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27 million galaxy morphologies quantified and cataloged with the help of machine learning | Penn Today – Penn Today

Research from Penns Department of Physics and Astronomy has produced the largest catalog of galaxy morphology classification to date. Led by former postdocs Jess Vega-Ferrero and Helena Domnguez Snchez, who worked with professor Mariangela Bernardi, this catalog of 27 million galaxy morphologies provides key insights into the evolution of the universe. The study was published in Monthly Notices of the Royal Astronomical Society.

The researchers used data from the Dark Energy Survey (DES), an international research program whose goal is to image one-eighth of the sky to better understand dark energys role in the accelerating expansion of the universe.

A byproduct of this survey is that the DES data contains many more images of distant galaxies than other surveys to date. The DES images show us what galaxies looked like more than 6 billion years ago, says Bernardi.

And because DES has millions of high-quality images of astronomical objects, its the perfect dataset for studying galaxy morphology. Galaxy morphology is one of the key aspects of galaxy evolution. The shape and structure of galaxies has a lot of information about the way they were formed, and knowing their morphologies gives us clues as to the likely pathways for the formation of the galaxies, Domnguez Snchez says.

Previously, the researchers had published a morphological catalog for more than 600,000 galaxies from the Sloan Digital Sky Survey (SDSS). To do this, they developed a convolutional neural network, a type of machine learning algorithm, that was able to automatically categorize whether a galaxy belonged to one of two major groups: spiral galaxies, which have a rotating disk where new stars are born, and elliptical galaxies, which are larger, and made of older stars which move more randomly than their spiral counterparts.

But the catalog developed using the SDSS dataset was primarily made of bright, nearby galaxies, says Vega-Ferrero. In their latest study, the researchers wanted to refine their neural network model to be able to classify fainter, more distant galaxies. We wanted to push the limits of morphological classification and trying to go beyond, to fainter objects or objects that are farther away, Vega-Ferrero says.

To do this, the researchers first had to train their neural network model to be able to classify the more pixelated images from the DES dataset. They first created a training model with previously known morphological classifications, comprised of a set of 20,000 galaxies that overlapped between DES and SDSS. Then, they created simulated versions of new galaxies, mimicking what the images would look like if they were farther away using code developed by staff scientist Mike Jarvis.

Once the model was trained and validated on both simulated and real galaxies, it was applied to the DES dataset, and the resulting catalog of 27 million galaxies includes information on the probability of an individual galaxy being elliptical or spiral. The researchers also found that their neural network was 97% accurate at classifying galaxy morphology, even for galaxies that were too faint to classify by eye.

We pushed the limits by three orders of magnitude, to objects that are 1,000 times fainter than the original ones, Vega-Ferrero says. That is why we were able to include so many more galaxies in the catalog.

Catalogs like this are important for studying galaxy formation, Bernardi says about the significance of this latest publication. This catalog will also be useful to see if the morphology and stellar populations tell similar stories about how galaxies formed.

For the latter point, Domnguez Snchez is currently combining their morphological estimates with measures of the chemical composition, age, star-formation rate, mass, and distance of the same galaxies. Incorporating this information will allow the researchers to better study the relationship between galaxy morphology and star formation, work that will be crucial for a deeper understanding of galaxy evolution.

Bernardi says that there are a number of open questions about galaxy evolution that both this new catalog, and the methods developed to create it, can help address. The upcoming LSST/Rubin survey, for example, will use similar photometry methods to DES but will have the capability of imaging even more distant objects, providing an opportunity to gain even deeper understanding of the evolution of the universe.

Mariangela Bernardi is a professor in the Department of Physics and Astronomy in the School of Arts & Sciences at the University of Pennsylvania.

Helena Domnguez Snchez is a former Penn postdoc and is currently a postdoctoral fellow at Instituto de Ciencias del Espacio (ICE), which is part of the Consejo Superior de Investigaciones Cientficas (CSIC).

Jess Vega Ferrero is a former Penn postdoc and currently a postdoctoral researcher at the Instituto de Fsica de Cantabria (IFCA), which is part of the Consejo Superior de Investigaciones Cientficas (CSIC).

The Dark Energy Survey is supported by funding from the Department of Energys Fermi National Accelerator Laboratory, the National Center for Supercomputing Applications, and the National Science Foundations NOIRLab. A complete list of funding organizations and collaborating institutions is at The Dark Energy Survey website.

This research was supported by NSF Grant AST-1816330.

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PODCAST: rise of the machine (learning) – BlueNotes

Jason is working on a few of the complex processes weve been wanting to automate for some time now and hes seeing some positive results.

[Were] looking to automate the home loan process - very document driven - trying to condense that, trying to extract data they can send into our decision systems for me to make a decision, Jason says.

The really exciting part is in today's world, using the old school techniques [such as neutral networks and gradient boosted models], we can make a decision after all those processes have been conducted within four seconds.

A faster decision means customers dont need to find supplementary documentation or spend time waiting for approval. They can get their answer and focus on whats important: getting into their new home.

But its not just the home loan process thats seen the benefit of new technologies. Our Institutional team has been using machine learning for the past few years and Sreeram says even three years ago the team saw the promise the tool held. Now, theyre seeing results.

I'm excited because it is really good for our staff. You know, there's so much value added from an individual point of view because banking can be notoriously paper intensive, he says.

This is a combination of technologies and capabilities. The machine nowthe transfer of paper to image, the quality and accuracy of imaging, the ability to read, the ability to interpret and then the ability to process; this is coming together for the first time, at least in my career.

We have seen cases where 50 per cent of the manual effort before has been. We have seen cases where our internal times have improved roughly 40 to 50 per cent. So I think it's absolutely made things better.

Although Sreeram reminds us that comes with its challenges and caution and management of governance, as with any technology.

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Rackspace Technology Works with Brave Software to Improve Machine Learning Functionality in the Web Browser – Yahoo Finance

SAN ANTONIO, April 08, 2021 (GLOBE NEWSWIRE) -- Rackspace Technology (NASDAQ: RXT), a leading end-to-end, multicloud technology solutions company, announced today its relationship with Brave Software which provides a free, open-source private and secure web browser for PC, Mac, and mobile environments.

Brave gives users a fast and private web experience, helps advertisers achieve better conversions and increases publishers revenue share. "Its machine learning functionality helps to match advertisements in the Brave Ads content categories for which Brave users would have the most interest, while preserving user privacy

Brave worked with AWS Premier Consulting Partner, Onica, a Rackspace Technology company, to improve the scalability of Braves software, increased the teams efficiency, and reduced infrastructure costs by 50 percent. Rackspace Technology used a wide range of AWS services to build cloud infrastructure tailored to Braves needs.

Before working with Rackspace Technology, Braves processes for training and deploying machine learning models were slower, involving manual steps spanning several days. Brave needed a more robust pipeline and fully automated processes.

Working with Rackspace Technology and AWS was beneficial to the continued success and scaling of Brave, said Jimmy Secretan, VP of Services and Operations, Brave Software. It substantially improved the way we created and deployed new models, which has helped us to be much more responsive to advertisers needs."

Rackspace Technology is one of only a few providers to have achieved AWS Machine Learning Competency status, said Jeff Deverter, CTO, Solutions at Rackspace Technology. This unique combination of expertise in AWS services and machine learning made us an ideal partner for Brave.

To learn more about Rackspace Technologys work and capabilities please visit

About Rackspace TechnologyRackspace Technology is a leading end-to-end multicloud technology services company. We can design, build and operate our customers cloud environments across all major technology platforms, irrespective of technology stack or deployment model. We partner with our customers at every stage of their cloud journey, enabling them to modernize applications, build new products and adopt innovative technologies.

Story continues

Media ContactNatalie SilvaRackspace Technology Corporate

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i.MX 8M plus eval kit with machine learning and voice and vision capabilities – Electropages

09-04-2021 | Mouser Electronics | Design & Manufacture

Mouser now stocks the i.MX 8M Plus evaluation kit from NXP Semiconductors. The comprehensive kit offers a complete evaluation platform for the new i.MX 8M Plus embedded multi-core heterogeneous applications processors the first in the family to combine a dedicated NPU for advanced machine learning inference at the edge in industrial and IoT applications.

The device plus evaluation kit comprises a compact compute module with onboard i.MX 8M Plus Quad processor and a larger baseboard bring out the wide connectivity required for product evaluation. The processor integrates four Arm Cortex-A53 cores running at up to 1.8GHz, plus an 800MHz Arm Cortex-M7 core for low-power real-time processing. Utilising the integrated NPU, the i.MX 8M Plus processor can simultaneously identify multiple highly complex neural network functions, including human pose and emotion detection, multi-object surveillance, and the recognition of more than 40,000 English words.

The kit is excellent for furthering designs in applications such as surveillance, robot vision, smart retail, home health monitors, building control, smart home, smart city, and industrial IoT.

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Machine Learning in Healthcare Market: Find Out Essential Strategies to expand The Business and Also Check Working in 2021-2029 KSU | The Sentinel…

The recently released report byMarket Research Inctitled as Global Machine Learning in Healthcaremarket is a detailed analogy that gives the reader an insight into the intricacies of the various elements like the growth rate, and impact of the socio-economic conditions that affect the market space. An in-depth study of these numerous components is essential as all these aspects need to blend-in seamlessly for businesses to achieve success in this industry.

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Top key players::

Intel Corporation, IBM Corporation, Nvidia Corporation, Microsoft Corporation, Alphabet Inc (Google Inc.), General Electric (GE) Company, Enlitic, Inc., Verint Systems, General Vision, Inc., Welltok, Inc., iCarbonX

The geographical segmentation includes study of global regions such asNorth America, Latin America, Asia-Pacific, Africa, Middle Eastand Europe. The report also draws attention to recent advancements in technologies and certain methodologies which further help to boost the outcome of the businesses. Furthermore, it also offers a comprehensive data of cost structure such as the cost of manpower, tools, technologies, and cost of raw material. The report is an expansive source of analytical information of different business verticals such as type, size, applications, and end-users.

This market research report on the Global Machine Learning in HealthcareMarket is an all-inclusive study of the business sectors up-to-date outlines, industry enhancement drivers, and manacles. It provides market projections for the coming years. It contains an analysis of late augmentations in innovation, Porters five force model analysis and progressive profiles of hand-picked industry competitors. The report additionally formulates a survey of minor and full-scale factors charging for the new applicants in the market and the ones as of now in the market along with a systematic value chain exploration.

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Additionally, this report recognizes pin-point investigation of adjusting competition subtleties and keeps you ahead in the competition. It offers a fast-looking perception on different variables driving or averting the development of the market. It helps in understanding the key product areas and their future. It guides in taking knowledgeable business decisions by giving complete constitutions of the market and by enclosing a comprehensive analysis of market subdivisions. To sum up, it equally gives certain graphics and personalized SWOT analysis of premier market sectors.

Rendering to the research report, the global Machine Learning in Healthcaremarket has gained substantial momentum over the past few years. The swelling acceptance, the escalating demand and need for this markets product are mentioned in this study. The factors powering their adoption among consumers are stated in this report study. It estimates the market taking up a number of imperative parameters such as the type and application into consideration. In addition to this, the geographical occurrence of this market has been scrutinized closely in the research study.

Further information:

In this study, the years considered to estimate the size ofMachine Learning in Healthcareare as follows:

History Year: 2015-2019

Base Year: 2020

Forecast Year 2021 to 2029.

Table of Contents:

Machine Learning in Healthcare Market Overview

Impact on Machine Learning in Healthcare Market Industry

Machine Learning in Healthcare Market Competition

Machine Learning in Healthcare Market Production, Revenue by Region

Machine Learning in Healthcare Market Supply, Consumption, Export and Import by Region

Machine Learning in Healthcare Market Production, Revenue, Price Trend by Type

Machine Learning in Healthcare Market Analysis by Application

Machine Learning in Healthcare Market Manufacturing Cost Analysis

Internal Chain, Sourcing Strategy and Downstream Buyers

Marketing Strategy Analysis, Distributors/Traders

Market Effect Factors Analysis

Machine Learning in Healthcare Market Forecast (2021-2029)


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Market Research Inc is farsighted in its view and covers massive ground in global research. Local or global, we keep a close check on both markets. Trends and concurrent assessments sometimes overlap and influence the other. When we say market intelligence, we mean a deep and well-informed insight into your products, market, marketing, competitors, and customers. Market research companies are leading the way in nurturing global thought leadership. We help your product/service become the best they can with our informed approach.

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Graphs, quantum computing and their future roles in analytics – TechRepublic

Graphs are used in mathematics, engineering and computer science, and they are growing as a technology in IT analytics. Here's how they relate to quantum computing.

Image: iStock/monsitj

A graph is a collection of points, called vertices, and lines between those points, are called edges. Graphs are used in mathematics, engineering and computer science, and they are growing as a technology in IT analytics.

"Graphs can be much more flexible than other [artificial intelligence] techniques, especially when it comes to adding new sources of data," said Steve Reinhardt, VP of product development at Quantum Computing Inc., which produces quantum computing software that operates on graphs. "For instance, if I'm storing patient data and I want to add a dimension to track the unlikely event of testing positive for coronavirus after being vaccinated, graphs only consume storage proportional to the number of patients encountering the rare event."

SEE: The CIO's guide to quantum computing (free PDF) (TechRepublic)

Graphs can be heady stuff, so let's break that down.

A database software, such as SQL or NoSQL, would be a logical technology to use if you want to plot the many different relationships between data. Analytics programs then operate on this data and how it is interrelated to derive insights that answer a specific business query.

Unfortunately, to process all of the data relationships in Reinhardt's patient example, a relational database must go through all patient records and store them in order to identify that subset of patients who tested positive for the coronavirus after being vaccinated. For an average hospital, this processing could involve hundreds of thousands of patient records and all of their multiple relationships to the coronavirus and the vaccine.

Now let's put that same problem into a graph. The graph uses data points, lines connecting those points and vertices which show where the lines intersect because they have a common shared context. This shared context enables the graph to identify a subset of patients who tested positive for COVID-19 after they had a vaccine and only store that subset of data for processing. Because a graph can intelligently identify a subset of data through its relationships before data gets processed, processing time is saved.

SEE: Big data graphs are playing an important role in the coronavirus pandemic (TechRepublic)

As IT expands into more data sources for its analytics and data stores, processing will grow more complex and cumbersome. This is where a combination of graphs and quantum computing will one day be able to process data faster than traditional methods.

"Graphs have a rich set of well-understood techniques for analyzing them," Reinhardt said. "Some of these are well-known from analyzing graphs that occur naturally, such as the PageRank algorithm that Google originally used to gauge the importance of web pages, and the identification of influencers in social networks. This is why we are focused on making these algorithms more practically usable."

That sounds good to IT, where there is an issue of understanding enough about graphs and quantum computing to put them to use.

SEE: Research: Quantum computing will impact the enterprise, despite being misunderstood (TechRepublic)

"The goal is to develop solutions so users need to know nothing about the details of quantum computers, including low-level architectural features such as qubits, gates, circuits, couplers and QUBOs," Reinhardt said. "Today, quantum processors are almost never faster than the best classical methods for real-world problems, so early users need to have appropriate expectations. That said, the performance of quantum processors has been growing dramatically, and the achievement of quantum advantage, superior quantum performance on a real-world problem, may not be far off, so organizations that depend on a computing advantage will want to be prepared for that event."

And that is the central point: While graphs and quantum computing are still nebulous concepts to many IT professionals, it isn't too early to start placing them on IT roadmaps, since they will certainly play roles in future analytics.

Learn the latest news and best practices about data science, big data analytics, and artificial intelligence. Delivered Mondays

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615 Million Euros Awarded to Quantum Delta NL for Quantum Research in the Netherlands – HPCwire

April 9, 2021 Quantum Delta NL, a research programme in which Leiden University participates, has been awarded 615 million euros from the National Growth Fund to help develop the Netherlands into a top player in quantum technology. This has been announced at the presentation of the honoured proposals in The Hague.

Quantum Delta NL is a cooperation of companies and research institutes in which the research has been organised in five hubs at the universities of Delft, Leiden, Amsterdam, Twente and Eindhoven.

The research groupApplied Quantum Algorithms (aQa)at the Leiden institutes for physics and computer science develops quantum algorithms for chemical and material science applications, in cooperation with Google, Shell, Volkswagen and Total.

Great enthusiasm

Research into quantum computing has been going on for twenty years, bringing real world application ever closer, says Carlo Beenakker, professor in Theoretical Physics and Deputy Chair of Quantum Delta NL. I seegreat enthusiasm in my students to apply abstract concepts from quantum physics to the solution of practical problems. This is the revolutionary technology of their generation.

The goal of aQa is to make quantum algorithms practically applicable, pertaining to questions ofsocietal and economical relevance. We cooperate narrowly with our industrial partners to render these large investments as useful as possible, says computer science researcher Vedran Dunjko. Recently, he published in the journal Natureabout artificial intelligence implemented through quantum computers.

Quantum technology

Quantum Delta NLs ambition is to position the Netherlands as a Silicon Valley for quantum technology in Europe during the coming seven years. The programme provides for the further development of the quantum computer and the quantum internet, which will be open for end users in business and societal sectors, including education.

It aims for a flourishing ecosystem where talent is fostered at all levels, and where cooperation happens over institutional borders to develop a new European high-tech industry.

Source: Leiden University

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Data Mining Tools Market 2021 Is Booming Across the Globe by Share, Size, Growth, Segments and Forecast to 2027 | Top Players Analysis- IBM, SAS…

The Global Data Mining Tools Market report dissects the complex fragments of the market in an easy to read manner. This report covers drivers, restraints, challenges, and threats in the Data Mining Tools market to understand the overall scope of the market in a detailed yet concise manner. Additionally, the market report covers the top-winning strategies implemented by major industry players and technological advancements that steers the growth of the market.

Key Players Landscape in the Data Mining Tools Report

IBMSAS InstituteOracleMicrosoftTeradataMathWorksH2O.aiIntelAlteryxSAPRapidminerKnimeFICOSalford SystemsBlueGraniteAngoss SoftwareMegaputer IntelligenceBiomax InformaticsFrontline SystemsSuntec IndiaDataikuWolfram ResearchReltioSenticNetBusiness Insight

Note: Additional or any specific company of the market can be added in the list at no extra cost.

Here below are some of the details that are included in the competitive landscape part of the market report:

This market research report enlists the governments and regulations that can provide remunerative opportunities and even create pitfalls for the Data Mining Tools market. The report confers details on the supply & demand scenario in the market while covering details about the product pricing factors, trends, and profit margins that helps a business/company to make crucial business decisions such as engaging in creative strategies, product development, mergers, collaborations, partnerships, and agreements to expand the market share of the company.

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An Episode of Impact of COVID-19 Pandemic in the Data Mining Tools Market

The COVID-19 pandemic had disrupted the global economy. This is due to the fact that the government bodies had imposed lockdown on commercial and industrial spaces. However, the market is anticipated to recover soon and is anticipated to reach the pre-COVID level by the end of 2021 if no further lockdown is imposed across the globe.

In this chapter of the report, DataIntelo has provided in-depth insights on the impact of COVID-19 on the market. This chapter covers the long-term challenges ought to be faced due to the pandemic while highlights the explored opportunities that benefited the industry players globally. The market research report confers details about the strategies implemented by industry players to survive the pandemic. Meanwhile, it also provides details on the creative strategies that companies implemented to benefit out of pandemic. Furthermore, it lays out information about the technological advancements that were carried out during the pandemic to combat the situation.

What are the prime fragments of the market report?

The Data Mining Tools report can be segmented into products, applications, and regions. Here below are the details that are going to get covered in the report:




BFSIHealthcare and Life SciencesTelecom and ITGovernment and DefenseEnergy and UtilitiesManufacturingOthers


North America, Europe, Asia Pacific, Middle East & Africa, and Latin America

Note: A country of your own choice can be added to the list at no extra cost. If more than one country needs to be added, the research quote varies accordingly.

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Below is the TOC of the report:

Executive Summary

Assumptions and Acronyms Used

Research Methodology

Data Mining Tools Market Overview

Global Data Mining Tools Market Analysis and Forecast by Type

Global Data Mining Tools Market Analysis and Forecast by Application

Global Data Mining Tools Market Analysis and Forecast by Sales Channel

Global Data Mining Tools Market Analysis and Forecast by Region

North America Data Mining Tools Market Analysis and Forecast

Latin America Data Mining Tools Market Analysis and Forecast

Europe Data Mining Tools Market Analysis and Forecast

Asia Pacific Data Mining Tools Market Analysis and Forecast

Asia Pacific Data Mining Tools Market Size and Volume Forecast by Application

Middle East & Africa Data Mining Tools Market Analysis and Forecast

Competition Landscape

If you have any doubt about the report, please feel free to contact us @

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DataIntelo has extensive experience in the creation of tailored market research reports in several industry verticals. We cover in-depth market analysis which includes producing creative business strategies for the new entrants and the emerging players of the market. We take care that our every report goes through intensive primary, secondary research, interviews, and consumer surveys. Our company provides market threat analysis, market opportunity analysis, and deep insights into the current and market scenario.

To provide the utmost quality of the report, we invest in analysts that hold stellar experience in the business domain and have excellent analytical and communication skills. Our dedicated team goes through quarterly training which helps them to acknowledge the latest industry practices and to serve the clients with the foremost consumer experience.

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Data Mining Tools Market 2021 Is Booming Across the Globe by Share, Size, Growth, Segments and Forecast to 2027 | Top Players Analysis- IBM, SAS...

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Data Mining Software Market is Flourishing at Healthy CAGR with Growing Demand, Industry Overview and Forecast to 2026 The Bisouv Network – The…

Global Data Mining Software Market report provides a scrupulous overview of the global market by closely analyzing a range of factors relating to the Data Mining Software market, such as key segments, regional market trends, market dynamics, suitability for investment, and key market players. In addition, the analysis offers sharp insights into current and future trends & developments in the global market for Data Mining Software .

Objectives of the Report

Get a Sample Copy of the Data Mining Software Market Report 2021-2026 Including TOC, Figures, and [emailprotected] Mining Software -market

A Brief Outlook of the Leading Organizations in the Data Mining Software market, Focusing on Companies such as

Data Mining Software Market: Product Type Segment Analysis:

Data Mining Software Market: Application Segment Analysis:

Leading regions covered in this research report:

North America (United States, Canada, and Mexico), Europe (Germany, France, UK, Russia, and Italy), Asia-Pacific (China, Japan, Korea, India and Southeast Asia), South America (Brazil, Argentina, Colombia, etc.), Middle East & Africa (Saudi Arabia, UAE, Egypt, Nigeria, and South Africa).

Also, the Data Mining Software Market report provides a Detailed Analysis of Market-Definitions, Classifications, Applications, and Market Overview;product specifications; manufacturing processes; cost structures, raw materials, and so on. The report considers theimpact of the novel COVID-19 pandemic on the Data Mining Software marketwith Competitive Intensity and How the Competition Will Take Shape in Coming Years. The report also covers the trade scenario, Porters Analysis, PESTLE analysis, value chain analysis, and company market share.

For more Customization, Connect with us at Mining Software -market

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Data Mining Software market along with Report Research Design:

Data Mining Software Market Historic Data (2015-2020):

Data Mining Software Market Influencing Factors:

Data Mining Software Market Forecast (2021-2026):

If you are an investor/shareholder in the Data Mining Software Market, the provided study will help you to understand the growth model of the Data Mining Software Industry after the impact of COVID-19. Request for sample report Mining Software -market

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Datamine expands CIS presence with acquisitions of GeoMineSoft and Database Systems – International Mining

Posted by Paul Moore on 8th April 2021

Mining software solutions major Datamine recently acquired GeoMineSoft and Database Systems LLP, leading firms in geology and mine consulting serving clients in Kazakhstan and Russia. GeoMineSoft and Database Systems LLP provide a range of software solutions designed for the mining industry, in addition to specialised consulting for operations.

With a range of solutions from field data collection with mobile access, to laboratory process automation, automation of production processes , and management of mining and geological data GeoMineSoft supports the mine value chain in accessing deeper insights and process improvements in their operations. The company says it offers: Software solutions for collecting, storing and managing data for enterprises operating in the exploration and mining industries and development and implementation of software solutions for the automation of production processes of enterprises.

It is best known for MineVision, a flexible and efficient platform for collecting, verifying, storing and managing data from a variety of sources. This solution plays the role of an integrating link between various software systems used in the enterprise, and also connects various divisions of the enterprise, ensuring the continuity and integrity of the flow of incoming data.

Recent GeoMineSoft work includes in 2019-2020a project for the implementation of the MineVision mining and geological data management system at the Mine of the Decade of Independence of Kazakhstan of TNK Kazchrome JSC). It covered exploration and exploration data management plus implementation of mobile electronic documentation for core drilling and underground mine workings using GeoSearch CORE and GeoSearch UG solutions.

GeoMineSoft will continue to be led by their knowledgeable team of experts, including Managing Director, Ablaykhan Shapenov, and Irina Kim remains as Managing Director at Database Systems LLP. Having successfully completed projects in Kazakhstan and in Russia since their initiation in 2015, GeoMineSoft will provide local insights and additional support to the Datamine network, and gain access to the international team and expertise of the Datamine group of 28 offices in 20 countries through this acquisition.


Datamine expands CIS presence with acquisitions of GeoMineSoft and Database Systems - International Mining

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