IBM
Armonk, NY, United States
Armonk, NY, United States

The International Business Machines Corporation is an American multinational technology and consulting corporation, with headquarters in Armonk, New York, United States. IBM manufactures and markets computer hardware and software, and offers infrastructure, hosting and consulting services in areas ranging from mainframe computers to nanotechnology.The company was founded in 1911 as the Computing-Tabulating-Recording Company through a merger of the Tabulating Machine Company, the International Time Recording Company, and the Computing Scale Company. CTR was changed to "International Business Machines" in 1924, using a name which had originated with CTR's Canadian subsidiary. The acronym IBM followed. Securities analysts nicknamed the company Big Blue for its size and common use of the color in products, packaging, and logo.In 2012, Fortune ranked IBM the No. 2 largest U.S. firm in terms of number of employees , the No. 4 largest in terms of market capitalization, the No. 9 most profitable, and the No. 19 largest firm in terms of revenue. Globally, the company was ranked the No. 31 largest in terms of revenue by Forbes for 2011. Other rankings for 2011/2012 include No. 1 company for leaders , No. 1 green company in the U.S. , No. 2 best global brand , No. 2 most respected company , No. 5 most admired company , and No. 18 most innovative company .IBM has 12 research laboratories worldwide, bundled into IBM Research. As of 2013 the company held the record for most patents generated by a business for 22 consecutive years. Its employees have garnered five Nobel Prizes, six Turing Awards, ten National Medals of Technology, and five National Medals of Science. Notable company inventions include the automated teller machine , the floppy disk, the hard disk drive, the magnetic stripe card, the relational database, the Universal Product Code , the financial swap, the Fortran programming language, SABRE airline reservation system, DRAM, copper wiring in semiconductors, the silicon-on-insulator semiconductor manufacturing process, and Watson artificial intelligence.IBM has constantly evolved since its inception, acquiring properties such as Kenexa and SPSS and organizations such as PwC's consulting business , spinning off companies like printer manufacturer Lexmark , and selling off product lines like its personal computer and server businesses to Lenovo . In 2014 IBM announced that it would "offload" IBM Micro Electronics semiconductor manufacturing to Global Foundries. This transition is in progress as of early 2015. Wikipedia.

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News Article | April 18, 2017
Site: marketersmedia.com

— Manufacturing analytics is a statistical tool which helps in rule based analysis of manufacturing data and information and enables the users to better understand the process. This also helps in improvements, identification and reinforcement of the best practices. Manufacturing analytics gives the access to identify the problem before it happened which could affect the product, yield or cost. For the better understanding of report, this market has been segmented on the basis of types, application, deployment model and end user industries. Types include – software and services. Services have been further segmented as managed and professional. On the basis of application, the market has been segmented as predictive asset management, inventory management, supply chain analysis, power and energy, emergency, sales & customer support and others. By deployment, the market has been segmented as on demand and on premise. The end users for manufacturing analytics are electronics equipment manufacturing industries, automotive and aerospace manufacturing industries, food and beverages manufacturing industries, machinery and industrial equipment manufacturing industries, chemicals and materials manufacturing industries, pharma and life science industry, paper, pulp, plastic, and rubber manufacturing industries and others. The prominent players in the market of manufacturing analytics are- • Computer Science Corporation (CSC) • Zensar Technologies Ltd. • SAS Institute, Inc. • Oracle Corporation • SAP SE • IBM • Tableau Software • Statsoft, Inc. • 1010data, Inc. • Alteryx, Inc. The reports also covers brief analysis of Geographical Region includes: Americas North America US Canada Europe Western Europe Germany France Italy Spain U.K Rest of Western Europe Eastern Europe Asia– Pacific Asia China India Japan South Korea Rest of Asia Pacific The Middle East& Africa For more information, please visit https://www.marketresearchfuture.com/reports/manufacturing-analytics-market


News Article | May 18, 2017
Site: www.prnewswire.co.uk

According to a report compiled by TMR, the world neuromorphic chip market could rise at a solid CAGR of 19.0% to attain a valuation of approximately US$1.8 bn by 2023. Leading companies such as Intel Corporation could look to focus on heavy investments in research and development for fortifying their position in the world neuromorphic chip market. In 2015, the company had invested a colossal amount of funds in research and development for developing cutting-edge proprietary technologies and fortifying its status in the world neuromorphic chip market. In the same year, IBM once again took a leading position in the world neuromorphic chip market on the back of several patents awarded in the U.S. Craving Need for Artificial Intelligence Gives Impetus for Growth The authors of the report have foreseen the international neuromorphic chip market to receive a strong momentum due to a rising demand for artificial intelligence (AI). This could include the furtherance of machines and computer programs that are adequately proficient to update themselves when introduced to real-time data. The range of applications in the international neuromorphic chip market has been expected to be significantly enhanced on account of innovations in the integrated circuit miniaturization field. The scalability and small size of neuromorphic chips facilitate their easy implementation into a variety of end-use products. Get PDF Sample for this Research Report @ http://www.transparencymarketresearch.com/sample/sample.php?flag=S&rep_id=9446 The benefit of improved neural network-based computations along with the speed of machine learning in large supercomputers could bode well for the international neuromorphic chip market. The growth of the international neuromorphic chip market has been projected to be forwarded by an aggressive demand from the Internet of things (IoT) technology. High cost of manufacture could be one of the chief deterrents in the worldwide neuromorphic chip market. The integration of biological synapses into microscopic-sized hardware requiring space of a mere single micron has been predicted to be a major challenge of manufacturers operating in the worldwide neuromorphic chip market. Hardware designing complexities and insufficient availability of competent infrastructure and technological resources could be other challenges in the worldwide neuromorphic chip market. However, the future of the worldwide neuromorphic chip market has been envisaged to be on the brighter side owing to growth opportunities birthing from applications in a wide gamut of products in different sectors such as healthcare, semiconductor and electronics, and automotive. Service and industrial robotics could be a telling application level opportunity prevailing in the worldwide neuromorphic chip market. Inflating adoption of software for perpetual online learning, data modeling, predictive analysis, and real-time data streaming and growing demand for sensors have been prophesied to be other reliable opportunities in the worldwide neuromorphic chip market. Get more information from Research Report Press Release: http://www.transparencymarketresearch.com/pressrelease/neuromorphic-chip-market.htm The information presented in this review is based on a TMR report, titled, "Neuromorphic Chip Market (Function - Signal Processing, Data Processing, and Image Recognition; Application - Defense and Aerospace, Automotive, Medical, and Industrial) - Global Industry Analysis, Size, Share, Growth, Trends and Forecast 2015 - 2023." The global neuromorphic chip market has been segmented as presented below: Transparency Market Research (TMR) is a market intelligence company, providing global business information reports and services. Our exclusive blend of quantitative forecasting and trends analysis provides forward-looking insight for thousands of decision makers. TMR's experienced team of Analysts, Researchers, and Consultants, use proprietary data sources and various tools and techniques to gather, and analyze information. Our business offerings represent the latest and the most reliable information indispensable for businesses to sustain a competitive edge. Each TMR syndicated research report covers a different sector - such as pharmaceuticals, chemicals, energy, food & beverages, semiconductors, med-devices, consumer goods and technology. These reports provide in-depth analysis and deep segmentation to possible micro Levels. With wider scope and stratified research methodology, TMR's syndicated reports thrive to provide clients to serve their overall research requirement.


News Article | May 18, 2017
Site: www.prnewswire.com

According to a report compiled by TMR, the world neuromorphic chip market could rise at a solid CAGR of 19.0% to attain a valuation of approximately US$1.8 bn by 2023. Leading companies such as Intel Corporation could look to focus on heavy investments in research and development for fortifying their position in the world neuromorphic chip market. In 2015, the company had invested a colossal amount of funds in research and development for developing cutting-edge proprietary technologies and fortifying its status in the world neuromorphic chip market. In the same year, IBM once again took a leading position in the world neuromorphic chip market on the back of several patents awarded in the U.S. Craving Need for Artificial Intelligence Gives Impetus for Growth The authors of the report have foreseen the international neuromorphic chip market to receive a strong momentum due to a rising demand for artificial intelligence (AI). This could include the furtherance of machines and computer programs that are adequately proficient to update themselves when introduced to real-time data. The range of applications in the international neuromorphic chip market has been expected to be significantly enhanced on account of innovations in the integrated circuit miniaturization field. The scalability and small size of neuromorphic chips facilitate their easy implementation into a variety of end-use products. Get PDF Sample for this Research Report @ http://www.transparencymarketresearch.com/sample/sample.php?flag=S&rep_id=9446 The benefit of improved neural network-based computations along with the speed of machine learning in large supercomputers could bode well for the international neuromorphic chip market. The growth of the international neuromorphic chip market has been projected to be forwarded by an aggressive demand from the Internet of things (IoT) technology. High cost of manufacture could be one of the chief deterrents in the worldwide neuromorphic chip market. The integration of biological synapses into microscopic-sized hardware requiring space of a mere single micron has been predicted to be a major challenge of manufacturers operating in the worldwide neuromorphic chip market. Hardware designing complexities and insufficient availability of competent infrastructure and technological resources could be other challenges in the worldwide neuromorphic chip market. However, the future of the worldwide neuromorphic chip market has been envisaged to be on the brighter side owing to growth opportunities birthing from applications in a wide gamut of products in different sectors such as healthcare, semiconductor and electronics, and automotive. Service and industrial robotics could be a telling application level opportunity prevailing in the worldwide neuromorphic chip market. Inflating adoption of software for perpetual online learning, data modeling, predictive analysis, and real-time data streaming and growing demand for sensors have been prophesied to be other reliable opportunities in the worldwide neuromorphic chip market. Get more information from Research Report Press Release: http://www.transparencymarketresearch.com/pressrelease/neuromorphic-chip-market.htm The information presented in this review is based on a TMR report, titled, "Neuromorphic Chip Market (Function - Signal Processing, Data Processing, and Image Recognition; Application - Defense and Aerospace, Automotive, Medical, and Industrial) - Global Industry Analysis, Size, Share, Growth, Trends and Forecast 2015 - 2023." The global neuromorphic chip market has been segmented as presented below: Transparency Market Research (TMR) is a market intelligence company, providing global business information reports and services. Our exclusive blend of quantitative forecasting and trends analysis provides forward-looking insight for thousands of decision makers. TMR's experienced team of Analysts, Researchers, and Consultants, use proprietary data sources and various tools and techniques to gather, and analyze information. Our business offerings represent the latest and the most reliable information indispensable for businesses to sustain a competitive edge. Each TMR syndicated research report covers a different sector - such as pharmaceuticals, chemicals, energy, food & beverages, semiconductors, med-devices, consumer goods and technology. These reports provide in-depth analysis and deep segmentation to possible micro Levels. With wider scope and stratified research methodology, TMR's syndicated reports thrive to provide clients to serve their overall research requirement.


Global Market Insights announces the new study based market Report on intelligent transportation systems market.  The ITS market size is predicted to hit USD 47.5 billion at a CAGR of over 13% from 2015 to 2022. The report covers in-depth analysis on the basis of region, application and competitive segmentation. The report focuses on all aspects of market with analysis of drivers, impediments, challenges, opportunities and its impact on the market scenario. Rising numbers of vehicles result in high traffic congestion and thus, necessitates highly developed public traffic management systems. ITS combines information and communication technologies used in traffic management and transportation systems, which helps reduce traffic congestion and thus enhance the safety, sustainability and efficiency of transportation networks. Increasing need for enhancing the existing transportation network performance and growing focus on the road is forecast to have a positive impact on the growth. Request for a sample of this research report @ https://www.gminsights.com/request-sample/detail/178 The system is being extensively adopted in several verticals including collision avoidance systems, fleet management, weather condition detection, traffic monitoring systems, asset monitoring, automotive telematics, parking availability systems, variable traffic message signs, traffic enforcement cameras and traffic signal control systems. ATMS market size is expected to witness substantial growth over USD 18.4 billion during the predicted time frame. APTS systems segment used in bus arrival notification systems, real time passenger information systems, etc. is anticipated to exhibit growth at a CAGR of over 13% in next 7 years. Browse key industry insights spread across 112 pages with 59 market data tables & 50 figures & charts from the report, “Intelligent Transportation System (ITS) Market Size By Application (Road Safety and Security, Traffic Management, Freight Management, Parking Management, Public Transport, Environment Protection, Road User Charging, Automotive Telematics), By Product (ATMS, ATPS, ATIS, APTS, Cooperative vehicle systems),Industry Analysis Report, Regional Analysis , Application Potential, Price Trends, Competitive Market Share & Forecast, 2015 – 2022” in detail along with the table of contents: The report covers the analysis of ITS at regional scale, including U.S., Canada, China, Brazil, Mexico, Argentina, Peru, Chile, Korea, Philippines, Indonesia, Taiwan, Japan, Australia, Singapore, New Zealand, Malaysia and India. Asia Pacific market size is forecast to see major growth. Significant implementation of this technology in China and India has impelled developments in sensor technologies. The Latin America market is predicted to experience growth at over 13%during the anticipated period. Positive regulatory initiatives, coupled with promotional activities are expected to propel the demand in the North America region. Germany market size is projected to experience growth over 11% by 2022. Make an inquiry for buying this report @ https://www.gminsights.com/inquiry-before-buying/705 The report features an analysis of competitive industry with the key industry players profiled thoroughly. It covers market trends, business strategies of participants. Some of the companies dominating the market are Q-Free ASA, Xerox Corp, Kapsch Traffic, Hitachi Ltd., Siemens AG, TomTom NV, SWARCO AG, Garmin International, IBM Corp, and Denso. The report offers a 3600 view of the complete ITS market with regards to the geography, application areas and competitive landscape with the qualitative analysis of each and every feature of the segmentation. All the numbers, at every level of detail, are anticipated till 2022 to provide the potential size details of this market. Global Market Insights, Inc., headquartered in Delaware, U.S., is a global market research and consulting service provider; offering syndicated and custom research reports along with growth consulting services. Our business intelligence and industry research reports offer clients with penetrative insights and actionable market data specially designed and presented to aid strategic decision making. These exhaustive reports are designed via a proprietary research methodology and are available for key industries such as chemicals, advanced materials, technology, renewable energy and biotechnology.


Zhang L.,University of California at Berkeley | Wang L.,IBM | Zhou K.,University of California at Berkeley | Zhang W.-B.,University of California at Berkeley
IEEE Transactions on Intelligent Transportation Systems | Year: 2012

Dynamic all-red extension (DARE) has recently attracted research interest as a nontraditional intersection collision-avoidance method, for which the prediction of red-light running (RLR) and its related hazardous situations is a crucial part. We propose a probabilistic framework to model and predict RLR hazards for DARE. The RLR hazard, which is quantified by a predictive encroachment time, has contributory factors, including the speed, distance, and car-following status of the violator and the empirical distribution of the entry time of conflict traffic. An offline data analysis procedure is developed to set the parameters for RLR hazard prediction. Online-wise, a 2-D normal model is developed to predict the vehicle's stop-go maneuver based on speeds at advanced detectors and the car-following status. Additionally, unlike most prediction models that are designed to minimize mean errors, our model identifies two types of errors, namely, the false alarm and a missed report. The capability of distinguishing these two types of errors is crucial to the effectiveness of dynamic operations. To quantify the tradeoff between these two types of errors in DARE, a system operating characteristic (SOC) function is then defined. Effectiveness of the proposed model and its prediction algorithm is demonstrated using data collected from a field intersection. At a false-alarm rate of less than 5% (or equivalently about one false trigger per 8 h), the algorithm reached a correct detection rate of over 70% to more than 80%. Performance evaluation results showed that the proposed DARE framework can effectively predict the RLR hazards. © 2011 IEEE.

Document Keywords (matching the query): car following.


Ahn H.-I.,IBM | Spangler W.S.,IBM
Annual SRII Global Conference, SRII | Year: 2014

Social media has been valuable sources to predict the future outcomes of some events such as box-office movie revenues or political elections. This paper focuses on periodic forecasting problems of product sales based on social media analysis and time-series analysis. In particular, we present a predictive model of monthly automobile sales using sentiment and topical keyword frequencies related to the target brand over time on social media. Our predictive model illustrates how different time scale-based predictors derived from sentiment and topical keyword frequencies can improve the prediction of the future sales. © 2014 IEEE.

Document Keywords (matching the query): automobile sales, social media analysis, time series analysis, predictive modeling, sentiment analysis, topical keyword analysis.


An approach is provided to automate predictive vehicle maintenance. In the approach, a vehicles information handling system receives vehicle data transmissions from a number of other vehicles in geographic proximity to the vehicle. Both the vehicle and the other vehicles correspond to various vehicle types that are used to identify those other vehicles that are similar to the vehicle. The sets of received vehicle data transmissions that are received to similar vehicles are analyzed with respect to a plurality of vehicle maintenance data corresponding to the vehicle. The analysis of the vehicle data transmissions resulting in predictive vehicle maintenance recommendations pertaining to the first vehicle.

Claims which contain your search:

1. A method implemented by an information handling system to automate predictive vehicle maintenance, the method comprising: receiving, at the information handling system included in a first vehicle, a plurality of wireless vehicle data transmissions from a plurality of other vehicles in geographic proximity to the vehicle, wherein the first vehicle corresponds to a first vehicle type; identifying a set of received vehicle data transmissions corresponding to a set of the plurality of the other vehicles with vehicle types that are similar to the first vehicle type; and analyzing the identified set of received vehicle data transmissions with respect to a plurality of vehicle maintenance data corresponding to the first vehicle, the analysis resulting in a set of one or more predictive vehicle maintenance recommendations pertaining to the first vehicle.

2. The method of claim 1 further comprising: comparing the one or more predictive vehicle maintenance recommendations to one or more criteria; and alerting a user of the first vehicle to at least one of the one or more predictive vehicle maintenance recommendations based on the comparison.

4. The method of claim 1 further comprising: utilizing predictive analysis according to a standard asset health assessment model utilizing aggregated information selected from a group consisting of reliability information, condition event, condition measurement, diagnostic, prognostic, health assessment, and work management.

8. An information handling system included in a first vehicle, the information handling system comprising: one or more processors; a memory coupled to at least one of the processors; a nonvolatile storage device; a wireless communication adapter; and a set of instructions stored in the memory and executed by at least one of the processors to automate predictive vehicle maintenance, wherein the set of instructions perform actions of:receiving, at the wireless communication adapter, a plurality of vehicle data transmissions from a plurality of other vehicles in geographic proximity to the vehicle, wherein the first vehicle corresponds to a first vehicle type;identifying a set of received vehicle data transmissions corresponding to a set of the plurality of the other vehicles with vehicle types that are similar to the first vehicle type; andanalyzing the identified set of received vehicle data transmissions with respect to a plurality of vehicle maintenance data corresponding to the first vehicle, the analysis resulting in a set of one or more predictive vehicle maintenance recommendations pertaining to the first vehicle.

9. The information handling system of claim 8 wherein the actions performed further comprise: comparing the one or more predictive vehicle maintenance recommendations to one or more criteria; and alerting a user of the first vehicle to at least one of the one or more predictive vehicle maintenance recommendations based on the comparison.

11. The information handling system of claim 8 wherein the actions performed further comprise: utilizing predictive analysis according to a standard asset health assessment model utilizing aggregated information selected from a group consisting of reliability information, condition event, condition measurement, diagnostic, prognostic, health assessment, and work management.

15. A computer program product stored in a computer readable medium, comprising computer instructions that, when executed by an information handling system, causes the information handling system to perform actions comprising: receiving, at the information handling system included in a first vehicle, a plurality of wireless vehicle data transmissions from a plurality of other vehicles in geographic proximity to the vehicle, wherein the first vehicle corresponds to a first vehicle type; identifying a set of received vehicle data transmissions corresponding to a set of the plurality of the other vehicles with vehicle types that are similar to the first vehicle type; and analyzing the identified set of received vehicle data transmissions with respect to a plurality of vehicle maintenance data corresponding to the first vehicle, the analysis resulting in a set of one or more predictive vehicle maintenance recommendations pertaining to the first vehicle.

16. The computer program product of claim 15 wherein the actions performed further comprise: comparing the one or more predictive vehicle maintenance recommendations to one or more criteria; and alerting a user of the first vehicle to at least one of the one or more predictive vehicle maintenance recommendations based on the comparison.

18. The computer program product of claim 15 wherein the actions performed further comprise: utilizing predictive analysis according to a standard asset health assessment model utilizing aggregated information selected from a group consisting of reliability information, condition event, condition measurement, diagnostic, prognostic, health assessment, and work management.

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