The rapid adoption of cryptocurrency and other popular blockchain applications has captured our global economys attention. Even as the value of cryptocurrencies slid from their all-time highs, the promise of these digital assets and the infrastructure being developed to support them has been transformative.

As with most emerging technologies, policymakers are still exploring the best approaches to regulating these new digital assets and business models. Questions about consumer protection, security, and the applicability of existing laws are to be expected; however, the environmental impact of these energy-intensive business practices has prompted considerable study and regulatory activity across the globe, including attention in the United States.

To understand the increasing energy demands associated with major cryptocurrencies predominantly, Bitcoin and Ethereum it is important to understand how many cryptocurrencies are generated in the first instance. Many countries, including China, have banned cryptocurrency mining, and, with the United States becoming the largest source of cryptocurrency mining activity, Congress began active investigations and hearings into the energy demands and environmental impacts in January 2022.

Proof of What? Why certain cryptocurrencies create high energy demands.

Not all cryptocurrencies or blockchain platforms, for that matter are created equal in their energy demands. The goal of most major cryptocurrency platforms is to create a decentralized, distributed ledger, meaning that there is no one authority to verify the authenticity of transactions and ensure that assets are not spent twice, for example. There needs to be a trustworthy mechanism a consensus system to verify new transactions, add those transactions to the blockchain, and to confirm the creation of new tokens. Bitcoin alone has well over 200,000 transactions per day,[1] so it should not come as a surprise that these platforms take an enormous amount of processing power to maintain.

There are currently two primary ways that network participants lend their processing power, which are framing part of the modern energy policy debates around cryptocurrency. The first form is proof of work, which is the original method that Bitcoin and Ethereum 1.0 employ. When a group of transactions (a block) needs to be verified, all of the mining computers race to solve a complex math puzzle, and whoever wins gets to add the block to the chain and is rewarded in coins. The competitive nature of proof of work consensus systems has led to substantial increases in computing power provided by institutional cryptocurrency mining operations and, with that, higher energy demands.

The second form is proof of stake, which newer platforms like Cardano and ETH2 use, promises to require considerably less energy to operate. With this method, validators stake their currency for a chance at verifying new transactions and updating the blockchain. This method rewards long-term investment in a particular blockchain, rather than raw computing power. A validator is picked based on how much currency they have staked and how long it has been staked for. Once the block is verified, other validators must review and accept the data before its added to the blockchain. Then, everyone who participated in validating the block is rewarded with coins.

While proof of stake consensus systems are becoming more common, the dominant and most valuable cryptocurrencies are still generated through energy-intensive proof of work systems.

Turning out the lights on Crypto: China bans domestic mining and other countries follow.

China has been incredibly influential in the modern cryptocurrency debate around energy use. For several years, China was the cryptocurrency mining capital of the world, providing an average of two-thirds of the worlds processing power dedicated to Bitcoin mining through early 2021.[2] In June 2021, however, China banned all domestic cryptocurrency mining operations, citing the environmental impacts of Bitcoin mining energy demands among its concerns.[3]

As Bitcoin miners fled China, many relocated to neighboring countries, such as Kazakhstan, and the United States became the largest source of mining activity an estimated 35.1% of global mining power.[4] The surge in Bitcoin mining activity in Kazakhstan has not been without its controversy. Many Kazakhstan-based crypto mining operations are powered by coal plants, and there has been considerable unrest sparked by rising fuel costs.[5]

With some countries experiencing negative impacts from cryptocurrency mining operations, several countries have followed Chinas lead in banning cryptocurrencies. According to a 2021 report prepared by the Law Library of Congress, at least eight other countries Egypt, Iraq, Qatar, Oman, Morocco, Algeria, Tunisia, and Bangladesh have banned cryptocurrencies.[6] Many other countries have impliedly banned cryptocurrency or cryptocurrency exchanges, as well.[7]

U.S. Congress shines its spotlight on the energy demands of cryptocurrency mining.

Now home to over a third of the global computing power dedicated to mining bitcoin, the United States has turned its attention to domestic miners and their impacts on the environment and local economies.

In June 2021, U.S. policymakers were still predominantly focused on the consumer protection and security concerns raised by digital currencies; however, Senator Elizabeth Warren alluded to her growing concerns about the environmental costs of, particularly, proof of work mining.[8] On December 2, 2021, Senator Warren sent a letter requesting information on the environmental footprint of New York-based Bitcoin miner Greenridge Generation.[9] The letter observed that, [g]iven the extraordinarily high energy usage and carbon emissions associated with Bitcoin mining, mining operations at Greenridge and other plants raise concerns about their impacts on the global environment, on local ecosystems, and on consumer electricity costs.[10] Senator Warrens concerns sparked several rounds of congressional oversight and inquiries into the environmental impacts of, particularly, proof of work cryptocurrencies, over the past month.

Committee Hearing on Cleaning up Cryptocurrency begins oversight and investigation into the energy impacts of blockchains.

On January 20, 2022, the U.S. House of Representatives Committee on Energy and Commerces Subcommittee on Oversight and Investigations held a hearing, where the externalities of cryptocurrency mining were the focus of the agenda. An early indicator of the Subcommittees views on the issue, the title for the hearing was Cleaning up Cryptocurrency: The Energy Impacts of Blockchains.[11]

The hearing focused heavily on the amount of energy used to power proof of work cryptocurrency mining. Bitcoin Mining has been widely criticized for the massive amounts of power it consumes globally, more than 204 terawatt-hours as of January 2022. Although some operations are attempting to utilize renewable energy, the machines executing these algorithms consume enormous amounts of energy primarily sourced from fossil fuels.

The five industry experts testifying before the House Energy and Commerce Oversight Subcommittee had competing views on how regulators should address the energy consumption of cryptocurrencieswith some experts opining that the computational demands were a feature, not a bug.[12] Two of the experts Brian Brooks, CEO of Bitfury Group, and Professor Ari Juels, Faculty member at Cornell Tech debated the technical merits between proof of work and proof of stake systems, described earlier in this article.[13] Similarly, Gregory Zerzan, an attorney with Jordan Ramis, P.C. who previously held senior positions in the United States Government, encouraged the Subcommittee not to lose sight of the fact that cryptocurrencies are but one aspect of a larger innovation, blockchain.[14] Although the viewpoints of the experts varied considerably, there was a clear consensus among the experts: energy-efficient alternatives should guide the path forward.

John Belizaire, the founder and CEO of Soluna Computing, said that cryptocurrency mining could further accelerate the transition to renewable energy sources from an energy perspective.[15] Renewables currently suffer from one significant deficiency intermittency. An example of this challenge is the so-called duck curve, which illustrates major differences between the demands for electricity as compared to the amount of renewable energy sources available throughout the day. For example, when the sun is shining, there is significantly more power than consumers need for a few hours per day; however, solar energy does not provide nearly enough energy when demand spikes in the late afternoon and evening.[16] While there has been progress in the development of lithium battery storage a critical piece in solving the issues mentioned above for the time being, deploying these batteries at scale is still too expensive.

In addressing gaps in battery storage, Belizaire testified that Computing is a better battery.[17] Computing, he states, is an immediately deployable solution that can allow renewables to scale to their full potential today.[18] Belizaire highlighted that, unlike other industrial consumers, cryptocurrency miners can turn their systems off when necessary, giving miners the ability to absorb excess energy from a given areas electrical grid rather than straining it. This ability to start and stop or pause computing processes can increase grid resilience by absorbing excess energy from renewable resources that provide more power than the grid can handle. Brooks shared similar hopes for how Bitcoin mining could help stabilize electric grids, support the viability of renewable energy projects, and drive innovation in computing and cooling technology.[19]

Steve Wright, the former general manager of the Chelan County Public Utility District in Washington, testified that the portability of cryptocurrency operations could be a benefit in terms of locating operations based on underutilized transmission and distribution capacity availability.[20] Still, with ambitious goals to expand transmission and increase and integrate large amounts of carbon-free emitting generation, Wright testified that substantial collaboration and coordination will be necessary to avoid cryptocurrency mining exacerbating an already very difficult problem.[21]

Congressional Democrats continue the investigation into domestic mining operations and the Cryptomining Industry response.

The January 20, 2022 Hearing made clear that policymakers are doing their due diligence into the impact that the United States could experience as the number of domestic cryptocurrency mining operations increase. Commentary from the Hearing forecasted that scrutinizing the sources and costs of energy used in cryptocurrency mining would be a priority for Democrat members of Congress.

To that end, on January 27, 2022, eight Democrat members of Congress led by Senator Elizabeth Warren sent letters to six cryptomining companies raising concerns over their extraordinarily high energy uses.[22] Citing the same concerns raised in her December 2021 letter to Greenridge, Senator Warren and her colleagues observed that Bitcoin minings power consumption has more than tripled from 2019 to 2021, rivaling the energy consumption of Washington state, and of entire countries like Denmark, Chile, and Argentina.[23] To assist Congress in its investigation, Riot Blockchain, Marathon Digital Holdings, Stronghold Digital Mining, Bitdeer, Bitfury Group, and Bit Digital were all asked for information related to their mining operations, energy consumption, possible impacts on the climate and local environments, and the impact of electricity costs for American consumers.[24] Senator Warren and her colleagues requested written responses by no later than February 10, 2022, so this increased oversight will likely continue.

Even with increased oversight, current trends in crypto mining and renewables could soon make such inquiries a moot point. Amid the heated debate over the environmental impact of cryptocurrencies, miners are increasingly committed to changing the negative reputation that it has built over the years especially as these operations move to the United States. In November of last year, Houston-based tech company Lancium announced that it raised $150 million to build bitcoin mines across Texas that will run on renewable energy.[25] In 2022, the company plans to launch over 2,000 megawatts of capacity across its multiple sites.[26] Bitcoin mining company Argo Blockchain, a company listed on the London Stock Exchange, secured a $25 million loan to fund its green mining operation.[27] The 320-acre site will only use renewable energy, the majority being hydroelectric.[28] This deal is set to transform Argos mining capacity and is expected to be completed in the first half of 2022.[29]

Capital Markets also appear to have a growing appetite for the development of green crypto mining. In April of last year, Gryphon Digital Mining raised $14 Million Series A to launch a zero-carbon footprint Bitcoin mining operation powered exclusively by renewables.[30] In a raise that closed in just over two weeks, institutional investors who were significantly oversubscribed accounted for over thirty percent of the round.[31]

As congressional, social, and economic pressures grow, it is evident that there is going to be a big focus on the sustainability of Bitcoin mining. As such, we may very well see announcements, like the deals mentioned above, well into 2022 and beyond.

[4] See Bitcoin Mining Map.

[17] See, e.g., Belizaire Statement, p.4.

[19] See generally Brooks Statement, pp.8-10.

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Crossing the Wires of Energy and Cryptocurrency Policy: U.S. Congress Investigates the Environmental Impact of Crypto Mining - JD Supra

The way things are going, in five years, the amount of renewable power wasted in California each year will be equivalent to the amount of power LA uses each year, Barath Raghavan, co-author of the study, said in a media statement.

In Raghavans view, this state of affairs means that information batteries may have a role to play in countries greener future.

The main principle behind these devices is that when renewable energy is available in excess, it is used to speculatively perform computations in large, energy-intensive data centers. These data centersfrom Google and Facebook to Hollywood movie renderingconsume 10 to 50 times the energy of a typical commercial building, according to the Office of Energy Efficiency and Renewable Energy. The stored computed results can then be used later when green energy is less plentiful.

We had the observation that if we can predict possible computations that might occur in the future, we can do those computations now, while there is energy available, and store the results, which now have embodied energy, Raghavan said.

As an example, the scientist mentioned that every day, YouTube data centers transcode more than 700,000 hours of videos to different resolutions. Many of these computations are predictable and can be performed at a time when there is excess green energy. At this point, the data is stored on servers for later use, when there is less renewable energy available on the grid essentially moving electricity consumption from one time period to another.

In the scientific sense, Raghavan explained that batteries are stores of potential energy to do useful work, electrical or otherwise. Most storage of energy into batteries converts one type of energy into another kind of potential energy, for instance, electrical into gravitational. In this case, information provides energy in the same way as a battery because electrical energy is turned into what might be called informational potential energy.

In addition to taking advantage of task predictability, the system is also flexible: the computations that are completed in advance do not need to match exactly with the computations completed at a later time.

We support pre-computing many fragments of computation and then later can pick and choose small pieces of computation done before, like puzzle pieces, and assemble them together to quickly compute a totally new computational task, the researcher said.

For certain types of workloads, the information battery system is expected to offer better efficiency than lithium-ion batteries. The specific efficiency depends on multiple factors, such as the types of computation conducted and the predictability of power. But unlike lithium-ion batteries, storing data is cost-effective in terms of both money and energy.

While the idea itself is relatively simple, its proponents said that the challenge is determining what computation to perform, where and when, and how these computations should be done to efficiently retrieve the results later.

Raghavan and co-author Jennifer Switzer tackle those challenges by providing a design and proof of concept implementation of the zero-carbon system that includes recurrent neural networks for predicting the future availability of renewable energy and upcoming tasks in data centers.

It also includes a cache where functions are stored and a modified compiler to automatically modify code to store and retrieve results. The infrastructure would be geographically distributed, comprising many small, distributed data centers, each located in a region of the country where wind or solar production is known to be high.

With this system, companies would be using power that would have been dumped, and everybody else benefits because the grid operator doesnt have to spin up natural gas power in the evening hours to compensate for demand, Raghavan said.

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Information batteries: the latest proposal to dethrone lithium-ion batteries - MINING.COM - MINING.com

DENVER, Feb. 4, 2022 /PRNewswire/ -- EnerCom, Inc., a nationally-recognized management consultancy, and Colorado School of Mines, a leading public research university focused on science and engineering, announced its Keynote speakers and presenting company line-up for The Energy Venture Investment Summit presented by Colorado School of Mines and EnerCom, in-person and on campus in Golden, Colorado, on Wednesday, February 16th & Thursday, February 17th, 2022.

With more than 30 participating companies, key themes for the event will include carbon capture and storage, hydrogen production, solar, advanced materials testing equipment and technology, and treatments to optimize oil and gas operations and resource production.

Qualified members of the investment community are invited to register to attend the Summit to hear presentations and meet with management teams from the conference's lineup.

Keynote Speakers and Panel Discussions Include:

Venture Company lineup includes:

Please visit the conference website atwww.theenergyventuresummit.comfor the most up to date list of presentations and schedule of events.

Summit Format: The Summit will provide invited presenters a venue to give a thirteen-minute, in-person presentation that will be webcast live to registered participants on the Summit website at http://www.theenergyventuresummit.com and recorded for replay to a world-wide audience on The Energy Venture Investment Summit website, and EnerCom's http://www.oilandgas360.com news website. Presenting companies and investors will have an opportunity to meet over cocktails and preview their presentations at Wednesday afternoon's Kickoff Networking Event.

Attendee Registration Cost: There is no cost to attend The Energy Venture Investment Summit for qualified investors, with other registration classifications available at a minimal cost. All attendees can register for the Summit at http://www.theenergyventuresummit.com. In addition to in-person and online access to all company presentations, panel discussions and keynote speakers, registration includes the opportunity for investors and management teams to meet over cocktails during the Summit kickoff prior to their presentations, as well as at Thursday's breakfast and keynote lunch.

Investor One-on-One Meetings: Investors will have the opportunity to request and schedule One-on-One meetings with presenting companies through EnerCom during the Summit.

About EnerCom, Inc.

EnerCom (Energy Communications) has a rich history of working with clients to differentiate and deliver targeted messages to investors. EnerCom, Inc. is an internationally-recognized management consulting firm advising companies on Environmental, Social & Governance (ESG), investor relations, corporate strategy/board advisory, marketing, analysis and valuation, media, branding, and visual communications design.

For more information about EnerCom and its services, please visit http://www.enercominc.com/ or call +1 303-296-8834.

About Colorado School of Mines

Colorado School of Mines is a public research university focused on science and engineering, where students and faculty together address the great challenges society faces today particularly those related to the Earth, energy and the environment.

For more information about Colorado School of Mines, please visit https://www.mines.edu/ or contact Emilie Rusch, Director of Communications at 303-273-3361 or [emailprotected].

About Oil and Gas 360

Oil & Gas 360 (www.oilandgas360.com) is a one-stop source of news, information, and analysis from the professionals atEnerCom, Inc.The website is dedicated to all things energy: people, technologies, transactions, trends, and macro-economic analysis that impact our industry.

Event Sponsors Include:

About ENGAGE

ENGAGE's mission is to simplify the B2B transaction process by automating financial workflows. Simply digitizing paper processes has been commercialized by many solutions, however, ENGAGE is the first to use predictive scheduling and data validation to reduce touchpoints and eliminate redundant processes, thus changing the way transactions are scheduled, managed and approved. Additionally, layering on ENGAGE's E-invoicing platform automates your workflows end-to-end, from scheduling services all the way through payment processing. One automated platform, order to payment.

For a complete list of services and to learn more about ENGAGE, please visit https://engagemobilize.com/.

About Nabors Industries

Nabors Industries (NYSE: NBR) is a leading provider of advanced technology for the energy industry. With operations in approximately 20 countries, Nabors has established a global network of people, technology and equipment to deploy solutions that deliver safe, efficient and responsible energy production. By leveraging its core competencies, particularly in drilling, engineering, automation, data science and manufacturing, Nabors aims to innovate the future of energy and enable the transition to a lower carbon world. For more information, please visit http://www.nabors.com.

About Haynes and Boone

Haynes and Boone, LLP is an energy focused corporate law firm, providing a full spectrum of legal services and solutions to clients across the oil and gas industry, including the upstream, midstream, and downstream sectors. Lawyers from our Denver office and 15 other offices work as a team to meet the legal needs of our domestic and international clients involved in oil and gas. We represent private and public oil and gas companies, financial institutions, investment funds and other investors. Our team of more than 100 energy lawyers and landmen understands the physical and financial energy markets, and the firm has been helping both operators and lenders complete some of the largest financings and M&A transactions in recent years. The BTI Industry Power Rankings, published by BTI Consulting Group, Inc., named Haynes and Boone a "Leading Recommended" firm for the energy industry in 2017, ranking our firm among the top three percent of all law firms. For more information, please visit http://www.haynesboone.com.

About Moss Adams

Moss Adams is a fully integrated professional services firm dedicated to assisting clients with growing, managing and protecting prosperity.

With more than 3,400 professionals and staff across more than 25 locations in the West and beyond, we work with many of the world's most innovative companies and leaders. Our strength in the middle market enables us to advise clients at all intervals of developmentfrom start-up, to rapid growth and expansion, to transition. For more information, please visit http://www.mossadams.com.

About Gary Climate Solutions

Gary Climate Solutions is a Carbon Capture and Storage developer focused on generating high-integrity carbon offsets. We partner with industrial emitters to provide a turnkey solution for capturing, transporting and permanently storing carbon dioxide through geologic sequestration at, or close to, the emission source.

We believe Carbon Capture and Storage (CCS) is the key climate change mitigation pathway. Its mature technology solutions and significant geologic capacity make it vital to decarbonization efforts. Our expertise in the operation of carbon capture facilities and the design, construction and operation of subsurface disposal wells lends itself to our ability to minimize the impact on the communities in which we operate and manage long-term subsurface risk. For more information, please visit: http://www.garyclimatesolutions.com

About City of Golden

Golden, Colorado is rich with culture, outdoor activities, scenic beauty, thriving businesses and friendly people, but the City's origins are largely thanks to another valuable resource gold. A small amount of gold discovered in Clear Creek attracted the area's earliest settlers in the mid-19th century and Golden City quickly became an important supply stop for gold miners seeking their fortunes in the adjacent mountains. Farmers soon discovered the rich soil in the valley that is now home to the Coors complex, and Golden City further swelled as coal mining and clay extraction industries settled in the area, utilizing the region's ample natural resources. Today, with the official name of City of Golden, the town continues to thrive. It offers residents and visitors an abundance of recreational, cultural and culinary opportunities. Come live, work and play with us in our modern town with an old west flair. For more information, please visit http://www.cityofgolden.net/.

SOURCE EnerCom, Inc.

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EnerCom and Colorado School of Mines Announce Keynote Panels, Presentations and Participating Companies for The Energy Venture Investment Summit,...

The term KDD stands for Knowledge Discovery in Databases. It refers to the broad procedure of discovering knowledge in data and emphasizes the high-level applications of specific Data Mining techniques. It is a field of interest to researchers in various fields, including artificial intelligence, machine learning, pattern recognition, databases, statistics, knowledge acquisition for expert systems, and data visualization.

The main objective of the KDD process is to extract information from data in the context of large databases. It does this by using Data Mining algorithms to identify what is deemed knowledge.

The Knowledge Discovery in Databases is considered as a programmed, exploratory analysis and modeling of vast data repositories.KDD is the organized procedure of recognizing valid, useful, and understandable patterns from huge and complex data sets. Data Mining is the root of the KDD procedure, including the inferring of algorithms that investigate the data, develop the model, and find previously unknown patterns. The model is used for extracting the knowledge from the data, analyze the data, and predict the data.

The availability and abundance of data today make knowledge discovery and Data Mining a matter of impressive significance and need. In the recent development of the field, it isn't surprising that a wide variety of techniques is presently accessible to specialists and experts.

The knowledge discovery process(illustrates in the given figure) is iterative and interactive, comprises of nine steps. The process is iterative at each stage, implying that moving back to the previous actions might be required. The process has many imaginative aspects in the sense that one cant presents one formula or make a complete scientific categorization for the correct decisions for each step and application type. Thus, it is needed to understand the process and the different requirements and possibilities in each stage.

The process begins with determining the KDD objectives and ends with the implementation of the discovered knowledge. At that point, the loop is closed, and the Active Data Mining starts. Subsequently, changes would need to be made in the application domain. For example, offering various features to cell phone users in order to reduce churn. This closes the loop, and the impacts are then measured on the new data repositories, and the KDD process again. Following is a concise description of the nine-step KDD process, Beginning with a managerial step:

1. Building up an understanding of the application domain

This is the initial preliminary step. It develops the scene for understanding what should be done with the various decisions like transformation, algorithms, representation, etc. The individuals who are in charge of a KDD venture need to understand and characterize the objectives of the end-user and the environment in which the knowledge discovery process will occur ( involves relevant prior knowledge).

2. Choosing and creating a data set on which discovery will be performed

Once defined the objectives, the data that will be utilized for the knowledge discovery process should be determined. This incorporates discovering what data is accessible, obtaining important data, and afterward integrating all the data for knowledge discovery onto one set involves the qualities that will be considered for the process. This process is important because of Data Mining learns and discovers from the accessible data. This is the evidence base for building the models. If some significant attributes are missing, at that point, then the entire study may be unsuccessful from this respect, the more attributes are considered. On the other hand, to organize, collect, and operate advanced data repositories is expensive, and there is an arrangement with the opportunity for best understanding the phenomena. This arrangement refers to an aspect where the interactive and iterative aspect of the KDD is taking place. This begins with the best available data sets and later expands and observes the impact in terms of knowledge discovery and modeling.

3. Preprocessing and cleansing

In this step, data reliability is improved. It incorporates data clearing, for example, Handling the missing quantities and removal of noise or outliers. It might include complex statistical techniques or use a Data Mining algorithm in this context. For example, when one suspects that a specific attribute of lacking reliability or has many missing data, at this point, this attribute could turn into the objective of the Data Mining supervised algorithm. A prediction model for these attributes will be created, and after that, missing data can be predicted. The expansion to which one pays attention to this level relies upon numerous factors. Regardless, studying the aspects is significant and regularly revealing by itself, to enterprise data frameworks.

4. Data Transformation

In this stage, the creation of appropriate data for Data Mining is prepared and developed. Techniques here incorporate dimension reduction( for example, feature selection and extraction and record sampling), also attribute transformation(for example, discretization of numerical attributes and functional transformation). This step can be essential for the success of the entire KDD project, and it is typically very project-specific. For example, in medical assessments, the quotient of attributes may often be the most significant factor and not each one by itself. In business, we may need to think about impacts beyond our control as well as efforts and transient issues. For example, studying the impact of advertising accumulation. However, if we do not utilize the right transformation at the starting, then we may acquire an amazing effect that insights to us about the transformation required in the next iteration. Thus, the KDD process follows upon itself and prompts an understanding of the transformation required.

5. Prediction and description

We are now prepared to decide on which kind of Data Mining to use, for example, classification, regression, clustering, etc. This mainly relies on the KDD objectives, and also on the previous steps. There are two significant objectives in Data Mining, the first one is a prediction, and the second one is the description. Prediction is usually referred to as supervised Data Mining, while descriptive Data Mining incorporates the unsupervised and visualization aspects of Data Mining. Most Data Mining techniques depend on inductive learning, where a model is built explicitly or implicitly by generalizing from an adequate number of preparing models. The fundamental assumption of the inductive approach is that the prepared model applies to future cases. The technique also takes into account the level of meta-learning for the specific set of accessible data.

6. Selecting the Data Mining algorithm

Having the technique, we now decide on the strategies. This stage incorporates choosing a particular technique to be used for searching patterns that include multiple inducers. For example, considering precision versus understandability, the previous is better with neural networks, while the latter is better with decision trees. For each system of meta-learning, there are several possibilities of how it can be succeeded. Meta-learning focuses on clarifying what causes a Data Mining algorithm to be fruitful or not in a specific issue. Thus, this methodology attempts to understand the situation under which a Data Mining algorithm is most suitable. Each algorithm has parameters and strategies of leaning, such as ten folds cross-validation or another division for training and testing.

7. Utilizing the Data Mining algorithm

At last, the implementation of the Data Mining algorithm is reached. In this stage, we may need to utilize the algorithm several times until a satisfying outcome is obtained. For example, by turning the algorithms control parameters, such as the minimum number of instances in a single leaf of a decision tree.

8. Evaluation

In this step, we assess and interpret the mined patterns, rules, and reliability to the objective characterized in the first step. Here we consider the preprocessing steps as for their impact on the Data Mining algorithm results. For example, including a feature in step 4, and repeat from there. This step focuses on the comprehensibility and utility of the induced model. In this step, the identified knowledge is also recorded for further use. The last step is the use, and overall feedback and discovery results acquire by Data Mining.

9. Using the discovered knowledge

Now, we are prepared to include the knowledge into another system for further activity. The knowledge becomes effective in the sense that we may make changes to the system and measure the impacts. The accomplishment of this step decides the effectiveness of the whole KDD process. There are numerous challenges in this step, such as losing the "laboratory conditions" under which we have worked. For example, the knowledge was discovered from a certain static depiction, it is usually a set of data, but now the data becomes dynamic. Data structures may change certain quantities that become unavailable, and the data domain might be modified, such as an attribute that may have a value that was not expected previously.

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KDD Process in Data Mining - Javatpoint