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Published By: DATAVERSITY     Published Date: Jul 24, 2014
Will the “programmable era” of computers be replaced by Cognitive Computing systems which can learn from interactions and reason through dynamic experience just like humans? With rapidly increasing volumes of Big Data, there is a compelling need for smarter machines to organize data faster, make better sense of it, discover insights, then learn, adapt, and improve over time without direct programming. This paper is sponsored by: Cognitive Scale.
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data, data management, cognitive computing, machine learning, artificial intelligence, research paper
    
DATAVERSITY
Published By: CData     Published Date: Jan 04, 2019
The growth of NoSQL continues to accelerate as the industry is increasingly forced to develop new and more specialized data structures to deal with the explosion of application and device data. At the same time, new data products for BI, Analytics, Reporting, Data Warehousing, AI, and Machine Learning continue along a similar growth trajectory. Enabling interoperability between applications and data sources, each with a unique interface and value proposition, is a tremendous challenge. This paper discusses a variety of mapping and flattening techniques, and continues with examples that highlight performance and usability differences between approaches.
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data architecture, data, data management, business intelligence, data warehousing
    
CData
Published By: MapR Technologies     Published Date: Aug 01, 2018
How do you get a machine learning system to deliver value from big data? Turns out that 90% of the effort required for success in machine learning is not the algorithm or the model or the learning - it's the logistics. Ted Dunning and Ellen Friedman identify what matters in machine learning logistics, what challenges arise, especially in a production setting, and they introduce an innovative solution: the rendezvous architecture. This new design for model management is based on a streaming approach in a microservices style. Rendezvous addresses the need to preserve and share raw data, to do effective model-to-model comparisons and to have new models on standby, ready for a hot hand-off when a production model needs to be replaced.
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MapR Technologies
Published By: Skytree     Published Date: Nov 23, 2014
Critical business information is often in the form of unstructured and semi-structured data that can be hard or impossible to interpret with legacy systems. In this brief, discover how you can use machine learning to analyze both unstructured text data and semi- structured log data, providing you with the insights needed to achieve your business goals.
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log data, machine learning, natural language, nlp, natural language processing, skytree, unstructured data, semi-structured data, data analysis
    
Skytree
Published By: Reltio     Published Date: May 22, 2018
"Forrester's research uncovered a market in which Reltio [and other companies] lead the pack,” the Forrester Wave Master Data Management states. "Leaders demonstrated extensive and MDM capabilities for sophisticated master data scenarios, large complex ecosystems, and data governance to deliver enterprise-scale business value.” Reltio executes the vision for next-generation MDM by converging trusted data management with business insight solutions at scale and in the cloud. Machine learning and graph technology capabilities enable a contextual data model while also maintaining temporal and lineage changes of the master data.
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Reltio
Published By: Reltio     Published Date: Nov 16, 2018
Big data is growing faster than the capabilities available to manage and analyze it. Get this vendor comparison to learn how a modern master data management platform will help you to achieve better outcomes.
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Reltio
Published By: Syncsort     Published Date: Jul 17, 2018
In most applications we use today, data is retrieved by the source code of the application and is then used to make decisions. The application is ultimately affected by the data, but source code determines how the application performs, how it does its work and how the data is used. Today, in a world of AI and machine learning, data has a new role – becoming essentially the source code for machine-driven insight. With AI and machine learning, the data is the core of what fuels the algorithm and drives results. Without a significant quantity of good quality data related to the problem, it’s impossible to create a useful model. Download this Whitepaper to learn why the process of identifying biases present in the data is an essential step towards debugging the data that underlies machine learning predictions and improves data quality.
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Syncsort
Published By: Syncsort     Published Date: Oct 25, 2018
In most applications we use today, data is retrieved by the source code of the application and is then used to make decisions. The application is ultimately affected by the data, but source code determines how the application performs, how it does its work and how the data is used. Today, in a world of AI and machine learning, data has a new role – becoming essentially the source code for machine-driven insight. With AI and machine learning, the data is the core of what fuels the algorithm and drives results. Without a significant quantity of good quality data related to the problem, it’s impossible to create a useful model. Download this Whitepaper to learn why the process of identifying biases present in the data is an essential step towards debugging the data that underlies machine learning predictions and improves data quality.
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Syncsort
Published By: Attivio     Published Date: Mar 14, 2018
Did you ever consider all of the examples of machine learning in your personal life? Google’s page ranking system, photo tagging on Facebook, and customized product recommendations from Amazon are all driven by machine learning under the hood. How do these same techniques improve productivity for your business? Search is the new data and content curation. Improved relevance translates to faster search results and better business outcomes across the line. Download the Five-Minute Guide to Machine Learning to find out how self-learning technologies drive increasingly relevant answers and better context for cognitive search.
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Attivio
Published By: Converseon     Published Date: Apr 02, 2018
Separating signals from noisy social listening data has long been a problem for data scientists. Poor precision due to slag, sarcasm and implicit meaning has often made it too challenging to effectively model. Today, however, new approaches that leverage active machine learning are rapidly over taking aging rules-based techniques and opening up use of this data in new and important ways. This paper provides some detail on the evolution of text analysis including current best practices and how AI can be used by data scientists to use this data for meaningful analysis.
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Converseon
Published By: Semantic Web Company     Published Date: Jun 27, 2018
Get a comprehensive introduction to AI technologies and learn why semantics should be a fundamental element of any AI strategy. Semantic enhanced artificial intelligence (Semantic AI) is based on the fusion of semantic technologies and machine learning. In this white paper, you will understand how to align the work of data scientists and subject matter experts to increase the business value of your data lake.
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Semantic Web Company
Published By: Wave Computing     Published Date: Jul 06, 2018
This paper argues a case for the use of coarse grained reconfigurable array (CGRA) architectures for the efficient acceleration of the data flow computations used in deep neural network training and inferencing. The paper discusses the problems with other parallel acceleration systems such as massively parallel processor arrays (MPPAs) and heterogeneous systems based on CUDA and OpenCL, and proposes that CGRAs with autonomous computing features deliver improved performance and computational efficiency. The machine learning compute appliance that Wave Computing is developing executes data flow graphs using multiple clock-less, CGRA-based System on Chips (SoCs) each containing 16,000 processing elements (PEs). This paper describes the tools needed for efficient compilation of data flow graphs to the CGRA architecture, and outlines Wave Computing’s WaveFlow software (SW) framework for the online mapping of models from popular workflows like Tensorflow, MXNet and Caffe.
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Wave Computing
Published By: Hewlett Packard Enterprise     Published Date: Jan 31, 2019
The bar for success is rising in higher education.  University leaders and IT administrators are aware of the compelling benefits of digital transformation overall—and artificial intelligence (AI) in particular. AI can amplify human capabilities by using machine learning, or deep learning, to convert the fast-growing and plentiful sources of data about all aspects of a university into actionable insights that drive better decisions. But when planning a transformational strategy, these leaders must prioritize operational continuity. It’s critical to protect the everyday activities of learning, research, and administration that rely on the IT infrastructure to consistently deliver data to its applications.
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Hewlett Packard Enterprise
Published By: TIBCO Software GmbH     Published Date: Jan 15, 2019
By processing real-time data from machine sensors using arti?cial intelligence and machine learning, it’s possible to predict critical events and take preventive action to avoid problems. TIBCO helps manufacturers around the world predict issues with greater accuracy, reduce downtime, increase quality, and improve yield.
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TIBCO Software GmbH
Published By: TIBCO Software GmbH     Published Date: Jan 15, 2019
Gradient Boosting Machine (GBM) modeling is a powerful machine learning technique for advanced root cause analysis in manufacturing. It will uncover problems that would be missed by regression-based statistical modelling techniques and single tree methods, but can easily be used by analysts with no expertise in statistics and modelling to solve complex problems. It is an excellent choice for advanced equipment commonality analysis and will detect interactions between process factors (for example, machines, recipes, process dates) that are responsible for bad product. It can also be used to identify complex nonlinear relationships and interactions between product quality measurements (for example, yield, defects, field returns) and upstream measurements from the product, process, equipment, component, material, or environment.
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TIBCO Software GmbH
Published By: TIBCO Software GmbH     Published Date: Jan 15, 2019
Whether you know it as Industry 4.0, the 4th Industrial Revolution, or Smart Industry, Manufacturing is going through a deep transformation, with changes that are centered around digitalization. While most industries are already on this digitalization path, the disruption is more visible and pronounced in manufacturing because it is expanding virtual data and processes into environments that have been fundamentally about physical products. This transformation has already started, and its impact is expected to be massive. Technical, economic, and social changes are expected across the whole manufacturing ecosystem, with jobs shifting from offshoring back to nearshoring. Strong technology elements driving this digital revolution include 3D printing, robotizing and automation, smart factory with IoT and machine learning, and supply chain digitization. Their impact is profound.
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TIBCO Software GmbH
Published By: Larsen & Toubro Infotech     Published Date: Jan 31, 2019
LTI helped a leading global bank digitize its traditional product ecosystem for AML transaction monitoring. With the creation of a data lake and efficient learning models, the bank successfully reduced false positives and improved customer risk assessment. Download Complete Case Study.
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Larsen & Toubro Infotech
Published By: PwC     Published Date: Jun 20, 2018
Digital Fitness Assessment 2.0 is here. The time to upskill is now. Let’s face it--every industry is being disrupted. To stay profitable and lead the way, it’s critical to be deliberate about making sure your employees have the skills they’ll need to take you there. The Digital Fitness Assessment will help your employees build their future through a custom experience that targets specific skills and behaviors to each employee. Get a demo here. [Please confirm link works and is tracked] What if you could give every employee a blueprint for tomorrow's most-needed skills? Your people know they need new skills--and most are eager to get them. But, what most companies offer is, well, standardized. That can make it boring and not applicable. To stand out and bring your workforce into the future, you need a creative, personal--and even enjoyable--learning experience. The Digital Fitness Assessment will help your employees build their future. Get a demo here. [Please confirm link works and is
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PwC
Published By: PwC     Published Date: Jul 19, 2018
What if you could give every employee a blueprint for tomorrow's most-needed skills? Your teams know they need new skills--and most are eager to get them. But, what most companies offer is, well, standardized. That can make it boring and not applicable. To stand out and bring your workforce into the future, you need a creative, personal--and even enjoyable--learning experience. The Digital Fitness Assessment will help your employees build their future. Get a demo here.
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PwC
Published By: HotSchedules     Published Date: Jan 17, 2019
What could you be saving by implementing a learning management system? This guide will show you the provable return on investment in an eLearning strategy; how an LMS can help your business lower print costs, reduce employee turnover, positively impact sales, and much more. Whether you’re exploring the marketplace or building out your business case for online learning, this guide has the information you want. As you prepare to move to a new LMS consider Clarifi Talent Development, the learning and performance management system designed for today’s workforce. It is the choice for leading brands like Brinker, Newk’s and Subway.
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HotSchedules
Published By: HotSchedules     Published Date: Jan 17, 2019
Founded in 2011 by entrepreneurs Allen Reagan and Walt Powell, Flix Brewhouse is the world’s only first-run movie theatre and fully functioning microbrewery. The operation faced some challenges as the business grew including a lack of of structured training program and disengaged training sessions. With the help of Clarifi Talent Development Flix Brewhouse was able to turn it all around. With a fully functioning training program, Flix Brewhouse now has 71 custom training courses with increased sales and improved service times. With 660 active users in their online training academy, they are able to maintain high-quality service for their customers. Learn how they do it!
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HotSchedules
Published By: Oracle     Published Date: Feb 11, 2019
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Oracle
Published By: Oracle     Published Date: Feb 11, 2019
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Oracle
Published By: MobileIron     Published Date: Feb 12, 2019
The types of threats targeting enterprises are vastly different than they were just a couple of decades ago. This paper examines some current mobile threat defense approaches to help organizations understand where traditional solutions may fall short — and how machine learning-based threat defense can expand upon those capabilities by providing immediate, on-device protection against mobile attacks.
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MobileIron
Published By: FICO EMEA     Published Date: Jan 25, 2019
Communications service providers (CSPs) have long recognized the potential of data analytics. Yet their early efforts to pull actionable intelligence from the oceans of data they have access to were largely unsuccessful. Many tried a 'big bang' approach to building a central repository without knowing what they wanted to do with the data in it. The arrival of artificial intelligence (AI) – its machine learning subset in particular – has changed their thinking and approach. For this Quick Insights report, we surveyed 64 professionals from CSPs around the world who are applying, leveraging and/ or planning to deploy advanced analytics in some capacity at various points across the customer lifecycle.
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analytics, artificial intelligence, customer lifecycle, insights, telecom credit lifecycle, customer acquisition, optimisation
    
FICO EMEA
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