (e.g., ECM), process-centric (e.g., BPM) or data-centric (e.g., BI) applications. As the market moves toward mainstream usage, text analytics technologies (and their relatives in text classification and search) will necessity to integrate more tightly into information and process management platforms – potentially being subsumed into them as native capability – and solutions will shift to leveraging analytics in conjunction with broader information management and technologies. 2009 will be a captious year for text analytics to start making this transition.
  • Other replies too looked at traffic conditions and at experiences in the adoption and expend of text technologies. Lexalytics CEO Jeff Catlin foresees:

  • The needy economy will shift the sale of text analytics to larger companies. We're already seeing this in their sales, which used to be about two-thirds minuscule companies and one-third large companies. Now it's mostly larger companies. The upside of this is that astronomical companies are spending on search and text analytics as they search to save money and extend efficiency due to lower staffing levels.
  • Technical push: Their push this year is going to be on empowering the customers with easy-to-use linguistic tools that will allow users to build and deploy some of their own text analytics bits and pieces using their core frameworks. The first of these for us is a user-driven entity recognizer that will allow users to note up domain-specific text (let’s disclose medical text) with entities (diseases); and then after marking up a hundred or so stories, the system will build a "model" or recognizer for that nature of entity so that it can deduce other diseases from how they’re described in the text. We'll be releasing their first version of this by late February/early March and hope to further enhance the tool over the course of the year to further empower the users. Their initial market research indicates that publishers/media would find such tools very valuable because of the amount of time/money they expend on maintaining lists.
  • I hope the number of vendors to contract as those with weaker or narrower offerings find it increasingly difficult to sell in this very challenging environment. To obtain a scurry of it in 2009, it seems that at a minimum, vendors necessity to provide: entity extraction, concept extraction and possibly sentiment analysis.
  • Craig Norris, CEO at Attensity, similarly famed three varieties of challenge, in his case relating to technology, implementation and seeing occasion in adverse traffic conditions:

  • Getting sentiment right: Many of the vendors in the text-analytics space hold pretty advantageous text classification technology – where the application classifies terms based on dictionary entries or predefined lists. The output looks advantageous – but because only terms are looked at, there tend to be a lot of incorrect positives – negations are missed, relationships (such as why someone is unhappy) are never found. Many customers hold purchased technology that only goes this far. They will be challenged this year by consumers of the data who find the data is, in fact, wrong. Natural language processing (NLP) technology dote Attensity's not only finds sentiment; it can find sentiment in context, determining accurately whether it's negative or positive, the degree [or intensity] of it, and the “why” behind it.
  • Getting to Action: Another challenge this year will be around getting past high-level views of the data (general ratings of sentiment, general views of issues) to the root cause so that users can buy action and companies can regain existent value. For example, knowing not only that a customer is unhappy with a product, but too that they intend to return it if they don't regain a convoke back or a fix for their problem is captious to being able to remedy a situation and save a customer. Being able to know this is where the existent ROI is for text analytics products. This is only viable with an approach that not only finds sentiment but can too understand the relationships between the issues and the reasons for the issues.
  • The economy: Certainly the economy will be a challenge for any technology solution vendor this year. Their offering has proven to enable two things to serve companies in a taut economy: revenue preservation – find out if customers are going leave, understand why and save them; and revenue growth – understand if customers are having issues ("cries for help") and delight them by acting on those issues. This generates improbable word of mouth and ultimately growth.
  • Solutions Focus

    Vendor prognosticators with a solutions focus involve Keith Collins, CTO at SAS, and Manya Mayes, SAS Chief Text Mining Strategist, see as challenges for 2009:

  • A broader set of vertical/horizontal offerings including more automated unstructured (text, voice, image) capabilities must be delivered for customer/product/competitive intelligence. SAS is doing this, for example, with SAS Text Miner integration into SAS Warranty Analysis. Automated capabilities involve graphics, sentiment analysis, net promoter scores, and key performance indicators (KPIs) from text analysis results.
  • Solutions providers must buy text analytics and search across the breadth of their offerings: For SAS, text+DI [data integration], text+BI[business intelligence], text+DataFlux, text+JMP, text+SOO [Service Operations Optimization], etc. SAS customers are requesting these capabilities, and they are pile software and planning road maps accordingly.
  • Customers are consolidating software and needing to settle on one vendor that can wield outright approaches to text – text analytics/mining/business analytics/search/categorization.
  • Collaboration and mobile BI are captious needs, and vendors necessity to be agile and scurry technology swiftly to meet the needs of consumers.
  • Yves Schabes, President of Teragram, a SAS company, focuses his response on one particular challenge – increasing workers’ efficiency:

    Given the current economic situation, large organizations are forced to preserve up a speedily pace of traffic with fewer resources, and it is therefore captious that these organizations hold outright of their content organized correctly so that employees can expend more time fulfilling their jobs and less time searching for information on their enterprise's system.

    Neil Hartley, CEO of Leximancer, sees “an industry that has been around for a long time and yet has seen microscopic in the way of being operationalized within traffic processes.” Neil continues:

    Yes, there are proofs of concept, elude by experienced vendor staff but these [prototypes], in my experience, rarely regain adopted by the traffic a) because of the setup and maintenance required, and b) because the source data vocabulary changes making the initial setup redundant. Autonomy is the exception, but their deployments are largely search/retrieval-based and less qualitative.

    What the traffic needs is a elevated degree of automation (without the necessity for inordinate setup and maintenance) together with clarity in the analysis and the faculty to apply control to the process where needed. This is exactly what Leximancer provides.

    The other major trend I see is the necessity to obtain gregarious media actionable, and this is something we’ve focused on heavily on their blog. I mediate customer attitudes on gregarious media or microblogging sites are a leading indicator for the business. The traffic that waits for these trends to be reflected in their formal feedback programs may find it is too late to buy efficient action.

    Technology and Applications

    I had expected that a higher harmony of 2009 “challenges” responses would heart on the technology and its applications. Responses I’ve already quoted touch on those areas with perhaps greater attention to traffic and market concerns. Eric Martin, Product Marketing Manager at SPSS, focused solely on solutions, which to me reflects both Eric’s self-possession in his company’s market position and his own background: Eric earned a Ph.D. in immunology and cellular biology and, dote many others, started using text mining on biomedical articles before joining text analytics pioneer LexiQuest, which was later acquired by SPSS. Challenge areas Eric sees for 2009 are:

  • Blog analysis: Already sweltering and will probably regain bigger in 2009. There's a lot of confusion though between blogs and other kindhearted of user-generated content or Web 2.0 data. That's too very solution-oriented and not only requires text analytics but too data mining, Web scraping capabilities, etc.
  • Sentiment analysis: Not an option anymore in most projects. partake of Voice of the Customer, blog analysis applications, etc. elevated quality out-of-the-box results are demanded more and more.
  • Automated translation: too getting more and more market traction prior to text analysis.
  • Matthew Hurst of Microsoft Live Labs is likewise a techie at heart, and he’s too very interested in user-generated and other online content. Matthew says, “As reported elsewhere in research and industry literature, the majority of textual data being published online today is from the many genres of gregarious media. To fully leverage this data, the key challenges are:

  • Comprehensive and complete data acquisition: Due to the gregarious nature of this content, missing documents or authors is dote missing the replies to answers or key voices in the choir.
  • Tuning or recreating criterion tools to deal with less formal content: Tokenization, sentence segmentation, part-of-speech (POS) tagging, parsing, etc. outright hold different qualities and requirements in the gregarious space.
  • Development and upkeep of broad (product) ontologies: This is a key requirement for grounding any analytics.
  • Lastly, Ren Mohan, Co-Chairman and CTO of IxReveal, replied not only with the three challenges his company hears most often from their clients, but too with a pair of “megatrends” he is dawn to see. Challenges are:

  • Speed to implementation: Clients remain concerned about implementation taking months before realizing any benefit. Immediate payback, more dote a pair of weeks, seems to be the mantra!
  • Systems should dynamically and quickly react to changing original information: Clearly, clients finish not want to scurry through an extensive process of rebuilding their analysis when requirements for analysis change. outright their customers preserve changing their analysis as original information unfolds.
  • Finding actionable intelligence in data that is not grammatical: Less time in convoke handling, texting, microscopic patience, quick notes by data sources – this implies an increasing amount of text data has embedded meanings but is less grammatical. So, their clients are asking us to serve them analyze such data as well.
  • Ren’s “megatrends” are:

  • The line between structured and unstructured is blurring and a original trend is emerging: Users want a original approach altogether. As opposed to converting unstructured to structured, users would dote to see it the other way, and they want it quickly and easily. In the ISO [insurance industry] fraud/claims management conference, the keynote presenter asked how many wished their systems could respond with Google-like access and ease; 100% of the audience raised their hands. When he asked how many of their systems behave dote that today, no hands went up! They are outright using SQL. So, they see a original trend in analysis going from structured (SQL dote complexity) to unstructured (search dote simplicity)!
  • Thus far, text analytics has been available only for mega corporations! Now, the trend is catching on in the consumer/desktop user world as well. Products like uReka! from IxReveal and SearchWiki from Google are examples. They give users a way to store and reuse search queries and links, and personalize them. This is the dawn of analysis as users hold to now mediate (analyze) about content databases, links, gregarious networks, etc.
  • Ren’s conclusion: “These are truly exciting times!” Indeed.

    A Live Challenge?

    Here are thoughts on one more potential challenge for 2009, a live challenge, of a different variety.

    As an aside while forwarding his buy on 2009 text-analytics challenges, Lexalytics’ Jeff Catlin suggested a bake-off to be held at the 2009 Text Analytics Summit, which is slated for June 1-2 (preceded by tutorials) in Boston. Jeff says in his blog, “Our market is still dominated by too much flashy marketing and not enough down in the dirt numbers for ‘apples to apples’ comparisons.” Jeff is right, although I’ll add that I believe that most of the flashy marketing actually fronts very capable products. Perhaps they can regain something going for this year – a commercially oriented version of the TREC (Text REtrieval Conference) challenge. remain tuned, and finish reckon attending this year’s summit.

    And if you’re a current or prospective text analytics user and would dote to narrate me about challenges you countenance or hope to face, delight finish regain in touch by email at grimes@altaplana.com or by phone at (301) 270-0795).

    SOURCE: Perspectives on Text Analytics in 2009

  • Seth GrimesSeth Grimes

    Seth is a traffic intelligence and determination systems expert. He is founding chair of the Text Analytics zenith and principal consultant at Washington, D.C., based Alta Plana Corporation. Seth consults, writes, and speaks on information-systems strategy, data management and analysis systems, IT industry trends, and emerging analytical technologies. Seth chairs the Sentiment Analysis Symposium and the Text Analytics Summit.

    Editor’s Note: More articles and resources are available in Seth's BeyeNETWORK Expert Channel. be positive to visit today!

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    References :


    Dropmark : http://killexams.com/demo-download/IBMSPSSMBPDM.pdf
    Dropmark-Text : http://killexams.dropmark.com/367904/12907899
    Blogspot : http://killexamsbraindump.blogspot.com/2017/12/pass4sure-ibmspssmbpdm-real-question.html
    Wordpress : https://wp.me/p7SJ6L-2ol
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