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Microsoft Corporation is an American multinational corporation headquartered in Redmond, Washington, that develops, manufactures, licenses, supports and sells computer software, consumer electronics and personal computers and services. Its best known software products are the Microsoft Windows line of operating systems, Microsoft Office office suite, and Internet Explorer web browser. Its flagship hardware products are the Xbox game consoles and the Microsoft Surface tablet lineup. It is the world's largest software maker measured by revenues. It is also one of the world's most valuable companies.Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975, to develop and sell BASIC interpreters for Altair 8800. It rose to dominate the personal computer operating system market with MS-DOS in the mid-1980s, followed by Microsoft Windows. The company's 1986 initial public offering, and subsequent rise in its share price, created three billionaires and an estimated 12,000 millionaires from Microsoft employees. Since the 1990s, it has increasingly diversified from the operating system market and has made a number of corporate acquisitions. In May 2011, Microsoft acquired Skype Technologies for $8.5 billion in its largest acquisition to date.As of 2013, Microsoft is market dominant in both the IBM PC-compatible operating system and office software suite markets . The company also produces a wide range of other software for desktops and servers, and is active in areas including Internet search , the video game industry , the digital services market , and mobile phones . In June 2012, Microsoft entered the personal computer production market for the first time, with the launch of the Microsoft Surface, a line of tablet computers.With the acquisition of Nokia's devices and services division to form Microsoft Mobile Oy, the company re-entered the smartphone hardware market, after its previous attempt, Microsoft Kin, which resulted from their acquisition of Danger Inc. Wikipedia.

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Forest Hill, MD, May 31, 2017 (GLOBE NEWSWIRE) -- The Apache Software Foundation (ASF), the all-volunteer developers, stewards, and incubators of more than 350 Open Source projects and initiatives, announced today that Apache® SystemML™ has graduated from the Apache Incubator to become a Top-Level Project (TLP), signifying that the project's community and products have been well-governed under the ASF's meritocratic process and principles. Apache SystemML is a machine learning platform optimal for Big Data that provides declarative, large-scale machine learning and deep learning. SystemML can be run on top of Apache Spark, where it automatically scales data, line by line, to determine whether code should be run on the driver or an Apache Spark cluster. "Today, the machine learning revolution is leading to thousands of life-altering innovations such as self-driving cars and computers that detect cancer," said Deron Eriksson, Vice President of Apache SystemML. "Apache SystemML enables and simplifies this process by executing optimized high-level algorithms on Big Data using proven technologies such as Apache Spark and Apache Hadoop MapReduce." The core of Apache SystemML has been created from the ground up with the following design principles in mind: Using Apache SystemML, data scientists are able to implement algorithms using high-level language concepts without knowledge of distributed programming. Depending on data characteristics such as data size/shape and data sparsity (dense/sparse), and cluster characteristics such as cluster size and memory configurations, SystemML's cost-based optimizing compiler automatically generates hybrid runtime execution plans that are composed of single-node and distributed operations on Apache Spark or Apache Hadoop clusters for best performance. "SystemML allows Cadent to implement advanced numerical programming methods in Apache Spark, empowering us to leverage specialized algorithms in our predictive analysis software," said Michael Zargham, Chief Scientist at Cadent Technology. "SystemML is like SQL for Machine Learning, it enables Data Scientists to concentrate on the problem at hand, working in a high-level script language like R, and all the optimizations and rewrites are handled by the very powerful SystemML optimizer that considers data and available resources to produce the best execution plan for the application," said Luciano Resende, Architect at the IBM Spark Technology Center and Apache SystemML Incubator Mentor. "IBM Watson Health VBC is using Apache SystemML on Apache Spark to build risk models on a very large EHR data set to predict emergency department visits," said Steve Beier, Vice President of Value Based Care Platform and Analytics at IBM Watson Health. "The models identify high-risk patients so that they can be targeted with preemptive strategies, thus potentially reducing care costs while at the same time leading to optimal outcomes for patients." SystemML originated at IBM Research - Almaden in 2010, and was submitted to the Apache Incubator in November 2015. SystemML initiated compressed linear algebra research, a differentiating feature in SystemML, which received the VLDB 2016 Best Paper. "The Apache Incubator is all about open collaboration and communication and was invaluable for everyone involved in SystemML," added Eriksson. "The Apache SystemML community sincerely encourages everyone interested in machine learning and deep learning to help build our community around this revolutionary technology." Catch Apache SystemML in action at the Big Data Developers Silicon Valley MeetUp on 8 June 2017 in San Francisco, CA. Availability and Oversight Apache SystemML software is released under the Apache License v2.0 and is overseen by a self-selected team of active contributors to the project. A Project Management Committee (PMC) guides the Project's day-to-day operations, including community development and product releases. For downloads, documentation, and ways to become involved with Apache SystemML, visit http://systemml.apache.org/ and https://twitter.com/ApacheSystemML About the Apache Incubator The Apache Incubator is the entry path for projects and codebases wishing to become part of the efforts at The Apache Software Foundation. All code donations from external organizations and existing external projects wishing to join the ASF enter through the Incubator to: 1) ensure all donations are in accordance with the ASF legal standards; and 2) develop new communities that adhere to our guiding principles. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF. For more information, visit http://incubator.apache.org/ About The Apache Software Foundation (ASF) Established in 1999, the all-volunteer Foundation oversees more than 350 leading Open Source projects, including Apache HTTP Server --the world's most popular Web server software. Through the ASF's meritocratic process known as "The Apache Way," more than 620 individual Members and 6,000 Committers successfully collaborate to develop freely available enterprise-grade software, benefiting millions of users worldwide: thousands of software solutions are distributed under the Apache License; and the community actively participates in ASF mailing lists, mentoring initiatives, and ApacheCon, the Foundation's official user conference, trainings, and expo. The ASF is a US 501(c)(3) charitable organization, funded by individual donations and corporate sponsors including Alibaba Cloud Computing, ARM, Bloomberg, Budget Direct, Capital One, Cash Store, Cerner, Cloudera, Comcast, Confluent, Facebook, Google, Hortonworks, HP, Huawei, IBM, InMotion Hosting, iSigma, LeaseWeb, Microsoft, ODPi, PhoenixNAP, Pivotal, Private Internet Access, Produban, Red Hat, Serenata Flowers, Target, WANdisco, and Yahoo. For more information, visit https://www.apache.org/ and https://twitter.com/TheASF © The Apache Software Foundation. "Apache", "SystemML", "Apache SystemML", "Hadoop", "Apache Hadoop", "Spark", "Apache Spark", and "ApacheCon" are registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries. All other brands and trademarks are the property of their respective owners.


Forest Hill, MD, May 31, 2017 (GLOBE NEWSWIRE) -- The Apache Software Foundation (ASF), the all-volunteer developers, stewards, and incubators of more than 350 Open Source projects and initiatives, announced today that Apache® SystemML™ has graduated from the Apache Incubator to become a Top-Level Project (TLP), signifying that the project's community and products have been well-governed under the ASF's meritocratic process and principles. Apache SystemML is a machine learning platform optimal for Big Data that provides declarative, large-scale machine learning and deep learning. SystemML can be run on top of Apache Spark, where it automatically scales data, line by line, to determine whether code should be run on the driver or an Apache Spark cluster. "Today, the machine learning revolution is leading to thousands of life-altering innovations such as self-driving cars and computers that detect cancer," said Deron Eriksson, Vice President of Apache SystemML. "Apache SystemML enables and simplifies this process by executing optimized high-level algorithms on Big Data using proven technologies such as Apache Spark and Apache Hadoop MapReduce." The core of Apache SystemML has been created from the ground up with the following design principles in mind: Using Apache SystemML, data scientists are able to implement algorithms using high-level language concepts without knowledge of distributed programming. Depending on data characteristics such as data size/shape and data sparsity (dense/sparse), and cluster characteristics such as cluster size and memory configurations, SystemML's cost-based optimizing compiler automatically generates hybrid runtime execution plans that are composed of single-node and distributed operations on Apache Spark or Apache Hadoop clusters for best performance. "SystemML allows Cadent to implement advanced numerical programming methods in Apache Spark, empowering us to leverage specialized algorithms in our predictive analysis software," said Michael Zargham, Chief Scientist at Cadent Technology. "SystemML is like SQL for Machine Learning, it enables Data Scientists to concentrate on the problem at hand, working in a high-level script language like R, and all the optimizations and rewrites are handled by the very powerful SystemML optimizer that considers data and available resources to produce the best execution plan for the application," said Luciano Resende, Architect at the IBM Spark Technology Center and Apache SystemML Incubator Mentor. "IBM Watson Health VBC is using Apache SystemML on Apache Spark to build risk models on a very large EHR data set to predict emergency department visits," said Steve Beier, Vice President of Value Based Care Platform and Analytics at IBM Watson Health. "The models identify high-risk patients so that they can be targeted with preemptive strategies, thus potentially reducing care costs while at the same time leading to optimal outcomes for patients." SystemML originated at IBM Research - Almaden in 2010, and was submitted to the Apache Incubator in November 2015. SystemML initiated compressed linear algebra research, a differentiating feature in SystemML, which received the VLDB 2016 Best Paper. "The Apache Incubator is all about open collaboration and communication and was invaluable for everyone involved in SystemML," added Eriksson. "The Apache SystemML community sincerely encourages everyone interested in machine learning and deep learning to help build our community around this revolutionary technology." Catch Apache SystemML in action at the Big Data Developers Silicon Valley MeetUp on 8 June 2017 in San Francisco, CA. Availability and Oversight Apache SystemML software is released under the Apache License v2.0 and is overseen by a self-selected team of active contributors to the project. A Project Management Committee (PMC) guides the Project's day-to-day operations, including community development and product releases. For downloads, documentation, and ways to become involved with Apache SystemML, visit http://systemml.apache.org/ and https://twitter.com/ApacheSystemML About the Apache Incubator The Apache Incubator is the entry path for projects and codebases wishing to become part of the efforts at The Apache Software Foundation. All code donations from external organizations and existing external projects wishing to join the ASF enter through the Incubator to: 1) ensure all donations are in accordance with the ASF legal standards; and 2) develop new communities that adhere to our guiding principles. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF. For more information, visit http://incubator.apache.org/ About The Apache Software Foundation (ASF) Established in 1999, the all-volunteer Foundation oversees more than 350 leading Open Source projects, including Apache HTTP Server --the world's most popular Web server software. Through the ASF's meritocratic process known as "The Apache Way," more than 620 individual Members and 6,000 Committers successfully collaborate to develop freely available enterprise-grade software, benefiting millions of users worldwide: thousands of software solutions are distributed under the Apache License; and the community actively participates in ASF mailing lists, mentoring initiatives, and ApacheCon, the Foundation's official user conference, trainings, and expo. The ASF is a US 501(c)(3) charitable organization, funded by individual donations and corporate sponsors including Alibaba Cloud Computing, ARM, Bloomberg, Budget Direct, Capital One, Cash Store, Cerner, Cloudera, Comcast, Confluent, Facebook, Google, Hortonworks, HP, Huawei, IBM, InMotion Hosting, iSigma, LeaseWeb, Microsoft, ODPi, PhoenixNAP, Pivotal, Private Internet Access, Produban, Red Hat, Serenata Flowers, Target, WANdisco, and Yahoo. For more information, visit https://www.apache.org/ and https://twitter.com/TheASF © The Apache Software Foundation. "Apache", "SystemML", "Apache SystemML", "Hadoop", "Apache Hadoop", "Spark", "Apache Spark", and "ApacheCon" are registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries. All other brands and trademarks are the property of their respective owners.


Forest Hill, MD, May 31, 2017 (GLOBE NEWSWIRE) -- The Apache Software Foundation (ASF), the all-volunteer developers, stewards, and incubators of more than 350 Open Source projects and initiatives, announced today that Apache® SystemML™ has graduated from the Apache Incubator to become a Top-Level Project (TLP), signifying that the project's community and products have been well-governed under the ASF's meritocratic process and principles. Apache SystemML is a machine learning platform optimal for Big Data that provides declarative, large-scale machine learning and deep learning. SystemML can be run on top of Apache Spark, where it automatically scales data, line by line, to determine whether code should be run on the driver or an Apache Spark cluster. "Today, the machine learning revolution is leading to thousands of life-altering innovations such as self-driving cars and computers that detect cancer," said Deron Eriksson, Vice President of Apache SystemML. "Apache SystemML enables and simplifies this process by executing optimized high-level algorithms on Big Data using proven technologies such as Apache Spark and Apache Hadoop MapReduce." The core of Apache SystemML has been created from the ground up with the following design principles in mind: Using Apache SystemML, data scientists are able to implement algorithms using high-level language concepts without knowledge of distributed programming. Depending on data characteristics such as data size/shape and data sparsity (dense/sparse), and cluster characteristics such as cluster size and memory configurations, SystemML's cost-based optimizing compiler automatically generates hybrid runtime execution plans that are composed of single-node and distributed operations on Apache Spark or Apache Hadoop clusters for best performance. "SystemML allows Cadent to implement advanced numerical programming methods in Apache Spark, empowering us to leverage specialized algorithms in our predictive analysis software," said Michael Zargham, Chief Scientist at Cadent Technology. "SystemML is like SQL for Machine Learning, it enables Data Scientists to concentrate on the problem at hand, working in a high-level script language like R, and all the optimizations and rewrites are handled by the very powerful SystemML optimizer that considers data and available resources to produce the best execution plan for the application," said Luciano Resende, Architect at the IBM Spark Technology Center and Apache SystemML Incubator Mentor. "IBM Watson Health VBC is using Apache SystemML on Apache Spark to build risk models on a very large EHR data set to predict emergency department visits," said Steve Beier, Vice President of Value Based Care Platform and Analytics at IBM Watson Health. "The models identify high-risk patients so that they can be targeted with preemptive strategies, thus potentially reducing care costs while at the same time leading to optimal outcomes for patients." SystemML originated at IBM Research - Almaden in 2010, and was submitted to the Apache Incubator in November 2015. SystemML initiated compressed linear algebra research, a differentiating feature in SystemML, which received the VLDB 2016 Best Paper. "The Apache Incubator is all about open collaboration and communication and was invaluable for everyone involved in SystemML," added Eriksson. "The Apache SystemML community sincerely encourages everyone interested in machine learning and deep learning to help build our community around this revolutionary technology." Catch Apache SystemML in action at the Big Data Developers Silicon Valley MeetUp on 8 June 2017 in San Francisco, CA. Availability and Oversight Apache SystemML software is released under the Apache License v2.0 and is overseen by a self-selected team of active contributors to the project. A Project Management Committee (PMC) guides the Project's day-to-day operations, including community development and product releases. For downloads, documentation, and ways to become involved with Apache SystemML, visit http://systemml.apache.org/ and https://twitter.com/ApacheSystemML About the Apache Incubator The Apache Incubator is the entry path for projects and codebases wishing to become part of the efforts at The Apache Software Foundation. All code donations from external organizations and existing external projects wishing to join the ASF enter through the Incubator to: 1) ensure all donations are in accordance with the ASF legal standards; and 2) develop new communities that adhere to our guiding principles. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF. For more information, visit http://incubator.apache.org/ About The Apache Software Foundation (ASF) Established in 1999, the all-volunteer Foundation oversees more than 350 leading Open Source projects, including Apache HTTP Server --the world's most popular Web server software. Through the ASF's meritocratic process known as "The Apache Way," more than 620 individual Members and 6,000 Committers successfully collaborate to develop freely available enterprise-grade software, benefiting millions of users worldwide: thousands of software solutions are distributed under the Apache License; and the community actively participates in ASF mailing lists, mentoring initiatives, and ApacheCon, the Foundation's official user conference, trainings, and expo. The ASF is a US 501(c)(3) charitable organization, funded by individual donations and corporate sponsors including Alibaba Cloud Computing, ARM, Bloomberg, Budget Direct, Capital One, Cash Store, Cerner, Cloudera, Comcast, Confluent, Facebook, Google, Hortonworks, HP, Huawei, IBM, InMotion Hosting, iSigma, LeaseWeb, Microsoft, ODPi, PhoenixNAP, Pivotal, Private Internet Access, Produban, Red Hat, Serenata Flowers, Target, WANdisco, and Yahoo. For more information, visit https://www.apache.org/ and https://twitter.com/TheASF © The Apache Software Foundation. "Apache", "SystemML", "Apache SystemML", "Hadoop", "Apache Hadoop", "Spark", "Apache Spark", and "ApacheCon" are registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries. All other brands and trademarks are the property of their respective owners.


Forest Hill, MD, May 31, 2017 (GLOBE NEWSWIRE) -- The Apache Software Foundation (ASF), the all-volunteer developers, stewards, and incubators of more than 350 Open Source projects and initiatives, announced today that Apache® SystemML™ has graduated from the Apache Incubator to become a Top-Level Project (TLP), signifying that the project's community and products have been well-governed under the ASF's meritocratic process and principles. Apache SystemML is a machine learning platform optimal for Big Data that provides declarative, large-scale machine learning and deep learning. SystemML can be run on top of Apache Spark, where it automatically scales data, line by line, to determine whether code should be run on the driver or an Apache Spark cluster. "Today, the machine learning revolution is leading to thousands of life-altering innovations such as self-driving cars and computers that detect cancer," said Deron Eriksson, Vice President of Apache SystemML. "Apache SystemML enables and simplifies this process by executing optimized high-level algorithms on Big Data using proven technologies such as Apache Spark and Apache Hadoop MapReduce." The core of Apache SystemML has been created from the ground up with the following design principles in mind: Using Apache SystemML, data scientists are able to implement algorithms using high-level language concepts without knowledge of distributed programming. Depending on data characteristics such as data size/shape and data sparsity (dense/sparse), and cluster characteristics such as cluster size and memory configurations, SystemML's cost-based optimizing compiler automatically generates hybrid runtime execution plans that are composed of single-node and distributed operations on Apache Spark or Apache Hadoop clusters for best performance. "SystemML allows Cadent to implement advanced numerical programming methods in Apache Spark, empowering us to leverage specialized algorithms in our predictive analysis software," said Michael Zargham, Chief Scientist at Cadent Technology. "SystemML is like SQL for Machine Learning, it enables Data Scientists to concentrate on the problem at hand, working in a high-level script language like R, and all the optimizations and rewrites are handled by the very powerful SystemML optimizer that considers data and available resources to produce the best execution plan for the application," said Luciano Resende, Architect at the IBM Spark Technology Center and Apache SystemML Incubator Mentor. "IBM Watson Health VBC is using Apache SystemML on Apache Spark to build risk models on a very large EHR data set to predict emergency department visits," said Steve Beier, Vice President of Value Based Care Platform and Analytics at IBM Watson Health. "The models identify high-risk patients so that they can be targeted with preemptive strategies, thus potentially reducing care costs while at the same time leading to optimal outcomes for patients." SystemML originated at IBM Research - Almaden in 2010, and was submitted to the Apache Incubator in November 2015. SystemML initiated compressed linear algebra research, a differentiating feature in SystemML, which received the VLDB 2016 Best Paper. "The Apache Incubator is all about open collaboration and communication and was invaluable for everyone involved in SystemML," added Eriksson. "The Apache SystemML community sincerely encourages everyone interested in machine learning and deep learning to help build our community around this revolutionary technology." Catch Apache SystemML in action at the Big Data Developers Silicon Valley MeetUp on 8 June 2017 in San Francisco, CA. Availability and Oversight Apache SystemML software is released under the Apache License v2.0 and is overseen by a self-selected team of active contributors to the project. A Project Management Committee (PMC) guides the Project's day-to-day operations, including community development and product releases. For downloads, documentation, and ways to become involved with Apache SystemML, visit http://systemml.apache.org/ and https://twitter.com/ApacheSystemML About the Apache Incubator The Apache Incubator is the entry path for projects and codebases wishing to become part of the efforts at The Apache Software Foundation. All code donations from external organizations and existing external projects wishing to join the ASF enter through the Incubator to: 1) ensure all donations are in accordance with the ASF legal standards; and 2) develop new communities that adhere to our guiding principles. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF. For more information, visit http://incubator.apache.org/ About The Apache Software Foundation (ASF) Established in 1999, the all-volunteer Foundation oversees more than 350 leading Open Source projects, including Apache HTTP Server --the world's most popular Web server software. Through the ASF's meritocratic process known as "The Apache Way," more than 620 individual Members and 6,000 Committers successfully collaborate to develop freely available enterprise-grade software, benefiting millions of users worldwide: thousands of software solutions are distributed under the Apache License; and the community actively participates in ASF mailing lists, mentoring initiatives, and ApacheCon, the Foundation's official user conference, trainings, and expo. The ASF is a US 501(c)(3) charitable organization, funded by individual donations and corporate sponsors including Alibaba Cloud Computing, ARM, Bloomberg, Budget Direct, Capital One, Cash Store, Cerner, Cloudera, Comcast, Confluent, Facebook, Google, Hortonworks, HP, Huawei, IBM, InMotion Hosting, iSigma, LeaseWeb, Microsoft, ODPi, PhoenixNAP, Pivotal, Private Internet Access, Produban, Red Hat, Serenata Flowers, Target, WANdisco, and Yahoo. For more information, visit https://www.apache.org/ and https://twitter.com/TheASF © The Apache Software Foundation. "Apache", "SystemML", "Apache SystemML", "Hadoop", "Apache Hadoop", "Spark", "Apache Spark", and "ApacheCon" are registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries. All other brands and trademarks are the property of their respective owners.


Forest Hill, MD, May 31, 2017 (GLOBE NEWSWIRE) -- The Apache Software Foundation (ASF), the all-volunteer developers, stewards, and incubators of more than 350 Open Source projects and initiatives, announced today that Apache® SystemML™ has graduated from the Apache Incubator to become a Top-Level Project (TLP), signifying that the project's community and products have been well-governed under the ASF's meritocratic process and principles. Apache SystemML is a machine learning platform optimal for Big Data that provides declarative, large-scale machine learning and deep learning. SystemML can be run on top of Apache Spark, where it automatically scales data, line by line, to determine whether code should be run on the driver or an Apache Spark cluster. "Today, the machine learning revolution is leading to thousands of life-altering innovations such as self-driving cars and computers that detect cancer," said Deron Eriksson, Vice President of Apache SystemML. "Apache SystemML enables and simplifies this process by executing optimized high-level algorithms on Big Data using proven technologies such as Apache Spark and Apache Hadoop MapReduce." The core of Apache SystemML has been created from the ground up with the following design principles in mind: Using Apache SystemML, data scientists are able to implement algorithms using high-level language concepts without knowledge of distributed programming. Depending on data characteristics such as data size/shape and data sparsity (dense/sparse), and cluster characteristics such as cluster size and memory configurations, SystemML's cost-based optimizing compiler automatically generates hybrid runtime execution plans that are composed of single-node and distributed operations on Apache Spark or Apache Hadoop clusters for best performance. "SystemML allows Cadent to implement advanced numerical programming methods in Apache Spark, empowering us to leverage specialized algorithms in our predictive analysis software," said Michael Zargham, Chief Scientist at Cadent Technology. "SystemML is like SQL for Machine Learning, it enables Data Scientists to concentrate on the problem at hand, working in a high-level script language like R, and all the optimizations and rewrites are handled by the very powerful SystemML optimizer that considers data and available resources to produce the best execution plan for the application," said Luciano Resende, Architect at the IBM Spark Technology Center and Apache SystemML Incubator Mentor. "IBM Watson Health VBC is using Apache SystemML on Apache Spark to build risk models on a very large EHR data set to predict emergency department visits," said Steve Beier, Vice President of Value Based Care Platform and Analytics at IBM Watson Health. "The models identify high-risk patients so that they can be targeted with preemptive strategies, thus potentially reducing care costs while at the same time leading to optimal outcomes for patients." SystemML originated at IBM Research - Almaden in 2010, and was submitted to the Apache Incubator in November 2015. SystemML initiated compressed linear algebra research, a differentiating feature in SystemML, which received the VLDB 2016 Best Paper. "The Apache Incubator is all about open collaboration and communication and was invaluable for everyone involved in SystemML," added Eriksson. "The Apache SystemML community sincerely encourages everyone interested in machine learning and deep learning to help build our community around this revolutionary technology." Catch Apache SystemML in action at the Big Data Developers Silicon Valley MeetUp on 8 June 2017 in San Francisco, CA. Availability and Oversight Apache SystemML software is released under the Apache License v2.0 and is overseen by a self-selected team of active contributors to the project. A Project Management Committee (PMC) guides the Project's day-to-day operations, including community development and product releases. For downloads, documentation, and ways to become involved with Apache SystemML, visit http://systemml.apache.org/ and https://twitter.com/ApacheSystemML About the Apache Incubator The Apache Incubator is the entry path for projects and codebases wishing to become part of the efforts at The Apache Software Foundation. All code donations from external organizations and existing external projects wishing to join the ASF enter through the Incubator to: 1) ensure all donations are in accordance with the ASF legal standards; and 2) develop new communities that adhere to our guiding principles. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF. For more information, visit http://incubator.apache.org/ About The Apache Software Foundation (ASF) Established in 1999, the all-volunteer Foundation oversees more than 350 leading Open Source projects, including Apache HTTP Server --the world's most popular Web server software. Through the ASF's meritocratic process known as "The Apache Way," more than 620 individual Members and 6,000 Committers successfully collaborate to develop freely available enterprise-grade software, benefiting millions of users worldwide: thousands of software solutions are distributed under the Apache License; and the community actively participates in ASF mailing lists, mentoring initiatives, and ApacheCon, the Foundation's official user conference, trainings, and expo. The ASF is a US 501(c)(3) charitable organization, funded by individual donations and corporate sponsors including Alibaba Cloud Computing, ARM, Bloomberg, Budget Direct, Capital One, Cash Store, Cerner, Cloudera, Comcast, Confluent, Facebook, Google, Hortonworks, HP, Huawei, IBM, InMotion Hosting, iSigma, LeaseWeb, Microsoft, ODPi, PhoenixNAP, Pivotal, Private Internet Access, Produban, Red Hat, Serenata Flowers, Target, WANdisco, and Yahoo. For more information, visit https://www.apache.org/ and https://twitter.com/TheASF © The Apache Software Foundation. "Apache", "SystemML", "Apache SystemML", "Hadoop", "Apache Hadoop", "Spark", "Apache Spark", and "ApacheCon" are registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries. All other brands and trademarks are the property of their respective owners.


— Transportation Predictive Analytics Market, By Component (Hardware, Software), By Transport Type (Roadway, Railway, Aviation, Maritime), By End-User (Public Enterprises, Private Enterprises) - Forecast 2022. The increasing data volumes across transportation sector and various private agencies are the key drivers governing the market growth of the transportation predictive analytics market. The transportation predictive analytics market aims to provide the predictive analysis of logistics data and can be used to transform the way companies do business, particularly in terms of cost efficiency, operational efficiency, cost saving, dynamic pricing and collection and visualization of data. Along with technology development, new adoptions of cloud computing, software based storage devices and internet of things (IoT) have boosted the transportation predictive analytics market. The study indicates that rising demand of integrated security & safety, cost saving, dynamic pricing and operational efficiency of data is driving the transportation predictive analytics market. It has been observed that the biggest restraints in the transportation predictive analytics are high initial cost and technical challenges related to integrating transportation predictive analytics and simulation software with the current systems, are the factors likely to decline the progression of market and The transportation predictive analytics market is segmented on the basis of component and transport type. The component segment consists of hardware and software. The hardware components further consists of servers and storages. The software solutions can be deployed on the basis of on-premise service, private, public or hybrid cloud. The segmentation on the basis of transport type includes roadway and railway transportation which acquires high risk and therefore efficient softwares are implemented for proper tracking of machines running on road and railway track. The global transportation predictive analytics market is expected to grow at USD ~1,900 Million by 2022, at ~22% of CAGR between 2016 and 2022. Study Objectives of Transportation Predictive Analytics Market: • To provide detailed analysis of the market structure along with forecast of the various segments and sub-segments of the transportation predictive analytics market. • To provide insights about factors affecting the market growth. • To analyze the Transportation predictive analytics market based porter’s five force analysis etc. • To provide historical and forecast revenue of the market segments and sub-segments with respect to four main geographies and their countries- North America, Europe, Asia, and Rest of the World (ROW). • To provide country level analysis of the market with respect to the current market size and future prospective. • To provide country level analysis of the market for segment on the basis of component, transport type and end users. • To provide strategic profiling of key players in the market, comprehensively analyzing their core competencies, and drawing a competitive landscape for the market. • To track and analyze competitive developments such as joint ventures, strategic alliances, mergers and acquisitions, new product developments, and research and developments in the Transportation predictive analytics market . Key Players: The prominent players in the transportation predictive analytics market are • Cubic Corporation (U.S.) • Microsoft Corporation (U.S) • International Business Machines Corporation (U.S.) • Xerox Corporation (U.S.) • SAP SE (Germany) • Space Time Insight, Inc. (U.S.) • Predikto Inc. (U.S.) • Cyient Insights (India) • Tiger Analytics (U.S.) • T-Systems (Germany) Segments: Transportation predictive analytics market for segment on the basis of component, transport type and end-user. Transportation predictive analytics market by Component: • Hardware • Software Transportation predictive analytics market by Transport Type: • Roadway • Railway • Aviation • Maritime Transportation predictive analytics market by End-User: • Public Enterprises • Private Enterprises Regional Analysis: The regional analysis of transport predictive analytics market is being studied for region such as Asia Pacific, Americas, Europe and Rest of the World. North America market is mainly dominating the market because of advanced infrastructure growth and transport operations. Asia-Pacific market is identified as fastest growing market due to rapid advancement and urbanization of countries and investment in the transportation sector and major automotive players in that region. Ask Question for Expert at https://www.marketresearchfuture.com/enquiry/2672 . For more information, please visit https://www.marketresearchfuture.com/reports/transportation-predictive-analytics-market-2672


Wiseguyreports.Com Adds “Packaging Automation Solution -Market Demand, Growth, Opportunities and Analysis of Top Key Player Forecast To 2022” To Its Research Database This report offers an overview of the market trends, drivers, and barriers with respect to the Packaging Automation Solution market. It also provides a detailed overview of the market of different regions across United States, Europe, China, Japan, India, Southeast Asia and Others. The report categorizes Packaging Automation Solution market by By Product Type, By Function, By Software and Service, and application. Detailed analysis of key players, along with key growth strategies adopted by them is also covered in this report on Packaging Automation Solution market is valued at XX million USD in 2016 and is expected to reach XX million USD by the end of 2022, growing at a CAGR of XX% between 2016 and 2022. This report focuses Global market, it covers details as following: Rockwell Automation (U.S.)  ABB Ltd. (Switzerland)  Mitsubishi Electric Corp. (Japan)  Schneider Electric SE (France)  Emerson Electric Co. (U.S.)  Swisslog Holding AG (Switzerland)  Siemens AG (Germany)  Automated Packaging Systems, Inc. (U.S.)  Kollmorgen (U.S.)  BEUMER Group GmbH & Co., KG (Germany) By Regions, this report covers (we can add the regions/countries as you want)  North America  China  Europe  Southeast Asia  Japan  India Main types of products  Packaging Automation Solution Market, by Product Type  Automated Packagers  Packaging Robots  Automated Conveyors and Sortation Systems  Packaging Automation Solution Market, by Function  Case Packaging  Palletizing  Labeling  Bagging  Filling  Packaging Automation Solution Market, by Software and Service  Software  Services Packaging Automation Solution Market, by Key Consumer  Food and Beverages  Healthcare  Logistics and Warehousing  Chemical  Retail  Semiconductor and Electronics  Aerospace and Defense  Automotive   Others Global Predictive Analytics Market Research Report 2017-2022 by Players, Regions, Product Types & Applications  Chapter One Methodology and Data Source  1.1 Methodology/Research Approach  1.1.1 Research Programs/Design  1.1.2 Market Size Estimation  1.1.3 Market Breakdown and Data Triangulation  1.2 Data Source  1.2.1 Secondary Sources  1.2.2 Primary Sources  1.3 Disclaimer Chapter Six Global Key Players Profile  6.1 Alteryx, Inc. (US)  6.1.1 Alteryx, Inc. (US) Company Details and Competitors  6.1.2 Alteryx, Inc. (US) Key Predictive Analytics Models and Performance  6.1.3 Alteryx, Inc. (US) Predictive Analytics Business SWOT Analysis and Forecast  6.1.4 Alteryx, Inc. (US) Predictive Analytics Sales Volume Revenue Price Cost and Gross Margin  6.2 AgilOne (US)  6.2.1 AgilOne (US) Company Details and Competitors  6.2.2 AgilOne (US) Key Predictive Analytics Models and Performance  6.2.3 AgilOne (US) Predictive Analytics Business SWOT Analysis and Forecast  6.2.4 AgilOne (US) Predictive Analytics Sales Volume Revenue Price Cost and Gross Margin  6.3 Angoss Software Corporation (Canada)  6.3.1 Angoss Software Corporation (Canada) Company Details and Competitors  6.3.2 Angoss Software Corporation (Canada) Key Predictive Analytics Models and Performance  6.3.3 Angoss Software Corporation (Canada) Predictive Analytics Business SWOT Analysis and Forecast  6.3.4 Angoss Software Corporation (Canada) Predictive Analytics Sales Volume Revenue Price Cost and Gross Margin  6.4 Domino Data Lab (US)  6.4.1 Domino Data Lab (US) Company Details and Competitors  6.4.2 Domino Data Lab (US) Key Predictive Analytics Models and Performance  6.4.3 Domino Data Lab (US) Predictive Analytics Business SWOT Analysis and Forecast  6.4.4 Domino Data Lab (US) Predictive Analytics Sales Volume Revenue Price Cost and Gross Margin  6.5 Dataiku (US)  6.5.1 Dataiku (US) Company Details and Competitors  6.5.2 Dataiku (US) Key Predictive Analytics Models and Performance  6.5.3 Dataiku (US) Predictive Analytics Business SWOT Analysis and Forecast  6.5.4 Dataiku (US) Predictive Analytics Sales Volume Revenue Price Cost and Gross Margin  6.6 Exago, Inc. (US)  6.6.1 Exago, Inc. (US) Company Details and Competitors  6.6.2 Exago, Inc. (US) Key Predictive Analytics Models and Performance  6.6.3 Exago, Inc. (US) Predictive Analytics Business SWOT Analysis and Forecast  6.6.4 Exago, Inc. (US) Predictive Analytics Sales Volume Revenue Price Cost and Gross Margin  6.7 Fair Isaac Corporation (FICO) (US)  6.7.1 Fair Isaac Corporation (FICO) (US) Company Details and Competitors  6.7.2 Fair Isaac Corporation (FICO) (US) Key Predictive Analytics Models and Performance  6.7.3 Fair Isaac Corporation (FICO) (US) Predictive Analytics Business SWOT Analysis and Forecast  6.7.4 Fair Isaac Corporation (FICO) (US) Predictive Analytics Sales Volume Revenue Price Cost and Gross Margin 6.8 GoodData Corporation (US)  6.8.1 GoodData Corporation (US) Company Details and Competitors  6.8.2 GoodData Corporation (US) Key Predictive Analytics Models and Performance  6.8.3 GoodData Corporation (US) Predictive Analytics Business SWOT Analysis and Forecast  6.8.4 GoodData Corporation (US) Predictive Analytics Sales Volume Revenue Price Cost and Gross Margin  6.9 International Business Machines (IBM) Corporation (US)  6.9.1 International Business Machines (IBM) Corporation (US) Company Details and Competitors  6.9.2 International Business Machines (IBM) Corporation (US) Key Predictive Analytics Models and Performance  6.9.3 International Business Machines (IBM) Corporation (US) Predictive Analytics Business SWOT Analysis and Forecast  6.9.4 International Business Machines (IBM) Corporation (US) Predictive Analytics Sales Volume Revenue Price Cost and Gross Margin  6.10 Information Builders (US)  6.10.1 Information Builders (US) Company Details and Competitors  6.10.2 Information Builders (US) Key Predictive Analytics Models and Performance  6.10.3 Information Builders (US) Predictive Analytics Business SWOT Analysis and Forecast  6.10.4 Information Builders (US) Predictive Analytics Sales Volume Revenue Price Cost and Gross Margin  6.11 Kognitio Ltd. (UK)  6.12 KNIME.com AG (Switzerland)  6.13 MicroStrategy, Inc. (US)  6.14 Microsoft Corporation (US)  6.15 NTT DATA Corporation (Japan)  6.16 Oracle Corporation (US)  6.17 Predixion Software (US)  6.18 RapidMiner (US)  6.19 QlikTech International (US)  6.20 Sisense, Inc. (US)  6.21 SAP SE (Germany)  6.22 SAS Institute, Inc. (US)  6.23 Tableau Software, Inc. (US)  6.24 TIBCO Software, Inc. (US)  6.25 Teradata Corporation (US) For more information, please visit https://www.wiseguyreports.com/sample-request/1760873-global-packaging-automation-solution-market-research-report-2017-2022-by-players


News Article | June 14, 2017
Site: marketersmedia.com

— Transportation Predictive Analytics Market, By Component (Hardware, Software), By Transport Type (Roadway, Railway, Aviation, Maritime), By End-User (Public Enterprises, Private Enterprises) - Forecast 2022. The increasing data volumes across transportation sector and various private agencies are the key drivers governing the market growth of the transportation predictive analytics market. The transportation predictive analytics market aims to provide the predictive analysis of logistics data and can be used to transform the way companies do business, particularly in terms of cost efficiency, operational efficiency, cost saving, dynamic pricing and collection and visualization of data. Along with technology development, new adoptions of cloud computing, software based storage devices and internet of things (IoT) have boosted the transportation predictive analytics market. The study indicates that rising demand of integrated security & safety, cost saving, dynamic pricing and operational efficiency of data is driving the transportation predictive analytics market. It has been observed that the biggest restraints in the transportation predictive analytics are high initial cost and technical challenges related to integrating transportation predictive analytics and simulation software with the current systems, are the factors likely to decline the progression of market and The transportation predictive analytics market is segmented on the basis of component and transport type. The component segment consists of hardware and software. The hardware components further consists of servers and storages. The software solutions can be deployed on the basis of on-premise service, private, public or hybrid cloud. The segmentation on the basis of transport type includes roadway and railway transportation which acquires high risk and therefore efficient softwares are implemented for proper tracking of machines running on road and railway track. The global transportation predictive analytics market is expected to grow at USD ~1,900 Million by 2022, at ~22% of CAGR between 2016 and 2022. Study Objectives of Transportation Predictive Analytics Market: • To provide detailed analysis of the market structure along with forecast of the various segments and sub-segments of the transportation predictive analytics market. • To provide insights about factors affecting the market growth. • To analyze the Transportation predictive analytics market based porter’s five force analysis etc. • To provide historical and forecast revenue of the market segments and sub-segments with respect to four main geographies and their countries- North America, Europe, Asia, and Rest of the World (ROW). • To provide country level analysis of the market with respect to the current market size and future prospective. • To provide country level analysis of the market for segment on the basis of component, transport type and end users. • To provide strategic profiling of key players in the market, comprehensively analyzing their core competencies, and drawing a competitive landscape for the market. • To track and analyze competitive developments such as joint ventures, strategic alliances, mergers and acquisitions, new product developments, and research and developments in the Transportation predictive analytics market . Key Players: The prominent players in the transportation predictive analytics market are • Cubic Corporation (U.S.) • Microsoft Corporation (U.S) • International Business Machines Corporation (U.S.) • Xerox Corporation (U.S.) • SAP SE (Germany) • Space Time Insight, Inc. (U.S.) • Predikto Inc. (U.S.) • Cyient Insights (India) • Tiger Analytics (U.S.) • T-Systems (Germany) Segments: Transportation predictive analytics market for segment on the basis of component, transport type and end-user. Transportation predictive analytics market by Component: • Hardware • Software Transportation predictive analytics market by Transport Type: • Roadway • Railway • Aviation • Maritime Transportation predictive analytics market by End-User: • Public Enterprises • Private Enterprises Regional Analysis: The regional analysis of transport predictive analytics market is being studied for region such as Asia Pacific, Americas, Europe and Rest of the World. North America market is mainly dominating the market because of advanced infrastructure growth and transport operations. Asia-Pacific market is identified as fastest growing market due to rapid advancement and urbanization of countries and investment in the transportation sector and major automotive players in that region. For more information, please visit https://www.marketresearchfuture.com/reports/transportation-predictive-analytics-market-2672


News Article | September 25, 2017
Site: www.prnewswire.com

NEW YORK, Sept. 25, 2017 /PRNewswire/ -- Advancements in technologies, such as machine learning, Artificial Intelligence (AI), and predictive maintenance to enhance fleet management is one of the drivers for the growth of the vehicle analytics market Read the full report: https://www.reportlinker.com/p05113894 The vehicle analytics market size is expected to grow from USD 1,124.1 million in 2017 to USD 3,637.4 million by 2022, at a Compound Annual Growth Rate (CAGR) of 26.5%. Advancements in technologies, such as machine learning, artificial intelligence (AI), and predictive maintenance to enhance fleet management and increasing use of real-time data collected from sensors, and Global Positioning System (GPS) tracking devices are some of the factors that have significantly fueled the growth of vehicle analytics solutions. However, network coverage limitations and high initial setup costs are the restraining factors for the vehicle analytics market. "Safety and security management application is estimated to hold the largest market size in 2017 in the vehicle analytics market. The trend is expected to continue during the forecast period" Many applications, such as automatic crash notification, remote alerting and theft tracking, roadside assistance, and concierge services make use of sensors and other RFID tags embedded inside the car to determine the overall health of the car. The need to monitor and analyze the data generated from myriads of IoT sensors and wireless connections to improve safety and security features inside the car is one of the major factor contributing to the substantial growth of the application. "Asia Pacific (APAC) is expected to have the highest growth rate during the forecast period in the vehicle analytics market by region" The top countries contributing to the growth of vehicle analytics market are China, Japan, Singapore, South Korea and India. The market is chiefly driven by the substantial growth of cab aggregator companies and by the astronomical growth and development witnessed around the development of smart cities and IoT proliferation. Vehicle Analytics companies should focus more on the emerging economies such as China, India, Bangladesh, Malaysia, and Thailand, as these countries hold the maximum potential of adoption of advanced technologies in the near future. Moreover, massive adoption of cloud technologies in the APAC region is also one of the compelling trends that signify the humungous growth opportunities for new and advanced technologies in the region. The Chinese market is currently keeping pace with the US and North America in terms of manufacturing the number of cars annually. The trend witnessed in the region is expected to even surpass/outgrow the US and Europe market in future. APAC market offers several untapped and unexplored opportunities which if explored properly could be significant in propelling the growth for the vehicle analytics solution and service providers. In the process of determining and verifying the market size for several segments and subsegments gathered through secondary research, extensive primary interviews were conducted with key people. The break-up of the profile of the primary participants is as follows: • By Company: Tier 1 – 25 %, Tier 2 –35%, and Tier 3 –40% • By Designation: C level – 16%, Director level – 35%, and Others – 49% • By Region: North America – 42%, Europe – 22%, and APAC – 36% The vehicle analytics market comprises following major vendors: 1. Acerta Analytics Solutions (Canada) 2. Agnik LLC (US) 3. Amodo (Croatia) 4. Automotive Rentals (ARI) (US) 5. Azuga (US) 6. C-4 Analytics, LLC (US) 7. CloudMade (UK) 8. Digital Recognition Network (US) 9. EngineCAL (India) 10. Genetec Inc. (Canada) 11. HARMAN International (US) 12. IBM (US) 13. Inquiron (Dubai) 14. INRIX (US) 15. Inseego Corp. (US) 16. Intelligent Mechatronic Systems (Canada) 17. Microsoft (US) 18. Noregon (US) 19.Pivotal Software, Inc. (US) 20. Plotly (Canada) 21. Procon Analytics (US) 22. SAP (Germany) 23. Teletrac Navman (US) 24. WEX Inc. (US) 25. Xevo Inc. (US) Research Report The report segments the vehicle analytics market on the basis of applications (predictive maintenance, warranty analytics, traffic management, safety and security management, driver and user behavior analysis, dealer performance analysis, infotainment, usage based insurance and road charging); components (software, and services) deployment models (on-premises and on-demand); end-user (service providers, automotive dealers, fleet owners, regulatory bodies ,and insurers), and regions (North America, Europe, APAC, Middle East and Africa (MEA), and Latin America). Reasons to Buy the Report • To get a comprehensive overview of the global vehicle analytics market • To gain wide-ranging information about the top players in this market, their product portfolios, and the key strategies adopted by them • To gain insights into the major countries/regions, in which the vehicle analytics market is flourishing across various industry verticals Read the full report: https://www.reportlinker.com/p05113894 About Reportlinker ReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place. https://www.reportlinker.com __________________________ Contact Clare: clare@reportlinker.com US: (339)-368-6001 Intl: +1 339-368-6001


News Article | November 20, 2015
Site: www.rdmag.com

Volvo Cars and Microsoft HoloLens have announced a joint venture to develop next-generation automotive technologies, including autonomous driving and revamping the customer purchasing experience with augmented reality. “Imagine enhancing your car buying experience at the dealership by viewing the complete inside of the vehicle you are interested in,” writes Scott Erickson, senior director of Microsoft HoloLens. “With the power of holograms, we have the ability to open the car up completely, take a closer look at the engine, inspect the chassis or watch the drivetrain and transmission in action.” The HoloLens is a fully holographic computer powered by Windows 10, with applications spanning the fields of education, design, health care and entertainment. “HoloLens offers the freedom to create a bespoke experience which customers can steer themselves,” said Björn Annwall, SVP of Sales, Marketing and Customer Service at Volvo. According to Annwall, the HoloLens may revolutionize car sales by freeing salespeople from the showroom environment, and further allowing for pop-up stores. “You can have access to the full array of options, features and possibilities associated with every car make and model,” writes Erickson. “Imagine then seeing the car you’ve configured, at full scale, as a high-definition hologram projected into your garage, long before the care has even been manufactured.” A video from Microsoft HoloLens shows users donning the HoloLens headset and controlling projected holograms with hand motions. The new tool is also being used by Microsoft collaborators, such as NASA’s Jet Propulsion Laboratory, the Cleveland Clinic and the Case Western Reserve Univ., among others. Media criticism While the video looks impressive, Microsoft has come under criticism for misrepresentation. On Thursday, the two companies invited journalists to partake in a demonstration of the system. The New York Times reports “HoloLens has a narrow field of view: the portion of one’s vision on which it can project digital images. Imagine a small rectangle suspended several feet in front of your eyes, a shape that travels wherever you turn your head. That is the HoloLens’ canvas. HoloLens doesn’t project anything into a subject’s peripheral vision or above or below that rectangle.” Other focus areas   Another area of focus, according to Volvo, is autonomous driving. Starting in 2017, the company will begin a program called “Drive-Me,” which will give 100 self-driving cars to real customers in the Swedish city Gothenburg. The company said it’s the world’s largest autonomous driving experiment. Further, the two companies plan on using information gathered by cars and drivers to improve the driving experience, and predictive analysis to improve safety.

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