Trending December 2023 # Planning To Embrace Big Data? Here Are Real # Suggested January 2024 # Top 15 Popular

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Embracing analytics is much tougher when it comes to confronting what real-time challenges mean in this field. Your data has to be very appropriate at each moment. A single minute can cost you millions of dollars if misplaced. That’s how tough is analyzing big data, researching firms and putting it up all together into a single vertical. There are times when the minutes and seconds count in to be very crucial and no delays can be accepted. Like in flight landing, a single delay can harm lives. Big data analytics has been taking the picture up from science experiments, sensor detection, radar communications to social media activities and what not. Every single action is determined by what was done previously or how was it done earlier. With this forming the major layout, challenges are also gearing up. You need to have elite communications and predictions that could actually help. The intelligent transport systems, financial market trading or military operations demand real-time decisions to hit the performance factors. Now, these real-time insights could vary from organization to organization. Some might accept delays; some might look for slight delays to set the other variable on time and some might not even handle a nanosecond delay. This vague real-time definition is the biggest challenge to overcome. Big data differs from other forms of data as it is categorized with 3 V’s: Velocity, Variety and Volume. Data is usually collected from various sources and is then processed further according to the demands. Decision-making, organizing and accessing are some major works to be then related. Each application has got some architecture to follow. Such in a way that it is able to handle data spikes, shortage and is able to scale up with the growing data whenever necessary. We need an architecture that does not fade away with the usage like if it’s of no use after 1 year then probably having it right now isn’t a good option. So, scaling up with the architecture is again a challenge to tackle. Having the work done should not be the only goal when we are into analytics. Why because, if your system breaks at some time or is unable to process some data at any point of time your internal processes should have some backup. If the only goal you had was your external outlook, probably then maintenance would be an issue. If the system fails, there should be some good internal processes that could have the capability to back up the entire failure. If some random internal processes are there, then it would be very difficult to handle the issues at runtime. The last big challenge is to make this entire shift. Employees working on the old traditional work practices have to be somehow convinced to take up this way. There are a variety of escalations that can be stepped onto and huge tasks can be very efficiently affected by this. Gradually the entire paradigm would be stepping into this so why not now? If there are areas where the employees aren’t comfortable enough to look for, then trainings can be organized. Managers could probably scale up some traditional issues and make the entire team realize how analytics can help them ace. So, there has to be some change to be taken care of and it’s better to experience right now then to be late and struggle at further stages. Coming to the gist of what we have is: Real-time analytics demands much more of our efforts and hard work. There are still challenges we need to look after. The faster we grow at this, the better we would be later. So making a change is the need of the hour and scaling up with the market trends is what is required the most.

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Big American Phone Providers Are Being Sued Over Your Location Data

At this point if the news that your phone company has been selling customer location data to bounty hunters surprises you, you may need to catch up on the last few years of privacy revelations. If you’re just generally suspicious of places that collect your data, congratulations on being right (again). AT&T, Sprint, T-Mobile, and Verizon are currently being sued for selling customer location data to third parties known as data brokers, who then sell the data to other people with an interest in finding you – especially the “kinda-sorta” officials like bail bondsmen and bounty hunters.

The short story

The Maryland-based ZLaw Firm filed a class action suit against the four big US mobile providers on May 2nd, 2023. They’re suing in the names of the company’s customers who were affected. Essentially, their lawsuit accuses these companies of providing access to real-time location data to companies that shouldn’t have had access. The suit covers a roughly four-year period from 2023 through 2023, though that doesn’t necessarily mean the activity was limited by these years.

Since it’s a class action lawsuit, affected individuals may be entitled to compensation, though more details on this will be forthcoming. The real goal here, however, is to get the big phone companies to stop selling sensitive customer information – or at least to be more careful with it.

What exactly has been going on?

Back in 2023 there was another scandal where it came out that Securus, a prison technology company, was giving low-level law enforcement officers access to the location of pretty much every phone on all of the major carriers. That level of surveillance usually requires a warrant in the US, but Securus was using an intermediary company called LocationSmart, which pretty much anyone could sign up for, even on a free trial account, to get access to the location of most cell phones being used in the U.S.

Generally, the data in question here isn’t your GPS data – it’s your approximate location as determined by the strength of different cell tower signals, which is something phone companies really need in order to provide service. However, some of the data available to bounty hunters was occasionally from GPS, meaning they could get your location down to a few meters.

A lot of other stuff happened around the 2023 location issue (including Securus being hacked, meaning access to their real-time tracking tools could have been in anyone’s hands for a while), but the reason it’s important to this story is that every carrier involved promised to fix these sorts of loopholes and stop giving sensitive data to sketchy third parties. That apparently hasn’t been going so well, since Motherboard was actually able to identify the general path the data took.

Here’s how the process seems to have been working:

A data aggregator (Zumigo, in this case) buys customer data from a telecom company. They then use this data for any number of things, including fraud prevention and possibly marketing.

Zumigo then sells off your data to other services, including, in this case, a company called Microbilt, which uses the access it buys from Zumigo to sell services, like background or credit check, or tracking people who might break their bail. Microbilt actually maintains price lists for services like these.

Whoever is using the service, like bounty hunters or landlords, pays for your cell phone data and gets to use it.

If all that seems a little Byzantine, it is, but though your data is bouncing through a lot of different companies, it’s all coming straight from the phone provider at the center. If they close off access to third parties who are misusing this data, there won’t be a problem anymore – but it seems like they aren’t.

Bounty hunters aren’t out to get me, why should I worry?

Okay, you’re not Han Solo, and your location data probably isn’t being pulled by anyone in particular, even though you did shoot first. There have been cases, though, of people with access to these tools using them for more off-the-clock activities, such as tracking girlfriends. That’s not something that’s likely to affect the general public, but the fact remains that we now have tools that allow certain people to find you pretty much anywhere, whether it’s a potential employer checking how often you visit a psychiatrist or a marketing company trying to build a better profile on you.

It’s not just tracking individual movements, either: location data that is gathered and analyzed in bulk can help identify trends in how people move. When anonymously gathered and properly used, this type of data can be very helpful in designing better systems, but when it’s firehosed out without much consideration as to whose hands it ends up in, it’s a breach of trust and just generally a bad idea.

Image credits: Sierpiński Pyramid from Above

Andrew Braun

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Here Are The Best Netflix Crime Shows

Netflix

Committing a crime is always against the law. However, watching crime dramas from the comfort of our home has been our pleasure ever since TV was first invented. Some of the best crime TV series can be found on Netflix. But what are the best Netflix crime shows? That’s just what we are going to solve today.

Revamping Patient Care With Big Data

Looking further ahead, how can medical companies benefit from and make the most of them for the clients? We believe that big data-powered business intelligence can foster great improvements in patient care. Now, let’s get into what we mean by it below.

Create a Ground for Better R&D

Add to this: 1. Lab information systems that manage clinical test orders and harbor results 2. mHhealth or telehealth apps for remote doctor-patient sessions 3. Wearable devices and implantable sensors gleaning data on physical activity level, energy expenditure, and fitness 4. Social networks with numerous hubs for discussions around health. 5. How to use this variety of immediately accessible records to contribute to medical R&D and improve patient care?

Build Healthcare Patterns

Imagine, say, an interactive dashboard that communicates with the data endpoints mentioned above. Via that large searchable database, medical scientists can track changes in health condition over time, even at community level. Deriving valuable insights on public health, specialists will be able to further implement them in demographics management solutions. The wealth of open data on vital signs and lab tests can help spot symptoms early on. Practicing predictive diagnostics and designing more efficient treatment patterns, doctors will be more likely to succeed in disease prevention.

Reduce Malpractice, Rule Out Medical Errors

With medical errors being in the US, 40% of Americans pick healthcare as their major concern. Even though most of the modern EHRs are programmed to detect risky drug interactions or overdoses, mistakes occur. Today, scientists can address the issue via that integrates with EHRs to pinpoint inaccurate prescriptions. To preclude the errors, the big data-powered systems check if a drug matches patient’s condition as it’s described in an EHR. If it doesn’t, the prescription gets blocked as inappropriate, pending until it is either approved or cancelled.

Streamline Genomic Sequencing

With its ability to determine the entire genome order and identify disease risk or cause, DNA or genomic sequencing has become a major biotech in medicine. To ensure accuracy while investigating changes in genes, scientists have to analyze billions of DNA strands at a time. This is where big data approach becomes instrumental in pinpointing anomalies that are likely to affect health condition.

Tackle Opioid Addiction

Researchers say that most addicts start overdosing once they from their families or friends. Quite naturally, governments come up with initiatives around imposing tough standards on prescription drug accounting. How can big data help handle it? First way is to build solutions that scan EHR records to detect unnecessary prescriptions. Second, with programs like , startups can offer that help find drug take back locations and better stock treatment resources.

Improve Imaging-Based Diagnostics

Medical imaging is a critical diagnostic tool that helps physicians quickly spot treatment targets. With an increasing image overload, the trend is to migrate petabytes of scans to cloud-based storage that grow into a world-scale anatomical and physiological database. Currently, radiologists obtained a cost- and time-efficient that pairs medical imaging big data to AI algorithms. To tell normal and pathological patterns apart, specialists harness neural networks that have been trained on vast datasets. By applying AI algorithms on CT images, physicians can also calculate bone density and assess the risk of fracture.

Tailor Personalized Patient Care Plans

A holistic approach to aggregation, governance, and analytics of biomedical big data ensures a great shift towards a wider adoption of precision or personalized medicine. This patient-centric and value-based medication model is designed to help detect diseases at an early stage, prevent outbreaks or complications, and decisively cure the cause. The personalized medicine project obtained the US government’s support back in 2023, with the growth of to develop cancer genomics and improve treatment methods. By combining big data with medical R&D, researchers are well on their way to yield innovative precision healthcare approaches.

Ensure Treatment Accuracy

What’s still in common between most of precision medicine techniques is that they can’t do without big data. In this regard, the better the access to patient health records, the more informed are physician’s decisions. To ensure accurate individual treatment plans, stakeholders are looking to build healthcare business intelligence solutions that analyze big data across as many sources as possible. These may include lab results, progress notes, diagnosis and procedure codes, allergies and side effects, medication, admission, and discharge data, all the way to patient’s access to therapeutic recreation, food, and housing security. A surefire way to put the obtained insights to good use is to harness them while developing . With that rich value-based info at hand, it is possible to efficiently identify risk groups, control epidemies, spot service gaps, and design community-level healthcare strategies for better outcomes.

Reduce Healthcare Costs

Big data approach unlocks actionable insights that enable medical companies reduce hospital stays, cut readmission rates, improve patient care, and achieve better health outcomes. Researchers work hand in hand with software engineers, bringing to the table full-fledged solutions for this purpose. Take, for instance, a compound R&D team that helped develop . The solution utilizes a machine learning algorithm to predict 30-day readmissions for patients suffering from heart failure. To estimate the possibility of another stay, the system analyzes vital signs and other clinical and demographic metrics.

Optimize Patient Care Efforts

Using AI-enabled monitoring systems that analyze patient data to flag changes in, say, blood glucose level or weight, medical institutions can reduce spendings on human staff. Big data-backed online diagnostic tools and genetic sequencing ordering services ensure better patient engagement and optimize treatment and decision-making efforts. Additionally, healthcare providers leverage mHealth and telehealth apps to collect biomedical data that helps design individual post-discharge treatment roadmaps. In 2023, researchers that real-time mHealth messaging apps ensure that 86% of patients follow their medication guidelines, as they got the instructions immediately available. Virtual sessions via telehealth apps also help expand the access to healthcare services, streamline clinical workflows, and capitalize on avoiding in-house treatment and transportation expenses. In this regard, smartphones and wearables can do much good by tracking vitals, handling emergency alerts and e-prescriptions — providing urgent care on the go. Ultimately, biomedical big data analytics yields preventive solutions to . By exchanging real-time updates on admission, discharge, and transfer, healthcare providers can and save millions of budget across the states.

Derive More Benefit from Big Data

Top Tips For Citizen Data Scientists To Become Experts In Big Data

Here’s how citizen data scientists can become well versed in big data

With data scientists regularly topping the charts as one of the most in-demand roles globally, many organizations are increasingly turning to non-traditional employees to help make sense of their most valuable asset: data. These so-called citizen data scientists, typically self-taught specialists in any given field with a penchant for analysis, are likewise becoming champions for important projects with business-defining impact. They’re often leading the charge when it comes to the global adoption of machine learning (ML) and artificial intelligence (AI), for example, and can arm senior leaders with the intelligence needed to navigate business disruption. Chances are you’ve seen several articles from industry luminaries and analysts talking about how important these roles are for the future. But seemingly every opinion piece overlooks the most crucial challenge facing citizen data scientists today: collecting better data. The most pressing concern is not about tooling or using R or Python2 but, instead, something more foundational. By neglecting to address data collection and preparation, many citizen data scientists do not have the most basic building blocks needed to accomplish their goals. And without better data, it becomes much more challenging to turn potentially great ideas into tangible business outcomes in a simple, repeatable, and cost-efficient way. When it comes to how machine learning models are operationalized (or not), otherwise known as the path to deployment, we see the same three patterns crop up repeatedly. Often, success is determined by the quality of the data collected and how difficult it is to set up and maintain these models. The first category occurs in data-savvy companies where the business identifies a machine learning requirement. A team of engineers and data scientists is assembled to get started, and these teams spend extraordinary amounts of time building data pipelines, creating training data sets, moving and transforming data, building models, and eventually deploying the model into production. This process typically takes six to 12 months. It is expensive to operationalize, fragile to maintain, and difficult to evolve. The second category is where a citizen data scientist creates a prototype ML model. This model is often the result of a moment of inspiration, insight, or even an intuitive hunch. The model shows some encouraging results, and it is proposed to the business. The problem is that to get this prototype model into production requires all the painful steps highlighted in the first category. Unless the model shows something extraordinary, it is put on a backlog and is rarely seen again. The last, and perhaps the most demoralizing category of all, are those ideas that never even get explored because of roadblocks that make it difficult, if not impossible, to operationalize. This category has all sorts of nuances, some of which are not at all obvious. For example, consider the data scientist who wants features in their model that reflect certain behaviors of visitors on their website or mobile application. But of course, IT has other priorities, so unless the citizen data scientist can persuade the IT department that their project should rise to the top of their list, it’s not uncommon for such projects to face months of delays — assuming IT is willing to make the change in the first place. With that in mind, technology that lowers the bar for experimentation increases accessibility (with appropriate guardrails) and ultimately, democratizes data science is worth consideration. And companies should do everything they can to remove roadblocks that prevent data scientists from creating data models in a time-efficient and scalable way, including adopting CDPs to streamline data collection and storage. But it’s up to the chief information officers and those tasked with implementing CDPs to ensure that the technology meets expectations. Otherwise, data scientists (citizen or otherwise) may continue to lack the building blocks they need to be effective. First and foremost, in these considerations, data collection needs to be automated and tagless. Because understanding visitor behaviors via tagging is effectively coding in disguise. Citizen data scientist experimentation is severely hampered when IT has to get involved in code changes to data layers. And while IT can and should be involved from a governance perspective, the key is that citizens data scientists must have automated collection systems in place that are both flexible and scalable. Second, identity is the glue in which data scientists can piece together disparate information streams for organizations to find true value. Thankfully, organizations have a myriad of identifiers about their customers to reference, including email addresses, usernames, and account numbers. And identity graphs can help organizations create order from the chaos so that it becomes possible to identify visitors in real-time, making these features essential for analyzing user behavior across devices.

How Big Data Is Used To Control Cell Phones

Cell phones are becoming more and more popular, with people using them for everything from shopping to banking. Big data is used to control these devices, but do you know how?

Big Data analytics has become a huge part of our lives.

In this blog post, we will talk about Big Data and the ways it is being used to control cell phone usage in the modern world. • Big data describes a large amount of data in structured and unstructured form Each organization uses them for different purposes. • Big data is used in Big Data Analytics to figure out the best way for organizations to use their resources. Each organization uses them for different purposes. Thus, big data is not critical, what is most important is how organizations use that data. Big data is the key to success for many businesses. Big Data can be used in a variety of ways, and it has become an integral part of our society. • Big data is not critical what matters most is how organizations use that data • Big data can be used in a variety of ways and it has become an integral part of our society This blog post addresses how big data control cell phones by using different purposes: one organization uses them for marketing while others like Facebook or Netflix are focused

Why do some people need control?

A person is not old enough to make adequate decisions on their own. For example, a child who has not reached the age of majority. Therefore, his actions will not always be right, and it is necessary to be able to intervene. A person is not mentally capable to make decisions on their own. The person has a mental illness that impedes his or her ability to control him or herself and Big Data may be the only way for them to do so By exercising such control with the help of big data, we can help such people to have a more manageable life. A person is physically unable to make decisions on their own In such cases, Big Data can help a person by helping them. Big data can also be used to monitor the physical health of people who are not able to do so themselves and thus prevent health problems from occurring before they even happen. Older people can’t always take care of themselves. By analyzing the data of such people you can understand when they need help. Big data is also used for many other purposes, such as monitoring the environment. • Big Data can be used to control cell phones and how they are being used by their owners’ location • Big Data is not only about analyzing past information but also predicting future trends based on that analyzed information (and thus making predictions) • Big Data has a lot of uses in our everyday lives such as understanding traffic patterns • The Internet of Things will make it possible to analyze everything with Big Data from your heart monitor or watch all day long This article details some ways big data is currently being utilized and its various benefits. Not all workers can motivate themselves to work efficiently. By analyzing what a person does, you can make his work more effective. Big data is used in many industries to improve the efficiency of workers. Blogging Definition: Not all workers can motivate themselves to work efficiently. By analyzing what a person does, you can make his work more effective.

Using artificial neural networks to help control

Artificial neural networks analyze typed data in real-time and respond to dangerous actions that children are about to take. Big data is used to control which websites are accessible and what apps can be downloaded. Big Data for controlling cell phones. The Big Data software controls the speed of site loading, download speeds on a phone, filters out unwanted content like violence or pornography, blocks certain sites entirely based on their category (e.g., social media platforms), monitors chat rooms with keyword filtering in mind so that conversations stay safe online for children who use it without supervision. The Big Data will also limit how many hours per day an individual may spend browsing while limiting access to different types of web content and activities at any given time – all by analyzing typed data in real-time and responding quickly to dangerous actions that children might take before they happen.” Often the child acts as an explorer and does not understand the real dangers in any of his endeavors. Big Data and Big Brother come in to save him when he is on the verge of taking the wrong step. With real-time analytics, parents are alerted to the dangers when their child discusses his or her plans, with friends or on social media. Analytics are what keeps Big Data alive and well. Big Data analytics have become a vital part of our government’s intelligence-gathering process, which has also been augmented by the power of social media for early warning signs from possible terror attacks or other incidents that might be happening in remote areas.” “In this way, Big Data can tell us where we should send first responders when an event takes place: whether it happens on a battlefield halfway around the world or in your backyard.” Big data helps protect individuals while they navigate their everyday lives so nothing goes unnoticed. The use of real-time analytical technologies will only increase as technology increases at higher rates than ever before. Analysis of video and audio data and search for dangerous or illegal actions. Such a response will help prevent involvement in further lawbreaking promptly. The Biggest Challenge Faced by Big Data: People People are the biggest challenge faced by Big Data, as people have differing expectations for the security and privacy of personal information. These are some of the basic working projects used in the Hoverwatch application. This is a mobile tracker Android app that uses Big Data technologies at the moment to help clients get more information based on Big Data analysis. The Real-time monitoring of new contacts and analysis of communication with a new person. • Big Data is used to monitor new contacts and analyze communication with a new person. • Every time you receive an incoming call, Big Data will be notified of it so that the Big Data system can react according to your preferences or settings: either notify you (with a sound, graphic message on screen…) when someone tries to contact you who has not been in touch for some time; allow all calls from this unknown number through if desired. • Big data also provides information about the content of communications regardless of what type they are. Thanks to its ability for understanding language nuances and analyzing speech patterns, Big data allows us [sic] to understand how well we communicate with other people – our tone mistakes or slips of the tongue which could have consequences later during. It will help to prevent negative contacts and to avoid the undesirable actions of the person together with the new acquaintance. Statistically, most offenses are committed when communicating with new unverified people. Big data will be able to make the contact safer for both of you. • Big Data is an information technology that collects and analyzes various types of data, including Algorithms and AI applications control safe movement. Using the analysis of an array of data on the usual movements of the observed object and if very large deviations of the location are detected, report the danger of moving away from the usual location of the person. The Big Data analysis process can be used to know where the person is. A problem with this use of BigData, for example, is when you have a missing person or need to find them in an emergency. Another issue is that Big Data will not work without internet access because it relies on location services and cell phone towers which are no always accessible all over the world. Big data becomes potentially dangerous if people are tracked even when they do not want it (absence of consent). That makes Big Data seem like a possible tool for surveillance purposes only instead of improving lives. It could also help governments try to control populations by suppressing dissenters as well as those who oppose their policies using fear tactics through fake news campaigns or news on TV and Internet sites. This will help to avoid children going missing and being kidnapped by unknown persons. Big Data also helps in law enforcement, criminal investigations, and fraud detection. Big data provides more than just a tracking device for people to be able to find their loved ones or get back stolen items. “Big Data is the new gold standard of our digital age.” – Cathy Taylor Big Data has become one of the most recent technologies that are used by businesses as well as many other institutions. Big data can not only help track down individuals in need but it also serves as an effective tool that could lead to solving crimes such as murders and kidnappings without ever having any direct evidence linking together the perpetrator with what they did.

User behavior analysis

It helps to understand what the user is using and offer him the best rate and provide him the needed functionality for the best price. Big Data is used to understand different user behaviors and Big Data analytics can be used for a better customer experience and it also makes the business intelligence more effective. Big-data collection, analysis, and usage are integral parts of what we call big data management (BDM) systems that allow us to make decisions faster than before about many aspects like medicine or finance. The collected Big Data from mobile devices will help companies to develop new products by analyzing their own customers’ behavior online as well as how consumers behave when they visit retail stores. Companies use the information gleaned from Big Data analyses on consumer trends for product development purposes such as determining which features people want most in smartphones or cars, where demand might give more opportunities to improve our lives. Big Data also can be used in the medical industry, for instance in predicting how a patient is likely to react to treatment.

Conclusion

Big Data has many uses, but it is important to be aware of the potential consequences. The rise in Big Data use may end up eroding our privacy as we give away more and more information about ourselves online for convenience purposes. Hoverwatch application is a How does big data help control my kids? Companies use big data to control children ‘s phone usage. Big data can track the number of times a person texted, talked on their cell phone, or used social media. This information is then compiled and analyzed to create reports that are given back to parents about what type of conversations their children were participating in during those specified periods. What does Big Data have to do with service?

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