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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 universal 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 stupendous 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.


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example of a Modeler coast 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 accomplish it handy to advance, teach, and maneuver 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 complete 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 live attainable as three deployment alternatives:

  • Watson Studio on the IBM Cloud is the easiest mode to Get begun. It presents 3 tiers from a free starter tier with pay-as-you-go compute complete the option to an commercial enterprise edition.
  • Watson Studio local is their at the back of-the-firewall solution presenting a snug 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 modern 3rd deployment option within the Watson Studio household. It takes the most common 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. complete with a dote a glitter and straightforward computing device setting up.

    Watson Studio on the desktop will scale from an individual consumer complete the means up to a huge group, and should live 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 live 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 build in client.
  • It provides offline use. want to build a mannequin on a airplane devoid of WiFi? Watson Studio computing device will work 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 workable for your entire information to remain to your desktop.
  • limitless modeling devoid of overages. using Watson Studio laptop, that you can expand fashions without any incremental cost. This permits you to construct fashions iteratively on your computing device, then coast 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 ease of Watson Studio, any individual — coders or non-coders — may live 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 embrace tasks to organize your work, information assets company, and a facile determine for updates feature.

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

    After you register, seek the “IBM SPSS Modeler Subscription Trial” card, click on download, then you definately’ll survey 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, live a portion of in the dialogue on the Watson Studio desktop beta community. here which you could request questions about the beta, supply recommendations for future features, Get forum aid, and participate in useful surveys to influence product route. link the forum here via logging in with your IBMid.

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    essentially 365 days in the past, IBM Corp. introduced plans to mingle 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 deem 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 maneuver your Hadoop cluster and you can work for your Python fashions inside DSX, and then push it remotely to live executed the station your records is,” observed Piotr Mierzejewski (pictured), program director of progress DSX, information science and laptop researching at IBM. “That’s probably the most huge price 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 spend of data analytics tools in quite a few commercial enterprise environments. (* Disclosure beneath.)

    The latest version of DSX contains a couple of key aspects for builders, together with IBM’s SPSS Modeler that offers predictive analytics to unearth statistics patterns, attend conclusion 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 tender purchasers full-scale flexibility, whether the records resides on-premises or in the cloud.

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

    Watch the complete video interview under, and accomplish inevitable to check out greater of SiliconANGLE’s and theCUBE’s coverage of the DataWorks pinnacle eu. (* Disclosure: Hortonworks Inc. sponsored this segment of theCUBE. Neither Hortonworks nor different sponsors acquire editorial maneuver 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 artery you can aid us fulfill it. SiliconANGLE Media Inc.’s trade model is according to the intrinsic value of the content, now not promoting. in contrast to many online publications, they don’t acquire a paywall or flee banner promoting, as a result of they want to hold their journalism open, devoid of acquire an upshot 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 — engage a lot of challenging work, time and funds. maintaining the exceptional towering requires the steer 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 engage a second to engage a leer at a sample of the video content supported via their sponsors, tweet your help, and retain coming back to SiliconANGLE.


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    The Urban Attitudes Lab at Tufts University has conducted research on accessing "big data" on companionable 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 modern ways of accessing how people assume about the places they live, work and play. "We're looking to survey how tapping into companionable 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 attend governments learn what people are epigram and feeling, said Hollander. And while formal types of data can live used as proxies for how gay people are, people openly partake their sentiments on companionable networking sites.

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

    Hollander is too interested in extracting data from companionable sites because it can live 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 live connected to place; Hollander has approximated that about 10 percent of complete tweets are geotagged to location.

    In its first study earlier this year, the lab looked at using gargantuan data to learn about people's sentiments and civic interests in modern 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 modern Bedford area. Hollander said anyone can Get API information from Twitter to too mine data from an area as wee as a neighborhood containing a couple hundred houses. 

    For its first study, the Urban Attitudes lab at Tufts University created its own software to insulate and resolve tweets geotagged in the area of modern 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 wicked 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 resolve 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 prefatory findings presented at a Chicago conference on gargantuan data intimate 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 companionable 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 great datasets can live challenging, he said.   

    Maria Pina-Rocha, director of Management Information Systems for modern Bedford, said there's a municipal spend for the study's sentiment-combing software, especially for modern 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 artery to blow off steam. 

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

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

    Using the study's artery of categorizing sentiments on Twitter, Hollander said governments can mine the data to avow 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 Get a sense of people's feelings about a project. 

    Still, there are limits to this kind 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 artery he gleans public opinion, is too not a convincing statistical representation of the population, and that there might live some profit to the technology. 

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

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

    "The understanding of really mining the attitudes and opinions of people using the Internet, using companionable media, is really the future," Hollander said. "As government professionals, they really acquire to find ways to tap into this erudition foundation and not just rely on the old-fashioned fashion 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 live certain 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 trade 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 tough in domains that acquire long applied text analytics. These areas embrace life sciences, intelligence and law enforcement, and pecuniary services. While the text analytics domain isn’t unaffected by current trade conditions, economic pressures could actually spur uptake motivated by a quest for efficiency through automation.

    Sources and Perspectives

    To abide on top of trends, I try to talk to current and prospective users as often as I can, and I too try to retain up with researchers and fellow analysts. (You’ll find links for some of the folks I follow at my BeyeNETWORK channel.) I too entrap 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 execute you survey as the 3 (or fewer) most notable 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 portion 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 trade conditions. Next, Lexalytics CEO Jeff Catlin candidly characterized his response as “somewhat self-serving thoughts.” No doubt many of the vendors quoted survey the greatest challenges as residing in areas their tools address, so engage their views with a grain of salt.

    I’ll relay complete 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 assume that the most difficult challenges of the past are soundless with us, and this is the biggest. People either believe that not much at complete is workable or they believe that more is workable than reality. It is never the case where cessation users acquire realistic expectations.
  • Portability: Text analytics systems trained on a particular kind of input data execute not typically transfer well to other types of input. Cross-domain issues (e.g., from word to medical) are similar to transfer between intra-domain input documents (e.g., from medical word 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 bailiwick needs is a profound and existent success epic 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 acquire 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 need to exhibit more a priori trade 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 meet 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 trade value is even more important.
  • From an isolated to an integrated solution. Toward the cessation 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 headquarters applications. This modern spend cycle – not just for analytics, but for operational integration – provides an lively chance for text analytics vendors to deem whether to live a stand-alone application or to be more tightly integrated with partner products and offerings.
  • From a static to a scalable architecture. The gargantuan epic in 2008 was scale. Data volumes are going up. Usage requirements are going up. There's no reason to expect scalability requirements won’t continue to grow in 2009 and beyond. The winners will live those who can survey beyond today's data and user volumes and design for an order or two more magnitude in their offerings, predicting this inevitable future.
  • Aaron B. Brown of IBM similarly looks at architectural issues and too at trade concerns. Aaron is Program Director, ECM Discovery, for IBM Information Management Software and has Great market and technical insights. I interviewed him final year on “Text Analytics for Legal Compliance.” That exchange is soundless a ample read. Here are his thoughts on challenges for 2009:

  • Defining the trade case for text analytics. In the current economic situation, organizations are clamping down on modern projects and more than ever looking for difficult ROI savings to justify investment. To pass the funding bar, text analytics solutions, which typically Fall in the category of modern projects undertaken for trade optimization, need to reach with solid trade cases that demonstrate hard-dollar operational savings based on proven examples. Given the emerging nature of many text analytics solution areas, this will live 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 spend 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 live rapidly and seamlessly reconfigured by the trade analyst as they explore data sets and interactively refine their understanding of what needs to live extracted. Further, these applications add pressure to democratize text analytics, as the trade user can no longer wait for a linguistic or data processing expert to reconfigure the analytics each time a modern insight is reached. The modern 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 trade user without specialized text or linguistic expertise.
  • Expanding mainstream spend cases for text analytics. To date, text analytics has had tough traction in inevitable niche solution markets. However, it has yet to live widely adopted as a horizontal capability powering broad trade optimization spend 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 need 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 live a faultfinding year for text analytics to start making this transition.
  • Other replies too looked at trade conditions and at experiences in the adoption and spend of text technologies. Lexalytics CEO Jeff Catlin foresees:

  • The poverty-stricken economy will shift the sale of text analytics to larger companies. We're already seeing this in their sales, which used to live about two-thirds wee companies and one-third great companies. Now it's mostly larger companies. The upside of this is that gargantuan companies are spending on search and text analytics as they seek to redeem money and expand efficiency due to lower staffing levels.
  • Technical push: Their push this year is going to live 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 ticket up domain-specific text (let’s squawk 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 kind of entity so that it can deduce other diseases from how they’re described in the text. We'll live releasing their first version of this by late February/early March and hope to further enhance the implement 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 spend on maintaining lists.
  • I expect the number of vendors to condense as those with weaker or narrower offerings find it increasingly difficult to sell in this very challenging environment. To accomplish a fade of it in 2009, it seems that at a minimum, vendors need to provide: entity extraction, concept extraction and possibly sentiment analysis.
  • Craig Norris, CEO at Attensity, similarly eminent three varieties of challenge, in his case relating to technology, implementation and seeing chance in adverse trade conditions:

  • Getting sentiment right: Many of the vendors in the text-analytics space acquire pretty ample text classification technology – where the application classifies terms based on dictionary entries or predefined lists. The output looks ample – but because only terms are looked at, there minister to live a lot of fraudulent positives – negations are missed, relationships (such as why someone is unhappy) are never found. Many customers acquire purchased technology that only goes this far. They will live 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 live around getting past high-level views of the data (general ratings of sentiment, universal views of issues) to the root cause so that users can engage action and companies can Get existent value. For example, knowing not only that a customer is unhappy with a product, but too that they intend to recrudesce it if they don't Get a convoke back or a fix for their problem is faultfinding to being able to remedy a situation and redeem a customer. Being able to know this is where the existent ROI is for text analytics products. This is only workable 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 live a challenge for any technology solution vendor this year. Their offering has proven to enable two things to attend companies in a taut economy: revenue preservation – find out if customers are going leave, understand why and redeem them; and revenue growth – understand if customers are having issues ("cries for help") and delight them by acting on those issues. This generates fabulous word of mouth and ultimately growth.
  • Solutions Focus

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

  • A broader set of vertical/horizontal offerings including more automated unstructured (text, voice, image) capabilities must live delivered for customer/product/competitive intelligence. SAS is doing this, for example, with SAS Text Miner integration into SAS Warranty Analysis. Automated capabilities embrace graphics, sentiment analysis, net promoter scores, and key performance indicators (KPIs) from text analysis results.
  • Solutions providers must engage 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 maneuver complete approaches to text – text analytics/mining/business analytics/search/categorization.
  • Collaboration and mobile BI are faultfinding needs, and vendors need to live agile and coast 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, great organizations are forced to retain up a dote a glitter pace of trade with fewer resources, and it is therefore faultfinding that these organizations acquire complete of their content organized correctly so that employees can spend 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 itsy-bitsy in the artery of being operationalized within trade processes.” Neil continues:

    Yes, there are proofs of concept, flee by experienced vendor staff but these [prototypes], in my experience, rarely Get adopted by the trade 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 trade needs is a towering degree of automation (without the need for extreme setup and maintenance) together with clarity in the analysis and the ability to apply control to the process where needed. This is exactly what Leximancer provides.

    The other major trend I survey is the need to accomplish companionable media actionable, and this is something we’ve focused on heavily on their blog. I assume customer attitudes on companionable media or microblogging sites are a leading indicator for the business. The trade that waits for these trends to live reflected in their formal feedback programs may find it is too late to engage effective action.

    Technology and Applications

    I had expected that a higher balance of 2009 “challenges” responses would headquarters on the technology and its applications. Responses I’ve already quoted touch on those areas with perhaps greater attention to trade and market concerns. Eric Martin, Product Marketing Manager at SPSS, focused solely on solutions, which to me reflects both Eric’s aplomb 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 pungent and will probably Get bigger in 2009. There's a lot of confusion though between blogs and other benevolent 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. portion of Voice of the Customer, blog analysis applications, etc. towering property 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 companionable media. To fully leverage this data, the key challenges are:

  • Comprehensive and complete data acquisition: Due to the companionable nature of this content, missing documents or authors is dote missing the replies to answers or key voices in the choir.
  • Tuning or recreating yardstick tools to deal with less formal content: Tokenization, sentence segmentation, part-of-speech (POS) tagging, parsing, etc. complete acquire different qualities and requirements in the companionable 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 couple of “megatrends” he is beginning to see. Challenges are:

  • Speed to implementation: Clients remain concerned about implementation taking months before realizing any benefit. Immediate payback, more dote a couple of weeks, seems to live the mantra!
  • Systems should dynamically and quickly react to changing modern information: Clearly, clients execute not want to fade through an extensive process of rebuilding their analysis when requirements for analysis change. complete their customers retain changing their analysis as modern information unfolds.
  • Finding actionable intelligence in data that is not grammatical: Less time in convoke handling, texting, itsy-bitsy 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 attend them resolve such data as well.
  • Ren’s “megatrends” are:

  • The line between structured and unstructured is blurring and a modern trend is emerging: Users want a modern approach altogether. As opposed to converting unstructured to structured, users would dote to survey 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 complete using SQL. So, they survey a modern 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 artery to store and reuse search queries and links, and personalize them. This is the beginning of analysis as users acquire to now assume (analyze) about content databases, links, companionable 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 engage on 2009 text-analytics challenges, Lexalytics’ Jeff Catlin suggested a bake-off to live 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 soundless 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 Get something going for this year – a commercially oriented version of the TREC (Text REtrieval Conference) challenge. abide tuned, and execute deem attending this year’s summit.

    And if you’re a current or prospective text analytics user and would dote to disclose me about challenges you pan or expect to face, tickle execute Get 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 trade intelligence and conclusion systems expert. He is founding chair of the Text Analytics pinnacle 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 certain 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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