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Published By: Stardog Union     Published Date: Jul 27, 2018
When enterprises consider the benefits of data analysis, what's often overlooked is the challenge of data variety, and that most successful outcomes are driven by it. Yet businesses are still struggling with how to query distributed, heterogeneous data using a unified data model. Fortunately, Knowledge Graphs provide a schema flexible solution based on modular, extensible data models that evolve over time to create a truly unified solution. How is this possible? Download and discover: • Why businesses should organize information using nodes and edges instead of rows, columns and tables • Why schema free and schema rigid solutions eventually prove to be impractical • The three categories of data diversity including semantic and structural variety
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Stardog Union
Published By: Neo Technology     Published Date: Jun 28, 2015
The future of Master Data Management is deriving value from data relationships which reveal more data stories that become more and more important to competitive advantage as we enter into the future of data and business analytics. MDM will be about supplying consistent, meaningful views of master data and being able to unify data into one location, especially to optimize for query performance and data fit. Graph databases offer exactly that type of data/performance fit. Use data relationships to unlock real business value in MDM: - Graphs can easily model both hierarchical and non-hierarchical master data - The logical model IS the physical model making it easier for business users to visualize data relationships - Deliver insights in real-time from data relationships in your master data - Stay ahead of the business with faster development Download and read the white paper Your Master Data Is a Graph: Are You Ready? to learn why your master data is a graph and how graph databases like Neo4j are the best technologies for MDM.
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database, nosql, graph database, big data, master data management, mdm
    
Neo Technology
Published By: Ted Hills     Published Date: Jul 02, 2015
Entity-relationship (E-R) modeling is a tried and true notation for use in designing Structured Query Language (SQL) databases, but the new data structures that Not-Only SQL (NOSQL) DBMSs make possible can’t be represented in E-R notation. Furthermore, E-R notation has some limitations even for SQL database design. This article shows how a new notation, the Conceptual and Objective Modeling (COM) notation, is able to represent NOSQL designs that are beyond the reach of E-R notation. At the end, it gives a peek into the tutorial workshop to be given at the 2015 NOSQL Conference in San Jose, CA, US, in August, which will provide opportunities to apply COM notation to practical problems.
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nosql, sql, data modeling, data model, er modeling, entity relationship, database, relational, dbms, schema-less, xml, conceptual, logical, physical
    
Ted Hills
Published By: Profium     Published Date: Mar 28, 2017
Profium Sense is an AI powered graph database which has unique features such as triggered query evaluation and ability to change rules in runtime. With support for open standards, it provides a reliable backbone for your next-generation digital services.
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Profium
Published By: MariaDB     Published Date: Apr 02, 2018
Learn how to use JSON functions for semi-structured data This white paper explains step-by-step how to support semi-structured data with JSON functions, introduced in MariaDB Server 10.2, using a practical use case with sample data and queries for everything from creating, reading and querying JSON documents to enforcing data integrity with check constraints and functions. You will learn how to: • Create, read and update JSON documents • Index and query JSON documents • Enforce data integrity with JSON documents • Combine relational data and JSON documents • Return JSON documents as relational data • Return relational data as JSON documents
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MariaDB
Published By: TopQuadrant     Published Date: Apr 16, 2013
TopBraid Insight provides an ‘out-of-the-box’ Logical Data Warehouse that allows an agile, extensible approach to querying data from diverse data sources. This white paper describes the approach as applied to the Life Sciences industry.
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TopQuadrant
Published By: MindTouch     Published Date: Mar 08, 2019
Better with knowledge management. To operate effectively, chatbots need to be able to quickly match user intent to relevant information. This means querying the available knowledge sources for answers. As it turns out, the better organizations structure, organize, and publish this information, the better their chatbots will be. This puts next-gen knowledge management solutions square in the middle of the chatbot conversation.
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MindTouch
Published By: SAP     Published Date: May 18, 2014
In-memory technology—in which entire datasets are pre-loaded into a computer’s random access memory, alleviating the need for shuttling data between memory and disk storage every time a query is initiated—has actually been around for a number of years. However, with the onset of big data, as well as an insatiable thirst for analytics, the industry is taking a second look at this promising approach to speeding up data processing.
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sap, big data, real time data, in memory technology, data warehousing, analytics, big data analytics, data management, business insights, architecture, business intelligence, big data tools, analytical applications
    
SAP
Published By: AWS     Published Date: Sep 05, 2018
Amazon Redshift Spectrum—a single service that can be used in conjunction with other Amazon services and products, as well as external tools—is revolutionizing the way data is stored and queried, allowing for more complex analyses and better decision making. Spectrum allows users to query very large datasets on S3 without having to load them into Amazon Redshift. This helps address the Scalability Dilemma—with Spectrum, data storage can keep growing on S3 and still be processed. By utilizing its own compute power and memory, Spectrum handles the hard work that would normally be done by Amazon Redshift. With this service, users can now scale to accommodate larger amounts of data than the cluster would have been capable of processing with its own resources.
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AWS
Published By: Oracle CX     Published Date: Oct 19, 2017
The Software in Silicon design of the SPARC M7 processor, and the recently announced SPARC S7 processor, implement memory access validation directly into the processor so that you can protect application data that resides in memory. It also includes on-chip Data Analytics Accelerator (DAX) engines that are specifically designed to accelerate analytic functions. The DAX engines make in-memory databases and applications run much faster, plus they significantly increase usable memory capacity by allowing compressed databases to be stored in memory without a performance penalty. The following Software in Silicon technologies are implemented in the SPARC S7 and M7 processors: Note: Security in Silicon encompasses both Silicon Secured Memory and cryptographic instruction acceleration, whereas SQL in Silicon includes In-Memory Query Acceleration and In-Line Decompression. Silicon Secured Memory is the first-ever end-to-end implementation of memory-access validation done in hardware. It
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Oracle CX
Published By: Amazon Web Services     Published Date: Jul 25, 2018
Organizations are collecting and analyzing increasing amounts of data making it difficult for traditional on-premises solutions for data storage, data management, and analytics to keep pace. Amazon S3 and Amazon Glacier provide an ideal storage solution for data lakes. They provide options such as a breadth and depth of integration with traditional big data analytics tools as well as innovative query-in-place analytics tools that help you eliminate costly and complex extract, transform, and load processes. This guide explains each of these options and provides best practices for building your Amazon S3-based data lake.
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Amazon Web Services
Published By: Dell Server     Published Date: Aug 07, 2018
Drive a competitive advantage. Deploy and analyse more high-value data workloads faster with a 41% increase in transactions per second and a 50% reduction in average query response time with the Dell EMC PowerEdge Servers, powered by Intel® Xeon® Platinum processor.
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Dell Server
Published By: Oracle PaaS/IaaS/Hardware     Published Date: Jul 25, 2017
This ESG Lab review documents the results of recent testing of the Oracle SPARC M7 processor with a focus on in-memory database performance for the real-time enterprise. Leveraging new advanced features like columnar compression and on-ship in-memory query acceleration, ESG Lab compared the in-memory database performance of a SPARC T7 system with a SPARC M7 processor to an x86-based system.
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Oracle PaaS/IaaS/Hardware
Published By: Idera     Published Date: Nov 08, 2013
SQL Diagnostic Manager: Download Trial Today
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idera, sql diagnostic, free trial, download software, performance monitoring, query monitoring, history browsing, predictive alerting, software development
    
Idera
Published By: Lucidworks     Published Date: Aug 24, 2016
In reality, we developed search technologies, not primarily as a front-end service but to alleviate the challenges of the back end: our data. Search grew in direct response to data problems. The search story can’t be told without the data story, and high-volume data would be nearly useless without search technology. Download this eBook to learn more about search technologies.
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search, search query, search technologies, search apps, data problems, ecommerce search
    
Lucidworks
Published By: Lucidworks     Published Date: Aug 24, 2016
Online search functionality should feel seamless. Type what you’re looking for, and watch it appear instantly—like magic. No stumbling through category hierarchies or landing pages; just fast, accurate, search results pointing you to exactly what you want. Unfortunately, ecommerce search isn’t quite there yet. Download this eBook to learn 12 must-have query types to optimize your organization's eCommerce search.
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query types, search engine optimization, seo, search functionality, search results, ecommerce search
    
Lucidworks
Published By: Oracle     Published Date: Jul 05, 2017
Every user’s first interaction with your website begins with a series of DNS queries. The Domain Name System (DNS) is a distributed internet database that maps human-readable names to IP addresses, ensuring users reach the correct website when entering a URL. DNS mappings are maintained in special-purpose servers called DNS nameservers. When a user enters your company’s URL, a DNS query is routed to a DNS nameserver containing the address mappings for your company’s internet domain.
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advanced traffic steering, global internet visibility, open programming interfaces, risk management tools, secondary dns
    
Oracle
Published By: Oracle     Published Date: Nov 30, 2017
Every user’s first interaction with your website begins with a series of DNS queries. The Domain Name System (DNS) is a distributed internet database that maps human-readable names to IP addresses, ensuring users reach the correct website when entering a URL. DNS mappings are maintained in special-purpose servers called DNS nameservers. When a user enters your company’s URL, a DNS query is routed to a DNS nameserver containing the address mappings for your company’s internet domain.
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traffic, dns, internet, visibility, programming, interfaces, optimization, routing
    
Oracle
Published By: Oracle Dyn     Published Date: Dec 06, 2017
Every user’s first interaction with your website begins with a series of DNS queries. The Domain Name System (DNS) is a distributed internet database that maps human-readable names to IP addresses, ensuring users reach the correct website when entering a URL. DNS mappings are maintained in special-purpose servers called DNS nameservers. When a user enters your company’s URL, a DNS query is routed to a DNS nameserver containing the address mappings for your company’s internet domain.
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traffic, dns, internet, visibility, programming, interfaces, optimization, routing
    
Oracle Dyn
Published By: FICO     Published Date: Jun 07, 2016
With the increasing mainstream adoption of big data infrastructure — highly distributed file storage and query tools — more businesses are taking a new look at what business intelligence and analytics can do to grow revenue, increase profits and ultimately develop strategic relationships with customers.
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FICO
Published By: IBM     Published Date: Jan 27, 2017
In today’s highly distributed, multi-platform world, the data needed to solve any particular decision making need is increasingly likely to be found across a wide variety of sources. As a result, traditional manual approaches requiring prior collection, storage and integration of extensive sets of data in the analyst’s preferred exploration environment are becoming less useful. Data virtualization, which offers transparent access to distributed, diverse data sources, offers a valuable alternative approach in these circumstances.
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IBM
Published By: MarkLogic     Published Date: Nov 08, 2016
In this report, you will learn: 1. Why semantic technology is gaining traction now 2. How semantics is solving major challenges in the real world 3. How to model data as RDF and query with SPARQL Download now!
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MarkLogic
Published By: Oracle + Dyn     Published Date: Jun 27, 2017
"DNS has evolved from a set-it and forget it solution into a mission-critical layer of your online infrastructure. The capabilities of DNS go far beyond speeds, feeds and query resolution. Download this “Rethink DNS” eBook and discover smarter ways to use DNS to optimize performance, build resiliency and manage traffic across hybrid cloud environments. This eBook covers: • Why DNS is central to the user experience • Costs and revenue impact of latency or outages • Risks and challenges of in-house and managed DNS services • Selecting the right DNS provider for your business Download eBook! "
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Oracle + Dyn
Published By: Oracle     Published Date: Oct 20, 2017
The Software in Silicon design of the SPARC M7 processor, and the recently announced SPARC S7 processor, implement memory access validation directly into the processor so that you can protect application data that resides in memory. It also includes on-chip Data Analytics Accelerator (DAX) engines that are specifically designed to accelerate analytic functions. The DAX engines make in-memory databases and applications run much faster, plus they significantly increase usable memory capacity by allowing compressed databases to be stored in memory without a performance penalty. The following Software in Silicon technologies are implemented in the SPARC S7 and M7 processors: Note: Security in Silicon encompasses both Silicon Secured Memory and cryptographic instruction acceleration, whereas SQL in Silicon includes In-Memory Query Acceleration and In-Line Decompression. Silicon Secured Memory is the first-ever end-to-end implementation of memory-access validation done in hardware. It
Tags : 
    
Oracle
Published By: Lucidworks     Published Date: Jun 24, 2015
Using content, context, and the crowd to build better search applications.
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search platform, search query, search technologies, big data, high-volume data, database advancements, it management, knowledge management, platforms, data management, intent, context, business technology, data center
    
Lucidworks
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