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LIDAR Magizine 2015: The Birth of 3D Mapping Artificial IntelligenceJason Creadore 🌐
Artificial intelligence (AI) has the potential to take the LiDAR
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Streetlight poles and luminaires are ideal hosts for connecting sensors. They provide fine-grained information about the urban environment, and are used to provide adaptive lighting but also feed into many municipal systems and departments. Examples include minute-by-minute traffic analytics or street-by-street air quality monitoring.
But if street lighting is to become the catalyst for “smart city” applications, lighting professionals need to understand when to harness sensor data, and when to consider application data or predictive “big data”. The world is changing and we need to take a wider view. Keith will focus on deployed use cases to help to make sense of the practical and economic implications of these important developments.
Talk by Keith Henry AMILP, Telensa
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Using FME to Automate Data Integration in a CitySafe Software
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Intelligent Mobility: Business Value of IoT and ML in LogisticsBigML, Inc
BigML’s partners, A1 Digital, introduce how the Internet of Things and Machine Learning can bring business value in Logistics.
Speaker: Francis Cepero, Head of Vertical Market Solutions at A1 Digital.
*Intelligent Mobility 2021: Virtual Conference.
Conference presentation for the 2013 International LiDAR Mapping Forum (ILMF), which was held at the Hyatt Regency Denver at Colorado Convention Center in Denver, Colorado from February 11 - 13, 2013.
In this presentation we will be discussing the business benefits for data centre power and environmental monitoring and practical steps you can take to reduce risk and increase efficiency. Richard May bio.: Richard May is the Data Centre Power SME and Country Manager for Raritan UKI and Nordics. With over 17 years’ data centre experience, specialising in rack monitoring, metering and control, Richard works to support Raritan customers and partners; helping to maximise the efficiency of their existing data centres, and developing strategies for their new facilities.
DICE project defines a quality-driven development methodology and related tools that will markedly accelerate the development of business-critical data-intensive applications running on public or private clouds. A quality engineering toolchain offering simulation, verification, and numerical optimisation will leverage these extensions to drive the early design stages of the application development and guide software quality evolution. DevOps-inspired methods for deployment, testing, continuous integration and monitoring feedback analysis will be used to accelerate the incorporation of quality in data-intensive cloud application.
Challenges in the Design of a Graph Database Benchmark graphdevroom
Graph databases are one of the leading drivers in the emerging, highly heterogeneous landscape of database management systems for non-relational data management and processing. The recent interest and success of graph databases arises mainly from the growing interest in social media analysis and the exploration and mining of relationships in social media data. However, with a graph-based model as a very flexible underlying data model, a graph database can serve a large variety of scenarios from different domains such as travel planning, supply chain management and package routing.
During the past months, many vendors have designed and implemented solutions to satisfy the need to efficiently store, manage and query graph data. However, the solutions are very diverse in terms of the supported graph data model, supported query languages, and APIs. With a growing number of vendors offering graph processing and graph management functionality, there is also an increased need to compare the solutions on a functional level as well as on a performance level with the help of benchmarks. Graph database benchmarking is a challenging task. Already existing graph database benchmarks are limited in their functionality and portability to different graph-based data models and different application domains. Existing benchmarks and the supported workloads are typically based on a proprietary query language and on a specific graph-based data model derived from the mathematical notion of a graph. The variety and lack of standardization with respect to the logical representation of graph data and the retrieval of graph data make it hard to define a portable graph database benchmark. In this talk, we present a proposal and design guideline for a graph database benchmark. Typically, a database benchmark consists of a synthetically generated data set of varying size and varying characteristics and a workload driver. In order to generate graph data sets, we present parameters from graph theory, which influence the characteristics of the generated graph data set. Following, the workload driver issues a set of queries against a well-defined interface of the graph database and gathers relevant performance numbers. We propose a set of performance measures to determine the response time behavior on different workloads and also initial suggestions for typical workloads in graph data scenarios. Our main objective of this session is to open the discussion on graph database benchmarking. We believe that there is a need for a common understanding of different workloads for graph processing from different domains and the definition of a common subset of core graph functionality in order to provide a general-purpose graph database benchmark. We encourage vendors to participate and to contribute with their domain-dependent knowledge and to define a graph database benchmark proposal.
High Performance Distributed Computing with DDS and ScalaAngelo Corsaro
The past few years have witnessed a tremendous increase in the amount of real-time data that applications in domains, such as, web analytics, social media, automated trading, smart grids, etc., have to deal with. The challenges faced by these applications, commonly called Big Data Applications, are manifold as the staggering growth in volumes is com- plicating the collection, storage, analysis and distribution of data. This paper focuses on the collection and distri- bution of data and introduces the Data Distribution Ser- vice for Real-Time Systems (DDS) an Object Management Group (OMG) standard for high performance data dissemi- nation used today in several Big Data applications. The pa- per then shows how the combination of DDS with functional programming languages, or at least incorporating functional features like the Scala programming language, makes a very natural and effective combination for dealing with Big Data applications.
Sales and Operations Planning (S&OP) at CIMSA Eray Cakici
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Using FME to Automate Data Integration in a CitySafe Software
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1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
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Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
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Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 3
Putting dTIMS Outputs In Front of Asset Managers
1. RIMS Conference 2013
Innovation Awards
Putting dTIMS outputs in front of Asset
Managers:
Improved accessibility in favourite format
Presenter:
Ken Mitchell
Opus International Consultants - Napier
RIMS Conference - March 2013
2. The Challenge…
Traditionally dTIMS outputs have been difficult to
translate back to the roading network at a project
level.
How to provide accessible treatment forecasts
GIS outputs are developing but a simple
accessible output was required to indicate where
optimised decision making could make a difference
in Road Network Asset Management
How could we do it better?
RIMS Conference March 2013
3. The Situation…
Traditionally dTIMS outputs have focussed on
network level outputs and summaries
− Reseal /Rehab/AC 10 year averages
− Condition trends : e.g.: PCI, SCI, Roughness etc.
− Investment patterns linked to FWP/Trigger and
optimised treatment forecasts
Translating this back to the project level of the
network the analysis was performed on has been
limited.
What could we do?
RIMS Conference March 2013
4. The solution - Iteration 1
Extract dTIMS budget scenario treatment forecast outputs and
work on more accessible formats for the analysis results.
Trial an excel format that Asset Managers easily access and use to
and see results at a treatment length level. Not Rocket Science
V1: Basic sorted excel sheet, some conditional colour
V2: A slightly smarter version with more filtering, summary network length
“..engineers love excel…..” Anon
Intention: Keep it simple and accessible.
Client: Wairoa District Council
RIMS Conference March 2013
5. The solution - Iteration 1 v1
-Simple searchable, sorted, colour coded excel.
RIMS Conference March 2013
6. The solution - Iteration 1 v2
-filtered, sorted, colour coded, summarised excel.
Clients: Wairoa DC and Wellington CC
RIMS Conference March 2013
7. The solution - Iteration 2
Use Excel and conditional colour for PCI (Pavement Condition
Index) condition comparison between budget scenarios.
Intent:
Show the difference between the current non-optimised FWP
and the investment limited optimised treatment forecast.
Make it easy to see relative differences in condition (Green is
good, Red is Bad)
Client: Wairoa District Council
RIMS Conference March 2013
8. Iteration 2a: PCI Rainbows
Conditional Formatting, Sorting, Stats Highlight
Not Pretty, But you see the difference
RIMS Conference March 2013
9. The solution - Iteration 3
Use Excel Pivot tables to allow asset
managers to drill down into
summarised treatment and
investment forecasts to Road,
Treatment length, Hierarchy level
Issue: You need a certain level of
excel knowledge to be comfortable
using these?
Client:
RIMS Conference March 2013
10. Iteration 3: Pivot tables:
filtered, annual summary, easy search
This is not very difficult to achieve and simple to use once you
know how…
allows rapid drilldown and summarisation
RIMS Conference March 2013
11. Iteration 2-3: Putting it on a Map
dTIMS output to RAMM GIS
Wellington City has taken the data a step further and created a
RAMM user defined table (UDT) based on these model outputs
In UDT form it is viewable as a layer in RAMM GIS.
Putting the data in a spatial format will be native in dTIMS v9 but
that is still a bit further down the track for NZ
RAMM GIS is evolving and you need to keep and eye on the
releases. Look for it on your RAMM Apps page.
RIMS Conference March 2013
12. Taking it a step further:
Kirsten Brown, WCC Asset Data Analyst
“I created the UDT to help identify roads/sections for our reseals
programme. The initial list comprises all roads that have at least one
section recommended by TSA or dTIMS for the next year, excluding
those with reseal jobs on them in the current year.
The engineers visit each road to choose which roads/sections to reseal.
The TSA and job data is in RAMM as well as all the other data the
engineers require (seal age, length, type, carriageway hierarchy,
footpath and kerb & channel condition).
By adding the dTIMS data I was able to write a single RAMM SQL that
identified the roads to check and produced a results set with all the
required data in it.”
RIMS Conference March 2013
13. WCC RAMM GIS Layer
Yellow =
Reseal, Pink =
AC
Normal RAMM
filtering
Filtering changes
display
transparency
RIMS Conference March 2013
14. RAMM GIS Layer View with Filter
• Contextual data can also be displayed.
• Layers from other sources also able to be
displayed
• Comes with your RAMM database
RIMS Conference March 2013
15. Summary: Keep it Simple & Effective
Target turning data into Thanks to
usable information Wairoa District Council
Look at what you can do and Wellington City Council
try and keep it simple where
possible.
Use tools that people can
tolerate easily if required
Build on what works and
adapt as new tools develop
and people want more.
RIMS Conference March 2013