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Test Code : IBMSPSSMBPDM
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illustration of a Modeler stream running on Watson Studio computing device

IBM Watson Studio is an incredibly efficient records science offering. if you’re now not regularly occurring, Watson Studio is an integrated ambiance designed to execute it effortless to strengthen, teach, and manage fashions. It comprises a wide selection of tools used for every thing from records prep and blend to picture awareness. And now Watson Studio is on its solution to your windows or Mac computer laptop.

Overview of Watson Studio

Watson Studio contains lots of tools bar no one inside one built-in ambiance. the most widespread gear of Watson Studio are:

  • SPSS Modeler for information prep, blend, and modeling with out coding
  • data Refinery, a spreadsheet trend tool, that allows for quick, self-carrier facts preparation
  • Python, R, and Scala Jupyter Notebooks to create and share documents that embrace reside code, equations, visualizations, and explanatory textual content
  • Neural network Modeler for designing TensorFlow, Keras, PyTorch and Caffe buildings without coding
  • Interactive Dashboards to deliver sharable live studies so the conclusion users can learn their own insights
  • obtainable Deployment alternatives

    Watson Studio will quickly live accessible as three deployment options:

  • Watson Studio on the IBM Cloud is the simplest way to find began. It offers 3 tiers from a free starter tier with pay-as-you-go compute bar no one of the option to an enterprise edition.
  • Watson Studio local is their in the back of-the-firewall respond providing a at ease approach to add Watson Studio capabilities to your business. includes purchase options for a handful of users or the total corporation.
  • And coming soon: Watson Studio computing device
  • Introducing Watson Studio computer

    Watson Studio computing device is their fresh third deployment alternative in the Watson Studio family. It takes the most regular portions of what is obtainable within Watson Studio on the cloud and ports it to your home windows or Mac computing device for offline use, for evaluation of records information you don’t are looking to add, and for snappy performance. bar no one with a quick and straightforward computer setting up.

    Watson Studio on the computer will scale from an individual person bar no one the way as much as a huge team, and will live a noteworthy addition to the Watson Studio family unit together with their cloud and autochthonous flavors.

    The introductory main facets of Watson Studio laptop will live a SPSS Modeler canvas along side the records refinery spreadsheet editor. this could encompass backwards compatibility for SPSS Modeler streams.

    benefits of Watson Studio desktop
  • It’s fast, certainly for autochthonous info. expand individual productivity the utilize of a locally set in client.
  • It provides offline use. want to build a mannequin on a aircraft without WiFi? Watson Studio laptop will work with out an internet connection for weeks at a time.
  • Adhere to security and governance mandates together with your statistics. Watson Studio desktop allows your whole statistics to continue to live in your machine.
  • limitless modeling with out overages. using Watson Studio desktop, that you could improve fashions with no one incremental can charge. This means that you can build models iteratively to your computing device, then circulation it to Watson Studio on the Cloud to deploy.
  • It’s one Watson Studio, so no re-learning required. With the identical designs because the cloud and local models, switching between deployment alternatives is seamless with out a entertaining practising.
  • And identical to the relaxation of Watson Studio, anyone — coders or non-coders — should live able to set together, mix, explore, and mannequin their information in exactly minutes.
  • Watson Studio laptop’s efficient graphing skillsJoin the Beta

    The Beta for Watson Studio is starting now with two tools already ported to the laptop: a refreshed version of SPSS Modeler & statistics Refinery. it is going to furthermore embrace projects to disarrange your work, statistics property organization, and a light determine for updates characteristic.

    be a Part of the beta to give remarks on how you relish these equipment, and advocate which other tools they bring to the laptop. The Beta is a Part of the IBM SPSS Modeler Subscription Trial, so when you register for that you just’ll occupy entry to both downloads.

    After you register, survey for the “IBM SPSS Modeler Subscription Trial” card, click download, then you’ll observe the Watson Studio computing device Beta downloads on the right hand aspect of the web page.

    be Part of the discussion on the beta neighborhood

    After registering for the beta, connect within the discussion on the Watson Studio computing device beta community. here you could anticipate questions in regards to the beta, deliver suggestions for future elements, find discussion board guide, and participate in positive surveys to repercussion product path. connect the forum here via logging in with your IBMid.

    look forward to future announcements here about this enthralling addition for the Watson Studio household.

    Watson Studio computer task organizer

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

    IBM nowadays announced that it is bettering a few of its current software items with the Apache Spark open-source statistics processing engine, including the SPSS predictive analytics software.

    IBM bought SPSS for $1.2 billion in 2009. SPSS itself bar no one started at Stanford tuition in 1968 and is time-honored in statistics classes at universities.

    IBM is notably incorporating Spark into SPSS Modeler and SPSS Analytic Server, a spokesman advised VentureBeat in an electronic mail.

    Spark is additionally being utilized to IBM’s BigInsights, Streams, and DataWorks application, in accordance with a press release.

    The Sparkification of IBM comes after massive Blue introduced an incredible commitment to the open-source software in June. on the time, IBM introduced Spark as a carrier on its Bluemix cloud; that carrier is now often obtainable.

    IBM has made greater than 60 contributions to Spark on account that the June announcement, in accordance with the statement.


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

    essentially one year ago, IBM Corp. announced plans to combine its information science platform with Hortonworks Inc.’s software for statistics management. What IBM brought to the partnership become its statistics Science event, or DSX, answer, designed to let developers create analytics models and desktop researching tools without delay.

    given that then, IBM has been pleasant-tuning its statistics science mannequin by way of adding tools for DSX shoppers to entry tips kept within the Hortonworks information Platform information lakes.

    “DSX and Hortonworks are integrated to the factor where which you can control your Hadoop cluster and you may work for your Python fashions within DSX, and then propel it remotely to live done the region your statistics is,” celebrated Piotr Mierzejewski (pictured), application director of structure DSX, data science and desktop discovering at IBM. “That’s one of the vital big charge propositions.”

    Mierzejewski spoke with James Kobielus (@jameskobielus), host of theCUBE, SiliconANGLE Media’s cell livestreaming studio, at the DataWorks Summit in Berlin, Germany. They discussed current and soon-to-be brought aspects for DSX and the utilize of facts analytics gear in quite a lot of trade environments. (* Disclosure under.)

    The latest version of DSX includes a brace of key features for builders, including IBM’s SPSS Modeler that presents predictive analytics to unearth statistics patterns, wait on altenative making, and gain greater forecasting accuracy. “As of DSX 1.2, they now occupy integrated an SPSS modeler, redesigned and rebranded,” Mierzejewski pointed out. “this is remarkable know-how from IBM that has been around for ages.”

    in the next two months, IBM plans to add herbal language processing together with text and sentiment analysis to DSX, in response to Mierzejewski. The flush is to present purchasers full-scale flexibility, no matter if the records resides on-premises or in the cloud.

    “With DSX, they designed it to install, speed and scale in every single place,” Mierzejewski mentioned. “Now you could occupy this extendable collaborative platform that may install and speed in records facilities without wanting to access the cyber web. That’s crucial.”

    Watch the finished video interview under, and live certain to check out more of SiliconANGLE’s and theCUBE’s insurance of the DataWorks Summit eu. (* Disclosure: Hortonworks Inc. backed this section of theCUBE. Neither Hortonworks nor different sponsors occupy editorial control over content on theCUBE or SiliconANGLE.)

    photo: SiliconANGLE in view that you’re right here … … We’d relish to inform you about their mission and how you could wait on us fulfill it. SiliconANGLE Media Inc.’s company model is in keeping with the intrinsic value of the content material, no longer advertising. unlike many online publications, they don’t occupy a paywall or speed banner promoting, because they want to hold their journalism open, without occupy an outcome on or the should chase site visitors.

    The journalism, reporting and commentary on SiliconANGLE — along with live, unscripted video from their Silicon Valley studio and globe-trotting video groups at theCUBE — choose lots of tough work, time and funds. keeping the noteworthy elevated requires the wait on of sponsors who are aligned with their vision of ad-free journalism content.

    in case you relish the reporting, video interviews and other advert-free content right here, please choose a second to choose a survey at a pattern of the video content material supported with the aid of their sponsors, tweet your help, and hold coming back to SiliconANGLE.


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

    The Urban Attitudes Lab at Tufts University has conducted research on accessing "big data" on social 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 fresh ways of accessing how people contemplate about the places they live, work and play. "We're looking to observe how tapping into social 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 wait on governments learn what people are maxim and feeling, said Hollander. And while formal types of data can live used as proxies for how delighted people are, people openly share their sentiments on social networking sites.

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

    Hollander is furthermore interested in extracting data from social sites because it can live done on a 24/7 basis, which means not having to wait for government to administer surveys, relish the Decennial Census. Information from Twitter can furthermore live connected to place; Hollander has approximated that about 10 percent of bar no one tweets are geotagged to location.

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

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

    For its first study, the Urban Attitudes lab at Tufts University created its own software to sequester and resolve tweets geotagged in the region of fresh 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 Awful feelings to +5 for feelings of elation. The lab did this for the Twitter messages, and establish 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 furthermore used NVivo qualitative software to resolve 24 key words in a one-year sample of the city's meeting minutes. By searching for the selfsame words in Twitter posts, the researchers establish that "school," "health," "safety," "parks," "field" and "children" were used frequently across both mediums.

    Comparing tweets with the minutes, the lab's introductory findings presented at a Chicago conference on mountainous data suggest 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 social media sites, to cast a wider net. There is furthermore the possibility of analyzing the data for discussions, retweets, and people's positions on issues, although working with big datasets can live challenging, he said.   

    Maria Pina-Rocha, director of Management Information Systems for fresh Bedford, said there's a municipal utilize for the study's sentiment-combing software, especially for fresh Bedford's tourism business. Yet Pina-Rocha said she has reservations about whether Twitter is the right 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 survey at the data and it would play a role, but I'm not sure what weight I would give it," Pina-Rocha said. "I contemplate it is worthwhile to consider." 

    Still, Pina-Rocha said, social networking sites are the future public forums. "That's the way we're going these days -- people occupy 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 respond specific questions, such as: What is the overall emotion of a proposed municipal bike path? In this way, supplementing traditional information-gathering methods, he said, a city could find a sense of people's feelings about a project. 

    Still, there are limits to this type of data, Hollander said, because the sample is not representative of the wider population. For instance, younger users are more dynamic 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 furthermore not a valid statistical representation of the population, and that there might live some profit to the technology. 

    "It would organize what people are maxim and it might live a tool for planners and other government officials to find 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 fresh study contrasting Twitter posts from four Massachusetts cities with the recent election results. 

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

    "The view of really mining the attitudes and opinions of people using the Internet, using social media, is really the future," Hollander said. "As government professionals, they really occupy to find ways to tap into this lore groundwork and not just rely on the traditional vogue ways to understand how things are going." 


    Text Analytics Opportunities and Challenges for 2010 | killexams.com true questions and Pass4sure dumps

    My year-ago item, Perspectives on Text Analytics in 2009, was my second-most-viewed BeyeNETWORK article ultimate year. On the assumption that those readers weren’t bar no one employees of the vendors I quoted, given reader interest, I thought I’d revisit the topic. After all, users profit by knowing what vendor executives are seeing and planning for the year ahead. Given the nature of the text-analytics market – solutions are not standardized, much less commoditized – customers invest much more than money when they sign with a vendor, and they want to remain sure they’ll invest well.

    No single solution provider dominates text analytics. No single provider dominates any significant text-analytics market segment: not customer experience, media and publishing, monetary services and insurance, intelligence, life sciences, or e-discovery. This is pleasurable advice for current and prospective users. Each provider, to stay competitive, has to innovate, to stay ahead of evolving customer requirements. Further pleasurable news, judging from provider monetary results and from my discussions with halt users, is that most vendors are on target. They occupy done a pleasurable job tracking and meeting emerging needs. They requisite to stay on track in 2010.So I posed to vendor executives the selfsame question as ultimate year, just updated by a year:

    What attain you observe as the 3 (or fewer) most notable text-analytics technology, solution, or market challenges [or opportunities] in 2010?

    I’ll again relay responses with minimal editing, condensed in just a few cases. attain retain in intuition that the vendors quoted doubtless observe the greatest opportunities and challenges as residing in areas their solutions address. This focus is completely understandable; it simply reflects what’s most notable to them and their companies’ commercial success.Text Analytics in 2010 Let’s start with Ian Hersey, Global Chief Technology Officer and EVP, Products of the Attensity Group. Ian cofounded Inxight and later joined trade Objects and then SAP in a succession of acquisitions a brace of years back. According to Ian, there are two mountainous opening areas that will live front and focus in 2010:
  • Moving text analytics from a standalone application into a component of other applications. As with its larger cousin, the traditional BI solution, as a platform you occupy a limited set of very specialized users. But there's a requisite for analytics about textual content to live a Part of many applications that deal with textual content, for example, applications that wait on automate customer service in the contact focus should provide analytics about customer issues back to the business, not only to improve operational efficiency back to the summon focus management but furthermore to share more true time views into product or service issues with the repose of the business.
  • Incorporating social media into text analytics in ways that better profit businesses. Today they observe a number of players out there focused on sheer social media aggregation and lightweight analytics (e.g., buzz analysis, media monitoring), but they observe customers wanting to incorporate the social media into the selfsame analytical models as they utilize for their internal data and, more important, plug that social media into trade processes. An example again in the region of service is to live able to monitor forums for technical solutions to user issues and to vet and potentially incorporate those solutions into your own web self-service portal.
  • According to Ian, the biggest challenges in realizing these opportunities include:
  • The requisite to pre-build "out of the box" analytical models for the various applications. Today, high-end text analytics deployments involve definition of both the dimensions you want to utilize in your analysis as well as the tuning of the back-end text analysis componentry in order to achieve elevated levels of accuracy. Making that more self-service while still achieving trust in the data you create is perhaps the industry's biggest overall challenge.
  • The "language" of social media is different, and often context is fairly limited, which makes deriving value from much of it quite difficult. There is furthermore a LOT of it, so being able to effectively filter the data that's really valuable up front is very notable and not easy.
  • Breck Balwin of Alias-i is co-author of LingPipe natural-language processing software. The first of his two points takes us 180 degrees away from the requisite Ian Hersey sees to “pre-build ‘out of the box’ analytical models” –We are focusing on customizing text analytics solutions quickly and cheaply for customers. For example, most commercial sentiment classifiers are generic and performance suffers accordingly. They are focused on creating customized models at similar charge points with a combination of dynamic learning approaches (this maximizes the value of a human annotators efforts) and better user interfaces for getting training data.

    We occupy furthermore started to utilize crush sourcing as a means to create training data quickly [for utilize with in model structure via machine learning]. A nice example of this is their autism project which uses non-specialist volunteers to verify whether a highlighted phrase is a gene or a protein. occupy a survey at http://bionotate.hms.harvard.edu/autism/index.html. This project combines crush sourcing with machine learning and supports the elevated degree of customization necessary for elevated attribute analysis of the autism literature.

    Breck’s a noteworthy guy for lending Ian Hersey one of his “3 (or fewer)” points (even though Breck didn't know he was doing that). Thanks Breck! One of Ian’s points related to moving text analytics from a standalone application into a solution component, and others of his and Breck’s points focus on adapting the technology for users.

    Claire Thomas, text analytics lead at SAP, (independently) shares these views. Claire sees as notable 2010 text-analytics challenges –

  • Adoption by mainstream trade users remains the biggest challenge for their industry. They occupy made strides in showing their relevance for fresh user groups, with marketing departments interested in customer feedback analytics, and sentiment analysis being broadly applicable to various industries and initiatives. However, the vast majority of trade users still view text analytics as a specialized, emerging technology.
  • One mountainous blocker to mainstream adoption is integrating text analytics into solutions that compass beyond the small number of specialists using sheer text analytics applications. This requires text analytics to live accessible for the masses, but that can’t live at the expense of compelling results. That’s not an light balance to achieve, especially given the common view that modern technology should provide instant value.
  • We still requisite to find the trade world to observe textual data as a valuable asset to live managed. The issue is that while most structured data is inherently valuable, with unstructured data, there’s no control on the input, so it’s often arduous to uncover which Part of it is notable to store and analyze, not to mention what should live trusted. Additionally, unstructured data requires an extra step for sorting and sifting before any profiling and reporting can live done, posing yet another barrier to mainstream adoption.
  • Aaron B. Brown’s responses reflect Claire’s and others I’ve cited to this point. Perhaps SAP and IBM, Aaron’s employer (IBM trade Analytics & Process Optimization Software), occupy been passing around a pitcher of mega-vendor Kool Aid. Aaron wrote me:Market momentum has turned the spotlight onto three key challenges that kisser text analytics as it begins to inhabit [a] fresh mainstream role:
  • Meeting the growing solution need. Text analytics has traditionally establish its niche in focused solutions for voice-of-customer insight, intelligence, and, more recently, e-discovery. As these solutions are becoming mainstream, the innovation frontier is moving rapidly outward, and forward-thinking organizations are looking for fresh text analytics solutions that address a broader set of problems, including healthcare optimization, fraud mitigation, proactive attribute management, and more. This explosion of solution exact will challenge text analytics vendors and their technologies, in both the fresh demands that these applications set on the technology and the fresh requirements they impose for sophisticated analytical processing and seamless integration with broader information management capabilities (such as BI, BPM, collaboration, master data management, and predictive modeling) – both elements of creating complete solutions that fully address the trade need.
  • Quantifying the trade case for text analytics. Quantifying the ROI and creating the trade case for text analytics projects is still mostly an craft – particularly when the buyer knows there is insight trapped in unstructured information, but the exact value of that insight is unknown until well into the execution of the project. Organizations (and vendors) are still learning how to navigate these challenges, and this will remain a fundamental challenge to text analytics growth in 2010.
  • Closing the value loop by operationalizing the insight of text analytics. Historically, text analytics has served one of two usage patterns – exploratory text analytics to learn fresh insights from poorly understood content sets and operational text analytics to automate routine extraction of text for utilize in BI and reporting environments. To fully address its potential as a lever for trade optimization, text analytics has to evolve to integrate these two patterns and in doing so deliver accurate closed-loop performance management of content-rich trade processes. With exploratory text analytics identifying previously invisible process problems and bottlenecks, and operational text analytics enabling ongoing oversight of those problems through continuous delivery of text-based insight to reporting and predictive modeling tools, organizations can finally set a performance management lens on content-rich processes. Leading-edge organizations are already starting to attain this to improve healthcare delivery, reduce fraud and crime, proactively avoid product attribute issues, and more; the challenge in 2010 is to execute this transformative capability a mainstream reality.
  • A Focus on Sentiment Analysis All three respondents I’ve quoted so far occupy cited sentiment analysis. You’ll observe that text-analytics application recur in additional responses. Next, the response of Olivier Jouve, vice president of corporate evolution at SPSS, an IBM company. Olivier relates two challenges that IBM and SPSS occupy anticipated through their solutions:
  • Multi-lingual, multi-source sentiment analysis: Twitter, Facebook and other Web 2.0 media are the fresh critical sources for marketing. They present a variety of challenges for text analytics: they are voluminous, cryptic, multi-lingual, and deeply interconnected. Text analytics solutions require sophisticated data collection mechanisms, advanced multi-lingual analysis and the infrastructure to manage daily terabytes of data.
  • Associate buzz and sentiment to operational data: For a CMO, extraction of buzz and sentiment has limited value if it can't live connected to operational data relish marketing spend, basket analysis, shopper cart, or marketing campaigns. Predictive analytics, when associated with text analytics, allows [users] to detect early signals, measure or even anticipate crusade effectiveness, and act upon buzz measurement, for instance, to turn detractors into influencers.
  • David Seuss, CEO of Northern Light, says that vendors requisite imagination, however, in making the case to current and prospective sentiment-analysis users:The most notable challenge for the text analytics industry is developing applications beyond sentiment scoring that occupy a commercial market. There is limited money being earned... in any region other than reputation management based on sentiment scoring. The industry has demonstrated a stunning requisite of... fresh ideas that educate the market as to the potential text analytics has in trade research, sales opening discovery, competitive intelligence, strategic planning, product development, purchasing evaluations, and technology research.

    This is not an light task, as witnessed by the monetary Times's recent shuttering of the highly innovative Newssift.com [site, a monetary advice portal featuring faceted navigation and document-level sentiment analysis].

    If the text analytics industry is going to find itself out of the sentiment-scoring corner it has painted itself into, compelling applications... occupy to live developed, deployed, used, and publicized. Here is a fascinating statistic from one of their clients: At one big IT products and services firm, there are fewer than 50 staff members in the analyst relations/marketing communications department that might requisite sentiment scoring support. This selfsame arduous has 70,000 users in marketing, sales, competitive intelligence, strategic planning, product development, and technology research that can bar no one profit from text analytics applications applied to their needs.

    More sentiment analysis – I warned you – from Mattias Tyrberg, founder and CEO of Saplo, a Swedish start-up, but with a search-oriented twist:I believe that next year will focus on more advanced and personalized true time filters. The increased amount of information makes it impossible to read everything, but users attain not want to miss anything enthralling or important. The filters needs to live personalized because it's only then you can trust them and save time by not reading bar no one texts.

    In the selfsame way, sentiment analysis needs to live developed to live able to appropriate to each individual’s (or company’s) opinions. I believe that in 2010 they will live able to utilize better and more accurate sentiment analysis models that learn and appropriate from users.

    Market Growth Lexalytics CEO Jeff Catlin affirms other respondents’ themes. He sees search as a particular growth region and makes other points regarding text-analytics market growth:
  • Text analytics [TA] will become a mainstream feature set in enterprise search applications (though not by name). We’ve seen a steady march toward this in 2009, and it’s most notable in how accepted TA features are by the universal public. When I’m at a party now and uncover someone what they do, they sing “Oh yeah, I read something about that sort of stuff ultimate month” as opposed to the “Huh???” that I used to get. The outcome of this is that there are a lot more opportunities for TA in enterprise applications, and I suspect it will involve that one or two of the players may find picked up by a mountainous company.
  • Sentiment will complete its transition to a “checklist” feature that everyone who works in this space will occupy to provide. bar no one of the vendors (big and small) will claim to occupy sentiment. The consumers of this technology will furthermore find a bit more educated – we’re seeing this in RFP requests for particular capabilities of sentiment – which will wait on separate the wheat from the chaff. Unfortunately for us, sentiment won’t live a totally differentiating feature that you can hang a trade on anymore, as there will live lots of competition on the sentiment front.
  • The [differentiation between] larger TA players and the niche players will become even more obvious. The bigger players will integrate a number of useful and useable semantic features into their engines which will wait on with things relish ad hoc classification, concept roll-up, and relationship [extraction].
  • On the trade side, they anticipate 2010 to live a “Home Run” year for bar no one the TA vendors with growth rates of 75% to 200% not out of the norm. This is partly due to the mainstreaming of the technology, which is opening up a lot of additional verticals.
  • Technical Matters Of bar no one my respondents, only Manya Mayes, SAS chief text Mining strategist, chose to respond with technical challenges. I’m going to conclude that Manya is quite confident about SAS’s capacity to compete, hence her concern with further improving solution capabilities. According to Manya:
  • Handling data (or at least text) volumes is increasingly important. Semi-supervised learning could involve that rather than letting the model becoming dated and having to retrain the entire model, the fresh data could live added incrementally to the training data without requiring an entire rerun.
  • Text mining/text analytics will become critical enabling technologies to solutions such as risk management and customer intelligence.
  • Text cleansing is more notable than ever with Web data being so messy (acronyms, synonyms, misspellings etc.).
  • An Ecosystem Finally, responses from Sid Banerjee, CEO of Clarabridge, who relayed his thoughts and then expanded on them a bit in his blog. Sid’s third point, about a ally ecosystem, is particularly enthralling and matches something I’ve observed myself as a market observer. Sid’s responses:
  • Social media analysis will poke from "fad" to "function": Sentiment analysis is key, and it's finally pleasurable enough to find true insights... Social media [provides] a critical early-warning (before a customer buys) and customer-support (when a customer is having issues) indicator of customer experience… There are stakeholders in the Fortune 1000 now, with budget and interest, that didn't exist 2 years ago.
  • Enterprise expansion of text analytics will drive… application requirements: Systems will scale to support multi-terabyte solutions, enterprise security, [and] authentication, shielding [personal] data… Capital expenditure budgets will grow and more customers will originate deploying in-house text analytics solutions (though SaaS deployments will furthermore continue to grow).
  • Text Analytics will FINALLY live embraced by a affluent ally ecosystem in 2010. In 2009 there was some ally activity (text analytics vendors partnering with marketing services providers, database platform vendors selling text analytics solutions), but partnership activity did not drive elevated revenue growth for either side... They anticipate to observe strong and productive partnerships developing among text analytics vendors and data warehousing, CRM, and summon focus [vendors] and systems integrators.
  • Learning More I’ll conclude with a suggestion how you can learn more, whether you’re a fresh or experienced user or still evaluating solutions, whether your role is in trade or in information technology. For the broad set of text-analytics technologies and solutions, there’s no better venue than the Text Analytics Summit. This year’s summit, the 6th Boston summit, is slated for May 25-26. I anticipate to reprise my role as chair.

    And given fierce interest in sentiment analysis – in opinions, attitudes, mood, and other subjective information in social media, news, and enterprise feedback – I’ve organized a fresh conference, the 2010 Sentiment Analysis Symposium, April 13 in fresh York. The symposium will occupy a practical, solutions focus, bridging technology and trade concerns.

    I hope to observe many readers at these events. Whether you can execute it or not, if you’re a current or prospective text-analytics user and would relish to uncover me about challenges (and opportunities) you kisser or anticipate to kisser – about your text-analytics plans or experiences – gratify attain find in touch (grimes@altaplana.com or 301-270-0795).

  • Seth GrimesSeth Grimes

    Seth is a trade intelligence and determination systems expert. He is founding chair of the Text Analytics Summit 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. live sure to visit today!

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    References :


    Dropmark : http://killexams.com/demo-download/IBMSPSSMBPDM.pdf
    Dropmark-Text : http://killexams.dropmark.com/367904/12907899
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    Wordpress : https://wp.me/p7SJ6L-2ol
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