Category Archives: Data Mining
Ethiopia Embarks on a $250M Data and AI Venture with Hong Kong Firm – TradingView
Key points:
The Ethiopian government, through its investment arm, Ethiopian Investment Holdings, has signed a Memorandum of Understanding with Data Center Service, a subsidiary branch of the Hong Kong-based West Data Group. This partnership, valued at $250 million, aims to pioneer sophisticated data mining and artificial intelligence (AI) training facilities within Ethiopia.
State-owned Ethiopian Investment Holdings has signed a Memorandum of Understanding with Data Center Service, a subsidiary of Hong Kongs West Data Group. They will cooperate on a $250-million project to establishing cutting-edge infrastructure for bitcoin mining and AI training.
Kal Kassa, the CEO of Hashlabs Mining, revealed on an X post that through this joint venture, the Ethiopian government will delve into bitcoin mining operations. Hashlabs Mining highlights the countrys openness to mining activities since 2022, despite its stance against cryptocurrency trading.
The initiative seems to gain further complexity with the Ethiopian governments experimental sandbox for cryptographic products licensing, per a Bloomberg report dated February 7.
Additionally, Ethiopia, benefiting from low electricity rates thanks to the partially operational Grand Ethiopian Renaissance Dam, faces a dilemma. The nation boasts the worlds second-lowest electricity prices yet struggles to provide consistent electricity access to half its population. This disparity fuels the debate on the prioritization of resources in the country.
As per another report, the presence of 21 crypto miners in Ethiopia, predominantly Chinese, underscores the global interest in Ethiopias potential as a mining hub. This interest persists despite the crypto trading and mining ban in their home country, China.
Ethiopias government has also engaged with the crypto mining community, supported by entities like Project Mano and BitcoinBirr, coupled with its collaboration with Cardano blockchains IOHK to revamp its education system.
West Data Group, known for its blockchain-fueled fintech solutions and data centers globally, brings to the table its expertise in Bitcoin mining, digital currency investment, and trading. Established in 2017 with its first data center in Kentucky, the company has expanded its footprint to Texas, Kazakhstan, Angola, and Kenya, signaling a robust commitment to digital currency endeavors.
The rest is here:
Ethiopia Embarks on a $250M Data and AI Venture with Hong Kong Firm - TradingView
AI Data Mining Cloak and Dagger. Nightshade AI Poisoning and Anti-Theft | by Aleia Knight | Feb, 2024 – Medium
Probably the biggest use of AI, commercially, has been for art. Models like DALL-E or Midjourney create anything from fantasy landscapes of modern people lounging with dragons to making a 1:1 recreation of the Mona Lisa. The biggest pushback for these models came from artists who, while making their creations public, did not consent to have their creations' data mined for AI training models. Oftentimes, I see people having an AI model take art specifically from a certain artist and then having it create a commission, rather than paying the artist themself to make it.
impersonating real people online with bot accounts, text generation, and image generation.
The Deepfake situation alone has escalated to the point that it has gotten to the desks of White House representatives. A big push was this was the recent Taylor Swift situation in which a user was using AI to scrap images of her from around the internet and create nude images of her, that she never took and without her consent. Imagine, if this can happen to a realistic scale with a celebrity, what that could impact on a social and political level, especially in terms of image, trust, and information exchange.
Even more so, at the beginining of 2024, when a video was release of a fake robocall from President Joe Biden urging the voters of New Hampshire not to vote.
See more here:
Association between biochemical and hematologic factors with COVID-19 using data mining methods – BMC Infectious … – BMC Infectious Diseases
A total of 13,170 participants were recruited (n=5780 people infected to SARS-COV-2 (case) and n=7390 individuals without SARS-COV-2 (control)). Based on Table 1, participants with SARS-COV-2 were significantly older than the control group (59.298.54 versus 56.979.03 years, respectively). In addition, BMI, diastolic blood pressure (DBP), systolic blood pressure (SBP), blood urea nitrogen (BUN), sex, smoking status, serum zinc, copper, creatinine (Cr), cholesterol, triglyceride, high sensitivity C-Reactive Protein (hs-CRP), fasting blood glucose (FBG), serum phosphorus, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), serum gamma glutamyl transferase (Gamma-GT), creatine phosphokinase (CPK), serum calcium, serum total bilirubin, serum direct bilirubin, aspartate aminotransferase (AST), alanine transaminase (ALT), alkaline phosphatase (ALP), serum uric acid and magnesium showed significant differences between groups. Several hematological factors, white blood cells (WBC), red blood cells (RBC), hemoglobin, hematocrit, mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), red cell distribution width (RDW), platelet distribution width (PDW), and mean platelet volume (MPV) were higher compared to the control group (P-value<0.05).
We have attempted to use the LR, DT, and BF models to diagnostic COVID-19 tested participants and their biochemical and hematologic features. In this regard, the data were divided into two parts as training and test data (80%-20%), randomly. The models are validated using test data (20%) and built on the training dataset. Results of the LR algorithm illustrated that biochemical factors (Model I), such as age, smoking status, sex, DBP, SBP, BUN, BMI, hs-CRP, FBG, HDL-C, AST, ALT, CPK, total bilirubin, iron, magnesium, and Gamma-GT were correlated with COVID-19 status (P-value<0.05). In Model I, the BMI, BUN, age variables have been defined as the most crucial variable with high OR by the LR algorithm. With a unit increase in BMI, the chance of being Cov+was 1.092 times. With a year increase in age, the chance of being Cov+was 1.048 times, and with a unit increase in BUN, the chance of being Cov+was 1.041 (see Table 2). In Model II, BMI, age, hemoglobin, hematocrit, sex, MPV, smoking status, and MCHC were significant (P-value<0.05). The hemoglobin had an OR equal to 4.292, so, the chance of being Cov+was 4.292 times. The MPV had an OR equal to 1.550, so, the chance of being Cov+was 1.550 times. Table 3 showed the other variables and values of effect. In Model III, CPK, BMI, MPV, FBG, sex, BUN, Cr, iron, magnesium, total bilirubin, hemoglobin, hematocrit, MCHC, smoking status, age, WBC, HDL-C, and ALT were correlated with COVID-19 status (P-value<0.05). The total bilirubin and MPV had an OR 1.647 and 1.447, so, the chance of being Cov+was 1.647 and 1.447 times, respectively (see Table 4). Based on Table 5, for LR algorithm the accuracy of three models (Model I, II, and III) were 75.13%, 68.28%, and 69.63%, respectively. The other performance indices were given in Table 5 (a), (d), and (g).
In the training phase of DT, the important variables were selected and the final tree is given after pruning. Models I, II, and III runs with 17, 8, and 18 variables as input, respectively. In Model I, CPK, age, BUN, BMI, ALP, sex, total bilirubin, hs-CRP, FBG, and Gamma-GT, in Model II, age, MPV, sex, BMI, hemoglobin, and MCHC, and in Model III, CPK, Cr, BUN, BMI, FBG, age, MPV, MCHC, sex, and total bilirubin variables remained in models. Based on Table 5, the tree is made based on biochemical, hematologic, and both of the variables (Model I, Model II, and Model III, respectively) that had 73.24%, 70.53%, and 68.80% accuracy on the training data, respectively. The other performance indices were given in Table 5 (b), (e), and (h).
The rules from DTs for Model I, II, and III is shown in Table 6. Rule 1 in Model I was illustrated that in a subgroup with CPK>=114.09 & BUN>=30.00 & BMI>=26.77 & Age>=54.00 & Gamma-GT>=16.91, the chance or probability of having Cov+was 84.69%. In another subgroup, CPK<114.09 & CPK<88.06 & Sex(female) & ALT<9.00 led to a 6.57% chance of having Cov+. The rules from Model II, were illustrated that there was an 86.46% chance that participants with features such as Age>=54.00 & BMI>=26.77 & MPV>=9.60 & Sex(male) & Hemoglobin<15.8 be infected with COVID-19. Another rule was suggested that the probability of Cov+in individuals with Age<54.00 & MPV<9.10 was 12.26%. The rules from Model III, were illustrated that there was an 88.15% chance that participants with features such as CPK>=114.09 & BUN>=30.00 & BMI>=26.77 & Age>=54.00 & MPV>=9.60 & MCHC<35.6 be infected with COVID-19. Another rule was suggested that the probability of Cov+in individuals with CPK<114.09 & Cr<1.40 & Cr<1.00 & FBG<118.34 & Sex(female) was 9.90%. Other rules were stated in Table 6.
Hence, the CPK and BUN for Model I, age, BMI, and MPV for Model II, and CPK and BUN for Model III were defined as most crucial variables. The final DT is shown in Figs.2, 3, and 4.
Graphical representation of the classification tree introduced for SARS-COV-2 diagnosis for Model I
Graphical representation of the classification tree introduced for SARS-COV-2 diagnosis for Model II
Graphical representation of the classification tree introduced for SARS-COV-2 diagnosis for Model III
In the final step, for another analysis we applied BF for analyzing the data based on COVID-19. The factors included in the BF algorithm were 17, 8, and 18 variables for Model I, II, and III, respectively. Moreover, we set the following specifications for Model I: Number of Trees in the Forest: 29 for Model I, 13 for Model II, and 53 for Model III, Number of Terms Sampled per Split: 4 for Model I, 2 for Model II, and 4 for Model III, Training Rows: 10,536, Test Rows: 2634, Minimum Splits per Tree: 10, Minimum Size Split: 13 for all three models. Confusion matrix and evaluation indices for comparison of the models I, II, III were stated in Table 5 (c), (f), and (i). Additionally, the crucial variables related to COVID-19 based on BF algorithm were: CPK, BUN, FBG, BMI, total bilirubin, and age in Model I, BMI, sex, MPV, and age in Model II, and CPK, Cr, FBG, BMI, BUN, total bilirubin, sex, MPV, and age for Model III. As one can check the obtained features from BF algorithm were equal to the obtained factors from LR and DT algorithms.
Read more here:
Google To Block Location Data Mining In Maps 12/18/2023 – MediaPost Communications
Privacy concerns and the potential for geofence warrants havepromptedGoogle to work on storing Maps user location-history data on the device rather than in the cloud. This is a change that will make it more difficult for anyone, including law enforcement,to access the data.
Google has faced pressure for years to change the way it stores user location history. The update to Maps will roll out during the next year on iOS and Android. The companyannouncedthechangesin a blog post this week.
The featureholding the data is called Timeline, which tracks all the places visited during a specific period of time. It originally launched during thesummer of 2015.
advertisement
advertisement
The idea seemed interesting atthe time, especially for Google. It allowed people to visit the places they visited in a tab on Google Maps.
The feature must be turned on manually, and is off by default. Users can delete allor part of the information at any time or disable the setting entirely.
Marlo McGriff, director of product at Google Maps, and the author of the post, wrote that users will receive anotification on their when the update applies to their account.
The change comes several months after a Bloomberg Businessweek investigation found police increasingly used warrants to obtain search andlocation data. This practice has been going on for many years. It just took a search warrant and lots of waiting for Google, Meta and other platforms with location information to release the data topolice.
Google also plans to change its auto-delete settings, which previously was set to 18 months by default. The update resets the auto-delete to three months by default.
Keepingthe location data when upgrading to a new phone will require the user to save the data locally and then back it up to the cloud. Google will automatically encrypt it.
Deleting activity such assearches, directions, visits, and shares will become easier with a few taps. The delete feature will roll out on Android and iOS in the coming weeks.
Privacy advocates are also concerned aboutsomething called a reverse keyword search warrant, where police can ask a technologycompany to provide data on the people who have searched for a given term. JenniferLynch, the general counsel at the nonprofit Electronic Frontier Foundation, told Time magazine: Search queries can be extremely sensitive, even if youre just searching for anaddress.
Original post:
Google To Block Location Data Mining In Maps 12/18/2023 - MediaPost Communications
WEKA is Outdated: Here are the Best Data Mining Tools for 2024 – Analytics Insight
In the dynamic landscape of data mining, staying ahead of the curve is crucial for extracting meaningful insights efficiently. While WEKA has been a stalwart in the field, 2024 heralds the arrival of more advanced and versatile data mining tools. This article explores the evolving data mining terrain and presents a curated list of tools that outshine WEKA in the current technological landscape.
RapidMiner stands tall among data scientists for its user-friendly interface and powerful capabilities. With an extensive library of pre-built templates, it simplifies complex data mining tasks, making it a go-to choice for both beginners and experts.
KNIME, an open-source platform, has gained popularity for its flexibility and adaptability. With a modular workflow design, it enables seamless integration of various data mining components, offering a collaborative environment for data scientists and analysts.
Orange is celebrated for its visual programming interface, making it an ideal choice for those who prefer a graphical approach to data mining. With an array of visualizations, it allows users to comprehend complex patterns and relationships effortlessly.
SAS Enterprise Miner empowers organizations with robust data mining capabilities. Known for its advanced analytics and machine learning algorithms, it is a comprehensive tool for businesses seeking in-depth insights from their data.
While TensorFlow is renowned for its prowess in machine learning, its data mining capabilities have made significant strides. Widely adopted by developers and data scientists, TensorFlow offers a scalable and efficient platform for mining valuable patterns from vast datasets.
In the era of big data, Apache Spark MLlib stands out as a data mining tool capable of handling massive datasets with ease. Leveraging the power of Spark, it enables distributed data mining, making it a robust choice for organizations dealing with large-scale data.
Bringing data mining to the cloud, Microsoft Azure Machine Learning provides a scalable and efficient platform for extracting insights. With seamless integration with other Azure services, it simplifies the end-to-end data mining process.
For Python enthusiasts, Scikit-Learn remains a top choice. Its simplicity and integration with popular Python libraries make it an accessible yet powerful tool for data mining tasks.
As we bid farewell to the era where WEKA reigned supreme, the data mining landscape in 2024 is brimming with innovative and powerful tools. Whether you prioritize user-friendliness, open-source flexibility, or cloud-powered scalability, the tools mentioned above offer a diverse range of options to cater to your data mining needs. Embrace the future of data mining with these cutting-edge tools and unlock the full potential of your datasets.
Read the original here:
WEKA is Outdated: Here are the Best Data Mining Tools for 2024 - Analytics Insight
The Role of Data in Process Mining – TechiExpert.com
Imagine running a business is like tending to a garden. Everyone talks about being eco-friendly, but Janina Bauer from Celonis says it is not just talk. In fact, it is a big part of every decision.
Celonis, Global Head of Sustainability at Celonis, is like a super-smart gardener. They use something called process mining to help businesses run smoothly. It is like shining a light on how things move around in your garden and simultaneously finding better ways to do things.
Now, the problem is, some businesses see being eco-friendly as too expensive. Plus, their information is all over the place, like seeds scattered in different pots. Celonis helps in gathering all this info and make it easier for businesses to be green.
Why should businesses care? Well, Janina says successful ones are both green and make money. In todays world, where people want eco-friendly choices, being green is not just a department, but it is part of everything.
Janina, who loves green ideas, makes sure Celonis practices what it preaches. They are working hard to become eco-friendly and sets targets like a gardener aiming for perfect blooms.
In simple terms, being eco-friendly is not a headache. It is a chance to grow. As Janina says, being green and making money go together like flowers and sunshine. So, for businesses to be ready for the future, it is time to make being green a big part of the journey, not just a goal on paper.
And when businesses embrace being green, it is not just good for the planet. It is like giving their garden a boost, making it healthier and more beautiful. So, let us all be gardeners of the business world, making it bloom with green success.
Read more here:
2023 in data: the trends that shaped the mining sector – Mining Technology
An assessment of trends in the mining sector during 2023 reveals an industry grappling with rising supply costs and changing demand as the world shifts away from coal and towards renewable energies.
Inflation is driving up the prices of core products and services, in particular fuel and power, as well as maintenance and explosives. Meanwhile, metals used in batteries are a growing market and the net-zero energy transition is bringing with it new technologies and sustainability expectations.
According to GlobalData analytics, there were a total of 511 asset transactions in the mining sector between 1 January 2023 and 15 December 2023, with a value totaling $29,393m. Of these, 305 were acquisitions, worth $70,931m in total, and 12 were mergers, worth $565m.
On the trends visible within the 2023 deals, David Kurtz, Director of Mining and Construction at GlobalData, says: On mining M&A were seeing an increasing shift towards transition metals and battery commodities. Looking at announced M&A, we can see a roughly 40% increase in deals across lithium, cobalt and nickel this year compared to 2022, and its about four times higher compared to 2019, in both value and volume terms, as companies focus their investments on these battery metals as well as other future-facing commodities.
Indicative of these changing priorities in the industry, the highest value deal in the sector in 2023 was Coolabah Metals acquisition of full ownership of the Cannington Project in Australia. Coolabah Metal is an Australia-based minerals exploration company which identifies, acquires and develops copper, gold and base metal assets; it acquired the project from Thomson Resources on 15 March for $19,994.7m.
GlobalData analysis also indicates that gold saw the most deals by sector in 2023, with 235 deals. It was followed by copper, which saw 163, silver with 67, nickel with 53, and coal with 50.
Access the most comprehensive Company Profiles on the market, powered by GlobalData. Save hours of research. Gain competitive edge.
Your download email will arrive shortly
We are confident about the unique quality of our Company Profiles. However, we want you to make the most beneficial decision for your business, so we offer a free sample that you can download by submitting the below form
2023 saw a general decline in stock prices, as the prices of base metals followed a mostly downward trajectory over the year. This is a result of lower demand from Chinas heavy industry and real estate sectors, which have suffered the impacts of an economy hit by youth unemployment, and slow growth post-Covid.
In its recent report, the World Bank noted that metal prices fell 2% in Q3 2023, compared to Q2, following an overall downward trend. It expects an overall decline of 12% over the course of the year, and anticipates that this decline will continue into 2024, due to continued slowing demand from Chinese markets.
However, demand for minerals needed for battery production such as lithium, copper and nickel will continue to increase. The World Bank says: In the short-term, they have followed the downward trend in base metals prices [However,] firming global growth, along with policies to expand renewable energy infrastructure, are expected to underpin a rebound in metal and mineral prices in 2025. Global investment in clean energy infrastructure has grown by almost 28%between 2021 and 2023 and continues to rise rapidly, propelling a demand surge for copper, lithium, and nickel.
The launch of ChatGPT in November 2022 thrust Artificial Intelligence (AI) into the limelight for almost all sectors this year. In mining, AI received a consistently high number of mentions across company filings throughout the year.
Early examples of AI are already disrupting the mining industry: a GlobalData report noted the use of AI in resource expansion by SensOre, an Australian mineral resource company. The company has used AI and machine learning technologies on its giant Western Australia multidimensional data cube to identify lithium-rich pegmatite signatures during early field reconnaissance.
GlobalData also noted AI use by Benchmark Metals Inc, a Canadian provider of mineral resource exploration. The company has initiated an AI-guided, 20,000-metr drilling program to define high-grade gold and silver zones at Cliff Creek and Dukes Ridge in British Columbia, Canada.
AI is still in its infancy in mining, but the industry has high expectations. A recent GlobalData poll found that that 53% of respondents expect AI to live up to its promises, while only 6% considered it all hype and no substance.
The mining sector has seen a decline in active roles across 2023, in a downward trend driven by the steady phasing out of coal.
In particular, there was drop in active roles across Australias mining sector. The sector supported 4,007 jobs in June 2023, but had seen a 38.8% decrease in positions by November, when only 2,452 roles were active. This saw Germany overtake Australia in September, with 2,852 roles; Canada overtook it with 2,671 in September.
However, some parts of the industry saw a growth in jobs. The global mining supply chain grew from 6,085 jobs in January 2023 to 7,121 by June 2023. Environment-related jobs also grew, from 7,610 in January to 9,606 in June.
Within the supply chain, the highest number of active roles were for logisticians and project management specialists, followed by buyers and purchasing agents. Trends within the supply chain were likely driven by difficulties sourcing products and services; a recent survey found that 41% of respondents agreed that difficulties had motivated them to seek out more new suppliers in 2023 than in the past, and 27% strongly agreed.
Meanwhile, in the environment department, maintenance and repair workers had the most active roles, followed by electrical and electronics engineers. The aforementioned survey confirmed this trend in the data, finding that 83% of respondents had looked for (an) alternative supplier(s) in maintenance and repair in 2023, compared to 70% in 2022.
Give your business an edge with our leading industry insights.
The rest is here:
2023 in data: the trends that shaped the mining sector - Mining Technology
The Top 9 Data Mining Tools for 2024 and the Transformative Insights They Hold! – ET Now
In today's fast-paced digital era, data is the lifeblood of every business. And with the escalating amount of data being generated, it becomes crucial to have robust data mining tools to extract meaningful insights. Data mining tools leverage advanced algorithms, analytics, and machine learning techniques to discover patterns, trends, and relationships within data sets.
The Top 9 Data Mining Tools for 2024 and the Transformative Insights They Hold!
1. Apache Hadoop
Apache Hadoop stands at the forefront of data mining technology. It is an open-source software framework that enables distributed processing of large datasets across clusters of computers. With its ability to handle massive volumes of data, Hadoop allows businesses to unlock valuable insights that were previously hidden. Its scalability and fault tolerance make it an indispensable tool for handling big data.
2. KNIME
KNIME, an acronym for "Konstanz Information Miner," is an open-source data integration, processing, analysis, and visualization platform. With its drag-and-drop interface, KNIME enables data scientists and analysts to build custom workflows without the need for programming. This user-friendly tool empowers users to explore, clean, transform, and visualize data effortlessly.
3. RapidMiner
RapidMiner is a powerful data mining tool that offers an extensive range of capabilities. It provides an intuitive graphical interface for data preparation, modelling, evaluation, and deployment. RapidMiner supports various machine learning algorithms and allows users to develop custom models specific to their business needs. Its seamless integration with other languages, such as R and Python, enhances its versatility.
Tableau is a leading data visualization and business intelligence tool that aids in uncovering valuable insights from complex datasets. Its drag-and-drop interface enables users to create visually appealing and interactive dashboards, charts, and reports. Tableau's ability to connect with multiple data sources and its powerful analytics features make it an invaluable asset in data-driven decision-making.
5. SAS Enterprise Miner
SAS Enterprise Miner, developed by SAS Institute, is an advanced analytics tool that simplifies the data mining process. With its rich set of algorithms and automated modelling features, SAS Enterprise Miner allows users to discover patterns, build predictive models, and make accurate predictions. Its user-friendly interface and robust performance make it a top choice for businesses across various industries.
Microsoft SQL Server Analysis Services (SSAS) is a comprehensive data mining tool that enables businesses to analyse and gain insights from their data. SSAS provides rich data mining features, including decision trees, clustering, and association rules, to uncover hidden patterns and relationships. Its integration with other Microsoft products, such as Excel and Power BI, streamlines the data analysis process.
7. IBM SPSS Modeler
IBM SPSS Modeler is a data mining tool that offers a wide range of predictive analytics capabilities. With its intuitive interface and extensive library of algorithms, SPSS Modeler enables users to explore data, identify patterns, build models, and generate predictions. Its seamless integration with other IBM tools enhances its versatility and makes it a trusted choice for businesses worldwide.
8. Oracle Data Mining
Oracle Data Mining is a powerful tool that leverages the capabilities of Oracle Database to extract meaningful insights from large datasets. With its comprehensive set of algorithms, Oracle Data Mining facilitates predictive analytics, anomaly detection, and text mining. Its integration with popular programming languages, such as R and Python, further amplifies its usability.
9. RapidMiner Studio
RapidMiner Studio offers a comprehensive suite of data mining and predictive analytics tools. With its drag-and-drop interface and extensive library of pre-built operators, RapidMiner Studio simplifies the process of data preparation, modelling, evaluation, and deployment. Its collaborative features enable teams to work together seamlessly, fostering innovation and efficient decision-making.
Career Opportunities
As we continue to navigate the data-driven future, these top nine data mining tools for 2024 provide the necessary arsenal to harness the transformative insights hidden within vast datasets. From open-source platforms like Apache Hadoop and KNIME to industry leaders like Tableau and IBM SPSS Modeler, these tools cater to diverse business needs and empower organizations to make data-driven decisions. By leveraging the power of these data mining tools, businesses can unlock a competitive edge and drive innovation in their respective industries.
View original post here:
The Top 9 Data Mining Tools for 2024 and the Transformative Insights They Hold! - ET Now
How Databases and Data Mining are Reshaping Healthcare – TechiExpert.com
In the dynamic realm of modern healthcare, a digital revolution is reshaping how medical information is collected, analyzed, and utilized. Amid this transformation, data mining takes center stage, employing intricate algorithms to unveil hidden patterns within vast datasets, bolstered by the role of databases that store and feed information. This synergy empowers informed decision-making and proactive disease detection, while database technology serves as the foundation for efficient data management and navigation. The fusion of data mining and databases stands to pioneer groundbreaking advancements in medical research and informed healthcare decisions, propelling the industry into an era of data-driven discovery and improved patient outcomes.
Data mining, a sophisticated facet of information processing, has assumed a pivotal role within the vast landscape of medical data analysis. This technology, underpinned by complex algorithms and statistical techniques, has the remarkable ability to unveil subtle patterns and profound insights concealed within colossal datasets. The data mining process is a structured progression comprising several interconnected phases. Initially, data is meticulously prepared, cleansed, and transformed to ensure its quality and suitability for analysis. Subsequently, the mining phase commences, wherein algorithms meticulously traverse the data terrain, identifying correlations, trends, and anomalies that might elude human scrutiny. This analytical choreography ultimately seeks to distill valuable knowledge, previously buried under the sheer volume and complexity of the information.
It is important to note that data mining is not solitary. It thrives alongside its steadfast partner, the database. The database serves as the repository where data is stored and organized, forming the foundation for mining operations. Acting as the primary source, it fuels the mining process with information, ultimately turned into actionable insights. This synergy underscores the link between adept data management and fruitful data analysis. The seamless interplay of data mining and databases yields advantages in healthcare decision-making, early ailment spotting, and enhanced patient care, highlighting the dynamic prospects of this interdisciplinary data approach.
At the heart of this revolutionary expedition lies database technology, a multifaceted domain within software science that serves as the bedrock of efficient data management and utilization. Through an exploration of database architecture, storage mechanisms, design principles, and real-world applications, this technology equips us with the tools to navigate the vast expanse of available information. By harnessing the symbiotic relationship between data mining and database technology, the prospect of groundbreaking advancements in the medical realm comes to the fore, as clinical researchers embark on a journey to unveil hitherto concealed revelations nestled within intricate datasets. This powerful collaboration enables the extraction of intricate patterns, correlations, and predictive insights, fueling a more informed and proactive approach to healthcare decision-making.
Amidst the expanding data landscape, the fusion of data mining and database technology emerges as a dynamic catalyst for medical innovation. This synergy empowers scientists and healthcare professionals to explore patient records, genomic sequences, and diagnostic data, unearthing insights that can reshape disease understanding, treatment approaches, and patient outcomes. By capitalizing on data minings analytical capacity and the structured organization of databases, the complex fabric of medical information becomes more manageable, enabling us to navigate modern healthcare challenges with enhanced precision. This transformative journey is set to redefine medical research and practice, ushering in an era of data-driven discovery and elevated healthcare standards.
The ascent of big data unfolds as a dynamic force that holds transformative potential across healthcare domains. The burgeoning data influx empowers medical researchers, clinicians, and educators to glean unprecedented insights from diverse sourcesranging from electronic health records and genetic sequences to real-time patient monitoring data and clinical trial results. This influx not only revolutionizes diagnosis accuracy, personalized treatment strategies, and disease prevention initiatives, but also propels medical education into an era of interactive learning fueled by real-world patient data. Yet, as the big data resurgence beckons, it demands innovative approaches to data storage, security, analysis, and ethical considerations, underscoring the imperative of adapting healthcare practices to leverage this tidal wave of information for the collective benefit of patients, practitioners, and the broader medical community.
Within the healthcare domain, data has become a precious resource. Medical institutions grapple with mounting data generated daily. Electronic health records, administrative claims, and biometric data are but a fraction of the data treasure trove. To unlock its true potential, medical institutions worldwide are integrating diverse medical information systems, setting the stage for a holistic understanding of patient health and treatment outcomes.
Medical data possesses unique characteristics that distinguish it from other fields. Gathering medical data can be intricate, often relying on structured protocols and domain expertise for accurate interpretation. Yet, amidst this complexity lies unparalleled potential. The convergence of data mining and database technology equips medical researchers to unveil latent patterns and relationships that influence diagnosis, treatment, and patient outcomes.
The fusion of medical databases and data mining technology promises to reshape healthcare dynamics. This amalgamation facilitates remote collaboration, propels precision medicine, and transforms the healthcare management landscape. As we move ahead this data-driven journey, we stand at the crossroads of transformative changea change that is set to permeate medical research, education, and practice. Insights gleaned from data will illuminate the path toward improved healthcare outcomes, ushering in a future where every patients journey is guided by data-driven precision.
See original here:
How Databases and Data Mining are Reshaping Healthcare - TechiExpert.com
Book Review: The Hank Show, by McKenzie Funk – The New York Times
THE HANK SHOW: How a House-Painting, Drug-Running DEA Informant Built the Machine That Rules Our Lives, by McKenzie Funk
If there was a Mount Rushmore of the architects of the modern panopticon state, it might be composed of Metas Mark Zuckerberg, the Palantir head Peter Thiel, and Hoan Ton-That, founder of Clearview AI. But perhaps there should be a fourth, more chiseled visage up there, one you probably dont recognize: that of Hank Asher.
Like another tech mogul whos the subject of a recent biography, Asher had an abusive childhood, treated women in his life poorly, was prone to dark moods and explosive management, and had a highly gifted technical mind. Both reportedly met with Rudy Giuliani to potentially hire him as a consultant and lobbyist after his mayoral stint (Elon Musk was repulsed by Giulianis demeanor; Asher happily retained his services).
But Asher, who died in 2013, is a little-known figure in technology although the legacy of the data broker industry he started potentially has as much impact on our lives as the work of Silicon Valley household names.
Data brokers hoover up massive amounts of personal data public records, credit card transactions, social media, geolocation data and then synthesize it for their clients to use for things like advertising, risk assessment for insurance, or even law enforcement.
Over the course of three decades Asher, an eccentric former condo painter, would run three separate companies all of which did exactly this. In the process, he would become something of a Forrest Gump of the field. The Hank Show, by McKenzie Funk, a reporter at ProPublica, traces the origins of the industry from its inception a small-use case allowing local insurance agencies to run searches on driving records more quickly to the behemoth that quietly touches all of us today.
In the early 1990s, before the commercial internet was widely available, Ashers business partner had the idea of buying up bulk D.M.V. records in the state of Florida. At the time, these were usually sold for a penny-a-record fee, and typically so an insurer or credit bureau could request intel as needed. At a time when most computers relied on one processing unit that operated sequentially, Asher and his collaborator figured out how to connect multiple smaller devices and distribute processing tasks, so that his system was able to outpace competitors.
As Asher realized, a search could potentially provide not just a current address, but also a list of any other occupants at the same address; past residences; businesses registered to a name; the value of a home. Asher and his co-workers added still more search criteria: marriage and divorce records, bankruptcies, credit reports, gun licenses, voter registrations. And, as home internet exploded in the late 1990s, email addresses and online shopping habits could be logged, too.
Police departments and corporate clients like newspapers (including this one), white-shoe law firms and insurers signed on to access Ashers ever-expanding trove of data, and his influence ballooned. When his first company, DBT, went public in 1996, Ashers 36 percent stake was worth $111 million; DBT later became ChoicePoint, which had an annual revenue close to $1 billion by 2004, and could count the State Department as a client. (Indeed, by 1997 Ashers shady past was inhibiting enough to such government contracts that he was voted out as C.E.O.)
Then came 2000. During the presidential election, DBT Online was contracted by the state of Florida to clean up its voter rolls by removing felons. However, the methodology the company used to cross-reference names was flawed Willie Steen was mixed up with Willie OSteen and thousands of (mostly Black) voters were mistakenly barred from voting. The margin of error was large enough to have potentially swung the election for Al Gore.
Despite this setback for the methodology, Asher, who publicly denied any responsibility for the debacle, continued to loom large in the world of data mining. After Sept. 11, he took it upon himself to find the planes hijackers using a set of narrow parameters. He ended up with a list of 1,000 potential people, down from the governments original 120,000 names, which included five of the actual hijackers.
Asher, who himself had evaded charges for his role as a drug-smuggling pilot between the States and Central America in the 1980s, was focused on the potential for his data to help with law enforcement, especially for missing and exploited children, frequently donating his services, along with monetary contributions.
Data privacy is one of those topics in tech journalism that are hard to write about because, while everyone knows its awful, it can make for dry reading. By following the colorful character of Hank Asher, The Hank Show succeeds in demonstrating how truly sinister the credit bureaus may actually be worse even than Facebook. The strategy also serves to demonstrate the real-world stakes.
Its easy to ignore the fact that, say, LexisNexis has our data, because it doesnt infringe on our lives in quotidian, irritating ways (think pop-up ads). But when that data is sold in aggregate to law enforcement, immigration or hospitals, it starts to matter.
The collection of our online data what websites we visit, whom we follow on Instagram, how long we lingered on a TikTok video before scrolling, what we Google should be of grave concern to all of us. The combined 2022 revenues of Alphabet and Meta are close to half a trillion dollars, mostly from highly targeted advertising.
As Funk points out, Apples Ask App Not to Track feature struck a serious blow to these tech companies; the majority of American iPhone users opt out of the ad tracking when given the choice. However, the kinds of data that Asher and his fellow brokers were collecting your address from your utility bills, facial recognition cameras in public places did not provide that option. Seeing an Instagram ad for a product youre likely to buy isnt a dire consequence. Being falsely accused of a crime due to law enforcements increasing use of predictive policing tech or being deported because of D.M.V. data? These are.
Ashers earlier company was sold to LexisNexis, and his last company, TLO (The Last One), was sold after his death by his daughters to the credit bureau TransUnion. Today, reporters routinely use both LexisNexis and TLOxp to find a phone number for a source, glean information like past criminal charges, or locate relatives and neighbors of someone in the news.
Like (I assume) most tech reporters, Ive run the search on myself out of curiosity. Indeed, its unsettling: not just my current phone and address, my childhood home, my parents names, the names of my college roommates as potential associates. There are services that, for around $10 per month, will opt you out of data brokers that it finds have your information. I highly recommend them.
Katie Notopoulos is a writer in Connecticut. She was previously a technology reporter for BuzzFeed News.
THE HANK SHOW: How a House-Painting, Drug-Running DEA Informant Built the Machine That Rules Our Lives | By McKenzie Funk | 304 pp. | St. Martins Press | $30
See the rest here:
Book Review: The Hank Show, by McKenzie Funk - The New York Times