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COG-500 IBM Cognos 8 Analytic Applications(R) Developer

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COG-500 exam Dumps Source : IBM Cognos 8 Analytic Applications(R) Developer

Test Code : COG-500
Test designation : IBM Cognos 8 Analytic Applications(R) Developer
Vendor designation : IBM
: 60 real Questions

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IBM IBM Cognos 8 Analytic

IBM Cognos Analytics C2090–621 exam Questions — one hundred% success guaranteed | real Questions and Pass4sure dumps

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    IBM pushes automation, NLP in Cognos Analytics 11.1 supplant | real Questions and Pass4sure dumps

    IBM has brought a host of AI-powered outfit to its BI platform, Cognos Analytics, to enable more superior analytics,...

    whereas on the equal time making the platform simpler for buyers to use, the company observed.

    the brand fresh Cognos Analytics eleven.1 platform facets include an AI assistant, pattern detection, visualization strategies, improved storytelling equipment, and extra advanced analytics and predictive analytics capabilities.

    The update, revealed in a Sept. 27 IBM weblog post, represents a "huge funding," pointed out Greg Adams, vp of IBM enterprise Analytics, in an interview. the brand fresh tools, past adding extra advanced capabilities, additionally build the platform more straightforward to build consume of for nondata-oriented employees and national facts scientists -- a category of users that has been growing abruptly.

    Following a style

    the fresh points in Cognos Analytics 11.1 build "the platform much more straightforward to consume to generate insights, certainly the natural language search, which enables users to beget real conversations with the data," mentioned Wayne Eckerson, founder and major consultant at Eckerson neighborhood.

    IBM is [in the] middle of the pack, which is a tad staggering, given their Watson heritage. Wayne Eckerson, Eckerson community

    "It automates facts preparation and information modeling tasks, so analysts can seat of attention extra on insights. It additionally auto-generates insights based on a box of hobby selected through a user, using algorithms to tease out key drivers of a metric, as an example," he continued.

    still, Eckerson mentioned, these are aspects that "the entire BI carriers are racing to include."

    "Some are a bit bit ahead of others, but most will cessation up within the identical belt in 18 months. IBM is [in the] core of the pack, which is a tad fantastic, given their Watson heritage," he noted.

    past this year, BI software dealer Alteryx Inc. launched Alteryx 2018.3, which added superior visualization capabilities and set the stage for future updates that are referred to to boost the platform's augmented intelligence and sensible capabilities.

    Tableau additionally made a jog to add extra automation to its structures this 12 months with the acquisition of MIT startup Empirical methods. The startup labored on a platform to immediately mannequin data for analysis, and it provides predictive insights into that records -- aspects that Tableau mentioned it will drudgery to add into its personal structures.

    Cognos Analytics 11.1 Cognos Analytics eleven.1 allows quick comparisons of two visualizations in a fresh exploration tool. more straightforward to use

    in line with Adams, at IBM, "we comprehend to in fact assist individuals discover insights, to in fact be self-service potential americans should be automatically given insights, immediately beget issues dropped at their consideration, and they must beget things be in reality approachable."

    That strategy is mirrored within the fresh AI Assistant. Taking potential of fresh natural language processing (NLP) capabilities, the second in Cognos Analytics 11.1 enables users to classification in questions and commands, equivalent to a command to connect records sets.

    "the key with the conversation technology, it be now not anything that they wanted to bolt onto the side," Adams observed. "We really tried to inject it, so it's fragment of your routine circulation."

    advanced analytics

    the fresh AI technologies in Cognos Analytics eleven.1 drudgery smartly with the newly introduced superior analytics equipment, Adams referred to.

    The platform now has sample detection, which can automatically find and current relationships in statistics that is being explored.

    looking at a undeniable visualization, for instance, the platform will automatically discover connected visualizations that can be used, Adams stated. When picking information, the device will additionally provide recommendations for visualizations.

    With a fresh exploration device in Cognos Analytics 11.1, users can drag and drop two visualizations for a "quick facet-through-facet evaluation" that will immediately perceive for relationships, Adams said.

    The supplant moreover includes more desirable predictive analytics capabilities; greater statistics storytelling alternate options, with the means to create interactive narratives with sequenced visualizations and media; and more convenient-to-use reporting equipment.

    atmosphere aside and looking ahead

    whereas Adams acknowledged other BI systems beget lately integrated equivalent elements, he claimed IBM's attention to facts governance sets Cognos Analytics 11.1 apart.

    "one of the high-quality issues about Cognos is its business-grade, relied on ambiance," he noted, adding that IBM has additionally introduced a outfit to immediately discover AI bias.

    moreover the update, IBM has released a fresh subscription pricing model for the platform. The mannequin units the platform into three tiers: lite, top class and business. The Cognos Analytics 11.1 supplant is accessible for the highest tier as of Sept. 28, and it'll be accessible on the other tiers by means of the conclusion of the year, in keeping with IBM.

    Datawatch Unveils Monarch for IBM Watson Analytics and IBM Cognos Analytics | real Questions and Pass4sure dumps

    might moreover 19, 2016

    Joyce Wells

    Datawatch employer has announced the provision of Datawatch Monarch for IBM Watson Analytics and IBM Cognos Analytics. previous this yr, Datawatch printed that IBM would resell Datawatch Monarch to deliver stronger and sooner records entry and self-provider facts instruction (prep) to its users. Now, the trade says, those clients can leverage Monarch to prep and anatomize extra statistics faster, and occupy skills of IBM`s cognitive computing capabilities across a wide variety of data sources.

    providing the advantages of advanced analytics with out the complexity, Watson Analytics is a lore discovery service purchasable on the cloud that helps e-book exploration, automates predictive analytics, and makes it workable for dashboard and infographic introduction.

    Cognos Analytics offers self-provider capabilities that permit trade clients to create and/or customise dashboards and stories on their personal - and is available on-premise or on the cloud.

    in keeping with Datawatch, statistics that provides probably the most analytical value is often locked away in inaccessible multi-structured and unstructured files, such as textual content stories, web pages, PDFs, JSON and log files. additionally, in many situations, a firm`s supply facts is distinct and infrequently items itself in an evaluation-ready layout – ensuing in the requisite for large amounts of time to be spent on manual facts retrieval and prep procedures.

    The Datawatch Monarch self-carrier data prep acknowledge is targeted at assisting statistics analysts and generic enterprise clients to greater unexpectedly entry, manipulate, and blend information from distinctive sources. facts can then be prepared for evaluation in Watson Analytics and Cognos Analytics more at once.

    This acknowledge is directed at the proven fact that the vast majority of strategic enterprise decisions are according to incomplete or outdated information because of the time it takes for facts retrieval and manual prep techniques, based on Michael Morrison, president and CEO of Datawatch. Monarch, he pointed out, enables Watson Analytics and Cognos Analytics clients to attain utter of the information required to gleam a holistic view of the enterprise, and then prepare it for analysis in order that, in its space of prepping, they could consecrate their time to performing analysis that accelerates option making and provides enterprise value.

    To gain lore of greater about Datawatch Monarch for Watson Analytics and Cognos Analytics, evanesce to

    Have a fresh announcement about your enterprise? Contact

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    Ten fresh Features for Report Users in IBM Cognos 10 Report Studio | real questions and Pass4sure dumps

    Instead of just focusing on the major features of IBM Cognos 10 Report Studio, Roger Johnson, coauthor of IBM Cognos 10 Report Studio: Practical Examples, looks at some lesser-known tools and demur properties to further enhance your Report Studio projects. From the author of 

    The biggest additions to IBM Cognos Report Studio in version 10 beget been lively Reports and Statistics objects. These two fresh ways of presenting data provide many fresh options for report authors to create reports that match the analytical needs of the user community. Entire classes beget been created to focus on the creation of reports using these formats.

    Other capabilities in IBM Cognos 10 Report Studio include the integration of external data sources and the faculty to rescue Report Studio reports to be used directly by trade Insight Advanced authors. Using external data sources in Report Studio has been a customer request since IBM Cognos tools moved to a web-based architecture. This enhancement gives the report consumer fresh opportunities to process just the information needed for a specific situation. By using a common report definition between applications, users who aren't accustomed to intricate reports can receive assistance to build reports that beget features beyond those normally available to trade Insight Advanced users.

    While the features I've discussed to this point are promoted as key features, other useful options in version 10 will multiply the effectiveness of reports to your users. Some of these features were previously available through intricate programming by the report developer, but IBM Cognos trade Intelligence now makes this job much easier. As a report writer and instructor who can prize the faculty to create report designs designed to expound a story, I want to highlight 10 of these fresh features.

    Alternative Text

    The Alternative Text property has been added to graphical objects, such as images and charts, to allow screen readers to translate the text (see device 1). Another aspect of this feature that's applied in various properties is to provide localized text without using conditional formatting.

    Colored Regions, Plot belt Fill, Material Effects

    The Colored Regions, Plot belt Fill, and Material Effects properties allow charts to beget more creative designs in order to enhance the overall presentation of the charts. Mixed with other chart presentation options, these features assist report developers to give fresh polish to the presentation of content. device 2 shows the additional gradient options that are now available, along with the fresh properties for the charts.

    New Gradient Fill Types

    Moving beyond simple linear gradients, some fresh fill types provide more choices for enhanced presentations. Backgrounds behind pages, objects, and selected areas can now pomp a number of gradient designs. The fill types include rectangular frames and embedded circular gradients, with many parameters to customize the blending of colors (see device 3).

    Chart Combinations (Primary Axis, Secondary Axis, Primary Bottom, Secondary Bottom)

    IBM Cognos 8 trade Insight had the faculty to set Y1 and Y2 axes. Now combination charts can be stacked with two more axes for better analysis of related numbers. These options can be integrated into dashboard design to enhance the presentation of related measures. device 4 shows a chart with the additional axes selected.

    Query Calculations in Query Explorer supplant Calculated Member, Calculated Measure, Set Expression, and Intersection (tuple)

    In IBM Cognos 8 Report Studio, different objects were created for each of the dimensional functions. Now they're utter bundled into the Query Calculations (see device 5). This option simplifies the toolbox for report authors, while emphasizing that dimensional queries can beget enhanced calculations.

    Summarize little Slices and Exploded Slices

    With pie charts, two improvements include summarizing little slices and exploding slices. These two options greatly improve the presentation of pie charts to emphasize the most notable information (see device 6). In my classes, these options beget been requested for years, and now they're delivered in this release.

    Trendlines, Category Baselines, and Numeric Baselines

    Report Studio functions beget now improved on the predictive faculty behind charts, without having to consume external functions to compute the numbers (see device 7).

    Series Color Synchronization

    With the changes made to enhance series, this feature allows report writers to simplify the legends when the sequence information is repeated across the combinations. By changing the sequence Color property to Match (see the short arrow in device 8), the legend shows the different product lines and the nested measures differently (indicated by the longer arrow), increasing the effectiveness of the presentation.

    Custom Properties for Prompts

    Several fresh properties beget been added to enhance the prompt options. These changes allow report developers to create customized prompt options without the additional JavaScript programming required in earlier versions. Most of the prebuilt text labels with these prompts can be customized to meet the needs of individual reports (see device 9).

    Dimensional Set Definitions

    As a welcome addition to dimensional functions, the Set Definition option (see device 10) allows report authors to create intricate subsets that are presented graphically. This feature can build troubleshooting intricate sets much easier by providing a more modular approach to set development. This was the best fresh property for me, since it simplifies the development of sets that had to be full of nested functions.

    IBM Adds Analytics, Data Warehousing to Bluemix | real questions and Pass4sure dumps

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    IBM Research releases 'Diversity in Faces' dataset to forward study of fairness in facial recognition systems | real questions and Pass4sure dumps

    Credit: IBM

    Have you ever been treated unfairly? How did it build you feel? Probably not too good. Most people generally coincide that a fairer world is a better world, and their AI researchers couldn't coincide more. That's why they are harnessing the power of science to create AI systems that are more objective and accurate.

    Many of their recent advances in AI beget produced remarkable capabilities for computers to accomplish increasingly sophisticated and notable tasks, devotion translating speech across languages to bridge communications across cultures, improving intricate interactions between people and machines, and automatically recognizing contents of video to assist in safety applications.

    Much of the power of AI today comes from the consume of data-driven deep learning to train increasingly accurate models by using growing amounts of data. However, the strength of these techniques can moreover be a weakness. The AI systems learn what they're taught, and if they are not taught with robust and diverse datasets, accuracy and fairness could be at risk. For that reason, IBM, along with AI developers and the research community, requisite to be considerate about what data they consume for training. IBM remains committed to developing AI systems to build the world more fair.

    The challenge in training AI is manifested in a very patent and profound route with facial recognition technology. Today, there can be difficulties in making facial recognition systems that meet fairness expectations. The heart of the problem is not with the AI technology itself, per se, but with how the AI-powered facial recognition systems are trained. For the facial recognition systems to achieve as desired – and the outcomes to become increasingly accurate – training data must be diverse and proffer a breadth of coverage. For example, the training data sets must be large enough and different enough that the technology learns utter the ways in which faces vary to accurately recognize those differences in a variety of situations. The images must reflect the distribution of features in faces they notice in the world.

    Credit: IBM

    How execute they measure and ensure diversity for human faces? On one hand, they are familiar with how faces vary by age, gender, and skin tone, and how different faces can vary across some of these dimensions. Much of the focus on facial recognition technology has been on how well it performs within these attributes. But, as prior studies beget shown, these attributes are just a piece of the confound and not entirely adequate for characterizing the full diversity of human faces. Dimensions devotion puss symmetry, facial contrast, the pose the puss is in, the length or width of the face's attributes (eyes, nose, forehead, etc.) are moreover important.

    Today, IBM Research is releasing a fresh large and diverse dataset called Diversity in Faces (DiF) to forward the study of fairness and accuracy in facial recognition technology. The first of its benevolent available to the global research community, DiF provides a dataset of annotations of 1 million human facial images. Using publicly available images from the YFCC-100M Creative Commons data set, they annotated the faces using 10 well-established and independent coding schemes from the scientific literature. [1-10] The coding schemes principally include objective measures of human faces, such as craniofacial features, as well as more subjective annotations, such as human-labeled predictions of age and gender. They believe by extracting and releasing these facial coding scheme annotations on a large dataset of 1 million images of faces, they will accelerate the study of diversity and coverage of data for AI facial recognition systems to ensure more objective and accurate AI systems. Today's release is simply the first step.

    We believe the DiF dataset and its 10 coding schemes proffer a jumping-off point for researchers around the globe studying the facial recognition technology. The 10 facial coding methods include craniofacial (e.g., head length, nose length, forehead height), facial ratios (symmetry), visual attributes (age, gender), and pose and resolution, among others. These schemes are some of the strongest identified by the scientific literature, structure a solid foundation to their collective knowledge.

    Our initial analysis has shown that the DiF dataset provides a more balanced distribution and broader coverage of facial images compared to previous datasets. Furthermore, the insights obtained from the statistical analysis of the 10 initial coding schemes on the DiF dataset has furthered their own understanding of what is notable for characterizing human faces and enabled us to continue notable research into ways to improve facial recognition technology.

    The dataset is available today to the global research community upon request. IBM is disdainful to build this available and their goal is to assist further their collective research and contribute to creating AI systems that are more fair.

    While IBM Research is committed to continuing study and investigation of fairer facial recognition systems, they don't believe they can execute it alone. With today's release, they prick others to contribute to the growing cadaver of research and forward this notable scientific agenda.

    To request access to the DiF dataset, visit their webpage. To learn more about DiF, read their paper, "Diversity in Faces."

    Explore further: Experts disagree with prior study that claimed sheep beget face-recognition abilities comparable to humans

    More information: L. G. Farkas, Anthropometry of the Head and Face, Raven Press, 1994.

    A. Chardon I. Cretois and C. Hourseau, "Skin colour typology and suntanning pathways," International Journal of Cosmetic Science , Aug. 1991, 13(4), pp. 191-208.

    Y. Liu, K. L. Schmidt, J. F. Cohn, S. Mitra, "Facial asymmetry quantification for expression invariant human identification," Computer Vision and Image Understanding, Volume 91, Issues 1–2, July–August 2003, pp. 138-159.

    L. G. Farkas, et. al, "International anthropometric study of facial morphology in various ethnic groups/races," J Craniofac Surg. 2005 Jul;16(4), pp. 615-46.

    N. Ramanathan, R. Chellappa, "Modeling Age Progression in young Faces," Intl. Conf. on Computer Vision and Pattern Recognition (CVPR), 2006, pp. 387-394.

    A. C. Little, B. C. Jones, L. M. DeBruine, "Facial attractiveness: evolutionary based research," Philos Trans R Soc Lond B Biol Sci. 2011 Jun 12;366(1571), pp. 1638-59.

    X. Zhu, D. Ramanan, "Face Detection, Pose Estimation, and Landmark Localization in the Wild," Intl. Conf. on Computer Vision and Pattern Recognition (CVPR), 2012, pp. 2879-2886.

    A. Porcheron, E. Mauger, R. Russell, "Aspects of Facial Contrast dwindle with Age and Are Cues for Age Perception," PLoS One 8(3), Mar. 6, 2013

    Z. Liu, P. Luo, X. Wang, X. Tang, "Deep Learning puss Attributes in the Wild", Intl. Conf. on Computer Vision (ICCV), 2015, pp. 3730-3738.

    Journal reference: PLoS ONE

    Provided by: IBM

    This anecdote is republished courtesy of IBM Research. Read the original anecdote here.

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