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Cisco Cisco PSACAFE Advanced Collaboration

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No outcomes discovered, try current keyword!Soutec’s portfolio of items ranges from ordinary network infrastructure offerings and facts materiel to superior collaboration tools for assembly ... of its crew and the company's partnership with Cisco ...

Workspace Of the longer term On panoply today on the Dimension statistics Digital workplace middle of Excellence In Silicon Valley | killexams.com actual Questions and Pass4sure dumps

The capital of innovation will host an commercial enterprise technology playground that includes items from Apple, Cisco and VMware

long island, Oct. 11, 2018 /PRNewswire/ -- Dimension statistics, a USD eight billion global technology integrator, managed services provider and member of the NTT community, proclaims the outlet of a Digital office middle of Excellence at the NTT international Innovation hub in Silicon Valley.

Dimension information brand (PRNewsfoto/Dimension statistics)

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through its ubiquitous mesh of voice, video, end-user computing, mobility and protection applied sciences, the workspace of the future allows for continuous innovation of sparkling enterprise fashions, processes and results. The core of Excellence brings to life built-in products and functions from leading know-how businesses together with Apple, Cisco and VMware – demonstrating how Dimension records collaborates with agencies to coerce advantageous traffic outcomes and assist the most arrogate consume of technology tailored to their wonderful wants.

headquartered in the innovation capital of the world, the core showcases superior digital office technologies akin to Cisco collaboration and protection tools, and VMware Workspace ONE, the intelligence-pushed digital workspace platform, and demonstrates how Apple instruments are premiere for enterprise with Dimension information managed services choices to back businesses bring an any place, each time mobile experience.

core guests will hold the option to interact with consultants as they explore the latest improvements transforming the artery forward for work. the following technologies are on screen, introduced collectively via Dimension statistics Endpoint Lifecycle administration (ELM) services to enhance the user journey and provide prosperous analytics:  

  • safety options to tarry away from phishing attacks, give protection to compromised credentials and become alert of rogue functions
  • contemporary collaboration solutions to wirelessly current, whiteboard, video or audio conference and annotate shared content
  • intelligent, enterprise-grade instant charging stations that provide cloud management and analytics
  • Cloud-based mostly region utilization analytics
  • options to streamline the provisioning and administration of endpoints in a shared atmosphere, empowering frontline workers
  • structures to surface statistics on space and technology utilization, showing meeting schedules and workplace analytics
  • solutions to quite simply and securely bring and manage any app on any machine by artery of integrating access manage, software management and multi-platform endpoint administration
  • "because the personnel turns into extra far off and cellular, so does their reliance on pervasive, comfy connectivity and collaboration," observed appoint Sainsbury, Senior vp, Americas solutions at Dimension information. "Being capable of create a cozy digital office it is agile and on-the-go is increasingly Important for state-of-the-art agencies. The Digital space of work hub of Excellence offers a window into what the future of work will materialize to be, as organizations wait on discrete personalized workstyles. The effectiveness of which depends on a seamless, relaxed and productive experience."  

    applied sciences featured at the middle encompass Cisco protection Connector, Cisco Webex Board, Chargifi, CoWorkr sensors, GroundControl, bristle Workspace sustain Platform and VMware Workspace ONE, amongst others.  

    About Dimension DataFounded in 1983, Dimension information is a USD 8 billion international leader in designing, optimizing, and managing modern evolving expertise environments. This makes it feasible for its valued clientele to leverage data in a digital age, flip it into suggestions, and extract insights. Headquartered in Johannesburg, Dimension records employs 28,000 americans throughout forty seven countries. The traffic brings together the area's greatest technology offered by using market leaders and niche innovators with the provider back that valued clientele necessity for his or her groups – from consulting, technical, and assist features to a totally-managed provider. Dimension information is a proud member of the NTT group. consult with us at http://www.dimensiondata.com

    Contact:Evan JordanSenior Public relations ManagerDimension statistics(704) 943-9345evan.jordan@dimensiondata.com

    Story Continues


    Cisco partners with Smartworld for Expo 2020 network | killexams.com actual Questions and Pass4sure dumps

    Cisco has announced a partnership with methods integrator Smartworld to design and installation the network infrastructure for Expo 2020 Dubai.

    because the official Digital network associate of Expo 2020 Dubai, Cisco and its associate Smartworld will develop a smart community with a view to wait on digital journey for friends to Expo 2020, in addition to connect an estimated a hundred,000 IoT devices which should subsist deployed on the website.

    Cisco is working with Smartworld to set up components of Expo's digital IP network infrastructure, including Ciscointent-based networking solutions. As a Cisco Premier companion, smart technology functions DWC LLC, (Smartworld) has expert elements and expertise to construct an clever and relaxed community for Expo 2020.

    Cisco's intent-based networking portfolio represents a fundamental shift faraway from the lead and time-intensive strategies by which networks are historically managed. Designed for the demands of the digital period, intent-based networks trap and translate traffic intent into community policies, and spark off them across the infrastructure. They import on operational issues, cease security threats of their tracks, and proceed to gain information of, appropriate and give protection to.

    "Being a semi-govt main master systems integrator, Smartworld is committed to working with Cisco to wait on Expo 2020 convey a worldwide sustain enabled by artery of the latest expertise, in alignment with Expo's theme Connecting Minds, developing the future," spoke of Abdulqader Ali, CEO of Smartworld.

    Shukri Eid, Managing Director - East area, Cisco core East, stated, "As Digital community accomplice for Expo 2020, Cisco will provide its latest know-how improvements and a cozy, bright groundwork for connectivity via their intent-based networking solution. They look forward to working with Smartworld in making Expo 2020's technology infrastructure probably the most superior in the event's history."

    Mohammed Alhashmi, Senior vp, Innovation and Future know-how at Expo 2020 Dubai, referred to, "An innovative and seamless digital infrastructure is basic for a a success Expo 2020. we're delighted on the progress being made via their partner, Cisco, in collaboration with Smartworld, and we're assured that the sensible technology deployed at Expo 2020 will wow guests from everywhere the world."


    650-368 PSACAFE Advanced Collaboration Architecture bailiwick Engineer

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    Test Code : 650-368
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    free pdf : 50 actual Questions

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    The Semiconductor Industry Is in Transition | killexams.com actual questions and Pass4sure dumps

    Companies are birth to confess the potential of current markets and downstream revenue opportunities as they explore a more comprehensive “silicon to services” model that spans the data hub to the mobile edge. More specifically, with eroding ASPs (average selling prices) and increasingly prohibitive design costs at ever lower nodes, many companies are searching for fresh revenue streams across a wide sweep of verticals comprising the Internet of Things (IoT).

    However, with the IoT install foundation expected to enlarge by about 15% to 20% annually through 2020, security is currently perceived as both a major occasion and a considerable challenge for the semiconductor industry.

    In addition to services, the concept of open-source hardware (OSH) and edifice silicon from disaggregated, pre-verified chiplets is birth to gain grave traction as companies slouch to slash costs and reduce time-to-market for heterogeneous designs.

    Specific strategies to unlocking the full potential of silicon and services will undoubtedly vary, which is why it’s Important for us to explore a future in which semiconductor companies, along with various industries, organizations, and government offices, play an open and collaborative role in helping to sustainably monetize both silicon and services.

    Acquisitions and industry consolidation continued apace in 2016 and 2017:

  • Analog Devices acquired Linear Technology
  • Infineon acquired International Rectifier
  • ROHM acquired Powervation
  • Renesas acquired Intersil
  • Major semiconductor players positioned themselves to better compete across multiple verticals, including cloud-based computing, simulated intelligence (AI), and self-driving vehicles. According to KPMG, many companies increasingly survey Mergers & Acquisitions (M&A) as the only artery to drive actual revenue growth, giving a current emphasis to the “make vs. buy” question, with many selecting the “buy” answer.

    Concurrently, chip development costs continued to tower and saliently move the number of designs at advanced nodes. More specifically, the total number of Advanced Performance Multicore first-time SoC design starts has been roughly flat to only slightly up over the past five years. Although design price tags hold been steadily increasing since 40 nm, analysts are most concerned with the acceleration of design costs at 7 nm and 5 nm.

    As flush Wawrzyniak, a senior analyst at Semico Research confirms, design starts beyond 10 nm will subsist constrained by rising development costs. While the total number of designs that migrate to current nodes may not subsist appreciably different than previous process geometry upgrades, Wawrzyniak says the time frame for such transitions by the majority of companies will subsist more protracted.

    New models for both R&D and revenue are clearly needed, as heightened industry consolidation and eroding ASPs are unsustainable in the long term. This is precisely why the industry is looking toward the Internet of Things to create additional revenue streams, with McKinsey Global Institute (MGI) analysts estimating the IoT could hold an annual economic impact of $3.9 to $11.1 trillion by 2025 across multiple verticals. However, with the IoT install foundation expected to enlarge by about 15% to 20% annually through 2020, security is considered to subsist both a major occasion and challenge for semiconductor companies.

    As such, MGI recommends creating security solutions that allow for semiconductor companies to expand into adjacent traffic areas and develop current traffic models. For example, companies could wait on create end-to-end security offerings, which are essential to the IoT’s success. Ideally, says MGI, the industry should play a leading role when developing such offerings, to ensure they obtain their just partake of the value chain.

    From their perspective, end-to-end IoT security solutions deployed as a platform as a service (PaaS) are captious in helping semiconductor companies generate renewable, downstream revenue for specific services. For customers, PaaS offers an light artery for customers to securely develop, run, and manage applications and devices without the complexity of edifice and maintaining intricate infrastructure.

    Such security solutions, which could also leverage a hardware-based root-of-trust, should back device identification and mutual authentication (verification), routine attestation checks, secure over-the-air (OTA) device updates, catastrophe recovery and key management, as well as the decommissioning and reassignment of keys to better manage devices and mitigate various attacks, including distributed denial of service (DDoS).

    Smart Cities

    Inaccessible silicon—such as chips embedded in IoT smart-city infrastructure—could tender semiconductor companies the occasion to implement a long-term PaaS “silicon to services” model. Indeed, future smart-city infrastructure will almost certainly subsist designed with chips in difficult-to-reach locations, including subterranean water pipes, air-conditioning ducts, and under streets and parking lots.

    Intelligent street lighting, responsive signage, and next-gen Bluetooth beacons also require future-proofing to avoid constant physical maintenance and upgrades. Therefore, silicon powering smart-city infrastructure should subsist capable of supporting secure in-field feature configuration, along with various PaaS-based services fondness advanced analytics, predictive maintenance alerts, self-learning algorithms, and intelligent, proactive interaction with customers.

    Smart Homes

    The global smart-home market is projected to attain at least $40 billion in value by 2020. According to Markets and Markets, the growth of the smart-home space can subsist attributed to multiple factors, including significant advances in the IoT sector; increasing requirements for consumer convenience, safety, and security; a more pronounced necessity for energy-saving, and low-carbon-emission-oriented solutions. However, as they previously discussed, it’s captious to ensure that IoT security is implemented at the product design stage to obviate malicious actors from exploiting smart-home devices and causing service interruptions.

    In addition to potentially lucrative cybersecurity opportunities for semiconductor companies, smart-home devices tender the pledge of creating recurring revenue streams to back a sustainable “silicon to services” model. As an example, MarketingInsider's Christoper Dean highlights Amazon’s favorite reflect devices. With at least 15 million Echoes already sold, reflect users are likely to double as lively Amazon consumers, leveraging the device to monitor wishlists and ascertain items they’re subsequently prompted to purchase. Meanwhile, Nest is using thermostat data as a platform to tender energy-management services to utility companies across the United States, with companies paying for meaningful and actionable customer information on a subscription basis.

    Automotive

    According to IC Insights, chip sales for automotive systems and the IoT will grow 70% faster than total IC revenues between 2016 and 2021. Specifically, IC sales for automobiles and other vehicles are forecast to jump from $22.9 billion in 2016 to $42.9 billion in 2021, while revenues for IoT functionality will enlarge from $18.4 billion in 2016 to $34.2 billion in 2021.

    The projected enlarge in automotive chip sales is hardly surprising, as modern vehicles are essentially a network of networks equipped with a sweep of embedded communication methods and capabilities. However, this means vehicles are now more vulnerable to cyberattacks than ever before.

    Potential security vulnerabilities comprehend unprotected vehicle-to-vehicle communication, the unauthorized collection of driver or passenger information, seizing control of captious systems such as brakes or accelerators, intercepting vehicle data, tampering with third-party dongles, and altering over-the-air (OTA) firmware updates. In terms of the latter, automotive manufacturers are now focusing on providing secure OTA updates for various systems, with the global automotive OTA update market projected to grow at a CAGR of 18.2% from 2017 to 2022 and attain $3.89 billion by 2022.

    Automotive manufacturers are also working to ensure that the vehicle supply chain is kept free of stolen and counterfeit components. Nevertheless, a wide sweep of gray-market devices can silent subsist found powering high-value modules such as in-vehicle infotainment systems and headlights, as well as in captious safety systems including airbag modules, braking modules, and powertrain controls. Shielding vehicle peripherals and components against tampering by implementing a sweep of layered hardware and software security solutions hold therefore become a priority for a number of automotive manufacturers.

    In addition to implementing layered security solutions, the semiconductor industry would clearly capitalize from adopting an IoT "as-a-service" approach to the automotive sector. As an example, companies could deploy sensor-based vehicle systems that proactively detect potential issues and malfunctions. This solution, which, in its most optimal configuration, would combine silicon and services, could subsist sold as a hardware and software product, or deployed as a service with subscription fees generated on a monthly or annual basis.

    Medical and Healthcare

    Long-life implanted medical devices will undoubtedly require a high degree of future-proofing from the semiconductor industry to avoid frequent physical upgrades and maintenance. Srihari Yamanoor, an R&D design specialist at Stellartech Research Corp., notes that medical devices will ultimately subsist customized to meet the needs of individual patients, thereby increasing the application of precision medicine.

    Moreover, the health insurance industry is expected to leverage machine learning to optimize and reduce the cost of care, while digital health devices will also subsist used by the insurance industry to identify at-risk patients and provide assistance. Medical devices, especially implantable models, should therefore subsist designed to back a “silicon to services model” via in-field feature configuration and secure OTA updates, as well as PaaS-based services, including the collection and analysis of apropos data; proactive maintenance, advanced algorithms; and an intuitive UI for both patients and doctors.

    Open-Source Hardware and Disaggregated Chiplets

    Alongside services, open-source hardware offered by organizations and companies such as RISC-V and SiFive hold begun to positively disrupt the semiconductor industry by encouraging innovation, reducing development costs and accelerating time-to-market.

    The success of open-source software—as opposed to a closed, walled-garden approach—continues to set an Important precedent for the semiconductor industry. Faced with prohibitively expensive development costs, a number of companies are opting to avoid unnecessary toll collectors while placing more of an emphasis on open-source architecture as they work to create current service-centric revenue streams.

    In addition to open-source hardware, the concept of edifice silicon from pre-verified chiplets is birth to gain traction as the semiconductor industry moves to slash costs and reduce time-to-market for heterogeneous designs. According to Semiconductor Engineering's Ann Steffora Mutschler, the chiplet concept has been on the drawing board for some time, although it was historically perceived as a potential future direction rather than a tangible solution in the shadow of waning Moore’s Law. This perception is birth to shift as design complexity increases, particularly at advanced nodes (10/7 nm), and as current markets that require semi-customized solutions coalesce.

    The concept of pre-verified chiplets has piqued the interest of the U.S. Defense Advanced Research Projects Agency (DARPA), which recently rolled out its Common Heterogeneous Integration and IP Reuse Strategies (CHIPS) program. In collaboration with the semiconductor industry, the successful implementation of CHIPS would survey a sweep of IP blocks, subsystems, and chips combined on an interposer in a 2.5D-like package.

    The CHIPS initiative took hub stage in August 2017, when participants from the military, commercial, and academic sectors gathered at DARPA headquarters at the official kickoff meeting for the Agency’s Common Heterogeneous Integration and Intellectual Property (IP) Reuse Strategies program.

    As DARPA's Dr. Daniel Green circumstantial during the conference, the program seeks to develop a current technological framework in which different functionalities and blocks of intellectual property—among them, data storage, computation, signal processing, and managing the profile and stream of data—can subsist segregated into petite chiplets. They then can subsist mixed, matched, and combined onto an interposer, more or less fondness joining the pieces of a jigsaw puzzle. In fact, says Green, an entire conventional circuit board with a variety of different but full-sized chips could ultimately subsist shrunk down onto a much smaller interposer hosting a huddle of yet far smaller chiplets.

    According to DARPA, specific technologies that could emerge from the CHIPS initiative comprehend compact replacements for entire circuit boards, ultra-wideband radio frequency (RF) systems, and fast-learning systems for extracting bewitching and actionable information from much larger volumes of mundane data.

    Perhaps not surprisingly, the semiconductor industry is already eyeing a disaggregated approach in the profile of SerDes chiplets and specialized low-power, application-specific die-to-die interfaces. To subsist sure, viable silicon disaggregation can subsist achieved by stirring high-speed interfaces fondness SerDes to separate die in the profile of SerDes chiplets, shifting analog sensor IP to separate analog chips and implementing very low-power and low-latency die-to-die interfaces through MCM or through an interposer using 2.5D technology.

    Beyond leveraging known-good die for SerDes in more develope nodes (N-1) or vice versa, disaggregation is expected to facilitate the creation of multiple SKUs, while optimizing cost and reducing risk. More precisely, disaggregation will survey SoCs broken out into higher-yielding, smaller dies and allow companies to create specific designs with multiple variants. Indeed, die-to-die interfaces can more easily accommodate diverse applications across memory, logic and analog technology. In addition, die-to-die interfaces don’t require a matching line/baud rate and number of lanes, while FEC may or may not subsist required depending on latency requirements.

    It should subsist eminent that multiple companies are actively pursuing SoC/ASIC aggregation for switches and other systems. Similarly, the semiconductor industry is developing ASICs with die-to-die interfaces on leading FinFET nodes, while at least one next-generation server chip is being designed with disaggregated I/Os on a separate die.

    Conclusion

    Over the past five years, the semiconductor industry has faced numerous tangled challenges. These comprehend increased development costs, eroded ASPs, market saturation, and heightened, yet unsustainable M&A activity. Through 2018, the semiconductor industry continues to search a revert to stability and organic growth within the parameters of a current traffic paradigm that’s both viable and collaborative. Within this context, semiconductor companies are acknowledging the potential of current markets and downstream revenue opportunities as they explore a more comprehensive “silicon to services” model that spans the data hub to the mobile edge.

    This includes end-to-end IoT security solutions and PaaS-based services such as in-field feature configuration, advanced analytics, predictive maintenance alerts, self-learning algorithms, and intelligent, proactive interaction with customers. In addition to services, the concept of open-source hardware and edifice silicon from disaggregated, pre-verified chiplets is birth to gain traction as companies slouch to slash costs and reduce time-to-market for heterogeneous designs.

    Specific strategies to unlocking the full potential of semiconductors will undoubtedly vary, which is why it’s Important for us to explore a future in which the industry, along with various research organizations and government offices, plays an open and collaborative role in helping to sustainably monetize both silicon and services.

    For more information on this topic, check out Rambus’ site.

    Shrikant Lohokare, Ph.D., is Vice President and Executive Director, North America for the Global Semiconductor Alliance.


    Saudi's $500bn futuristic city Neom names advisory board | killexams.com actual questions and Pass4sure dumps

    Saudi Arabia's $500-billion futuristic city, Neom, has constituted its global advisory board comprising experts from several major sectors including urban planning, architecture, design, technology, sustainability, energy and manufacturing.

    The destination for the future of alive being developed in the northwest of Saudi Arabia, Neom said these members will provide industry insight and advise on key milestones besides forging strategic connections for the kingdom's ambitious project.  

    The members comprehend Sam Altman, president of YC Group and co-chairman of Open AI; Lord Norman Foster, founder and executive chairman of Foster + Partners; Peter R Voser, chairman of ABB Group; Masayoshi Son, the chairman and CEO of SoftBank Group Corporation; Marc Andreessen, the co-founder and general ally of Andreessen Horowitz; Tim Brown, the chief executive and president of Ideo, Timothy Collins, founder and CEO of Ripplewood Advisors; Alexandra Cousteau, senior advisor to Oceana; Dan Doctoroff, founder and CEO of Sidewalk Labs; Jean Fréchet, distinguished professor of chemistry; Travis Kalanick, CEO of City Storage Systems; Neelie Kroes, former vice president of the European Commission.

    The board also boasts high-profile members such as Andrew Liveris, former chairman and CEO of Dow Chemical and executive chairman of DowDuPont; Ernest J Moniz, president and CEO of the Energy Futures Initiative; Marc Raibert, founder and CEO of Boston Dynamics; Carlo Ratti, professor of urban technologies and planning director at MIT’s Senseable City Lab; John Rossant, founder and chairman at the NewCities Foundation and Rob Speyer, president and CEO of Tishman Speyer.

    Lauding the move, CEO Nadhmi Al Nasr said: "We welcome the global and diverse expertise of the advisory board and are confident that each of them will compose huge contributions to the development of the $500-billion giant city. It will also wait on shape Neom’s future through its circumstantial information and connections with potential long-term strategic partners."

    Those members hold previously been engaged with Neom in a meeting held in current York, US, in August to review Neom’s strategy and contend updates on the project.

    Each of these advisory board members has been carefully chosen for their expertise and has demonstrated apropos experience, a global traffic background, familiarity with large-scale projects, senior board-level roles, international influence and enthusiasm for the aims of Neom.

    Altman is an American entrepreneur, investor, programmer and blogger. He was named the top investor under 30 by Forbes in 2015 and one of the "Best young Entrepreneurs in Technology" by Businessweek in 2008. He is also the Chairman of two energy companies, Helion and Oklo.

    Lord Foster is the president of the Norman Foster Foundation. His company, Foster + Partners, is a global studio for architecture, urbanism and design with several notable projects including Reichstag in Berlin, the magnificient Court of British Museum, Boston Museum of Fine Arts, headquarters of Apple, Bloomberg and Comcast and airports in Hong Kong and Beijing.

    Voser is an industry veteran with several years of sustain in the industrial energy field. Before taking up his position as chairman of the board of technology giant ABB, Voser served as CEO at Shell, amongst other positions with the company. He currently serves as a board member at Roche, IBM and Temasek, as well as several non-profit organisations.

    Son is the founder of SoftBank, a global technology company that aspires to drive the Information Revolution and has expanded its traffic to cover a sweep of technologies, including telecommunications, AI, smart robotics, IoT and spotless energy. In 2017, SoftBank announced the first major close of the SoftBank Vision Fund to back the transformational companies at the forefront of the Information Revolution.

    Andreessen is a pioneer in the tech world who has founded software companies fondness Opsware, Mosaic, Netscape and Ning. He sits on the board of directors of Facebook, eBay and Hewlett Packard Enterprise among others. Andreessen was one of six inductees in the World Wide Web Hall of Fame announced at the First International Conference on the World-Wide Web in 1994.

    Brown is an industrial designer by training and has earned numerous design awards, as well as advising senior traffic leaders. As chief executive of Ideo, a company which focuses on the value of design thinking to traffic people and designers, Brown discusses the concept of design thinking around the world, including at the World Economic Forum in Davos and through TED Talks.

    Collins is currently the chairman of the Yale Som Advisory Board. He had founded investment hard Ripplewood in 1995, with his previous sustain at Onex Corporation, Lazard Freres & Company, Booz Allen Hamilton and Cummins Engine Company. He has also served on a number of corporate boards.

    Cousteau is an expert in environmental issues and is currently a National Geographic Emerging Explorer, filmmaker and global oceans expert.

    Doctoroff was earlier the president and CEO of Bloomberg. Before that Doctoroff had been the deputy mayor for Economic development and Rebuilding in current York and managing ally of Oak Hill Capital Partners. He presently serves on the boards of the University of Chicago, World Resources Institute, the US Olympic Committee, Bloomberg Philanthropies and Human Rights First. He also helped found several charitable organizations.

    Fréchet is a Professor emeritus at the University of California, Berkeley and vice-president for research at the King Abdullah University of Science and Technology (KAUST), Saudi Arabia. He is a leader in developing strategies and managing resources to back interdisciplinary, collaborative research bridging science and engineering. He is the author of over 800 publications with more than 106,000 citations and 200 patents.

    Kalanick is the chief executive of City Storage Systems, a holding company focused on redeveloping actual estate assets to fuel urban job creation and neighbourhood rejuvenation. He is also co-founder and former CEO of Uber. During his seven years at the helm in Uber, the company grew to operate in more than 70 countries, employed over 15,000 people and provided three million drivers with elastic work opportunities to complete over five billion rider trips. Prior to Uber, he founded Red Swoosh, a networking software company.

    Kroes is a former Minister for Transport, Public Works and Telecommunication in the Netherlands. She had earlier held the position of EU Commissioner - the first term as EU Commissioner for Competition Policy and the second term as commissioner in pervade of the Digital Agenda for Europe. In the eventual term, she was also vice-president of the European Commission. Kroes currently serves on various international company boards.

    Liveris, an industry veteran, ran Dow, a producer and marketer of chemical, materials, plastics and speciality chemicals for over 14 years and was amenable for transforming Dow and DuPont into the largest chemical company in the world. He has advised two US presidents, written a seminal reserve on the criticality of manufacturing to economic development and is on the boards of Saudi Aramco, WorleyParsons and IBM, and an advisor to the Public Investment Fund of Saudi Arabia.

    Dr Moniz had served as the 13th United States Secretary of Energy from 2013 to January 2017. He is the Cecil and Ida Green Professor of Physics and Engineering Systems emeritus and Special Advisor to the MIT President. Dr Moniz is also CEO of the Nuclear Threat Initiative and of the Energy Futures Initiative and the inaugural Distinguished Fellow of the Emerson Collective.

    Raibert leads the development of some of the world's most advanced robots. Before founding Boston Dynamics in 1992, he was a professor at MIT and Carnegie Mellon University, and is a member of the U.S. National Academy of Engineering.

    Ratti is an architect and engineer by training and currently teaches at MIT. He has co-authored over 500 publications and patents.

    Rossant founded the NewCities Foundation in 2010 with an train to shape the future of urban projects. He was previously amenable for the production of global forums, such as the e-G8 in Paris and the World Economic Forum in Davos and is also the CEO and chief curator of LA CoMotion, the annual conference and event on future mobility. He is a board member of the Fondation Tocqueville in Paris and Humanity in Action in current York.

    Speyer, as its president and CEO, has grown Tishman Speyer into a leading global actual estate company with $50 billion in assets. He is the chairman of the advisory board of the Mayor’s Fund to advance current York City, and in 2013 became the youngest-ever chairman of the actual Estate Board of current York. He currently serves on the advisory council of Exor as well as several charitable ventures.

    Neom has announced that several more industry experts are likely to associate the advisory board in the coming months.

    The Saudi futuristic city's emphasis on sustainability and innovation makes it a core pillar of Vision 2030, the kingdom’s ambitious blueprint to diversify its economy and enable wider societal transformation.

    Neom forms allotment of the Saudi giga-projects investment pool for the Public Investment Fund (PIF) of Saudi Arabia. The giga-projects are integrated economic ecosystems that will back the economic transformation of the Kingdom and act as a ferment for investment across various sectors, and in addition to Neom comprehend The Red Sea Project, and Qiddiya.-TradeArabia intelligence Service


    Knowledge Graph Search With Elasticsearch and Neo4j | killexams.com actual questions and Pass4sure dumps

    Editor's Note: This presentation was given by Luanne Misquitta and Alessandro Negro at GraphConnect current York in October 2017.

    Presentation Summary

    Knowledge graphs are key to delivering apropos search results to users, meeting the four criteria for relevance, which comprehend the query, the context, the user, and the traffic goal. Luanne Misquitta and Alessandro Negro report the tower of information graphs and their application in apropos search, highlighting a sweep of consume cases across industries before taking a abysmal dive into an e-commerce consume case they implemented for a customer.

    In supporting apropos search, storing multiple views in Elasticsearch supports snappily response for users while bar no one at all data is stored in the information graph. The talk covers key aspects of apropos search, including personalization and concept search and shows how using the privilege tool for the privilege job led to a powerful solution for the customer that serves as a pattern for others to use.

    Full Presentation: information Graph Search With Elasticsearch and Neo4j

    This blog deals with how to deliver apropos search results using Neo4j and Elasticsearch:

    Luanne Misquitta: The Forrester Wave for Master Data Management said that "knowledge graphs provide contextual windows into master data domains and links between domains."

    This is Important because over the eventual few years there hold been huge leaps from data to information to information and then, finally, to automated reasoning. What's key about information graphs is that they play a fundamental role because they accumulate data from a variety of sources and they link them together in a very organic form. You're also able to grow it, query it easily, maintain it easily and at the very time retain it apropos and up to date.

    Use Cases for information Graphs

    Knowledge graphs hold been on the tower for the eventual brace of years across industries and consume cases.

    In ecommerce, you hold multiple data sources. It is one of the primary consume cases for information graphs. There are multiple category hierarchies; you almost never hold a solitary hierarchy of products. You hold products that are allotment of multiple different hierarchies.

    This is quite a difficult problem to address if you don't hold a graph database. You hold the combination of not only products, categories, and data sources, but also marketing. Marketing strategies and promotions influence what you sell. The combination of these three elements merge well into the concept of a information graph.

    Enterprise networks connect partners, customers, employees, opportunities and providers. information graphs wait on you ascertain relationships between bar no one at all these connected data sets and therefore ascertain current opportunities.

    Finance has a textual corpora of fiscal documents, which contain a wealth of knowledge. There are many case studies such as the ICIJ's consume of Neo4j to dissect the Panama Papers, an analysis that linked together more than 11 million documents. You desist up with a very structured graph of entities, and how they are connected.

    Health is an Important consume case for information graphs that has had many practical applications over the eventual few years. information graphs unify structured and unstructured data from multiple data sources and integrate them into a graph-based model with very dynamic ontologies where data is characterized and organized around people, places and events. This enables you to generate a lot of information based on the co-occurrence of relationships and data.

    Knowledge graphs are being used for uncovering patterns in disease progression, causal relations involving disease, symptoms and the treatment paths for numerous diseases. current relationships hold been discovered that were previously unrecognized simply by putting bar no one at all this data together and applying a context around where it happened, who was involved and other natural events that might hold happened around this data.

    Criminal investigation and intelligence is an region where numerous papers hold been published recently about how information graphs helped in investigating cases. One of the most familiar cases has been around the region of human trafficking. information graphs hold helped, because it is a difficult bailiwick to track, and much of the information is obfuscated. Coded messages are posted on public forums and they profile a pattern. Tools and techniques such as natural language processing (NLP) decode these messages, compose sense of them, and apply context around them.

    Another key allotment of the total information graph is that, especially with law enforcement, you really necessity to hold traceability from your information graph back to your source of information. Otherwise, no one at all of this would hold up in a court of law. With the information graph, you also maintain the traceability of your data from the source to the point at which it clearly provides you the information or the evidence that you need.

    Data Sparsity

    Now we're going to talk about some of the problems you survey around data sparsity.

    When you talk about collaborative filtering, one of the classic problems is a gelid start. You cannot really recommend products or things to other people if you don't even hold other people in your graph or other purchases in your graph. This is commonly solved through things fondness tagging-based systems or reliance networks. If you don't hold collaborative filtering, even content-based recommendation suffers a lot of problems due to missing data or wrong data. If you don't hold an enhanced graph to set aside bar no one at all this data together, these are hard problems to solve.

    With text search as well, with sparse data, you desist up with user agnostic searches, and the world is stirring away from anonymous agnostic searches to apropos context-oriented searches. Similarly, apropos search is a problem. So, these are some of the areas that we'll paw upon in the later allotment of the presentation.

    Knowledge Bases on Steroids

    Knowledge graphs are what they used to know as information bases but on steroids.

    Knowledge graphs provide you an entity-centric view of your linked data. They're self-descriptive because the data that's described is really described in your graph.

    Therefore, you're capable of growing this data in an organic fashion, enhancing it as you ascertain more and more facts about your data. You hold an ontology that can subsist extended or revised, and it supports continuously running data pipelines. It's not a static system that's once built, remains that artery forever; it grows as your domain expands.

    Back to the law enforcement consume case, it provides you traceability into the provenance of data, and this is Important in many industries.

    Knowledge Graphs as Data Convergence

    A information graph is really a convergence of data from a variety of places (see below).

    You hold data sources, which could approach from bar no one at all over the place, in bar no one at all sorts of formats. You hold external sources to enrich the data you have. You hold user interaction, so as users play with your application or your domain, you learn more, you feed it back into the information graph, and, therefore, you better your information graph. You hold tools such as machine learning processes or NLP, which really, in the end, wait on you achieve your traffic goals.

    Relevant Search

    Alessandro Negro: Let's consider a specific application of information graphs — a specific consume case that implements an advanced search engine that delivers apropos search capabilities to the desist user.

    Relevance here is the exercise of improving search results, satisfying your data information needs in the context of a very specific user sustain while combining or balancing how ranking impacts on a business' inevitable needs.

    Let's walk through an implementation that combines graphs and, specifically, information graphs, with Elasticsearch in order to deliver services to the desist user.

    You will survey a actual architecture and a concrete infrastructure where, combining both, it's feasible to deliver a high-level set of services to the desist user. You will notice how information graphs could wait on in this direction, not only because they resolve issues related to data sparsity, but also because graphs picture a direct model for delivering these types of services to the desist user.

    Four Dimensions of apropos Search

    According to the definition stated before, relevance moves along four different dimensions. It revolves around four different elements: text, user, context and traffic goal.

    First of all, a search has to fill an information necessity that is expressed by the user using a textual query.

    So, in this context, information retrieval and natural language processing are key to provide search results that mostly fill the user intent expressed by the search query. But apropos search moves toward a more user-centric perspective of the search. That means that they can't deliver the very result set to the very user even if they discharge the very query.

    In this way, user modeling and the recommendation engine could wait on to customize the result set according to the user profile or the user preferences. While on the other hand, context expresses the special conditions under which that specific search has been performed. Contextual information fondness location, time, or even the weather could subsist helpful to further refine the search results according to the needs of the user while he was performing the query.

    Last but not least, the traffic goals drive the entire implementation, because a search exists only to fill a specific necessity of an organization in terms of revenue or in terms of a specific goal that it would fondness to achieve. Moreover, a apropos search has to store a lot of information related to search histories or feedback loops. And bar no one at all this data has to subsist accessed in a artery that shouldn't move the user sustain in terms of performance that will impact response time or the quality of the results.

    Knowledge Graphs for apropos Search

    Here is where information graphs approach in because they will survey how information graphs picture the privilege approach in terms of information structure for providing apropos search.

    In order to provide a apropos search, a search architecture has to wield high quality, high quantity of data that is heterogeneous in terms of schemas, sources, volume, and rate of generation. Furthermore, it has to subsist accessed as a unified data source. That means that it has to subsist normalized and accessed as a unified schema structure that satisfies bar no one at all the informational and navigational requirements of a apropos search that they saw before.

    Graphs are the privilege representation for bar no one at all the issues related to apropos search including information extraction, recommendation engines, context representation, and even a rules engine for representing traffic goals.

    Information extraction attempts to compose the semantic structure in the text explicit because they advert to text as unstructured data, but text has a lot of structure related to the grammar and the constraints of the language.

    You can extract this information using natural language processing. From a document, you can extract the sentences. And for each sentence, you can extract a list of tags. And then, the relationships between these tags based on the types of dependencies, mentions or whatever else.

    This is a connected set of data that can subsist easily stored in a graph. Once you store this data, you may easily extend the information that you hold about it, ingesting information from other information graphs fondness ConceptNet 5, which is an ontology structure that can subsist easily integrated in this basic set of information, adding current relationships to the graph.

    On the other hand, recommendation engines are also built using user-item interactions that are easily stored in a graph. They can also subsist built using a content-based approach in which you will necessity to consume informational extraction or a description or a list of features of the component you would fondness to recommend.

    In both cases, the output of the process could subsist a list of similarities that could subsist easily stored in a graph as current relationships between items or between users. And then, you could consume them to provide recommendations to the user that combine both approaches.

    Context, by definition, is a multidimensional representation of a status or an event. Mainly, it is a multidimensional array. It is a tensor. A tensor is easily represented in a graph where you hold an component that is one node and bar no one at all the other indexes that advert to that component are other nodes that point to that specific node.

    In this way, you can easily discharge any sort of operation on the tensor, such as slicing, for example, or whatever else. Even easier is adding current indexes to this tensor - just adding a current node that points it to the element.

    Finally, in order to apply a specific traffic goal, you hold to implement a sort of rule engine. And even in this case, a graph could wait on you not only to store the rule itself, but also to enforce the rule in your system.

    Use Case: E-Commerce Search Engine

    Let's consider a specific consume case: a search engine for an ecommerce site.

    Every search application has its own requirements and its own dramatically specific set of constraints in terms of expectations from the search. In the case of a web search engine, you hold millions of pages that are completely different from each other. And even the source of the information cannot subsist trusted.

    You might contemplate e-commerce search is a simpler case, but it is not. It's dependable that the set of documents is more controlled and in terms of numbers, reduced. But in an e-commerce site, the categories of navigation and the text search are the main "salespeople," not just a artery for searching for something. They also a artery for promoting something, for shortening path between the necessity and the buyer. They are very important.

    In terms of expectations, a search in an e-commerce site has a lot of things to do. But even more, in this case, you can accumulate data coming from multiple data sources: sellers, content providers, and marketers pushing a strategy or marketing propel in the system fondness promotions, offers, or whatever else.

    Also, you hold to consider the users. You hold to store user-item interactions and user feedback in order to customize the results provided to the user according to their history. And obviously, again, there are traffic constraints.

    Here is a simplified case of a information graph for an e-commerce site:

    The main element, in this case, is represented by the products. Here they hold just three products: an iPhone, a case for the iPhone, and an earphone. For every product, they hold some information fondness the list of features that report that product. But obviously this list of features will change according to the character of the product, so it will subsist different for a TV, a pair of shoes and so on.

    Starting from this information, it's light to process the textual description and the list of tags. Once they hold the tags, they are able to ingest data from ConceptNet 5 and know that a smartphone is a personal device. They will survey how this is valuable information later on for providing apropos search. But apart from the data that is common for this character of application, they also dissect their data and create current relationships between items.

    This is how relationships fondness "usually bought together" approach into play. It's also feasible to manually add relationships, fondness the relationship between a phone and a case. In this way, you are augmenting the amount of information that you hold step by step and using bar no one at all this information during the search and during catalog navigation.

    Infrastructure for the information Graph

    With bar no one at all these ideas in mind, they designed this infrastructure for one of their customers:

    The information graph is the main source of verity in this infrastructure, and several other sources feed the information graph. A machine-learning platform processes the graph continuously and extracts insights from the information graph and then stores the insights back into the information graph.

    Later we'll contend how the integration with Elasticsearch allows us to export multiple views with multiple scopes so that information graph queries attain not impact the front desist too much. They consume Elasticsearch as a sort of cache and also a powerful search engine on top of their information graph.

    Data stream for the information Graph

    For the data flow, they designed an asynchronous data ingestion process where they hold multiple data sources pushing data in multiple queues. A microservice infrastructure reacts to these events, processes them, and stores this intermediate data in one queue that then is processed by a solitary Neo4j Writer component that reads the data and stores it in the graph.

    This avoids any issues in terms of concurrency, and it also enables us to easily implement a priority-based mechanism that allows us to assign different priorities to an element.

    Storing Multiple Views in Elasticsearch

    In order to store multiple views in Elasticsearch, they created an event-based notification system that is highly customizable and capable of pushing several types of events. The Elasticsearch Writer reacts to these events, reads data from the information graph and creates current documents or updates existing documents in Elasticsearch.

    The Role of Neo4j

    Neo4j is the core of this infrastructure because it stores the information graph, which is the only source of truth. There is no other data somewhere else. bar no one at all the data that they would fondness to process is here. It bar no one at all converges here in this information graph.

    Neo4j is a valuable tool because it allows you to store several types of data: users, products, details about products and whatever else. It also provides an easy-to-query mechanism and an easy-to-navigate system for easily accessing this data.

    More than that, in the early stage of the apropos search implementation, it can wait on the relevance engineer to identify the most bewitching feature in the information graph that's useful for the implementation of the related search in the system.

    Moreover, once bar no one at all the data is in the graph, it goes through a process of extension that includes three different operations: cleaning, existing data augmentation and data merging. Because multiple sources can express the very concept in different ways, data merging is also important.

    Machine Learning for Memory-Intensive Processes

    Some of these processes can subsist accomplished in Neo4j itself with some plugins or with Cypher queries. Others necessity to subsist externalized because they are intensive in terms of computational needs and recollection requirements.

    We created a machine learning platform (see below) that, thanks to the Neo4j Spark connector, allows us to extract data from Neo4j, process it using natural language processing or even recommendation model building, and then store this current data in Neo4j, which will subsist useful later on for providing advanced features for the apropos search.

    Elasticsearch for snappily Results

    We are trying to consume the privilege tool for the privilege job. They consume Neo4j for storing the information graph and it is perhaps the most valuable tool for doing this, but obviously, in terms of textual search, it could subsist an issue if you want to discharge more advanced textual search in Neo4j. They added Elasticsearch on top of Neo4j in order to provide fast, reliable, and easy-to-tune textual search.

    We store multiple views of the very data set in Elasticsearch for solving several scopes and for serving several functionalities that are faceting. Faceting means any sort of aggregation of the result set that you want to have. You can serve a product details page or provide product variants aggregation. With Elasticsearch, you can also provide auto-completion or suggestions.

    Elasticsearch is not a database, and they don't want to consume it as a database. It is just a search engine for data from Neo4j in this consume case and is a valuable tool for textual search.

    Relevant Search on Top of information Graph

    Now let's explore using Elasticsearch to provide apropos search on top of the information graph that they built using the infrastructure covered earlier.

    What Is a Signal?

    In terms of apropos search, a signal is any component of a relevance-scoring calculation corresponding to meaningful and measurable information. At its simplest, it is just a bailiwick in a document in Elasticsearch.

    The complicated allotment is how you design this field, what you set aside inside it and how you can extract from the information graph and store data in Elasticsearch that really matters.

    There are two main techniques for controlling relevancy (see below). The first one is signal modeling. It is how you design the list of fields that compose your documents in order to react to some textual query. And the other one is the ranking function, or the ranking functions, that are how you compose several signals and assign each of them a specific weight in order to obtain the final score and the rank of the results.

    You always hold to balance precision and recall where, in this context, precision is the percentage of documents in the result set that are apropos and recall is the percentage of apropos documents in the result set.

    It's a complicated topic, but to simplify it, if you revert bar no one at all the documents to the user, you hold a recall of 100%, because you hold bar no one at all the apropos documents in the result set. With a recall of 100%, you hold a very low degree of precision. And even in this case, you can hold multiple sources in terms of data from the information graph that you can consume for modeling your signal.

    Personalizing Search

    Let me give you a brace of examples. The first approach is personalizing search.

    Here, they comprehend users as a current source of information so they can provide a customized result set. They would fondness to customize the result set according to the user profile or the user preferences.

    We hold two different approaches. The first one is a profile-based approach where they create a user profile that could subsist created manually by the user by filling out a form, or automatically, inferring user preferences from past searches.

    The second approach is behavioral-based. In this case, it's more related to a recommendation engine that analyzes the user-item interaction in order to compose explicit the relationship among users and items.

    Once you hold a user profile or behavioral information, you hold to tie this information to the query. And you can attain this in three different ways: at query time, changing the query according to the recommendation that you would fondness to make; at index time, changing the documents according to the recommendation that you would fondness to make; or using a combined approach.

    For example, if you would fondness to customize the results for the user at query time, you hold to change your query, specifying the list of products that the user may subsist interested in. In this way, you could easily boost the results if the results set includes some of the products that the user is known to subsist interested in. On the other hand, at index time, you would hold to store a list of users that may subsist interested in each product. And then you can easily boost results that specifically match in that case.

    Concept Search

    Another bewitching extension of the classical search is the concept search. In this case, they slouch from searching for strings to searching for things.

    The user could consume different words for expressing the very concept. This can subsist an issue in a classical text search environment where you hold to match the specific word that appears in the text. Using the techniques that they described before in terms of data enrichment, you can easily extend the information that you hold about each word. You can easily store those words in Elasticsearch and react to the user's query even if he is not using the terms that are on the page.

    There are different approaches to enriching this information. You can manually, or even automatically, add a list of tags that could subsist helpful in terms of the describing the content. You can write a list of synonyms. For example, for TV, you can hold synonyms fondness T.V., or television, or whatever else. And in this way, even if the text says TV, you can easily provide results if the user writes television, for example.

    There is a more advanced approach in which you consume machine learning tools for augmenting the knowledge. You can consume a simple co-occurrence as well as dormant Dirichlet Allocation. That means clustering your document set, and finding the set of the words that better describes that specific cluster and bar no one at all the documents in that cluster.

    Combining Neo4j and Elasticsearch: Two Approaches

    Here are two different approaches that they tried when integrating Neo4j and Elasticsearch.

    On the left side below is the first approach, which has two elements: the documents in Elasticsearch and the graph database.

    In this approach, when a user performed a query, it goes through two different phases. The first one is the classical textual search query that is performed entirely on Elasticsearch.

    The first result set is then manipulated by accessing the graph, and boosting the results according to whatever rule you'd fondness to use. This approach could work, but in terms of performance, could suffer a shrimp bit when you hold to attain tangled boosting operations.

    As a result, they moved to the second approach (see above right), in which there is a tighter connection between Elasticsearch and the information graph. Using this approach, as discussed earlier, the information graph is used for exporting multiple views into Elasticsearch.

    Part of the work is done at index time. When the user performs a query, the query itself goes through a first stage that is an enrichment. Accessing the graph, they can change the query, and then they discharge the query against Elasticsearch. This appears to subsist a more performant approach, because this operation itself is not related to every solitary component in the graph, but is narrowed based on the context or on the user, so it is very fast.

    We can consume Elasticsearch for what is putative to subsist just textual search. But at that point, they already hold a very tangled query that is created using the graph. And even the indexes are really changed according to the graph's structure.

    Conclusion

    Knowledge graphs are very Important because they consume graphs for representing tangled knowledge. information graphs consume an easy-to-query model that allows us to accumulate data from several sources. That means they can store users, user-item interactions, and whatever else they want. They can further extend Important data later on or change their information graph by adding current information.

    On top of this, they can implement a search engine fondness Elasticsearch, which is fast, reliable, and easy-to-tune and which provides other bewitching features, such as faceting and auto-completion.

    By combining Neo4j and Elasticsearch, you can deliver a high-level set of services to the desist user. The takeaway is to consume the privilege tool for the privilege job. You can consume a graph for representing tangled information and a simple tool fondness Elasticsearch to provide textual search capability and advanced search capability to the desist user.



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