Database Mining

The term database mining refers to the process of extracting information from a set database and transforming that into understandable information. The data mining process is also known as data dredging or data snooping. The consumer focused companies into retail, financial, communication, and marketing fields are using data mining for cost reduction and increase revenues. This process is the powerful technology, which helps the organisations to focus on the most important and relevant information from their collected data. Organisations can easily understand the potential customers and their behaviour with this process. By predicting behaviours of future trends the recruitment process outsourcing firms assists the multiple organisations to make proactive and profitable decisions in their business. The database mining term is originated from the similarities between searching for valuable information in large databases and mining a mountain for a vein of valuable crystal.

Recruitment process outsourcing firm helps the organisation for the betterment of their future by analyzing the data from distinctive dimensions or angles. From the business point of view, the data mining and data entry services leads the organisation to increase their profitability and customer demands. Data mining process is must for every organisation to survive in the competitive market and quality assurance. Now a day the data mining services are actively utilised and adapted by many organisations to achieve great success and analyse competitor growth, profit analysis, budget, and sales etc. The data mining is a form of artificial intelligence that uses the automated process to find required information. You can easily and swiftly plan your business strategy for the future by finding and collecting the equivalent information from huge data.

With the advanced analytics and modern techniques, the database mining process uncovers the in-depth business intelligence. You can ask for the certain information and let this process provide you information, which can lead to an immense improvement in your business and quality. Every organisation holds a huge amount of data in their database. Due to rapid computerisation of business, the large amount of data gets produced by every organisation and then database mining comes in the picture. When there are problems arising and challenges addressing in the database management of your organisation, the fundamental usage of data mining will help you out with maximum returns. Thus, from the strategic point of view, the rapidly growing world of digital data will depend on the ability of mining and managing the data.

Source: http://ezinearticles.com/?Database-Mining&id=7292341

Coal Mining: Timeless Black Gems

Coal is an abundant sedimentary rock and fossil fuel used primarily as an energy source for electricity and other industrial uses such as smelting and alloy production. Coal is seldom confused with charcoal, which is primarily of wooden origin. Coal was previously used as mere household heating commodities but when the industrial revolution began, coal mining started to became large-scale. It then became an important commodity to produce electricity as well as to provide primary energy for industries as well as transportation during the 18th century to the 1950s.

Coal mining can be a very dangerous activity most especially when it involves mining underground. Gases produced can be very toxic or highly flammable, capable of explosions which can instantly kill a team of miners. Fortunately, technology has enabled companies the capacity to effectively protect their workers from the hazards of coal mining. But not only that, they can also do the same or even higher output even with significantly less number or workers.

Coal mining can involve mining underground by shaft mining or, for a more accessible and easier way, open pit mining the rock strata coal beds or coal seams. However, there are several other ways in coal mining.

Coals near the surface can be extracted by using open cut mining methods. Explosives are first used to break through the surface of the mining area and after which it is removed by draglines or by shovel and a truck. With the coal seam exposed, drills are utilized to fracture and thoroughly mine it in strips. Area mining involves drilling holes against the surface of the mining area and then planting the drill holes with explosives. When the surface is exposed, there will be a coal seam exposed. This can be extracted, mined and transported with trucks immediately. If it is still hard enough, this can also be drilled and blasted with explosives. The coal can then be collected until there is none left in the strip - then the process can be repeated to create a new mining strip. This coal mining method is most ideal for flat terrain.

One particular coal mining method is controversial. This is the mountaintop removal mining - and just as its name says, it's literally removing the mountain top, making the ridges and hill tops look like flattened plateaus. It is controversial because it drastically alters the topography as well as disturbing the ecosystem. Valleys will be filled the extracted prize and streams will be covered. The objective to coal mining was to extract these valuable energy sources, but is it really worth the damaging the environment or even risk worse consequences?

Source:http://ezinearticles.com/?Coal-Mining:-Timeless-Black-Gems&id=6333094

The Coal Mining Industry And Investing In It

The History Of Coal Usage

Coal was initially used as a domestic fuel, until the industrial revolution, when coal became an integral part of manufacturing for creating electricity, transportation, heating and molding purposes. The large scale mining aspect of coal was introduced around the 18th century, and Britain was the first nation to successfully use advanced coal mining techniques, which involved underground excavation and mining.

Initially coal was scraped off the surface by different processes like drift and shaft mining. This has been done for centuries, and since the demand was quite low, these mining processes were more than enough to accommodate the demand in the market.

However, when the practical uses of using coal as fuel sparked industrial revolution, the demand for coal rose abruptly, leading to severe shortage of the coal output, gradually paving the way for new ways to extract coal from under the ground.

Coal became a popular fuel for all purposes, even to this day, due to their abundance and their ability to produce more energy per mass than other conventional solid fuels like wood. This was important as far as transportation, creating electricity and manufacturing processes are concerned, which allowed industries to use up less space and increase productivity. The usage of coal started to dwindle once alternate energies such as oil and gas began to be used in almost all processes, however, coal is still a primary fuel source for manufacturing processes to this day.

The Process Of Coal Mining

Extracting coal is a difficult and complex process. Coal is a natural resource, a fossil fuel that is a result of millions of years of decay of plants and living organisms under the ground. Some can be found on the surface, while other coal deposits are found deep underground.

Coal mining or extraction comes broadly in two different processes, surface mining, and deep excavation. The method of excavation depends on a number of different factors, such as the depth of the coal deposit below the ground, geological factors such as soil composition, topography, climate, available local resources, etc.

Surface mining is used to scrape off coal that is available on the surface, or just a few feet underground. This can even include mountains of coal deposit, which is extracted by using explosives and blowing up the mountains, later collecting the fragmented coal and process them.

Deep underground mining makes use of underground tunnels, which is built, or dug through, to reach the center of the coal deposit, from where the coal is dug out and brought to the surface by coal workers. This is perhaps the most dangerous excavation procedure, where the lives of all the miners are constantly at a risk.

Investing In Coal

Investing in coal is a safe bet. There are still large reserves of coal deposits around the world, and due to the popularity, coal will be continued to be used as fuel for manufacturing process. Every piece of investment you make in any sort of industry or a manufacturing process ultimately depends on the amount of output the industry can deliver, which is dependent on the usage of any form of fuel, and in most cases, coal.

One might argue that coal usage leads to pollution and lower standards of hygiene for coal workers. This was arguably true in former years; however, newer coal mining companies are taking steps to assure that the environmental aspects of coal mining and usage are kept minimized, all the while providing better working environment and benefits package for their workers. If you can find a mining company that promises all these, and the one that also works within the law, you can be assured safety for your investments in coal.

Source: http://ezinearticles.com/?The-Coal-Mining-Industry-And-Investing-In-It&id=5871879

Data Mining vs Screen-Scraping

Data mining isn't screen-scraping. I know that some people in the room may disagree with that statement, but they're actually two almost completely different concepts.

In a nutshell, you might state it this way: screen-scraping allows you to get information, where data mining allows you to analyze information. That's a pretty big simplification, so I'll elaborate a bit.

The term "screen-scraping" comes from the old mainframe terminal days where people worked on computers with green and black screens containing only text. Screen-scraping was used to extract characters from the screens so that they could be analyzed. Fast-forwarding to the web world of today, screen-scraping now most commonly refers to extracting information from web sites. That is, computer programs can "crawl" or "spider" through web sites, pulling out data. People often do this to build things like comparison shopping engines, archive web pages, or simply download text to a spreadsheet so that it can be filtered and analyzed.

Data mining, on the other hand, is defined by Wikipedia as the "practice of automatically searching large stores of data for patterns." In other words, you already have the data, and you're now analyzing it to learn useful things about it. Data mining often involves lots of complex algorithms based on statistical methods. It has nothing to do with how you got the data in the first place. In data mining you only care about analyzing what's already there.

The difficulty is that people who don't know the term "screen-scraping" will try Googling for anything that resembles it. We include a number of these terms on our web site to help such folks; for example, we created pages entitled Text Data Mining, Automated Data Collection, Web Site Data Extraction, and even Web Site Ripper (I suppose "scraping" is sort of like "ripping"). So it presents a bit of a problem-we don't necessarily want to perpetuate a misconception (i.e., screen-scraping = data mining), but we also have to use terminology that people will actually use.

Source:http://ezinearticles.com/?Data-Mining-vs-Screen-Scraping&id=146813

Commercial Kitchen Ventilation and Extraction - What You Need to Know

There are a number of things to consider when installing commercial kitchen ventilation and there are several different types of systems available - but all must comply with the "Standard for kitchen ventilation systems DW172". A commercial kitchen cannot operate effectively without a properly designed and functioning ventilation system. Getting the design of the correct system for YOUR premises can be complex. All systems are operation and site specific - how you move the air, where you move it to and what you have to do with it to ensure compliance not only with the relevant legislation, but also any local building and environmental constraints.

The factors that may need to be addressed include not only physically moving the air, but heat, humidity, smoke, fire, grease and odour. There are various filter and safety systems available that deal with any or all of these issues and the best system for you will depend on your site, its surroundings and your budget. You may also have to deal with noise from the fan(s) and any planning issues relating to external ducting.

In basic terms a ventilation system comprises a canopy over the production area with a fan linked by ducting to a filter bank within the kitchen extraction canopy which draws the air out to the external exhaust point. The fan is sized in direct relation to the amount of air that has to be moved, where it has to be moved to (the exhaust point) and how quickly (depending on the type of food being cooked).

In addition, mechanical provision must be made to replace 85% of the air that is being extracted. This is called "Make up Air", the other 15% is made up by natural means - general kitchen areas and windows etc.

Within the design, careful consideration must also be given to ensure adequate access for cleaning of the duct and servicing of the fans.

If the production equipment is gas, in accordance with British Standard (BS6173) you will have to fit a Gas Interlock system. This system automatically shuts off the gas supply to the cooking equipment in the event of a failure in the ventilation system.

You may also want to consider the installation of a Heat Recovery unit which reclaims the heat (and some of the fuel cost) from your kitchen that would normally be blasted straight out through from your extracton canopy.

Source:http://ezinearticles.com/?Commercial-Kitchen-Ventilation-and-Extraction---What-You-Need-to-Know&id=6438003

Websites Can Contractually Restrict Third Party Scraping of Their Data

E-commerce service providers can contractually prevent other websites from copying factual information from their website for commercial use, such as for price comparison purposes.

On 15 January 2015, the Court of Justice of the European Union (CJEU) confirmed in a preliminary ruling that websites not protected by a database right, are free to impose contractual restrictions on the use of their data. Interestingly, the CJEU acknowledged that the contractual restrictions could – if national law permits - be imposed through the website’s terms and conditions.

Let’s have a quick look at how this matter arose. Since the early days of online reservations, some websites discovered that they could attract a lot of visitors by comparing the online prices displayed by e-commerce websites selling competing goods and services. Originally such third party websites were called “content aggregators” and today one particular type, so-called “price comparison” websites, is widely-known.  To be able to aggregate such content and create added-value for the consumer, these websites use automated software that visits the e-commerce websites and copies the latter’s pricing information in real time. This practice is often referred to as “screen scraping” and frequently occurs in the online travel reservation business. Some of these third party websites do not only show the compared prices of airline tickets but act as an intermediary for booking travel packages, including car and hotel rental services on top of the airline ticket, often after adding a commission.

In response, low-cost airlines quickly started taking legal action against such screen scraping practices, fearing the loss of such additional, revenue-generating services to these third party websites and also through suffering reputational damage when consumers were not properly informed about issues such as flight changes and cancellations. In these circumstances there was one case between the low-cost airline, Ryanair, and the third party website owner, PR Aviation BV, in which the Dutch Supreme Court made a preliminary ruling request to the CJEU.

The CJEU, in its preliminary ruling on the scope of database protection and contractual freedom, ruled in Ryanair’s favour. It concluded that, in the absence of any database related copyright or sui generis protection on Ryanair’s website, Ryanair was expressly allowed to lay down contractual limitations on the use of its website by third parties. Ryanair would not have had such contractual freedom if its database enjoyed copyright or sui generis database protection (due to the restriction laid down in Article 15 of the Database Directive 96/9/EC). Ryanair’s terms and conditions, to which users had to visibly agree when searching for flights (but without needing to explicitly tick a box), indeed stated that the use of any automated system or software to extract data from its website for commercial purposes was prohibited. Ryanair even went as far as to explicitly state that other websites could not sell its flights and that price comparison websites had to enter into a written licence agreement with Ryanair,
to access Ryanair’s price, flight and timetable information for the sole purpose of price comparison.

As a consequence of the CJEU’s ruling, any website making available mere factual information not protected by any legal right, can still prevent others from using such information through its terms and conditions. Clearly, that website will have to demonstrate under applicable (national) law that the website visitor is contractually bound, in particular because it validly agreed to such terms and conditions. Depending on the applicable law, such agreement by the consumer could be considered as having taken place by ticking a box or merely after having been made aware of the website’s terms and conditions.

The CJEU’s ruling is likely to impact upon the business model of a number of content aggregating/price comparison websites. The ruling’s concrete relevance, however, will have to be assessed on a case-by-case basis.

Source:http://www.timelex.eu/en/blog/detail/websites-can-contractually-restrict-third-party-scraping-of-their-data

How You Can Identify Buying Preferences of Customers Using Data Mining Techniques

The New Gold Rush: Exploring the Untapped ‘Data Mining’ Reserves of Top 3 Industries

In a bid to reach new moms bang on time, Target knows when you’ll get pregnant. Microsoft knows Return on Investment (ROI) of each of its employee. Pandora knows what’s your current music mood. Amazing, isn’t it?

Call it the stereotype of mathematician nerds or Holy Grail of predictive analysts of modern day, Data Mining is the new gold rush for many industries.

Today, companies are mining data to predict exact actions of their prospective customers. That means, when a huge chunk of customer data is seen through a series of sophisticated, formatted and collective data mining process, it can help create future-ready content of marketing and buying messages, diminishing scope of errors and maximizing customer loyalty.

Also a progressive team of coders and statisticians help push the envelope as far as the marketing and business tactics are concerned by collecting data and mining practices that are empowering.

Mentioned below is a detailed low-down of three such industries (real estate, retail and automobile) where LoginWorks Software has employed the most talented predictive analysts and comprehensive behavioral marketing platforms in the industry. Let’s take a look.

Real Estate Industry Looks Past the Spray-And-Pray Marketing Tactic By Mining User Data.

A supremely competitive market that is to an extent unstructured too, the real estate industry needs to reap the advantageous benefits of data mining. And, we at LoginWorks Softwares understand this extremely well!

Our robust team of knowledge-driven analysts make sure that we predict future trends, process the old data and rank the areas using actionable predictive analytics techniques. By applying a long-term strategy to analyze the trend and to get hold of the influential factors that are invested in buying a property, our data warehouses excels in using classical techniques, such as Neural Network, C&R Tree, linear regression, Multilayer Perception Model and SPSS in order to uncover the hidden knowledge.

By using Big Data as the bedrock of our Predictive Marketing Platform, we help you zero-in on the best possible property available for your interest. Data from more than a dozen of reliable national and international resources to give you the most accurate and up-to-the minute data. Right from extracting a refined database of one’s neighbourhood insights to classic knowledge discovery of meaningful l techniques, our statisticians have proven accuracy. We scientifically predict your data by:

•    Understanding powerful insights that lead to property-buying decisions.
•    Studying properties and ranking them city-wise, based on their predictability of getting sold in the future.
•    Measuring trends at micro level by making use of Home Price Index, Market Strength Indicator, Automated Valuation Model and Investment analytics.

Our marketing platform consists of the mentioned below automated features:

Data Mining Techniques for Customer Relationship Management and Customer Retention in Retail Industry

Data mining to a retailer is what mining gold to a goldsmith would be! Priceless, to say the least. To understand the dynamics and suggestive patterns of customer habits, a retailer is always scouting for information to up his sales and generate future leads from existing and prospective consumers. Hence, sourcing your birth date information from your social media profiles to zooming upon your customer’s buying behaviour in different seasons.

For a retailer, data mining helps the customer information to transform a point of sale into a detailed understanding of (1) Customer Identification; (2) Customer Attraction; (3) Customer Retention; and (4) Customer Development. A retailer can score potential benefits by calculating Return on Investment (ROI) of its customers by:

•    Gaining customer loyalty and long-term association
•    Saving up on huge spend on non-targeted advertising and marketing costs
•    Accessing customer information, which leads to directly targeting the profitable customers
•    Extending product life cycle
•    Uncovering predictable buying patterns that leads to a decrease in spoilage, distribution costs and holding costs

Our specialised marketing team targets customers for retention by applying myriad levels of data mining techniques, in both technological and statistical perspective. We primarily make use of ‘basket’ analysis technique that unearths links between two distinct products and ‘visual’ mining techniques that helps in discovering the power of instant visual association and buying.

Role of Data Mining in Retail Sector

Spinning the Magic Wheel of Data Mining Algorithms in Automobile Industry of Today

Often called as the ‘industries of industries’. the automobile industry of today is robustly engrossed in constructing new plants, and extracting more production levels from existing plants. Like food manufacturing and drug companies, today, automakers are in an urgent need to build sophisticated data extraction processes to keep themselves all equipped for exuberantly expensive and reputation-damaging incidents. If a data analytics by Teradata Corp, a data analytics company, is to be believed then the “auto industry spends $45 billion to $50 billion a year on recalls and warranty claim”. A number potentially damaging for the automobile industry at-large, we reckon!

Hence, it becomes all the more imperative for an automobile company of repute to make use of enhanced methodology of data mining algorithms.

Our analysts would help you to spot insightful patterns, trends, rules, and relationships from scores and scores of information, which is otherwise next to impossible for the human eye to trace or process. Our avant-garde technicians understand that an automative manufacturing industry does not interact on one-to-one basis with the end consumers on a direct basis, hence we step into the picture and use our fully-integrated data mining feature to help you with the:

•    Supply chain procedure (pre-sales and post-sales services, inventory, orders, production plan).
•    Full A-Zee marketing facts and figures(dealers, business centers, social media handling, direct marketing tactics, etc).
•    Manufacturing detailing (car configurations/packages/options codes and description).
•    Customers’ inclination information (websites web-activities).

Impact of Big Data Analytics of Direct Vehicle Pricing

Bottom line

To wrap it all up, it is imperative to understand that the customer data is just as crucial for an actionable insights as your regular listings data. Behavioural data and predictive analysis is where the real deal lies, because at the end of the day it is all about targeting the right audience with the right context!

Move forward in your industry by availing LOGNWORKS SOFTWARES’ comprehensive, integrated, strategic and sophisticated Data Mining Services.

Source: http://www.loginworks.com/blogs/web-scraping-blogs/can-identify-buying-preferences-customers-using-data-mining-techniques/