Altair’s event processing and data visualization tools enable fleet operators to analyze critical data streaming in from sensors and other sources. This real-time visibility into vehicle and driver performance helps reduce operating costs, improve driver safety, and increase fleet productivity. Analysts can display maps showing the current position of all assets, examine route deviations, program alerts on any set of parameters, and compare drivers’ behavior. Analysts can design and modify analytical dashboards as needed without writing a single line of code.
Tips For Implementing Smart City TechnologyAlan Oviatt
The concept of smart cities was premised on integrating information, communications and Internet of Things (IoT) technologies like sensors and cameras in a secure fashion to manage a city's assets. One goal was more effective and cost-efficient management of city infrastructures and property, but equally important was responsiveness to emerging infrastructure events to help cities and their occupants. By Alan Oviatt
Tips For Implementing Smart City TechnologyAlan Oviatt
The concept of smart cities was premised on integrating information, communications and Internet of Things (IoT) technologies like sensors and cameras in a secure fashion to manage a city's assets. One goal was more effective and cost-efficient management of city infrastructures and property, but equally important was responsiveness to emerging infrastructure events to help cities and their occupants. By Alan Oviatt
FDOT Transportation Symposium presentation for connected and autonomous technology in highway work zones. Presented by Acey Roberts and Glenn Havinoviski, Wantman Group 1/19/21
Connected Lives: Where Smart Vehicles Meet the Intelligent RoadCognizant
The digital highway promises to enable an ever-expanding ecosystem encompassing intelligent transportation systems, smart cities and logistics systems, optimizing productivity and performance for businesses and individuals.
Transport for London: Using data to keep London movingWSO2
This talk was presented by Sriskandarajah Suhothayan (WSO2) and Roland Major (Transport for London) at the Strata Data Conference in London, May 23 2017.
Transport for London (TfL) uses a wide range of data for operational purposes, but the underlying data is typically held in multiple disconnected systems. Freedom of Information requests have helped prove the value of sharing this data. TfL is embarking on a journey to make more of this data open and available in real time.
TfL and WSO2 have been working together on broader integration projects. Roland Major and Sriskandarajah Suhothayan share the evolving big data and IoT architectures and services TfL is building to pull together these diverse datasets to better support operational teams and accelerate the identification and classification of disruption to improve response times for incidents. In particular, they explore WSO2’s solution, which emerged from the Data in Motion hackathon organized by TfL, AWS, and Geovation. The solution innovates TfL’s heterogeneous data sources through the combination of the TfL Unified API and its operational data sources, including traffic sensor, air quality, and passenger flow data, to provide better travel time and transit suggestions for Londoners and tourists using the WSO2 Data Analytics Server, WSO2 Complex Event Processor, and WSO2 API Manager, bringing together IoT and big data techniques to feed a real-time dashboard of current and predicted transport network status.
Mr. Paul Chang's presentation at QITCOM 2011QITCOM
QITCOM 2011
Presentation:
City Operations Centre for Managing City
Presenter:
Mr. Paul Chang - Business Development Executive for Emerging Markets, IBM
Optimizing Delivery Routes and Logistics with AI.pdfNiranjana P
Optimizing Delivery Routes and Logistics with AI, Artificial intelligence is revolutionizing delivery routes and logistics, transforming an age-old industry. By crunching vast amounts of data on traffic, weather, distance, and delivery windows, AI algorithms can craft the most efficient routes possible. This translates to faster deliveries, reduced costs (fuel, time, labor), and a happier customer experience. Imagine a world where your online order arrives ahead of schedule, and businesses save money on every trip. That's the power of AI in logistics.
CarStream: An Industrial System of Big Data Processing for Internet of Vehiclesijtsrd
As the Internet-of-Vehicles (IoV) technology becomes an increasingly important trend for future transportation, de-signing large-scale IoV systems has become a critical task that aims to process big data uploaded by fleet vehicles and to provide data-driven services. The IoV data, especially high-frequency vehicle statuses (e.g., location, engine parameters), are characterized as large volume with a low density of value and low data quality. Such characteristics pose challenges for developing real-time applications based on such data. In this paper, we address the challenges in de-signing a scalable IoV system by describing CarStream, an industrial system of big data processing for chauffeured car services. Photon is deployed within Google Advertising System to join data streams such as web search queries and user clicks on advertisements. It produces joined logs that are used to derive key business metrics, including billing for advertisers. Our production deployment processes millions of events per minute at peak with an average end-to-end latency of less than 10 seconds. We also present challenges and solutions in maintaining large persistent state across geographically distant locations, and highlight the design principles that emerged from our experience. Rakshitha K. S | Radhika K. R"CarStream: An Industrial System of Big Data Processing for Internet of Vehicles" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd14408.pdf http://www.ijtsrd.com/computer-science/database/14408/carstream-an-industrial-system-of-big-data-processing-for-internet-of-vehicles/rakshitha-k-s
ITS is the system defined as the electronics, advanced technology, communications or information processing used singly or integrated to enhance safety, mobility, and the economic vitality of the surface transportation system. The Intelligent Transport Systems (ITS) makes automobiles and the road traffic infrastructure intellectual and information-oriented in an integrated way to provide a safe and comfortable traffic system.
Implementing the ABT/DSM system in all power plants stabilizes the Grid frequency and ensures smooth load changes.
The system minimise penalties and increase incentives by providing Real-time DSM calculations and decision support for power generation companies.
inSis EMS provides a built-in DSM module that help power generation companies to effectively implement ABT/DSM system.
A distributed system can be viewed as an environment in which, number of computers/nodes are connected and resources are shared among these computers/nodes. But unfortunately, distributed systems often face the problem of traffic, which can degrade the performance of the system. Traffic management is used to improve scalability and overall system throughput in distributed systems using Software Defined Network (SDN) based systems. Traffic management improves system performance by dividing the work traffic effectively among the participating computers/nodes. Many algorithms were proposed for traffic management and their performance is measured based on certain parameters such as response time, resource utilization, and fault tolerance. Traffic management algorithms are broadly classified into two categories- scheduling and machine learning traffic management. This work presents the study of performance analysis of traffic management algorithms. This analysis can further help in the design of new algorithms. However, when multiple servers are assigned to compile the mysterious code, different kinds of techniques are used. One common example is traffic management. The processes are managed based on power efficiency, networking bandwidth, Processor speed. The desired output will again send back to the developer. If multiple programs have to be compiled then appropriate technique such as scheduling algorithm is used. So the compilation process becomes faster and also the other process can get a chance to compile. SDN based clustering algorithm based on Simulated Annealing whose main goal is to increase network lifetime while maintaining adequate sensing coverage in scenarios where sensor nodes produce uniform or non-uniform data traffic.
Faststream Technologies’s Fleet Management Solution Analysts offers a comprehensive solution for time tracking your fleet with Cloud backed IoT networking. With real-time access to captured data from sensors installed in vehicles, you get assured of compliance and fleet security norms. Add to it enhanced vehicle maintenance, prompt customer service, assured discipline from the drivers, and the location status.
This leading auto insurance provider chose StreamAnalytix to ingest, transform, enrich, analyze and store automotive telematics data in real time to build an end-to-end analytics application for driver profiling & individual risk assessment, and subsequently offer dynamic, usage based, plans to its customers.
Altair offers a unique set of simulation tools to evaluate product feasibility, optimize the manufacturing process, and run virtual try-outs for many traditional, subtractive, and additive manufacturing processes.
Smart Product Development: Scalable Solutions for Your Entire Product LifecycleAltair
Being connected to your products opens doors to recurring and value-based revenue streams. It not only solves your customer's toughest challenges; it also helps build a sustainable future for your company. Try SmartWorks IoT today, for free trial .
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FDOT Transportation Symposium presentation for connected and autonomous technology in highway work zones. Presented by Acey Roberts and Glenn Havinoviski, Wantman Group 1/19/21
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This talk was presented by Sriskandarajah Suhothayan (WSO2) and Roland Major (Transport for London) at the Strata Data Conference in London, May 23 2017.
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CarStream: An Industrial System of Big Data Processing for Internet of Vehiclesijtsrd
As the Internet-of-Vehicles (IoV) technology becomes an increasingly important trend for future transportation, de-signing large-scale IoV systems has become a critical task that aims to process big data uploaded by fleet vehicles and to provide data-driven services. The IoV data, especially high-frequency vehicle statuses (e.g., location, engine parameters), are characterized as large volume with a low density of value and low data quality. Such characteristics pose challenges for developing real-time applications based on such data. In this paper, we address the challenges in de-signing a scalable IoV system by describing CarStream, an industrial system of big data processing for chauffeured car services. Photon is deployed within Google Advertising System to join data streams such as web search queries and user clicks on advertisements. It produces joined logs that are used to derive key business metrics, including billing for advertisers. Our production deployment processes millions of events per minute at peak with an average end-to-end latency of less than 10 seconds. We also present challenges and solutions in maintaining large persistent state across geographically distant locations, and highlight the design principles that emerged from our experience. Rakshitha K. S | Radhika K. R"CarStream: An Industrial System of Big Data Processing for Internet of Vehicles" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd14408.pdf http://www.ijtsrd.com/computer-science/database/14408/carstream-an-industrial-system-of-big-data-processing-for-internet-of-vehicles/rakshitha-k-s
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Implementing the ABT/DSM system in all power plants stabilizes the Grid frequency and ensures smooth load changes.
The system minimise penalties and increase incentives by providing Real-time DSM calculations and decision support for power generation companies.
inSis EMS provides a built-in DSM module that help power generation companies to effectively implement ABT/DSM system.
A distributed system can be viewed as an environment in which, number of computers/nodes are connected and resources are shared among these computers/nodes. But unfortunately, distributed systems often face the problem of traffic, which can degrade the performance of the system. Traffic management is used to improve scalability and overall system throughput in distributed systems using Software Defined Network (SDN) based systems. Traffic management improves system performance by dividing the work traffic effectively among the participating computers/nodes. Many algorithms were proposed for traffic management and their performance is measured based on certain parameters such as response time, resource utilization, and fault tolerance. Traffic management algorithms are broadly classified into two categories- scheduling and machine learning traffic management. This work presents the study of performance analysis of traffic management algorithms. This analysis can further help in the design of new algorithms. However, when multiple servers are assigned to compile the mysterious code, different kinds of techniques are used. One common example is traffic management. The processes are managed based on power efficiency, networking bandwidth, Processor speed. The desired output will again send back to the developer. If multiple programs have to be compiled then appropriate technique such as scheduling algorithm is used. So the compilation process becomes faster and also the other process can get a chance to compile. SDN based clustering algorithm based on Simulated Annealing whose main goal is to increase network lifetime while maintaining adequate sensing coverage in scenarios where sensor nodes produce uniform or non-uniform data traffic.
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Car makers have to reduce consumption of vehicles and so, are continually looking for solutions to lighten components. For powertrain, components generally mean screwed assembly, contact and fitting interfaces, with different kind of loading to take into account (static and dynamic). Hence, we decided to apply with Altair assistance, a process of topology optimization on an assembly of gearbox housing in order to check its feasibility and efficiency. Several steps had to be solved from exhaustive identification of all mechanical constraints to execution of large models with Optistruct. By the end, the process has been defined and implemented on an existing gearbox and will be soon apply on the next one to design.
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Practical and hands-on experiences are doubtlessly a complementary and enriching form of educational path where it is very important the use of simulation software like HyperWorks. Team members have a real opportunity to lead their educational path by building and crafting their own thesis. Final papers are indeed part of a cluster of thesis which combines all the technological and organizational areas of development H2politO has envisioned and embraced.
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Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Analyze performance and operations of truck fleets in real time
1. ANALYZE PERFORMANCE AND OPERATIONS
OF TRUCK FLEETS IN REALTIME
Altair’s event processing and data visualization tools enable fleet operators to analyze critical data streaming in from sensors
and other sources. This real-time visibility into vehicle and driver performance helps reduce operating costs, improve driver
safety, and increase fleet productivity. Analysts can display maps showing the current position of all assets, examine route
deviations, program alerts on any set of parameters, and compare drivers’ behavior. Analysts can design and modify analytical
dashboards as needed without writing a single line of code.
Analytics Challenges for Fleet Operators
Trucks are now capable of streaming vast quantities of data from the field to headquarters. Large
numbers of sensors send telemetry on engine performance, fuel consumption, and driver behavior that
can be combined with GPS and third-party data sources like weather condition providers, police, and/
or public services to develop an understanding of road conditions, construction zones, traffic flow, and
other parameters. Using this data effectively can have a significant positive impact on the profitability
and service quality for a fleet operation, but the volume and velocity of the data make it difficult to
understand what the data is showing unless managers are equipped with the right analytics tools.
Amplify Real-Time Visibility with Artificial Intelligence
An operational view of truck and driver performance offers numerous obvious benefits, but those benefits
can be maximized by leveraging artificial intelligence (AI). AI tools that allow business users to create new
programs using drag and drop interfaces are necessary to fully exploit the fleet’s data resources.
Use cases for AI in this environment include:
• Predictive and preventative maintenance
• Remaining useful life (RUL)calculations
• Pro-active alerts for driver fatigue, delays, and potential mechanical failures
• Cost per engine hour and cost per distance traveled projections
• Profitability per journey
• Incorporation of weather data into route planning and management
Flexible, Code-Free Analytics Solutions for Decisionmakers
Fleet operations are inherently a real-time business. The people responsible for smooth operations
must be able to analyze information and respond quickly. Rigid analytics applications that require
custom coding or third-party consulting expertise to modify them can create barriers that prevent
decision-makers from identifying and mitigating small issues before they evolve into much larger,
more expensive problems.
IMPROVE FLEET
PROFITABILITY BY
LEVERAGING TELEMETRY
AND OTHER DATA
SOURCES EFFECTIVELY
AI AND REAL-TIME
VISIBILITY DELIVER
COMPLETE
OPERATIONAL VIEW
CODE-FREE SOLUTIONS
DEMOCRATIZE DATA
ANALYTICS
Altair Engineering, Inc. All Rights Reserved. / altair.com / Nasdaq: ALTR / Contact Us
SOLUTIONS
FLYER
2. A real-time dashboard like this
can process and display telemetry,
GPS, and image data for individual
vehicles and drivers as well as
aggregate and filter data for the
entire fleet. Data sets typically
include speed, engine RPM,
oil pressure, tire pressure, fuel
consumption, harsh braking
occurrences, actual speed
compared with posted speed
limits, location, traffic data,
weather and road conditions,
and much more.
Click here to watch a demo.
Business users need analytics platforms that enable them to modify their applications quickly, develop
new dashboards, and create new data processing algorithms. People who understand the questions
that must be answered and the nature of the source data are best positioned to design and update
analytics systems. The clear need is for tools that require no coding to connect to new data sources,
build and modify dashboards, and implement complex event processing algorithms.
Altair Data Analytics for Truck Fleet Monitoring
Altair enables fleet operators to develop, manage, and deploy stream processing applications
and dashboards that provide detailed views into the real-time performance of individual trucks
and drivers as well as aggregated views of groups of vehicles and drivers. Altair’s web-based,
cloud-ready tools can federate real-time data collected from individual truck gateways into
a single data stream and analyze historical data with granular detail.
Data Preparation: Access, cleanse, and format data from a wide variety of sources – including Excel,
CSV, PDF, TXT, JSON, XML, HTML, SQL databases, big data like Hadoop, and more – without any
manual data entry or coding.
Stream Processing: Connect directly to real-time sensor data being streamed over MQTT, Kafka,
Solace, and other message queues and build complex stream processing applications with
a full drag-and-drop interface.
Data Visualization: Connect to live and historical data sources and build and publish sophisticated
real-time dashboards without writing any code. Solve difficult problems quickly, understand complex
relationships in seconds, and identify issues requiring further investigation with just a few clicks.
Artificial Intelligence and Machine Learning: Altair’s industry-leading visual approach to analytic
modeling enables business users to minimize repetitive takes related to creating curated and
governed data sets, share knowledge across the enterprise, and reuse steps within connected
model workflows for faster analysis and sharing of insight.
Learn more about Altair Data Analytics at altair.com/data-analytics
Fleet managers must leverage
all the data available on vehicle
performance, driver behavior,
traffic, weather, and more so
they can respond in real time
to changing conditions. They
need agile analytics solutions
that enable them to adapt to
new challenges immediately.
Sam Mahalingam
CTO, Altair
#ONLYFORWARD
Fleet operations is a real-time business.
Decision-makers must have real-time views
of driver and vehicle performance at the
individual and aggregate levels.