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LinkGeoML – Automatic and accurate interlinking of geospatial data using machine learning

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The automatic and accurate interlinking of geospatial data poses an important scientific challenge, with direct application in several business fields. The major requirement is achieving high accuracy in identifying similar entities within datasets. For example, in a cadastral database, it is crucial that the land parcels, that were gathered from several different databases, are uniquely and clearly identified. In another example, for a geo-marketing company, it is of high importance to be able to accurately cross-reference the location/addresses of customers and companies, so that they are properly targeted. LinkGeoML aims at researching, developing and extending machine learning methods, utilizing the vast amount of available, open geospatial data, in order to implement automated and highly accurate algorithms for interlinking geospatial entities. The proposed methods will implement novel training features, based on domain knowledge and on the analysis of open and proprietary geospatial datasets.


Ackroo launches Ackroo BI

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Ackroo Inc., a loyalty marketing, gift card and payments technology and services provider, is pleased to announce that they have launched Ackroo BI, Ackroo's business intelligence data services product. As a data driven MarTech company, Ackroo now offers an end to end data solution that combines an Ackroo developed DataWarehouse for ingestion of ALL sales and transaction data, a storage and data transformation tool in order to process, store and sort the ingested information, plus an integrated data presentation and visualization tool for custom dashboards and reports. Clients can also choose to use their own visualization tool and just utilize Ackroo's Enterprise DataWarehouse and leverage the Ackroo data engineering services team to support their data needs. The solution will provide Ackroo merchants a centralized and unified data set to better understand not just their loyalty and gift card data but ALL purchase data in order to make better marketing and business decisions and to truly understand ROI. For Ackroo this means even further differentiation in the marketplace and an additional revenue stream that the Company expects will have a significant impact on their organic growth in the years ahead.


Python Vs R key differences in commands and syntaxes

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Python Vs R key differences in commands and syntaxes 5.0 (1 rating) Course Ratings are calculated from individual students' ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately. You will learn and reconcile the key differences in commands of R programming and Python. We have realized that professionals and students have to learn multiple languages to keep up to the needs of clients and organizations. R and Python are most common languages for a Data scientist/ Business Intelligence and big data developers and it often causes confusion between 2 languages. Steven is a IT/ETL data developer and data scientist and has extensive industry experience into large variety of technologies.


Labeling data for 3D object tracking and sensor fusion in Amazon SageMaker Ground Truth Amazon Web Services

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Amazon SageMaker Ground Truth now supports labeling 3D point cloud data. For more information about the launched feature set, see this AWS News Blog post. In this blog post, we specifically cover how to perform the required data transformations of your 3D point cloud data to create a labeling job in SageMaker Ground Truth for 3D object tracking use cases. Autonomous vehicle (AV) companies typically use LiDAR sensors to generate a 3D understanding of the environment around their vehicles. For example, they mount a LiDAR sensor on their vehicles to continuously capture point-in-time snapshots of the surrounding 3D environment.


Data analytics practices plagued with inefficiencies

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Data analytics practices are plagued with inefficiencies, according to a new report from automated data integration provider Fivetran. Polling circa 500 data professionals, the firm uncovered "surprising" information surrounding how data analysts spend their working days and the challenges they face. According to Fivetran, most data analysts spend less than half of the day actually analysing data. Much of the rest of the day is wasted as a result of various bottlenecks. For example, more than 60 percent reported wasting time waiting for engineering resources, multiple times a month.


NLP: Some useful notes about Text Processing

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Many times we listen to speak about machine learning, but it is important to know that there are other pipelines before machine learning which play a significant role in the study of Big Data. Some examples are ETL (extract, transform and load) or NLP(natural language processing). Nowadays, in particular, NLP pipeline is taking more and more space in Data Science. So, what is Natural Language Processing? Natural Language Processing is a process that permits Data Scientist or Data Analyst to extract important information from human language. For example, with NLP it is possible to find an important pattern by studying texts in posts or comments available on a social network.


Speech Analytics Market Future Aspect Analysis and Current Trends by 2017 to 2025 – Distinct Analysis & Reports

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Speech analytics technologies are used to extract information at customer contact points across various channels such as voice, chat, email, social channels, and surveys. Across the world, voice and phone interaction is the most common mode of communication used by consumers. Therefore, speech analytics is used in Voice User Interface (VUI) to derive insights at different contact points. In current times, organizations across various industry sectors are undertaking programs for transcripting and analyzing customer and organizational media. This is mainly to take logical decisions for customer and business management with the help of speech and text intelligence.


A deep "data lake" for coronavirus information

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An AI software provider has created a sprawling new "data lake" of information about the COVID-19 pandemic for researchers around the world. Why it matters: In just a few short months, researchers have generated an astounding amount of data about COVID-19. Putting much of that information in an easily readable source will enable researchers and policymakers to get the most out of big data. How it works: For all the rich data being produced about COVID-19, much of it is being compiled in separate silos by the government, academia and business, often in unreadable formats. Without an integrated data set, there's no easy way to produce the AI models used to analyze the many facets of the pandemic.


NVIDIA Accelerates Apache Spark, World's Leading Data Analytics Platform

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NVIDIA today announced that it is collaborating with the open-source community to bring end-to-end GPU acceleration to Apache Spark 3.0, an analytics engine for big data processing used by more than 500,000 data scientists worldwide. With the anticipated late spring release of Spark 3.0, data scientists and machine learning engineers will for the first time be able to apply revolutionary GPU acceleration to the ETL (extract, transform and load) data processing workloads widely conducted using SQL database operations. In another first, AI model training will be able to be processed on the same Spark cluster, instead of running the workloads as separate processes on separate infrastructure. This enables high-performance data analytics across the entire data science pipeline, accelerating tens to thousands of terabytes of data from data lake to model training, without changes to existing code used for Spark applications running on premises and in the cloud. "Data analytics is the greatest high performance computing challenge facing today's enterprises and researchers," said Manuvir Das, head of Enterprise Computing at NVIDIA.


Industry 4.0 Is Leading IoT Adoption in 2020, Boosting Demand for Integrated Data

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Manufacturing and processing plants might not be at the front of anyone's minds when it comes to tech adoption, but as illustrated by a recent IDC report, The Worldwide Internet of Things Spending Guide, the manufacturing industry is transforming into industry 4.0 and spearheading the adoption of IoT. Industry 4.0 is the newest industrial revolution, bringing automation, big data and AI into plants and factories around the world. One of the building blocks of industry 4.0 is the internet of things, or IoT. A recent report forecast that spending on IoT platforms would see a 40% CAGR between 2019 and 2024, resulting in spending that exceeds $12.4 billion. In 2019, leading industry corporations were expected to invest almost $200 billion in IoT solutions.