DETROIT Data mining is an often misunderstood term. Consumers hear it and they think privacy violation. Small businesses hear it and assume its only a luxury of Fortune 500 companies. In fact, neither assumption is strictly accurate. Appropriately sourced data mining can be ethical and safe for consumers and businesses alike.

And, even modest-sized companies use data mining to their advantage with the right tools and understanding.

In this article, we take a look at some of the many benefits of data mining and reflect on how your business can use it advantageously.

Data mining is the process of extracting insights from long string forms of informationusually done by identifying and interpreting patterns within the information set. Its a process that typically involves a combination of machine learning and human-driven interpretation, all combined to deliver actionable insights for business professionals.

The data sets themselves can be generated by a wide variety of sources, including internal data, and even customer surveys. While often seen as a practice exclusive to large businesses, with the proliferation of data technology, as well as the ever-increasing number of professionals with master degrees in data science, the practice has become more democratized than ever before.

Data mining helps businesses better understand their customers by extracting granular insights into their buying and product usage habits. This information is key when it comes to forming closer relationships with customers but it can also be used from a marketing perspective.

Marketers with reliable data access are not only able to enjoy customized marketing campaigns based on the specific interests of their customers, but they can also determine how and when to implement those insights.

For example, data mining might inform a business of how and when their customer base is most active on social media. With this information, they can make very specific ads on Twitter or Facebook cater precisely to their demographic.

If their customer base consists primarily of teenagers, they may target ads for the middle of the day, during school lunch breaks. Or, late afternoon, for when students get out of school.

If, on the other hand, the demographic consists mostly of business people, the ads might get posted around 7 PM when adults are most likely to be on social media.

Data mining also always you to make calculated adjustments to your product. By extracting information directly from customers who have used what you are setting, its easy to get a clear idea of what people like, and what they dont use as much.

For example, software companies update their products every several years. Though they are usually selling different versions of the same product, features are routinely tweaked to increase marketability.

This might mean tweaking features to make them more in line with what consumers are looking for. It might also mean adjusting how they describe their product, fixing ad language to make it more compatible with how consumers think of the software.

Looking at historic customer data, consumers can anticipate future product demand, using projections to accurately predict incoming revenue for a fiscal period. While is no crystal ball, it is accurate enough for businesses to make reasonable adjustments to their spending habits, equipping them to buckle down in lean periods or get aggressive with their investments during times where the cash is flowing.

Mined data can reveal two important customer insights.

Customer service has become very personalized. Nearly 80% of consumers expect this personal touch when dealing with businesses. Data mining helps you provide it, increasing profitability and making your customers happier with one move.

Data mining can also help your business recognize fraud by identifying abnormalities within patterns, and flagging unusual activity. While not a catch-all, data can help differentiate between legitimate transactions and ones that might not be.

The more transactions an algorithm can analyze, the better it gets at detecting fraud, meaning that theoretically, businesses that use data mining will be less susceptible to fraud as time goes on.

Data mining does come with its drawbacks. Most notably, complexity. Interpreting large sets of data is difficult, both as a practice and because of the technology it requires. For true data mining success, businesses must consider the services of a data professional.

Scalability is another issue. The tools required to do data mining impactfully are complicated, necessitating large databases that can be difficult for the layperson to manage.

Its also worth keeping in mind that data mining practices have been the source of some controversy amongst consumers. Though the information that is involved in data mining is typically anonymous (X customers like banana smoothies, not Sally likes banana smoothies) they can still generate bad pressparticularly in instances where that data has been sold.

Addressing the former concern is mostly a matter of hiring the right people and using the right tools while taking care of the latter can be solved with transparency. Handle data responsibly with the respect and attentiveness it requires, and there wont be any issues.

Bio: Ryan Ayers has consulted a number of Fortune 500 companies within multiple industries including information technology and big data. After earning his MBA in 2010, Ayers also began working with start-up companies and aspiring entrepreneurs, with a keen focus on data collection and analysis.

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How Data Mining Can Positively Impact Your Business - mitechnews.com

Picture that its June 30, 2021, Baltimores hottest day of the year at 99F.

Imagine every house with AC cranking it to 65. Suddenly, everything goes dark and the cool air cuts off from all the demand on the power grid. A second later, electricity returns because, somewhere, a major power consumer (like a factory, industrial plant or Bitcoin mine) shut off the power.

Also in 2021, China, which has more crypto mining activity than any country, decided to ban Bitcoin mining because it deemed the industry highly pollutive. This opened the door for the United States to become the top location for Bitcoin mining in the world. States like New York, Texas and Pennsylvania, as well as small towns like Coshocton, Ohio, eventually became places where industrial-size Bitcoin mines want to set up shop.

Bitcoin mining essentially involves computers doing intense math to validate transactions because the decentralized nature of cryptocurrencies needs a verification process to prevent bad actors from manipulating the currency. The process is called proof of work. The first person to solve the math equation and add their validated transactions to the overall ledger that is, the blockchain receives cryptocurrency as their reward.

The computational power needed to be first and reap the benefits is what drives Bitcoin mining energy demands to 132.48 terawatt-hours (TWh) annually across the world, according to estimates from the University of Cambridge that were reported by Business Insider. Those energy demands in the US, where 35.4% of Bitcoin mining takes place, translate to 0.85 pounds of carbon dioxide per kilowatt-hour (kWh) and 40 billion pounds of carbon dioxide.

That energy usage is not lost on utility services companies like Baltimores CPower Energy Management, a company that acts as a liaison between Bitcoin miners and power companies.

They use a lot of energy, David Chernis, a CPower account executive, told Technical.ly. Thats both a problem and a solution, in that they can also turn off very quickly if needed to.

CPower offers grid reliability and demand response services. On days over 90 degrees or during intense cold, theres stress on the power grid; high-energy consumers need to curtail power consumption so the community at large continues to get power smoothly.

If they were to turn off a traditional data center, imagine turning off Google. Whats going to happen? Youre going to see webpages go down, said CEO Daniel Lawrence of Baltimore-based cryptocurrency software firm OBM Inc. If you turn off a Bitcoin mine, the mine stops generating Bitcoin, but thats about it. Theres no user impact.

OBM Inc. created the crypto-mining management platform Foreman to help miners better manage the power consumption of their hundreds of devices that process transactions on the blockchain. This process aims to offset an environmental impact that enabled bans in countries like China, Egypt, Iraq, Qatar, Oman, Morocco, Algeria, Tunisia and Bangladesh.

The software allows companies like Standard Power, which builds and maintains enterprise-level Bitcoin mines for clients, to automate shutting down Bitcoin mines in five minutes (it used to take two hours to get this done manually).

In 2018, the company bought 125 acres of what used to be a paper mill to create a data center for bitcoin mining in Coshocton, a town roughly 78 miles northeast of Columbus. The WestRock Paper Mill had shut down in 2015, leaving hundreds jobless. Its an age-old story of manufacturing leaving a small town and its residents out to dry. Places like Coshocton are hoping the new market of cryptocurrency infrastructure can revitalize the town. Coshoctons Mayor Steve Mercer even boasted that refurbishing the plant into a cryptocurrency operation would lead to 100 jobs and $100 million of technological infrastructure.

At that level of scale, we have a community development project, essentially, said Maxim Serezhin, CEO of Standard Power.

What makes towns like Coshocton attractive to Bitcoin miners is the old industrial infrastructure, the proximity to natural resources (like Utica and Marcellus Shale gas formations) creating excess energy capacity for the state and the proximity to fiber data centers to reduce latency. In rural Pennsylvania, Bitcoin mines have set up shop using waste coal as fuel, sparking debates about issues ranging from the carbon footprint to the noise pollution from Bitcoin mine power generators using natural gas. These debates echo a larger conflict about what it takes to actually mine Bitcoin that has played out elsewhere as the recent crypto crash dovetailed with calls for more oversight.

OBM, CPower and Standard Power want to change the stigma around Bitcoin mining by offering a solution to energy concerns instead of a problem.

Its a partnership with the community where we give to the community the thing it needs most, at the time it needs it most, right at the time that the community needs power the most, Serezhin said. At those times, we are able to turn our business off and say, we can share the resource that we own with the community and our ability to do that fundamentally changes everything.

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How does Bitcoin mining keep the lights on? Some companies may have solutions - Technical.ly

Over the past couple of years, fintech in the country has grown at a pace helping expand financial services to the masses and making India a benchmark for a lot of global markets. India is labelled as Asias top fintech hub with a fintech adoption rate of 87% over the global average of 64%as explained by RBI Governor Mr Shaktikanta Das.

This present-day enthusiasm shared by Indian financial institutions about the fintech industry is well connected to a natural evolution of the banking sector in India. For the better part of the last decade, traditional financial institutions that gradually realised the potential of fintech companies, redefined their time-tested strategies of conducting business. And the biggest enabler is the tech or technology of fintech that is motivating traditional financial institutions to rethink their perspective and forge a partnership for the future.

Embracing the new approach Over the past 75 years of Indias Independence, a majority of the countrys financial institutions have made significant strides to increase the pace of their customer operations and this has been further accelerated with the entry of the fintechs in this area.

Leveraging on strengths of each other, while the traditional financial institutions have garnered immense inherent trust of their customers riding on their vintage and distribution, the new-age fintechs bring agile data mining capabilities to help them stay connected and relevant to their customers. The combined capabilities can now process historical data on customer behaviour thus slowly improving processes, bandwidth, reducing errors, creating value-added services as per needs of todays customers across income levels.

Erasing Customer fear and bringing confidence The partnership also helps new-age fintech companies, with their wide catalogue of financial products, designed to meet the needs of modern customers, gain adaptation faster. And a significant contributor to this phenomenon is the Indian consumers affinity to bank only on trust. This partnership has helped the fintechs to pinpoint the customer pain points and devise solutions to bridge past process gaps, thus making the end customer happier and showcasing the same bank as much more customer-friendly. And through the ease of availability of these services on banks mobile application, the end consumer is much less inhibited by the traditional way of transacting cash & is welcoming the digitised process with increased confidence. The ease and effortless usage have helped more and more customers who didnt adopt the banking system in the past to happily use it today. And SME/Kiranas/farmers largely fall in this segment in the new Bharat.

Technology as the enabler

A partnership between financial institutions and fintech institutions visible today is built on the shared agenda of better addressing the problems of the end customers and steered by their common vision of increasing financial inclusion using technology as a great enabler.

Over the past couple of years, the popularity of PaaS has grown exponentially among banks and financial institutions mainly because they offer scalability, ready availability and cost-benefit to the banks. Due to this, not only are banks better able to leverage advanced capabilities, but also meet customer expectations leading to more profitability and retention.

Big data, artificial intelligence and technology, powered by fintechs, generally have been accelerating change in payments, lending, insurance and wealth management.

Opportunities ahead While the bank fintech partnership spans across the entire spectrum of financial services, the merchant payments space off late has seen increased activity. The new approach combines integrated omni channel payment solutions with value added adjacencies leveraging payments data to deliver relevant and easy to use offerings for MSME/SME.

One platform solving all things as payments, business growth and working capital delivered by combining the best of capabilities from the bank (payment rails, distribution, underwriting) and the fintech (ease of use, data mining and agility) for a particular segment as MSME.

Redefining the way banks operate

By leveraging the PaaS model and forging equal opportunity partnerships, not only can banks better understand the exact needs of the modern customer, but also positively impact their bottom line. However, it is important to reiterate that for this partnership to truly realize its ultimate potential, banks and fintechs need to work together, as the partnership between trust and technology is at the heart of its success.

Views expressed above are the author's own.

END OF ARTICLE

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No longer Fintech v/s bank: It is Fintech with banks - Times of India

Seismic noise can be generated through continuous vibrations at different frequencies inside the earth. Typically, there are three commonseismic noise sources encountered in the field: natural seismic noise, man-made electromagnetic interference (EMI), and man-made seismic noise. This noise can monitor a mines seismicity and structural integrity to detect potential ground hazards.

Image Credit:calcassa/Shutterstock.com

The mining process is non-spontaneous and can induce stresses in the mining region. On average, the mining-induced deformation rate is greater than the average slip rate of tectonic plates by a magnitude of at least two orders.

Deformation and stress-relaxation caused by underground openings are followed by rock extractions, which initiate bulk seismic activity in the mines.

During stress-relaxation, the accumulated potential energy in the rock mass may be released suddenly or gradually.

A sudden inelastic deformation will lead to a seismic event.

The subsequent seismic waves vary in amplitude and frequency depending on the rocks strength and stress state, the seismic sources size, and the magnitude of the deformation rate during fracture.

The seismic response of rocks in mines can be monitored to quantify, prevent, control, and even predict potential rock mass destabilization resulting in rock bursts. Furthermore, blasting vibrations, energy release due to rock crack propagation, and mechanical vibrations induce microseismic events in underground mines.

Increased mining-induced stress can also cause the filling up of cracks, fractures, pores, and voids, which increases the elastic wave velocity.

Microseismic events of varying magnitudes due to rock fracturing can excite elastic waves in the surrounding rocks.

Although these events are of smaller magnitudes, their locations are superficial and closer to the surface. Therefore, they can inflict major damage and loss of life and have a high occurrence rate.

Although past data can be beneficial for comparing seismic rock masses to various mining scenarios, it is not the optimum indicator of future hazards due to the irregular nature of mass loading. Seismic monitoring is widely used to detect, locate, and estimate the size or strength of ground movements.

Seismicity recordings in the mines of the United States ranged from using small-scale arrays, collecting analog signals by geophones, and personal computer seismic unified data structures (PC-SUDS).

In Germany, seismicity monitoring at underground coal mines is conducted by 60 seismic stations, which use three-component 4.5-Hz geophones operating at 500 samples/second. About 20 mines in Poland use the seismic network developed by the central mining institute (CMI) comprising conventional and broadband 1-Hz short-period triaxial sensors.

In Australia, fracturing processes due to longwall caving have been delineated using microseismic monitoring systems developed from numerous temporary research networks. Most of these networks utilized a triaxial geophone sensor. These sensors are grouted into the drilled surface boreholes that provide real-time analysis of the microseismic data.

Methods such as ambient noise tomography record dispersive surface waves and create a three-dimensional (3D) model of the s-wave velocity structure. These passive methods provide a low-cost solution to image the subsurface, delineate structural integrity, and help avoid potential hazards.

Techniques such as velocity tomography use seismic waves produced from mining-induced microseismic activities such as explosions to structure the stress state or the rock mass conditions. However, the quality of results from velocity tomography can be affected by factors such as accurate arrival time picking, high-resolution source location, ray tracing, and efficient inversion algorithms.

Velocity tomography is primarily used to monitor rock mass stability, detect geological structure and discontinuities, and prevent rock burst hazards.

A recent study by Wu and team evaluated noise cross-correlations functions (CCFs) by analyzing one month of surface 17 geophone array data at a longwall coal mine. Their work established that the CCFs related to the ambient noise could be isolated and used to trace mining-induced signals, enabling further research on structural integrity.

Other recent studies have reported using passive seismic data to model the subsurface as an attractive approach for implementing mine safety and preventing hazards.

Seismic noise tomography is another environmentally friendly, inexpensive technique used in exploration and subsurface structure detection. This, coupled with the latest advancements in compact autonomous seismic recording stations and low-cost data collection spanning over a month, makes it an attractive prospect in future mining applications.

Methods such as velocity tomography are advantageous due to their broad monitoring range, allowing them to be utilized at a mine scale or a regional scale and carry out long-term continuous observations. However, it is limited due to deviations of the calculated source locations from their actual positions and can be less reliable in mined-out areas with lesser ray coverage.

Velocity tomography has efficiently analyzed mining-induced stress distribution in hard rock and underground coal mines. Hence, this methods ability to assess rock instability and rock disturbances can be helpful in early predicting and evaluating mining-induced hazards and enhanced ground control. However, the results of this method are not always effective and successful and hence require improvements in timeliness and accuracy.

Swanson P, Boltz MS, and Chambers D. Spokane, Seismic monitoring strategies for deep longwall coal mines. WA: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, DHHS (NIOSH) Publication No. 2017-102, RI 9700. https://www.cdc.gov/niosh/mining/works/coversheet1944.html

Mendecki, Aleksander & Lotter, Ernest. (2011). Modelling Seismic Hazard for Mines. https://www.researchgate.net/publication/236183155_Modelling_Seismic_Hazard_for_Mines

Mendecki, Aleksander & Van Aswegen, Gerrie &Mountfort, Peter. (1999). A guide to routine seismic monitoring in mines. https://www.researchgate.net/publication/236204233_A_guide_to_routine_seismic_monitoring_in_mines

Durkin, John, Greenfield, Roy J., Evaluation of the Seismic System for Locating Trapped Miners, Buereau of Mines Report of Investigations/1981, United States Department of the Interior. https://www.cdc.gov/niosh/mining/UserFiles/works/pdfs/ri8567.pdf

Nicola Ramm, Tjaart de Wit & Gerrit Olivier (2019) Passive Seismic Imaging for Mineral Exploration, ASEG Extended Abstracts, 2019:1, 1-3, DOI: 10.1080/22020586.2019.12073177. https://doi.org/10.1080/22020586.2019.12073177

Li, Y.; Deng, H.; Wen, L.; Qin, Y.; Xu, X. Method for Identifying and Forecasting Mining-Induced Earthquakes Based on Spatiotemporal Characteristics of Microseismic Activities in Fankou Lead/Zinc Mine.Minerals2022,12, 318. https://doi.org/10.3390/min12030318

Zhu, Qiankun& Zhao, Xingdong& Westman, Erik. (2021). Review of the Evolution of Mining-Induced Stress and the Failure Characteristics of Surrounding Rock Based on Microseismic Tomography. Shock and Vibration. 2021. 1-19. 10.1155/2021/2154857. https://www.researchgate.net/publication/354647108_Review_of_the_Evolution_of_Mining-Induced_Stress_and_the_Failure_Characteristics_of_Surrounding_Rock_Based_on_Microseismic_Tomography.

Santiago Rabade, SinMei Wu, FanChi Lin, Derrick J. A. Chambers; Isolating and Tracking Noise Sources across an Active Longwall Mine Using Seismic Interferometry.Bulletin of the Seismological Society of America2022; doi:https://doi.org/10.1785/0120220031

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Seismic Noise Analysis and its Role in Mining Hazard Prevention - AZoMining