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mining surplus | new and used mining equipment

mining surplus | new and used mining equipment features new and used mining equipment for sale from mining operations across Canada, the United States, South America, and Australia. profiles surface, mill plant process and underground mining equipment from copper, lead, zinc, gold and coal mining operations. Please use the search tools below to search our new and used mining equipment and parts listings. Mining Equipment Search Search our equipment listings or select a category below to browse our listings. Equipment Search MobileDozers (71)Excavators (50)Graders (28)Haul Trucks (53)Loaders (38)Rotary Drills (4)Scrapers (29)Shovels (32)Other (66)UndergroundRock Bolters (3)Scissor Lifts (2)Scooptrams (22)Utility Vehicles (9)U/G Drills (13)U/G Trucks (19)Boom Trucks (2)Ventilation (10)Other (8)Mill ProcessConveyors (5)Crushers (9)Feeders & Screens (1)Filters & Thickeners (1)Flotation (1)Grinding Mills (10)Pumps (4)Other (8)SupportAir Compressors (2)Backhoes (1)Forklifts (5)Generators (2)Mobile Cranes (35)Tires (11)Trucks (62)Welders (1)Other (47)ElectricalHV SwitchGear (2)Instrumentation (1)Motors (22)Other (2)OtherAssay & Laboratory (5)Attachments (21)Tanks (4)OtherBuildings (7)Complete Plants (3)Construction (24)Major Components (15)Miscellaneous (10)Safety (1) Surplus Parts Search To search for parts, use the search field below or try our advanced search. Parts Search Mining Surplus proudly lists equipment from the following mining companies. Featured Listings Support - Mobile CranesRough Terrain RT875E CranePrice: NegotiableMore InfoUnderground - ScooptramsEJC 210 Scooptram Price: NegotiableMore InfoOther - Major ComponentsD10T Caterpillar Dozer Push ArmsPrice: NegotiableMore InfoUnderground - U/G DrillsSimba H1254 U/G DrillPrice: $120,000.00More InfoSupport - ForkliftsGHL ZOOM BOOM ZB10044Price: NegotiableMore InfoMobile - DozersD11R DOZERPrice: NegotiableMore InfoUnderground - Utility VehiclesJCB 214Price: NegotiableMore InfoUnderground - U/G DrillsSimba H1254 U/G DrillPrice: $120,000.00More Info

used underground mining equipment for sale. freightliner equipment & more | machinio

used underground mining equipment for sale. freightliner equipment & more | machinio

Product Description Efficient and rapid detection of underground water sources, detectors for underground water wells FET series of multi-function earth electromagnetic field survey instrument-single electrode r...

OC-XYC-3 Hydraulic Water Truck Mounted Well Underground Mining Equipment Deep Hole Core Drilling Rig Product Description Brief Introduction of Core Drilling Rig: OC- XYC-200GT,XYC-3 truck-mounted drilling rig is...

Product Description FLA-130 130m small underground pneumatic rock bore well drilling rig with compressor FLA series well drilling rig with compressor is a type of light, high efficient and multifunctional drillin...

Product Description Yugong is specialized in the rock splitting machine, including the excavator mounted rock splitter and small hydraulic rock splitter .The hydraulic stone splitter machine is a tool to break r...

Technical Parameters Yugong is specialized in the rock splitting machine, including the excavator mounted rock splitter and small hydraulic rock splitter . The hydraulic stone splitter machine is a tool to break ...

China Drilling Rig Supplier DFU-300 Portable Underground Mining Equipment I .GeneralIntroduction DFU- 3 00 hydraulicundergrounddrillrigismainlyappliedinthefieldsofcoal,geology,metallurgyand engine...

Product Description Steel cable slow speed electric winch for lift JD Series Shaft Sinking Winch / Underground Mining Winch Electric winch is drum winding wire rope, is one kinds of small lifting equipment. It ca...

KD-10A load haul dumper KD-10AOverallperformanceparameters Dimension:LWH 772019502200mm Bucket:(Stackloading)5m3 DumpingReachAngle: 70 Turningangle: 40 SwingAngleofRearAxles8...

Low price Electric type underground deep water Borehole Drilling Machine for sale Product Description Introduction of Low price Electric type underground deep water Borehole Drilling Machine for sale 1. Easy to t...

Product Description Introduction of agriculture /farm electric water well drilling machines /cheap underground water finding machine 1. water well drilling machine has a hydraulic automatic feed mechanism, improv...

OC-XY-8 Underground Gold Mine Diamond Sampling Core Drilling Rig Machine Product Description Brief Introduction of Core Drilling Rig: OC-XY-8 drilling rig is a core drilling rig developed with the advantages of ...

Company Profile Luoyang MaiCheng Machinery Equipment Co., Ltd.,founded in2016 and located in the Industrial Cluster area of Yichuan, Luoyang, covers an area of more than 60,000 square meters, isan independent ...

F UK-12 Underground Truck Underground mining equipment Dump Truck Fuk-12 Main Configurations: DieselEngine: Brand: Germany DEUTZModel: BF6M1013FWType: Water coolingRated Power: 165KW/2...

how a data mining approach for search engine optimization works

how a data mining approach for search engine optimization works

Data mining in Search Engine Optimization is a new concept and has gained importance in the digital marketing field. It can be understood as a process that can be used for extracting useful information from a large amount of data. In other words, data mining is a process that can be used by companies for converting raw data into useful data with the help of a software.

Data Miningcan be understood as a set of methodologies that are used in analyzing data from different perspectives and dimensions for finding out previously unknown hidden patterns. This helps in classifying and grouping the data to create a summary of the identified relationships. Data mining tasks are divided into two parts:

Data mining and Big Data have become umbrella words that sum up the modern reality. Data mining focuses on evaluating the vast amount of data for discovering interesting trends and values that can be further used for improving efficiencies within an enterprise. The applications of data mining in a firm can be:

Data mining is being applied in different sectors and occupations. Today, customers are comfortable with organizations recording their online behavior and expect them to automate their communication experiences with the help of data mining. Rather, for many consumer-facing organizations, the ability to correctly predict a customers behavior is turning out to be a competitive edge in the industry. Today, organizations need to make use of the enhanced consumer understanding for driving customer service, successful marketing, product satisfaction, and ultimately growth.

There are several big data organizations and companies out there. However, Google is widely considered as a major one and thus, it is easy to expect that it would have a major influence on the search. There are four ways in which Big Data is changing SEO. These can be understood through the below points:

Generally, content is nothing more than a published piece of information. However, with Google emerging as one of the major curators of big data, content is continuously being thought of as a quantifiable entity by Google data mining tools. Thus, by converting content into data, search engines are easily able to analyze the data and deliver more relevant answers to the users.

This way, the results from Google are becoming more structured. The detailed analysis of semantic information has helped Google in developing rich snippets, local search packs as well as other unique results that provide more valuable results to the searchers.

Google and other search engines are apprehensive about reassembling content into quantifiable data. This is making it easier for marketers to gain insight into the type of data that the users search for. With the help of big data, digital marketers can easily keep a track as well as analyze the keyword, backlinks, on-page optimization, as well as other important search areas for optimizing their efforts.

The ultimate goal of an SEO or an online marketer is to increase conversions. This makes it important for them to find a correlation between the elements and other analytics, such as conversions, traffic, and page views for the business as well as the competitors.

With the help of Bing Webmaster Tools, Google Search Console, as well as other analytics systems, digital marketers can check how their web content is placed in the large network of content data. This helps in making suitable adjustments to the content based on its performance. Google Analytics data makes it easier for digital marketers to keep a track of the clicks, conversions, time on page, backlinks, as well as other important search factors.

Social media is another major source of getting insights into big data. Facebook has around 1 billion users whereas Twitter has more than 500 million users. Also, the web hosts around 156 million public blogs that are seeking to network on such platforms. Since Google is a big data company that is processing more than 20 PB of data every year, it cannot afford to ignore such information. Thus, signals from social media are an important factor for search and cannot be ignored by search engines.

Data mining is of paramount importance in SEO as with the help of data mining, a business can easily understand the behavior pattern of its customers and target them accordingly. Below are some points that show the level of importance of data mining for a business:

By leveraging the benefits of data mining, a business can easily understand its customers. This can be done by collecting useful information about the customers based on their previous search history and shopping preferences. This gives a good idea about the needs and wants of your customers. After understanding the customer better, you can apply the right efforts to entice the customers into buying your products or services.

A business can easily target the target audience with the help of data mining. Data mining gives a fair idea about your relevant and target customers. By knowing and understanding the likes and dislikes of the customers, a business can target them better with the help of customized techniques. This also enhances user experience and level of customer satisfaction.

The process of data mining helps a business in identifying customers who are attracted to a specific product and whether they can susceptible to purchasing the product or not. After gaining knowledge about the who and what of customer loyalty, one can use data mining for extracting the data and increasing the level of customer involvement. This also improves marketing actions and helps in acquiring new customers.

Search engines such as Google are keeping most of their analyses well hidden from the general public, which is the main reason why many marketers do not realize the real importance of big data on SEO. The use of data mining in web search engine helps in analyzing the content and at the same time delivering results that are relevant for the users. As a result, digital marketers who are focused on creating valuable content for users sure to benefit from the impact of data mining on SEO.

reading: mining and mineral use | geology

reading: mining and mineral use | geology

Some minerals are very useful. An ore is a rock that contains minerals with useful elements. Aluminum in bauxite ore (figure 1) is extracted from the ground and refined to be used in aluminum foil and many other products. The cost of creating a product from a mineral depends on how abundant the mineral is and how much the extraction and refining processes cost. Environmental damage from these processes is often not figured into a products cost. It is important to use mineral resources wisely.

Geologic processes create and concentrate minerals that are valuable natural resources. Geologists study geological formations and then test the physical and chemical properties of soil and rocks to locate possible ores and determine their size and concentration.

Surface mining allows extraction of ores that are close to Earths surface. Overlying rock is blasted and the rock that contains the valuable minerals is placed in a truck and taken to a refinery. As pictured in figure 2, surface mining includes open-pit mining and mountaintop removal. Other methods of surface mining include strip mining, placer mining, and dredging. Strip mining is like open pit mining but with material removed along a strip.

Placers are valuable minerals found in stream gravels. Californias nickname, the Golden State, can be traced back to the discovery of placer deposits of gold in 1848. The gold weathered out of hard metamorphic rock in the western Sierra Nevada, which also contains deposits of copper, lead, zinc, silver, chromite, and other valuable minerals. The gold traveled down rivers and then settled in gravel deposits. Currently, California has active mines for gold and silver and for non-metal minerals such as sand and gravel, which are used for construction.

Underground mining is used to recover ores that are deeper into Earths surface. Miners blast and tunnel into rock to gain access to the ores. How underground mining is approached from above, below, or sideways depends on the placement of the ore body, its depth, concentration of ore, and the strength of the surrounding rock.

The ores journey to becoming a useable material is only just beginning when the ore leaves the mine (figure 3). Rocks are crushed so that the valuable minerals can be separated from the waste rock. Then the minerals are separated out of the ore. A few methods for extracting ore are:

To extract the metal from the ore, the rock is melted at a temperature greater than 900C, which requires a lot of energy. Extracting metal from rock is so energy intensive that if you recycle just 40 aluminum cans, you will save the energy equivalent of one gallon of gasoline.

Although mining provides people with many needed resources, the environmental costs can be high. Surface mining clears the landscape of trees and soil, and nearby streams and lakes are inundated with sediment. Pollutants from the mined rock, such as heavy metals, enter the sediment and water system. Acids flow from some mine sites, changing the composition of nearby waterways (figure 4).

U.S. law has changed so that in recent decades a mine region must be restored to its natural state, a process called reclamation. This is not true of older mines. Pits may be refilled or reshaped and vegetation planted. Pits may be allowed to fill with water and become lakes or may be turned into landfills. Underground mines may be sealed off or left open as homes for bats.

Some minerals are valuable because they are beautiful. Jade has been used for thousands of years in China. Diamonds sparkle on many engagement rings. Minerals like jade, turquoise, diamonds, and emeralds are gemstones. A gemstone, or gem, is a material that is cut and polished for jewelry. Many gemstones, including many in figure 5, are minerals.

Gemstones are usually rare and do not break or scratch easily. Most are cut along cleavage faces and then polished so that light bounces back off the cleavage planes (figure6). Light does not pass through gemstones that are opaque, such as turquoise.

Gemstones are not just used in jewelry. Diamonds are used to cut and polish other materials, such as glass and metals, because they are so hard. The mineral corundum, of which ruby and sapphire are varieties, is used in products such as sandpaper.

Minerals are used in much less obvious places. The mineral gypsum is used for the sheetrock in homes. Window glass is made from sand, which is mostly quartz. Halite is mined for rock salt. Copper is used in electrical wiring, and bauxite is the source for the aluminum used in soda cans.

text mining in banking a brief overview of capabilities | emerj

text mining in banking a brief overview of capabilities | emerj

Raghav is serves as Analyst at Emerj, covering AI trends across major industry updates, and conducting qualitative and quantitative research. He previously worked for Frost & Sullivan and Infiniti Research.

Historically, banks have collected large volumes of data about customers and their own internal operations. A significant portion of these documents is textual information recorded mostly for compliance reasons.

This data might be siloed for different business functions inside a bank, and effective information search and discovery is challenging in its traditional form. AI systems could help navigate through the vast data stores to gain valuable business insights.

This was possible because the information that these early systems had to read and understand was relatively simple and highly structured. With NLP, the scope of what information can be made searchable has grown to include both semi-structured and unstructured free form text.

The graphic below shows the number of AI vendor product offering functions in our banking research. The column labeled Data Collection and Analysis (Text Data) shows the number of vendor products that were applicable to leveraging free form natural language text data.

Data Collection and Analysis (Text Data) accounted for 13.48% of all the AI vendor product offering functions we categorized in our report and 11.49% of the total funding raised by AI vendors in banking.

As an example, banks could use NLP-based software to search for specific information from internal legal documents. With an AI solution, users across the bank could search for only finance-related or fraud-related excerpts from these documents.

Banks could deploy an NLP solution wherein they first need to upload existing enterprise documents to the AI system. The algorithms are then tweaked to recognize attributes in these documents that are marked as being relevant for extraction, summarization, or abstraction.

Once the system is tested and deployed, employees at the bank could contextually search for information on a dashboard. The NLP algorithms can identify if the search queries are relevant based on the previously tagged datasets (usually called meta-data).

For example, if a bank has several million responses from customers to their open-ended text-based feedback form, NLP might help in cutting down the time taken to review these messages and unearth new insights about what customers really want.

Banks would need to upload existing customer messages to an NLP software to first categorize this data. In the case of sentiment analysis, the softwares algorithms are designed to parse out sentences, phrases, and other significant parts of each customers message and automatically tag these categories as positive, negative, or neutral sentiment.

For example, using NLP software to read through customer feedback on social media, banks might identify that customer posts from one particular geographical region are unusually high. NLP software might help identify that top customer issue is password and login issues. The bank can then alert the IT team in that region to take action to resolve the issue.

Expert System offers its AI-based Cogito software to banks and other financial industries and markets it with numerous capabilities. One such capability is enterprise search, which the company calls Cogito Discover.

The product also has a lesser focus on external search for investment research. Cogito Discover can purportedly help compliance officers search for relevant customer information or contracts that they can use to prove compliance in various business areas.

In addition, the company highlights Cogitos ability to purportedly classify text data and documents, as well as enrich that text data and its meta-data. For example, a user could upload a new document into the system, and Cogito Discover seems able to organize that document so that its searchable with the Cogito Discover search function. Its unclear how the product does this, but it isnt outside the range of capabilities for a machine learning-based software.

Nishant Chandra, PhD, Sr. Director of Data Products at Visa believes the best way to approach such an AI project is to first look at what academic use-cases exist for NLP and which of those can be repurposed for banking applications. He says:

What we want to do is take academic use cases and make it significantly useful in the production of AI in industry. For example, in natural language processing document summarization, the user can find the keywords and summarize it. The hierarchical approach to this is to take that document and create context. This can be applied to banking documents to allow search and retrieval of contextual information from within documents.

In enterprise applications, such software might need to understand banking jargon. In customer-facing applications, the software might need to be tweaked to identify sentiments on ambiguous customer messages.

Banks might find that even when a solution is deployed, alterations to the software might be inevitable. For instance, customer preferences might change or a new abbreviation or symbol might be used by customers on social media that needs to be given context for the software to understand it.

Banks might also acquire a new communication channel, such as a chatbot, or restructure their organizations and their internal data flows. All of these would require further upgrades and tweaks to the NLP algorithms.

NLP solutions are fairly well-established in banking. NLP products accounted for the largest share in our report in terms of vendor product offering functions. AI-based information search and discovery, text mining, and natural language generation (NLG) are applications of this technology which are now possible due to AI and machine learning techniques.

Banks are in one of the best positions for leveraging AI in the coming years because the largest banks have massive volumes of historical data on customers and transactions that can be fed into machine learning algorithms. We recently completed our Emerj AI in Banking Vendor Scorecard and Capability Map in which we explored which AI capabilities banks were taking advantage of the most and which they might be able to leverage in the future.

Investment in AI by banks and financial institutions for risk-related functions such as fraud and cybersecurity, compliance, and financing and loans has grown dramatically in the last half-decade compared to customer-facing functions.

Business process management (BPM) in banking involves the automation of operations management by identifying, modeling, analyzing, and improving business processes. Many banks already have some form of BPM for various process. For example, compliance processes at most banks tend to have some form of software automation in their workflows.

Hackers are cyberattackers are using more sophisticated methods to break into digital networks; they themselves have also started employing artificial intelligence techniques to bypass detection systems.

In the banking sector, supervisory organizations create and oversee the compliance rules that banks and other financial organizations need to follow. These compliance regulations are important for companies to carefully abide by, since non-compliance can potentially result in large fines or in extreme cases, even loss of banking licenses.

You've reached a category page only available to Emerj Plus Members. Members receive full access to Emerj's library of interviews, articles, and use-case breakdowns, and many other benefits, including:

mineral mapping, mining, geological mapping | satellite imaging corp

mineral mapping, mining, geological mapping | satellite imaging corp

Satellite imagery and aerial photography have proven to be important tools in support of mineral exploration projects. They can be used in a variety of ways. Firstly they provide geologists and field crews the location of tracks, roads, fences and inhabited areas. This is important for mapping out potential access corridors for exploration areas and considering the environmental impact of large project. The satellite map data is also useful for mapping outcrops and regolith systematics and vegetation cover across exploration blocks and over regional areas.

The Morenci satellite image above is an open-pit copper mine in southeast Arizona is North America's leading producer of copper. This processed and interpreted ASTER image used short wavelength infrared bands to highlight in bright pink the altered rocks in the Morenci pit associated with copper mineralization.

Satellite images can also benefit geologists, scientists, and exploration managers due to the multiple bands that the satellites carry which allow them to interpret wavelengths that cannot be seen by the human eye. Near infrared, short wave infrared, and thermal infrared can be used to identify the difference in structural features of the earth's surface.

Multispectral imaging and thematic mapping allows researchers to collect reflection data and absorption properties of soils, rock, and vegetation. This data could be utilized by trained photogeologists to interpret surface lithologies, identify clays, oxides, and soil types from satellite imagery.

In the example above, the left image displays visible and near infrared bands 3, 2, and 1 in red, green, and blue (RGB). Vegetation appears red, snow and dry salt lakes are white, and exposed rocks are brown, gray, yellow and blue. Rock colors may reflect the presence of iron minerals, and variations in albedo. The middle image displays short wavelength infrared bands 4, 6, and 8 as RGB. In this wavelength region, clay, carbonate, and sulfate minerals have diagnostic absorption features, resulting in distinct colors on the image. For example, limestones are yellow-green, and purple areas are kaolinite-rich. The right image displays thermal infrared bands 13, 12 and 10 as RGB. In this wavelength region, variations in quartz content appear as more or less red; carbonate rocks are green, and mafic volcanic rocks are purple.

Satellite Imaging Corporation (SIC) provides high resolution satellite maps for analysis and mapping applications such as Geographic Information System (GIS). Our imaging, Geographic Information System (GIS), Global Positioning System (GPS), and geodesy experts are highly experienced in image processing, orthorectification, georeferencing, feature extraction, and mosaicing for your specific project needs.

For many image requests, a matching image can be located in our global archives of satellite imagery. If no satellite map data is available in the archives, new satellite image data can be acquired through a satellite tasking process. Besides providing image data, Satellite Imaging Corporation performs many tasks in the background to ensure that we meet customer specifications and time schedules.

Satellite image data has been used by government, commercial, industrial, civilian, and educational communities throughout the world. The data is used to support a wide range of applications in such areas as global change research, agriculture, forestry, geology, resource management, geography, mapping, hydrology, and oceanography.

mining | britannica

mining | britannica

mining, process of extracting useful minerals from the surface of the Earth, including the seas. A mineral, with a few exceptions, is an inorganic substance occurring in nature that has a definite chemical composition and distinctive physical properties or molecular structure. (One organic substance, coal, is often discussed as a mineral as well.) Ore is a metalliferous mineral, or an aggregate of metalliferous minerals and gangue (associated rock of no economic value), that can be mined at a profit. Mineral deposit designates a natural occurrence of a useful mineral, while ore deposit denotes a mineral deposit of sufficient extent and concentration to invite exploitation.

When evaluating mineral deposits, it is extremely important to keep profit in mind. The total quantity of mineral in a given deposit is referred to as the mineral inventory, but only that quantity which can be mined at a profit is termed the ore reserve. As the selling price of the mineral rises or the extraction costs fall, the proportion of the mineral inventory classified as ore increases. Obviously, the opposite is also true, and a mine may cease production because (1) the mineral is exhausted or (2) the prices have dropped or costs risen so much that what was once ore is now only mineral.

Archaeological discoveries indicate that mining was conducted in prehistoric times. Apparently, the first mineral used was flint, which, because of its conchoidal fracturing pattern, could be broken into sharp-edged pieces that were useful as scrapers, knives, and arrowheads. During the Neolithic Period, or New Stone Age (about 80002000 bce), shafts up to 100 metres (330 feet) deep were sunk in soft chalk deposits in France and Britain in order to extract the flint pebbles found there. Other minerals, such as red ochre and the copper mineral malachite, were used as pigments. The oldest known underground mine in the world was sunk more than 40,000 years ago at Bomvu Ridge in the Ngwenya mountains, Swaziland, to mine ochre used in burial ceremonies and as body colouring.

Gold was one of the first metals utilized, being mined from streambeds of sand and gravel where it occurred as a pure metal because of its chemical stability. Although chemically less stable, copper occurs in native form and was probably the second metal discovered and used. Silver was also found in a pure state and at one time was valued more highly than gold.

According to historians, the Egyptians were mining copper on the Sinai Peninsula as long ago as 3000 bce, although some bronze (copper alloyed with tin) is dated as early as 3700 bce. Iron is dated as early as 2800 bce; Egyptian records of iron ore smelting date from 1300 bce. Found in the ancient ruins of Troy, lead was produced as early as 2500 bce.

One of the earliest evidences of building with quarried stone was the construction (2600 bce) of the great pyramids in Egypt, the largest of which (Khufu) is 236 metres (775 feet) along the base sides and contains approximately 2.3 million blocks of two types of limestone and red granite. The limestone is believed to have been quarried from across the Nile. Blocks weighing as much as 15,000 kg (33,000 pounds) were transported long distances and elevated into place, and they show precise cutting that resulted in fine-fitting masonry.

One of the most complete early treatments of mining methods in Europe is by the German scholar Georgius Agricola in his De re metallica (1556). He describes detailed methods of driving shafts and tunnels. Soft ore and rock were laboriously mined with a pick and harder ore with a pick and hammer, wedges, or heat (fire setting). Fire setting involved piling a heap of logs at the rock face and burning them. The heat weakened or fractured the rock because of thermal expansion or other processes, depending on the type of rock and ore. Crude ventilation and pumping systems were utilized where necessary. Hoisting up shafts and inclines was done with a windlass; haulage was in trucks and wheelbarrows. Timber support systems were employed in tunnels.

Great progress in mining was made when the secret of black powder reached the West, probably from China in the late Middle Ages. This was replaced as an explosive in the mid-19th century with dynamite, and since 1956 both ammonium nitrate fuel-blasting agents and slurries (mixtures of water, fuels, and oxidizers) have come into extensive use. A steel drill with a wedge point and a hammer were first used to drill holes for placement of explosives, which were then loaded into the holes and detonated to break the rock. Experience showed that proper placement of holes and firing order are important in obtaining maximum rock breakage in mines.

The invention of mechanical drills powered by compressed air (pneumatic hammers) increased markedly the capability to mine hard rock, decreasing the cost and time for excavation severalfold. It is reported that the Englishman Richard Trevithick invented a rotary steam-driven drill in 1813. Mechanical piston drills utilizing attached bits on drill rods and moving up and down like a piston in a cylinder date from 1843. In Germany in 1853 a drill that resembled modern air drills was invented. Piston drills were superseded by hammer drills run by compressed air, and their performance improved with better design and the availability of quality steel.

Developments in drilling were accompanied by improvements in loading methods, from handloading with shovels to various types of mechanical loaders. Haulage likewise evolved from human and animal portage to mine cars drawn by electric locomotives and conveyers and to rubber-tired vehicles of large capacity. Similar developments took place in surface mining, increasing the volume of production and lowering the cost of metallic and nonmetallic products drastically. Large stripping machines with excavating wheels used in surface coal mining are employed in other types of open-pit mines.

Water inflow was a very important problem in underground mining until James Watt invented the steam engine in the 18th century. After that, steam-driven pumps could be used to remove water from the deep mines of the day. Early lighting systems were of the open-flame type, consisting of candles or oil-wick lamps. In the latter type, coal oil, whale oil, or kerosene was burned. Beginning in the 1890s, flammable acetylene gas was generated by adding water to calcium carbide in the base of a lamp and then released through a jet in the centre of a bright metal reflector. A flint sparker made these so-called carbide lamps easy to light. In the 1930s battery-powered cap lamps began entering mines, and since then various improvements have been made in light intensity, battery life, and weight.

Although a great deal of mythic lore and romance has accumulated around miners and mining, in modern mining it is machines that provide the strength and trained miners who provide the brains needed to prevail in this highly competitive industry. Technology has developed to the point where gold is now mined underground at depths of 4,000 metres (about 13,100 feet), and the deepest surface mines have been excavated to more than 700 metres (about 2,300 feet).

how to use elasticsearch for natural language processing and text mining part 2 - dataconomy

how to use elasticsearch for natural language processing and text mining part 2 - dataconomy

This time well focus on one very important type of query for Text Mining. Depending on the data it can solve at least 2 different kinds of problems. This magical query Im referring to is the More Like This Query.

It will analyse your input text that comes either from the documents in the index or directly from the like text. It will extract the most important keywords from that text and run a Boolean Should query with all those keywords.

Keywords can be determined with a formula given a set of documents. The formula can be used to compare a subset of the documents to all documents based on word probabilities. It is called Tf-Idf and despite several attempts to find something new and fancy it is still a very important formula for TextMining.

If you have a very clean dataset of lets continue with the example news articles, you should easily be able to extract keywords that describe each section: Sports, Culture, Politics and Business.

But if you have to solve a real world Big Data problem, you will probably have a lot of noise in your data: links, words from another language, tags etc. If that garbage is not equally distributed you will have a problem. Tf-Idf will score very high all those rare mistakes in your dataset as they look very unique to the algorithm.

There are usually 2 types of recommendation engines: social and content based. A social recommendation engine is also referred to as Collaborative Filtering mostly known as Amazons People who bought this product also bought

The other type of recommendation engine is called Item based recommendation engine. This tries to group the datasets based on the properties of the entries. Think of novels or scientific papers as an example.

Depending on your dataset that same MLT query will return all duplicates. If you have data from several sources (news, affiliate ads, etc.) it is pretty likely to run into duplicates. For most end user applications this is unwanted behaviour.

You need to compare all documents pairwise (O(n)) The first inspected element will remain, all others will be discarded So you need a lot of custom logic to choose the first document to look at. It should be the best.

I have the next blog post in the works but dont worry, youll have enough time to let all of the knowledge in Part 1 and Part 2. sink in. For now, stay tuned for Part. 3 of the How to use Elasticsearch for NLP and Text Mining series, where well tackle Text Classification and Clustering.

After graduating in Comptational Linguistics she moved to Berlin and working as a NLP Java Developer she decided to become a full-time freelancer in 2014. She works as a freelance consultant and developer in TextMining, NLP and is a big fan of ElasticSearch. As a part-time digital nomad she traveled with communities like HackerParadise ( and NomadCruise ( She recently started a platform for freelancers working in NLP/AI and TextMining called

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text analysis & mining software | easy to use content analysis | wordstat

text analysis & mining software | easy to use content analysis | wordstat

WordStat is a flexible and easy-to-use text analysis software whether you need text mining tools for fast extraction of themes and trends, or careful and precise measurement with state-of-the-art quantitative content analysis tools. WordStat can be used by anyone who needs to quickly extract and analyze information from large amounts of documents. Our content analysis and text mining software can be used in many applications such as analysis of open-ended responses, business intelligence, content analysis of news coverage, fraud detection and more. WordStats seamless integration withSimStat our statistical data analysis tool QDA Miner our qualitative data analysis software andStatathe comprehensive statistical software from StataCorp, gives you unprecedented flexibility for analyzing text and relating its content to structured information, including numerical and categorical data.

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