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SPS-202 IBMSPSSMBPDM- IBM SPSS Modeler Business(R) partner Data Mining Associate

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SPS-202 exam Dumps Source : IBMSPSSMBPDM- IBM SPSS Modeler Business(R) partner Data Mining Associate

Test Code : SPS-202
Test appellation : IBMSPSSMBPDM- IBM SPSS Modeler Business(R) partner Data Mining Associate
Vendor appellation : IBM
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IBM IBMSPSSMBPDM- IBM SPSS Modeler

IBM Watson Studio: Product Overview and insight | killexams.com true Questions and Pass4sure dumps

down load the authoritative e-book: Cloud Computing 2019: the usage of the Cloud for aggressive advantage

See the total checklist of computing device gaining scholarship of SolutionsSee consumer experiences of IBM Watson Studio

bottom line

Watson is an umbrella for indecent IBM deep discovering and ersatz intelligence, in addition to laptop learning. The company became a pioneer in introducing AI technologies to the industry world. What this potential for patrons: Watson Studio is a exact contender for any company looking to deploy laptop studying and deep researching applied sciences.

The platform gives extensive materiel and technologies for facts scientists, builders and domain depend consultants that necessity to learn information, build models, and educate and set up computer gaining scholarship of fashions at scale. The admit contains materiel to participate visualizations and results with others. Watson Studio helps cloud, computer and local deployment frameworks.

The latter resides behind a firm’s firewall or as a SaaS solution working in an IBM deepest cloud. IBM Watson Studio is ranked as a “chief” within the Forrester Wave. It turned into a customers’ option 2018 recipient at Gartner Peer Insights.

Product Description

Watson Studio relies on a group of IBM tools and applied sciences to construct efficacious computing device getting to know purposes and services. This comprises IBM Cloud pretrained computing device studying fashions corresponding to visible recognition, Watson herbal Language Classifier, and others. The environment makes expend of Jupyter Notebooks together with other open supply tools and scripting languages to complement constructed-in collaborative undertaking points.

https://o1.qnsr.com/log/p.gif?;n=203;c=204660772;s=9478;x=7936;f=201812281334210;u=j;z=TIMESTAMP;a=20403954;e=i

The outcome is an ambiance that enables quick and powerful laptop gaining scholarship of building and character tuning of models. facts scientists and others can create a choice from a number of capacities of Anaconda, Spark and GPU environments.

Watson Studio supports better visual modeling through a drag-and-drop interface supplied by using IBM’s SPSS Modeler. furthermore, it accommodates automatic deep researching the expend of a drag-and-drop, no-code interface in Neural community Modeler.

Overview and lines consumer Base

statistics scientists, builders and zone bethink consultants.

Interface

Graphical drag-and-drop and command line.

Scripting Languages/formats Supported

supports Anaconda and Apache Spark. The latter offers Scala, Python and R interfaces.

codecs Supported

Most major information and file codecs are supported through open supply Jupyter Notebooks.

Integration

IBM Watson Studio connects a yoke of IBM items, together with SPSS Modeler and records Science event (DSX) together with open supply tools, with a view to bring a stalwart Predictive Analytics and laptop getting to know (PAML) solution.

The ambiance accommodates open data sets through Jupyter Notebooks, Apache Spark and the Python Pixiedust library. The cloud edition features interactivity with computer servers and R Studio, together with Python, R., and Scala coder for facts scientists.

Reporting and Visualization

Visualization via SPSS Modeler. stalwart logging and reporting capabilities are developed into the product.

Pricing

IBM has adopted a pay-as-you-go model. Watson Studio Cloud – middling expenses $ninety nine per month with 50 means unit hours per month covered. Watson Studio Cloud - industry runs $6,000 per month with 5,000 capability unit hours. Watson Studio laptop charges $199 per thirty days with limitless modeling. Watson Studio local – for enterprise records science teams N/A.

IBM Watson Studio Overview and contours at a look:

seller and contours

IBM Watson Studio

ML focus

wide information science focus of attention with cloud and desktop ML structures.

Key features and capabilities

powerful visible awareness and herbal classification equipment. flexible strategy that accommodates open source tools. Connects to IBM SPSS Modeler.

consumer feedback

particularly rated for points and capabilities. Some complaints revolving round using notebooks.

Pricing and licensing

Tiered mannequin from $99 per month per user to $6,000 per 30 days per consumer or more at enterprise level.


IBM sends Cognos, SPSS to the cloud | killexams.com true Questions and Pass4sure dumps

Two of IBM’s most established evaluation products, the Cognos company Intelligence and the SPSS predictive analytics equipment, are headed for the cloud, the newest in an ongoing shove by using IBM to port its substantial utility portfolio to the cloud.

having access to this sort of application from a hosted atmosphere, in condition of procuring the materiel outright, offers a number of advantages to purchasers.

“We manage the infrastructure, and this permits you to scale more effortlessly and Get indecent started with less upfront investment,” pointed out Eric Sall, IBM vice president of global analytics marketing.

IBM introduced these additions to its cloud functions, as well as a few fresh choices, at its perception user conference for records analytics, held this week in Las Vegas.

by 2016, 25 p.c of recent enterprise analysis deployments can be accomplished within the cloud, in keeping with Gartner.

Analytics might advocate groups in lots of techniques, in line with IBM. It may supply further insight in the procuring habits of consumers, as well as insight into how neatly its personal operations are performing. It could wait on safeguard systems from assaults and attempts at fraud, as well as guarantee that industry departments are assembly compliance requirements.

the fresh online edition of Cognos, IBM Cognos enterprise Intelligence on Cloud, can at the second be validated in a preview mode. IBM plans to present Cognos as a plenary industry provider early next year. clients can flee Cognos towards statistics they retain in the IBM cloud, or towards records they save on premises.

A plenary industry edition of the online IBM SPSS Modeler will be purchasable within 30 days. This materiel will encompass indecent the SPSS add-ons for records based predictive modeling, akin to a modeler server, analytics decision administration software and a information server.

past this year, IBM pledged to tender a total lot of its software portfolio as cloud services, many via its Bluemix set of platform functions.

besides Cognos and SPSS, IBM besides unveiled a number of fresh and up-to-date offerings at the conference.

One fresh carrier, DataWorks, gives a yoke of options for refining and cleaning facts so it's equipped for evaluation. The company has launched a cloud-based data warehousing carrier, called dashDB. a brand fresh Watson-primarily based provider, called Watson Explorer, offers a artery for users to request natural language questions on diverse units of interior information.

To palpate upon this text and other PCWorld content material, visit their fb page or their Twitter feed.

IBM Watson Studio is Coming to Your desktop | killexams.com true Questions and Pass4sure dumps

example of a Modeler stream running on Watson Studio computing device

IBM Watson Studio is an exceedingly efficacious information science offering. if you’re now not typical, Watson Studio is an built-in environment designed to create it easy to increase, educate, and exploit models. It comprises a great choice of materiel used for every puny thing from records prep and merge to picture consciousness. And now Watson Studio is on its solution to your windows or Mac desktop computer.

Overview of Watson Studio

Watson Studio contains lots of tools indecent inside one built-in atmosphere. the most regularly occurring materiel of Watson Studio are:

  • SPSS Modeler for facts prep, mix, and modeling devoid of coding
  • facts Refinery, a spreadsheet fashion tool, that permits quickly, self-service records practise
  • Python, R, and Scala Jupyter Notebooks to create and participate files that contain reside code, equations, visualizations, and explanatory text
  • Neural network Modeler for designing TensorFlow, Keras, PyTorch and Caffe constructions without coding
  • Interactive Dashboards to deliver sharable are vital reports so the conclusion users can learn their personal insights
  • obtainable Deployment options

    Watson Studio will quickly be purchasable as three deployment options:

  • Watson Studio on the IBM Cloud is the easiest artery to Get begun. It presents 3 tiers from a free starter tier with pay-as-you-go compute indecent the technique to an enterprise edition.
  • Watson Studio local is their behind-the-firewall admit proposing a relaxed artery so as to add Watson Studio capabilities to your commercial enterprise. contains purchase options for a handful of users or the total organization.
  • And coming quickly: Watson Studio desktop
  • Introducing Watson Studio computing device

    Watson Studio desktop is their fresh third deployment option inside the Watson Studio family. It takes essentially the most frequent parts of what's available inside Watson Studio on the cloud and ports it to your windows or Mac computing device for offline use, for evaluation of information data you don’t are looking to upload, and for snappy performance. indecent with a posthaste and simple desktop installing.

    Watson Studio on the computer will scale from someone user indecent of the artery as much as a gigantic team, and will be a fine addition to the Watson Studio family together with their cloud and native flavors.

    The initial leading features of Watson Studio computing device can be a SPSS Modeler canvas in conjunction with the statistics refinery spreadsheet editor. this will encompass backwards compatibility for SPSS Modeler streams.

    merits of Watson Studio laptop
  • It’s quickly, in particular for local files. multiply particular person productivity the usage of a in the neighborhood achieve in customer.
  • It provides offline use. want to construct a mannequin on a aircraft devoid of WiFi? Watson Studio laptop will toil with out an internet connection for weeks at a time.
  • Adhere to safety and governance mandates along with your facts. Watson Studio computing device allows for indecent your statistics to wait in your computing device.
  • unlimited modeling devoid of overages. using Watson Studio computing device, that you could boost models without any incremental cost. This allows you to build models iteratively on your laptop, then whisk it to Watson Studio on the Cloud to set up.
  • It’s one Watson Studio, so no re-learning required. With the very designs because the cloud and local types, switching between deployment options is seamless without a unique practicing.
  • And similar to the repose of Watson Studio, any one — coders or non-coders — may be in a position to achieve together, mix, explore, and model their statistics in precisely minutes.
  • Watson Studio laptop’s potent graphing knowledgeJoin the Beta

    The Beta for Watson Studio is beginning now with two materiel already ported to the computing device: a refreshed edition of SPSS Modeler & facts Refinery. it'll besides consist of tasks to prepare your work, statistics assets organization, and a effortless check for updates function.

    be allotment of the beta to supply comments on the artery you enjoy these tools, and advocate which other tools they carry to the computer. The Beta is allotment of the IBM SPSS Modeler Subscription Trial, so when you register for that you just’ll absorb access to both downloads.

    After you register, search for the “IBM SPSS Modeler Subscription Trial” card, click on down load, then you definately’ll see the Watson Studio laptop Beta downloads on the appropriate hand facet of the page.

    join the discussion on the beta neighborhood

    After registering for the beta, be a allotment of within the discussion on the Watson Studio computing device beta neighborhood. here that you would be able to request questions concerning the beta, supply strategies for future aspects, Get forum assist, and rob allotment in valuable surveys to absorb an repercussion on product direction. be a allotment of the discussion board perquisite here by means of logging in with your IBMid.

    wait for future announcements perquisite here about this pleasing addition for the Watson Studio household.

    Watson Studio computer chore organizer

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    IBMSPSSMBPDM- IBM SPSS Modeler Business(R) partner Data Mining Associate

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    Your guide to the top gigantic data certifications today | killexams.com true questions and Pass4sure dumps

    IDG

    IDG

    Data and gigantic data analytics are posthaste becoming the lifeblood of any successful business. Getting the technology perquisite can be challenging, but building the perquisite team with the perquisite skills to undertake gigantic data initiatives can be even harder.

    Not surprisingly, that challenge is reflected in the rising require for gigantic data skills and certifications. According to research by IT research solid Foote Partners, both noncertified advanced data analytics skills and certified gigantic data skills absorb gained value in recent years: with 74 advanced data analytics related skills and certifications rising in middling value by 6 percent in 2015, followed by 116 advanced data analytics related skills and certifications increasing 4.8 percent overall in market value in 2016. Additionally, Foote Partners research organize 123 related certified and noncertified gigantic data skills seeing a 0.3 percent gain in value in the first quarter of 2017.

    Organizations are on the hunt for data scientists and analysts with expertise in the techniques required to resolve gigantic data. They besides necessity gigantic data systems architects to translate requirements into systems, data engineers to build data pipelines, developers who know their artery around Hadoop clusters and other technologies, and systems administrators and managers to tie everything together.

    [ Find out the hottest data and analytics trends today. | Get ahead with the top certs for gigantic data, project management, agile, and the cloud. | Get weekly career tips by signing up for their CIO Leader newsletter. ]

    These skills are in tall require and are relatively rare. Individuals with the perquisite merge of undergo and skills can require tall salaries. The perquisite certifications can help.

    "Advanced data analytics capabilities are just too critical for staying competitive," David Foote, co-founder, chief analyst and chief research officer of Foote Partners, said in a statement released with the research. "They've expanded in popularity from a few industries to nearly every industry and market. And there is the Internet of Things, the next critical focus for data and analytics services. IDC is predicting a 30 percent CAGR over the next five years, while McKinsey is expecting IoT to absorb a $4 trillion to $11 trillion global economic repercussion by 2025 as businesses peer to IoT technologies to provide more insight."

    While the market value of noncertified advanced analytics skills has actually increased faster as a percentage of basis salary than the value of certified gigantic data skills, according to Foote Research, Foote believes pay premiums for both noncertified and certified skills will steadily climb over the next 12 to 24 months.

    If you're looking for a artery to Get an edge — whether you're job hunting, angling for a promotion or just want tangible, third-party proof of your skills — gigantic data certification is a noteworthy option. Certifications measure your scholarship and skills against industry- and vendor-specific benchmarks to prove to employers that you absorb the perquisite skillset. The number of gigantic data certs is expanding rapidly.

    Below is their guide to the most sought after gigantic data certifications to wait on you determine which cert is perquisite for you.

    If you would enjoy to submit a gigantic data certification to this directory, gratify email us.

    Analytics: Optimizing gigantic Data Certificate

    The Analytics: Optimizing gigantic Data Certificate is an undergraduate-level program intended for business, marketing and operations managers, data analyst and professionals, pecuniary industry professionals, and minuscule industry owners. The program brings together statistics, analysis, and written and oral communications skills. It introduces students to the tools needed to resolve great datasets, covering topics including importing data into an analytics software package, exploratory graphical and data analysis, building analytics models, finding the best model to explore correlation among variables and more.

    Organization: University of Delaware

    Price: $2,895 course fee

    How to prepare: A basic background in statistics and some prior college coursework is recommended.

    Certificate in Engineering Excellence gigantic Data Analytics and Optimization (CPEE)

    Offered in Hyderabad and Bengaluru, India, the Certificate in Engineering Excellence gigantic Data Analytics and Optimization is an intensive 18-week program that consists of 10 courses (lectures and labs) for students of indecent aspects of analytics, including working with gigantic data using Hadoop. It focuses on R and Hadoop skills, as well as statistical modeling, data analytics, machine learning, text mining and optimization. Students are evaluated on a real-world capstone project and a train of quizzes.

    Organization: International School of Engineering (INSOFE)

    Price: ₹3000 (INR) application fee and a program fee of ₹3,25,000 + 15 percent service tax.

    How to prepare: INSOFE admits students based on performance on its entrance exam and prior academic background and toil experience.

    Certification of Professional Achievement in Data Sciences

    The Certification of Professional Achievement in Data Sciences is a non-degree program intended to develop facility with foundational data science skills. The program consists of four courses: Algorithms for Data Science (CS/IEOR), Probability & Statistics (STATS), Machine Learning for Data Science (CS), and Exploratory Data Analysis and Visualization (STATS).

    Organization: Columbia University

    Price: $1,858 per credit (a minimum of 12 credits, including the four courses, are required to complete the program). In addition, there is an $85 non-refundable application fee for the on-campus program and $150 for the online program. The online program besides includes an additional non-refundable technology fee of $395 per course.

    How to prepare: An undergraduate degree and prior quantitative and introductory to computer programming coursework are required.

    Certified Analytics Professional

    The Certified Analytics Professional (CAP) credential is a universal analytics certification that certifies end-to-end understanding of the analytics process, from framing industry and analytic problems to acquiring data, methodology, model building, deployment and model lifecycle management. It requires completion of the CAP exam and adherence to the CAP Code of Ethics.

    Organization: INFORMS

    Price: $495 if you are an INFORMS member, or $695 if you're not. Team pricing is available for organizations. 

    How to prepare: A list of study courses and a train of webinars are available through registration.

    Cloudera Certified Associate (CCA) Data Analyst

    A SQL developer who earns the CCA Data Analyst certification demonstrates core analyst skills to load, transform and model Hadoop data to define relationships and extract meaningful results from the raw output. It requires passing the CCA Data Analyst Exam (CCA159), a remote-proctored set of eight to 12 performance-based, hands-on tasks on a CDH 5 cluster. Candidates absorb 120 minutes to implement a technical solution for each task. They must resolve the problem and arrive at an optimal approach in the time allowed.

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    The CCA Spark and Hadoop Developer credential certifies a professional has proven their core skills to ingest, transform and process data using Apache Spark and core Cloudera enterprise tools. It requires passing the remote-proctored CCA Spark and Hadoop Developer Exam (CCA175), which consists of eight to 12 performance-based, hands-on tasks on a Cloudera Enterprise cluster. Each question requires the candidate to unravel a particular scenario. Some cases may require a implement such as Impala or Hive, others may require coding. Candidates absorb 120 minutes to complete the exam.

    Organization: Cloudera

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    Cloudera Certified Professional (CCP): Data Engineer

    The CCP: Data Engineer credential certifies the talent to execute core competencies required to ingest, transform, store and resolve data in Cloudera's CDH environment. It requires passing the remote-proctored CCP: Data Engineer Exam (DE575), a hands-on, practical exam in which each user is given five to eight customer problems each with a unique, great data set, a CDH cluster and four hours. For each problem, the candidate must implement a technical solution with a tall degree of precision that meets indecent the requirements.

    Organization: Cloudera

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    How to prepare: Cloudera suggests professionals seeking this certification absorb hands-on undergo in the domain and rob the Cloudera Developer Training for Spark and Hadoop course.

    EMC Proven Professional Data Scientist Associate (EMCDSA)

    The EMCDSA certification demonstrates an individual's talent to participate and contribute as a data science team member on gigantic data projects. It includes deploying the data analytics lifecycle, reframing a industry challenge as an analytics challenge, applying analytic techniques and tools to resolve gigantic data and create statistical models, selecting the appropriate data visualizations and more.

    Organization: Dell EMC Education Services

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    Organization: IBM Professional Certification Program

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    How to prepare: IBM recommends a train of nine multi-day courses to prepare for the test.

    Mining Massive Data Sets Graduate Certificate

    Designed for software engineers, statisticians, predictive modelers, market researchers, analytics professionals, and data miners, the Mining Massive Data Sets Graduate Certificate requires four courses and demonstrates mastery of efficient, powerful techniques and algorithms for extracting information from great datasets enjoy the Web, gregarious network graphs and great document repositories. The certificate usually takes one to two years to complete.

    Organization: Stanford focus for Professional Development

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    How to prepare: At least six months of programming undergo in SAS or another programming language is required to enroll.

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    Perspectives on Text Analytics in 2009 | killexams.com true 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 industry sector. So far, the diversity of options has benefitted potential users; awareness (and realization) of the technology’s capabilities is building rapidly in areas such as media monitoring, publishing, customer-experience management and semantic search. Meanwhile, growth remains stalwart in domains that absorb long applied text analytics. These areas include life sciences, intelligence and law enforcement, and pecuniary services. While the text analytics domain isn’t unaffected by current industry conditions, economic pressures could actually spur uptake motivated by a quest for efficiency through automation.

    Sources and Perspectives

    To wait on top of trends, I try to talk to current and prospective users as often as I can, and I besides try to withhold up with researchers and fellow analysts. (You’ll find links for some of the folks I result at my BeyeNETWORK channel.) I besides ensnare 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 conclude you see as the 3 (or fewer) most well-known 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 allotment because “…for me, 2009 is too immediate a time horizon.” This thought applies not only to academic researchers but besides to anyone who’s thinking strategically, beyond this year’s crop of product releases and industry 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 rob their views with a grain of salt.

    I’ll relay indecent 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 reflect that the most difficult challenges of the past are noiseless with us, and this is the biggest. People either believe that not much at indecent is feasible or they believe that more is feasible than reality. It is never the case where cease users absorb realistic expectations.
  • Portability: Text analytics systems trained on a particular kind of input data conclude 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 domain needs is a profound and true success record 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, call center, survey groups. Over the past few years, solutions absorb 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 demonstrate more a priori industry value and relevance, and at the very 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 very time articulating and demonstrating industry value is even more important.
  • From an isolated to an integrated solution. Toward the cease 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 call focus applications. This fresh expend cycle – not just for analytics, but for operational integration – provides an racy opening for text analytics vendors to respect whether to be a stand-alone application or to be more tightly integrated with partner products and offerings.
  • From a static to a scalable architecture. The gigantic record in 2008 was scale. Data volumes are going up. Usage requirements are going up. There's no intuition to await 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 certain future.
  • Aaron B. Brown of IBM similarly looks at architectural issues and besides at industry concerns. Aaron is Program Director, ECM Discovery, for IBM Information Management Software and has noteworthy market and technical insights. I interviewed him ultimate year on “Text Analytics for Legal Compliance.” That exchange is noiseless a cordial read. Here are his thoughts on challenges for 2009:

  • Defining the industry case for text analytics. In the current economic situation, organizations are clamping down on fresh projects and more than ever looking for difficult ROI savings to warrant investment. To pass the funding bar, text analytics solutions, which typically Fall in the category of fresh projects undertaken for industry optimization, necessity to compass with solid industry 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 depend 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 industry 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 industry user can no longer wait for a linguistic or data processing expert to reconfigure the analytics each time a fresh insight is reached. The fresh challenge for text analytics is to enable extraction and analysis that are near instantaneous and at the very time are reconfigurable at interactive speeds by a industry user without specialized text or linguistic expertise.
  • Expanding mainstream expend cases for text analytics. To date, text analytics has had stalwart traction in certain niche solution markets. However, it has yet to be widely adopted as a horizontal capability powering broad industry 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 critical year for text analytics to start making this transition.
  • Other replies besides looked at industry conditions and at experiences in the adoption and expend 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 be about two-thirds minuscule companies and one-third great companies. Now it's mostly larger companies. The upside of this is that gigantic companies are spending on search and text analytics as they search to save money and multiply efficiency due to lower staffing levels.
  • Technical push: Their shove 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 tag up domain-specific text (let’s deliver 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 be 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 expend on maintaining lists.
  • I await the number of vendors to shrink as those with weaker or narrower offerings find it increasingly difficult to sell in this very challenging environment. To create a Go 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 famous three varieties of challenge, in his case relating to technology, implementation and seeing opening in adverse industry conditions:

  • Getting sentiment right: Many of the vendors in the text-analytics space absorb pretty cordial text classification technology – where the application classifies terms based on dictionary entries or predefined lists. The output looks cordial – but because only terms are looked at, there tend to be a lot of deceptive positives – negations are missed, relationships (such as why someone is unhappy) are never found. Many customers absorb 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 enjoy 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, universal views of issues) to the root antecedent so that users can rob action and companies can Get true value. For example, knowing not only that a customer is unhappy with a product, but besides that they intend to recrudesce it if they don't Get a call back or a fix for their problem is critical to being able to remedy a situation and save a customer. Being able to know this is where the true ROI is for text analytics products. This is only feasible with an approach that not only finds sentiment but can besides 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 wait on 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 unbelievable word of mouth and ultimately growth.
  • Solutions Focus

    Vendor prognosticators with a solutions focus include 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 include graphics, sentiment analysis, net promoter scores, and key performance indicators (KPIs) from text analysis results.
  • Solutions providers must rob 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 building software and planning road maps accordingly.
  • Customers are consolidating software and needing to settle on one vendor that can exploit indecent approaches to text – text analytics/mining/business analytics/search/categorization.
  • Collaboration and mobile BI are critical needs, and vendors necessity to be agile and whisk 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 withhold up a posthaste pace of industry with fewer resources, and it is therefore critical that these organizations absorb indecent 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 puny in the artery of being operationalized within industry 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 industry 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 industry needs is a tall degree of automation (without the necessity for excessive setup and maintenance) together with clarity in the analysis and the talent to apply control to the process where needed. This is exactly what Leximancer provides.

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

    Technology and Applications

    I had expected that a higher balance of 2009 “challenges” responses would focus on the technology and its applications. Responses I’ve already quoted palpate on those areas with perhaps greater attention to industry 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, enjoy 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 torrid 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 besides very solution-oriented and not only requires text analytics but besides data mining, Web scraping capabilities, etc.
  • Sentiment analysis: Not an option anymore in most projects. allotment of Voice of the Customer, blog analysis applications, etc. tall character out-of-the-box results are demanded more and more.
  • Automated translation: besides 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 besides 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 enjoy missing the replies to answers or key voices in the choir.
  • Tuning or recreating touchstone tools to deal with less formal content: Tokenization, sentence segmentation, part-of-speech (POS) tagging, parsing, etc. indecent absorb 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 besides with a yoke 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 enjoy a yoke of weeks, seems to be the mantra!
  • Systems should dynamically and quickly react to changing fresh information: Clearly, clients conclude not want to Go through an extensive process of rebuilding their analysis when requirements for analysis change. indecent their customers withhold changing their analysis as fresh information unfolds.
  • Finding actionable intelligence in data that is not grammatical: Less time in call handling, texting, puny 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 wait on them resolve such data as well.
  • Ren’s “megatrends” are:

  • The line between structured and unstructured is blurring and a fresh trend is emerging: Users want a fresh approach altogether. As opposed to converting unstructured to structured, users would enjoy 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 enjoy that today, no hands went up! They are indecent using SQL. So, they see a fresh trend in analysis going from structured (SQL enjoy complexity) to unstructured (search enjoy 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 absorb to now reflect (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 rob 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 noiseless 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. wait tuned, and conclude respect attending this year’s summit.

    And if you’re a current or prospective text analytics user and would enjoy to recommend me about challenges you puss or await to face, gratify conclude Get in palpate 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 industry intelligence and decision systems expert. He is founding chair of the Text Analytics peak 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!

  • Recent articles by Seth Grimes


    Are You Using the perquisite Tools for Your gigantic Data Projects? | killexams.com true questions and Pass4sure dumps

    Data scientists depend on tools/products/solutions to wait on them Get insights from data. Gregory Piatetsky of KDNuggets conducts an annual survey of data data professionals to better understand the different types of tools they use. Here are the results of the 2015 survey. He followed his initial posting with additional analyses to better understand which tools Go together. In the latter posting, he looked at pairs of tools and organize that some tools tend to Go together (usage of tools are correlated with each other). He offered the anonymized raw data set for free to encourage other people to resolve the data, which I did.

    Dimension Reduction through Principal Components Analysis

    His approach looked at pairs of tools to understand their relationship with the other. I took a slightly different approach. I applied principal components analysis. The current approach groups the tools by looking at the relationship among indecent tools simultaneously. In general, principal components analysis examines the statistical relationships (e.g., covariances) among a great set of variables and tries to intricate these correlations using a smaller number of variables (components).

    The results of the principal components analysis are presented in tabular format called the principal component matrix. The factor matrix is an NxM table (N = number of original variables and M = number of underlying components). The elements of a principal component matrix portray the relationship between each of the variables and the components. These elements portray the might of relationship between the variables and each of the underlying components. The results of the principal components analysis recommend us two things:

  • number of underlying components that narrate the initial set of variables
  • which variables are best represented by each component
  • Results

    This expend of principal components analysis is exploratory in nature. That is, I didn’t impose a pre-defined structure on the data itself. The pattern of relationships among the 95 tools drove the pattern of results. While human judgment comes into play in the determination of the number of components that best narrate the data, the selection of the number of components is based on the results. The goal of the current analysis was to intricate the relationship among the 95 tools with as few components as was necessary. Toward that end, there are a yoke of rules of thumb that I used to determine the number of components using the eigenvalues (output of principal components analysis). The first rule of thumb is to set the number of components based on the number of eigenvalues greater than union (1.0). Another artery is to plot (called a scree plot) the 95 eigenvalues to identify a pellucid breaking point along the eigenvalues.

    The plot of the eigenvalues appeared to wreck around the 13th and 14th eigenvalue. Therefore, I chose a 13-factor solution to intricate the relationships among the 95 data science tools.

    Table 1. Principal Component Matrix of 95 Data Science Tools - data from KDNuggets 2015 annual survey of data professionals. Click image to enlarge.Table 1. Principal Component Matrix of 95 Data Science Tools – data from KDNuggets 2015 annual survey of data professionals. Click image to enlarge.

    Based on a 13-factor solution, the principal component matrix (see Table 1) was significantly easy to interpret. Some of the cell values in the matrix in Table 1 are bold to portray values greater than .33. The components’ headings are based on the tools that loaded highest on that component. For example, four IBM products loaded highly on component 6, showing that usage of these tools by a given respondent tend to Go together (if you expend one of IBM’s tools, you tend to expend the other(s)); as a result, I labeled that component as IBM. Similarly, based on the tools that were highly related to the other 12 factors, the other 12 factors were labeled accordingly.

    Tool Groupings

    The results hint that 13 implement grouping narrate the data. I’ve listed the groupings below and include the tools that narrate each grouping (if number of votes are greater than 20). Tools that Fall within a specific group tend to be used together.

  • Hadoop, HBase, Hive, Mahout, MLlib, Other Hadoop/HDFS-based tools, Pig, Scala, Spark, SQL on Hadoop tools
  • Microsoft Azure ML, Microsoft Power BI, Microsoft SQL Server, Revolution Analytics
  • Dataiku, H2O (0xdata), Python, scikit-learn, Theano, Vowpal Wabbit
  • JMP, SAS Base, SAS Enterprise Miner
  • Gnu Octave, MATLAB, Orange, R, RapidMiner, Rattle, Weka
  • IBM Cognos, IBM SPSS Modeler, IBM SPSS Statistics, IBM Watson Analytics
  • Actian, C/C++, Perl, SQLang, Unix shell/awk/gawk
  • Caffe, Pylearn2
  • Pentaho and QlikView
  • Datameer and Zementis
  • XLSTAT for Excel
  • Other deep Learning tools, Other free analytics/data mining tools, Other Hadoop/HDF-based tools, Other paid analytics/data mining/data science software, Other programming languages
  • C4.5/C5.0/See5, Miner3D, Oracle Data Miner
  • The expend of specific gigantic Data, data mining and data science tools tend to occur together. Based on the current analysis of implement usage, the 90+ tools can be grouped into a smaller subsets of tools. The results of the current analysis are significantly consistent with the prior results. For example, Piatetsky organize that Pig usage was closely associate with Hadoop, Spark, scikit-learn, Unix shell/awk/gawk and Python. In the current analysis, they organize that Pig was besides associated with Hadoop and Spark. However, they organize that the expend of Pig was associated with HBase, Hive, Mahout, MLlib, Scala and SQL on Hadoop. It’s well-known to note that the former analysis used the top 20 tools while the latter analysis used indecent 90+ tools; this difference could intricate the differences between the two analyses.

    To improve your chances of success in your gigantic Data projects, it’s well-known that you select the perquisite data tools. No unique implement will conclude it all, and data professionals tend to expend more than one data-related implement (in this study, they organize that, on average, data professionals expend 5 data tools). One artery to wait on in your selection process is to identify the tools sets that other data professionals are using. The results hint that you respect looking at tools within components as potential candidates for tools you might consider.

    Some of the implement groupings are simply based on the parent company of the tools, including IBM, Microsoft and SAS. Perhaps cross-selling data science tools is a lot easier within a major brand than it is across different brands. This makes sense to the extent that a company’s products likely toil together better than with other vendors’ products. Still, more research is definitely needed to understand why certain tools tend to be used together.

    I’m interested in seeing what others find with these data. Here is the link to the anonymized data set (CSV format).



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