Tag: #data #technology #datascience #informationtechnology #programming

The First Beta of Android 15

May 15th, 2024
Today we’re releasing the first beta of Android 15. With the progress we’ve made refining the features and stability of Android 15, it’s time to open the experience up to both developers and early adopters, so you can now enroll any supported Pixel device here to get this and future Android 15 Beta and feature drop Beta updates over-the-air.

Source:google

Android 15 continues our work to build a platform that helps improve your productivity, give users a premium app experience, protect user privacy and security, and make your app accessible to as many people as possible — all in a vibrant and diverse ecosystem of devices, silicon partners, and carriers.

Android delivers enhancements and new features year-round, and your feedback on the Android beta program plays a key role in helping Android continuously improve. The Android 15 developer site has lots more information about the beta, including downloads for Pixel and the release timeline. We’re looking forward to hearing what you think, and thank you in advance for your continued help in making Android a platform that works for everyone.

They will have lots more to share as we move through the release cycle, and be sure to tune into Google I/Owhere you can dive deeper into topics that interest you with over 100 sessions, workshops, codelabs, and demos.

Edge-to-edge

To assist your app with going edge-to-edge, many of the Material 3 composables handle insets for you, based on how the composables are placed in your app according to the Material specifications.

Apps targeting Android 15 are displayed edge-to-edge by default on Android 15 devices. This means that apps no longer need to explicitly call Window.setDecorFitsSystemWindows(false) or enableEdgeToEdge()to show their content behind the system bars, although we recommend continuing to call enableEdgeToEdge() to get the edge-to-edge experience on earlier Android releases.

On the left: App targets SDK 34 (Android 14) and is not edge-to-edge on an Android 15 device. On the right: App targets SDK 35 (Android 15) and is edge-to-edge on an Android 15 device. Note the Material 3 TopAppBar is automatically protecting the status bar, which would otherwise be transparent by default.

The system bars are transparent or translucent and content will draw behind by default. Refer to “Handle overlaps using insets” (Views) or Window insets in Compose to see how to prevent important touch targets from being hidden by the system bars.

Smoother NFC experiences – part 2

Android 15 is working to make the tap to pay experience more seamless and reliable while continuing to support Android’s robust NFC app ecosystem. In addition to the observe mode changes from Android 15 developer preview 2, apps can now register a fingerprint on supported devices so they can be notified of polling loop activity, which allows for smooth operation with multiple NFC-aware applications.

Inter-character justification

Starting with Android 15, text can be justified utilizing letter spacing by using JUSTIFICATION_MODE_INTER_CHARACTER. Inter-word justification was first introduced in Android O, but inter-character solves for languages that use the white space for segmentation, e.g. Chinese, Japanese, etc.JUSTIFICATION_MODE_NONE

https://android-developers.googleblog.com/2024/04/the-first-beta-of-android-15.html?m=1

https://android-developers.googleblog.com/2024/04/the-first-beta-of-android-15.html?m=1

https://android-developers.googleblog.com/2024/04/the-first-beta-of-android-15.html?m=1

App archiving

Android and Google Play announced support for app archiving last year, allowing users to free up space by partially removing infrequently used apps from the device that were published using Android App Bundle on Google Play. Android 15 now includes OS level support for app archiving and unarchiving, making it easier for all app stores to implement it.

Apps with the REQUEST_DELETE_PACKAGESpermission can call the PackageInstaller requestArchive method to request archiving a currently installed app package, which removes the APK and any cached files, but persists user data. Archived apps are returned as displayable apps through the LauncherApps APIs; users will see a UI treatment to highlight that those apps are archived. If a user taps on an archived app, the responsible installer will get a request to unarchive it, and the restoration process can be monitored by the ACTION_PACKAGE_ADDED broadcast.

App-managed profiling

Android 15 includes the all new ProfilingManagerclass, which allows you to collect profiling information from within your app. We’re planning to wrap this with an Android Jetpack API that will simplify construction of profiling requests, but the core API will allow the collection of heap dumps, heap profiles, stack sampling, and more. It provides a callback to your app with a supplied tag to identify the output file, which is delivered to your app’s files directory. The API does rate limiting to minimize the performance impact.

Better Braille

In Android 15, we’ve made it possible for TalkBack to support Braille displays that are using the HID standard over both USB and secure Bluetooth.

This standard, much like the one used by mice and keyboards, will help Android support a wider range of Braille displays over time.

Key management for end-to-end encryption

We are introducing the E2eeContactKeysManagerin Android 15, which facilitates end-to-end encryption (E2EE) in your Android apps by providing an OS-level API for the storage of cryptographic public keys.

The E2eeContactKeysManager is designed to integrate with the platform contacts app to give users a centralized way to manage and verify their contacts’ public keys.

Secured background activity launches

Android 15 brings additional changes to prevent malicious background apps from bringing other apps to the foreground, elevating their privileges, and abusing user interaction, aiming to protect users from malicious apps and give them more control over their devices. Background activity launches have been restricted since Android 10.

App compatibility

With Android 15 now in beta, we’re opening up access to early-adopter users as well as developers, so if you haven’t yet tested your app for compatibility with Android 15, now is the time to do it. In the weeks ahead, you can expect more users to try your app on Android 15 and raise issues they find.

To test for compatibility, install your published app on a device or emulator running Android 15 beta and work through all of your app’s flows. Review the behavior changes to focus your testing. After you’ve resolved any issues, publish an update as soon as possible.

To give you more time to plan for app compatibility work, we’re letting you know our Platform Stability milestone well in advance.

At this milestone, we’ll deliver final SDK/NDK APIs and also final internal APIs and app-facing system behaviors. We’re expecting to reach Platform Stability in June 2024, and from that time you’ll have several months before the official release to do your final testing. The release timeline details are here.

Get started with Android 15

Today’s beta release has everything you need to try the Android 15 features, test your apps, and give us feedback. Now that we’ve entered the beta phase, you can enroll any supported Pixel device here to get this and future Android Beta updates over-the-air. If you don’t have a Pixel device, you can use the 64-bit system images with the Android Emulator in Android Studio. If you’re already in the Android 14 QPR beta program on a supported device, or have installed the developer preview, you’ll automatically get updated to Android 15 Beta 1.
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For the best development experience with Android 15, we recommend that you use the latest version of Android Studio Jellyfish (or more recent Jellyfish+ versions). Once you’re set up, here are some of the things you should do:

Try the new features and APIs - your feedback is critical during the early part of the developer preview and beta program. Report issues in our tracker on the feedback page.
Test your current app for compatibility - learn whether your app is affected by changes in Android 15; install your app onto a device or emulator running Android 15 and extensively test it.
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We’ll update the beta system images and SDK regularly throughout the Android 15 release cycle. Read more here.
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Graph Databases: What They Are and How to Get Started

August 1st, 2022
Graph databases facilitate processing and analytics of large sets of data, but IT is still in early stages of adoption. Now is a good time to think about whether they’re a fit and where to initiate.

B Siddharth Rao, Founder BetaDATA.india
- LinkedIn
- Twitter
For the past few years, graph databases have been pushed by vendors and pundits as a better way to scale database access and manage data. Enterprise IT was initially slow to move to graph databases, but now momentum is picking up.

MarketsandMarkets research predicts that graph database software sales will grow from $1.9 billion in 2021 to $5.1 billion by 2026. Emergen research projects that by 2030, the global graph database software market will be at $11.25 billion.

Graph databases began as a concept in the 1960s, when limitations in hierarchical databases like IBM’s IMS were circumvented with the help of what was then known as virtual records. However, it wasn’t until the 2010s that graph databases began to be noticed by companies.

Indeed, graph databases can surpass the performance of relational databases like SQL when it comes to processing large troves of data from disparate sources and systems.

Current use cases seem to confirm this.

In the financial sector, a graph database supports complex analytics by connecting many different data points that give companies insights into how, when, and where fraudulent activity begins to emerge. With the help of graph databases, companies can also see links between fraudulent activity and credit cards, addresses and transactions. Being able to detect and intercept fraud before it manifests is huge. In 2021, US consumers lost over $5.8 billion dollars to fraud.

In aerospace, Lockheed Martin Space is using graph databases to manage its large supply chain. “Think about the lifecycle of how a product is created,” said Tobin Thomas, CDAO at Lockheed Martin Space, in a Business of Data report. “[We’re] using technologies like graphs to connect the relationships together, so we can see the lifecycle based on particular parts or components and the relationships between every element.”

In healthcare, graph databases can connect a diversity of data points to observe how patients move from providers to specialists throughout the healthcare systems, and to better understand rates of disease occurrence and causative factors.

In a word, any organization faced with analysing a large spectrum of data points, with many of them seemingly unrelated, will benefit from using a graph database.

WHAT IS A GRAPH DATABASE?
A graph database is so named because it follows the point-to-point structure of a graph. The database store items in a data store that are linked to a collection of nodes and edges, with the edges representing the relationships between the nodes. These nodal relationships allow data in the store to be linked together directly and, in many cases, retrieved with one operation.

Graph databases use NoSQL, which is a boon to IT, which usually has staff with SQL skills—and the power of a graph database to discover and link thousands of different data relationships for analytics and insights makes it an ideal fit for analysis of Web, social media, and unstructured data. The point to point, non-columnar structure of a graph database makes it faster and more agile than its relational SQL database counterpart.

Getting Started With Graph Databases
Despite rosy market forecasts, only 12.7% of company respondents in a 2019 DATAVERSITY survey said they were using graph databases, and only one quarter of survey respondents said they were planning to use graph databases in the future.

One barrier to use has been understanding.

IT has well-honed skills in relational databases and understands the place of relational databases in the overall database landscape, but there is still fuzziness about how graph databases differ from relational databases and how graph databases can be used for advantage.

Given these gaps in knowledge and experience, what can IT do now to ensure that it doesn’t miss out on what could become a powerful analytics platform? Here are some ideas:

1. Find a use case
In criminal forensics, where it is important to connect many different data points (some of them seeming unrelated) to create a picture of a suspect, graph databases are useful. The same goes for a medical application that seeks to understand the origin of an illness, and why it affects some people but not others.

In both cases, large amounts of data need to be analysed. This is where graph databases shine, and where relational databases have their limits.

2. Start with one project
The first business use case should be tightly defined and projected. This gives your staff a chance to learn and to experiment with graph database technology. It also enables staff to define a methodology for working with graph databases. The DBA can begin thinking about how graph databases should fit in overall data architecture.

3. Look for a strategic vendor or consultant partner Graph database expertise is available.
Some vendors offer “startup” deals where companies can try a graph database product and tools for free and get help from support as they learn how to work with graph databases. If you have a vendor-partner that offers both help and user-friendly tools for graph database development, this is an option worth considering.
Data science, the soothsayer. A fact or a myth?

July 13th, 2022

We humans are very interguied with the future, Our future. What’s instored for us has been a money mining business of the fortune telling. But what if I say that we finally have a more reasonable solution to get these answers?

Data science proves that with logical calculated and authentic results. Here are a few reasons I think data science could or has the answers!

Picture credit -aiasia

Can Data science help predict future calamities?

Yes, data science can help predict future calamities. There are a number of ways that you can use data science to predict which events will happen in the future.

First, you can model the probability of an event happening given a set of inputs and outputs. For example, you can predict the probability of an earthquake occurring given a set of input parameters.

Second, you can build a predictive model that tries to predict the probability of an event happening and starts with no prior information. For example, you might build a predictive model that predicts the probability of an earthquake occurring given current input parameters.

Third, you can use machine learning to develop predictive models on historical data. For example, a machine learning algorithm could be trained on historical data to predict the probability of an earthquake occurring at a specific location given current parameters.

Picture credit – research for disaster management Maryland

The near future and recent researching

Hi!

I am Dr. Robert White, a professor of risk and insurance at the University of Maryland. I have studied data science for the last 10 years and I helped create an algorithm that could predict future calamities.

We can use data science to help predict future calamities, but it’s not easy. I’ve spent the past 10 years trying to find a way to predict earthquakes that would be reliable and accurate.

Can data science help predict future calamities? Absolutely!
Dr Robert white

Tech support BETADATA

Search more on machine learning and future calamities
Machine Learning Predictions for the Automobile Market Size and Forecast for 2028.

July 13th, 2022
Article for California State University
- WordPress
- @sidraodelhi
Machine Learning is becoming a mainstream technology with a lot of potential applications. The automotive industry is one of the fastest growing markets for machine learning. Many of the analytic systems, such as image analysis, speech recognition, and natural language processing, are now being implemented in the automotive industry. Machine learning is a powerful tool that can be used to make predictions and forecasts for the automotive industry. In the report, we cover predictions for the market size and forecasts for the automotive industry.

1. Introduction

Machine learning predictions for the automobile market size and forecast for 2028. Machine learning predictions for the automobile market size and forecast for 2028. Machine learning predictions for the automobile market size and forecast for 2028. Machine learning predictions for the automobile market size and forecast for 2028. Machine learning predictions for the automobile market size and forecast for 2028. Machine learning predictions for the automobile market size and forecast for 2028. Machine learning predictions for the automobile market size and forecast for 2028. Machine learning predictions for the automobile market size and forecast for 2028. Machine learning predictions for the automobile market size and forecast for 2028. Machine learning predictions for the automobile market size and forecast for 2028.

2. Machine Learning Predictions for the Automotive Market Size

Machine Learning Predictions for the Automobile Market Size: Machine Learning Predictions for the Automotive Market Size: Machine Learning Predictions for the Automotive Market Size: Machine Learning Predictions for the Automotive Market Size: Machine Learning Predictions for the Automotive Market Size: Machine Learning Predictions for the Automotive Market Size: Machine Learning Predictions for the Automotive Market Size: Machine Learning Predictions for the Automotive Market Size: Machine Learning Predictions for the Automotive Market Size: Machine Learning Predictions for the Automotive Market Size: Machine Learning Predictions for the Automotive Market Size: Machine Learning Predictions for the Automotive Market Size: Machine Learning Predictions for the Automotive Market Size: Machine Learning Predictions for the Automotive Market Size: Machine Learning Predictions for the Automotive Market Size: Machine Learning Predictions for the Automotive Market Size: Machine Learning Predictions for the Automotive Market Size: Machine Learning Predictions for the Automotive Market Size: Machine Learning Predictions for the Automotive Market Size: Machine

3. Machine Learning Predictions for the Automotive Market Forecast

Machine Learning Predictions for the Automobile Market Size and Forecast for 2028 Machine learning is a type of artificial intelligence that is capable of learning from data and making predictions. Machine learning is being used in the automotive industry in order to make predictions about the future of the market. Using machine learning, the automotive industry has been able to make predictions about the future of the automotive industry. Machine learning is predicted to make more than $2 billion in revenue by 2028. Machine learning is predicted to make a significant increase in revenue because of the rise of autonomous and electric vehicles.

4. Conclusion.

Machine learning predictions can be made by analyzing historical data sets and then applying the same techniques to new data sets. This allows the machine learning predictions to be made with accuracy. The machine learning predictions can be made by analyzing historical data sets and then applying the same techniques to new data sets. This allows the machine learning predictions to be made with accuracy. Machine learning predictions for the automobile market size and forecast for 2028. Machine learning predictions for the automobile market size and forecast for 2028. Machine learning predictions for the automobile market size and forecast for 2028. Machine learning predictions for the automobile market size and forecast for 2028. Machine learning predictions for the automobile market size and forecast for 2028. Machine learning predictions for the automobile market size and forecast for 2028.

betadata.india@gmail.com

New Delhi, Delhi
India
Data Collection Safety, the Steps to Take When Collecting Data

July 10th, 2022
B Siddharth Rao tech|security|review

Data collection is becoming an increasingly important part of the world we live in. Companies, schools, and others are collecting data from all around us. This data can come from a variety of sources from a customer survey to a data-driven marketing campaign to an email that you just want to send.

If you’re collecting personal information, then you need to make sure that you are doing everything in your power to protect the safety of the personal information you collect. This blog post will tell you all you need to know about taking the right precautions in order to collect data in a safe and secure way.

1. The importance of data collection

Collecting data is important because it helps the company make better decisions. Data collection is a vital part of the business world. Without data, companies cannot make decisions about the products they are selling, the services they are providing, or the marketing campaigns they are conducting. Data collection can be as simple as collecting customer feedback or as complex as collecting information about the world’s climate. Regardless of the type of data you collect, it is important to keep in mind the steps to take when collecting and storing data.

2. The steps to take when collecting data

There are many steps to take when collecting data. You should always take the time to do a risk assessment, which will help you decide if you should collect the data or not. You should also make sure that you are collecting the data for the right purpose. You should also make sure that you are collecting the data in a way that is as safe as possible. There are many things that you can do to make sure that the data is collected safely. For example, you can ask the person being interviewed how they would feel if they were being watched while they are being interviewed. You can also ask if they would be okay with you recording the interview in a video or audio file. These are just a few of the many things that can be done to make sure that the data is collected safely.

3. Conclusion.

It is important to take precautions when collecting data. It is important to know the steps to take when collecting data. It is also important to know the associated risks of collecting data. In order to ensure that you do not have any accidents, you should always practice the following steps: – Be sure to have a data collection plan. – Be sure to have an emergency plan. – Be sure to have a backup plan. – Be sure to have a contact list. – Be sure to have a list of what you are collecting data for. – Be sure to have a list of what you are collecting data from. – Be sure to have a list of what you are not collecting data from. – Be sure to have a list of what you are not collecting data for. – Be sure to have a list of what you are not collecting data from. – Be sure to have a list of what you are not collecting data for. – Be sure to have a list of what you are not collecting data from. – Be sure to have a list of what you are not collecting data for. – Be sure to have a list of what you are not collecting data from.

4.Data collection is an essential part of many businesses.

Without it, many companies would not be able to function. However, there are some regulations that have to be followed when collecting data. There are some safety steps that have to be taken in order to ensure the safety of the data you are collecting. These include: – Doing the research to ensure the data you are collecting is not dangerous. – Making sure the data you are collecting is not illegal. – Notifying the data subject that you are collecting data. – Making sure the data you are collecting is not personal information. – Making sure the data you are collecting is legal. – Making sure that the data you are collecting is not being collected in a way that violates the law. – Making sure the data is being collected in a way that is ethical. – Making sure that the data is being collected in a way that is not violating the law. – Making sure the data is not being collected in a way that is not ethical. – Making sure the data is not being collected in a way that is
Data Warehouse vs. Data Mart vs. Data Lake

July 7th, 2022
What is what and where are the Differences?

Photo by Salmen Bejaoui on Unsplash

In addition to Data Warehouses, which are now firmly established in the corporate world, Data Lakes are also becoming increasingly common, and one often hears of Data Marts, but what is actually what?

The Data Warehouse

The Data Warehouse is an analytical, usually relational database (SQL) or hybrid system (Mix of SQL and NoSQL) created from different data sources. The goal is usually to store historical data for later analysis. Data Warehouses often have extensive computing and storage resources for running complicated queries and generating reports. They are often used as data sources for business intelligence and machine learning systems. New approaches, technologies and especially the cloud are changing the field a lot and offer new opportunities. Data Warehouses are classic relational systems that work with structured data. Exceptions are new cloud-based Data Warehouse technologies such as BigQuery or Snowflake which can also work with unstructured data and are column-based.

The Data Lake

The Data Lake, on the other hand, is a large pool of raw data for which no use has yet been determined. A Data Warehouse is a repository for structured, filtered data that has already been processed for a specific purpose [1].While Data Warehouses use the classic ETL process in combination with structured data in a relational database, a Data Lake uses paradigms such as ELT and a schema on-read as well as often unstructured data [2].

A Data Lake can also be used as the basis for a Data Warehouse, so that the data is then made available in structured form in the Data Warehouse from there.

The difference between Data Warehouse versus Data Lake .

So what is a Data Lakehouse?

It is not just about integrating a Data Lake with a Data Warehouse, but rather integrating a Data Lake, a Data Warehouse, and purpose-built storage to enable unified governance and ease of data movement[3]. From my own experience it has often shown that Data Lakes can be realised much faster. Once all data is available, Data Warehouses can still be built on top of it as a hybrid solution.

Hybrid Data Lake-house concept

This makes rigid and classically planned Data Warehouses a thing of the past. This greatly accelerates the provision of dashboards and analyses and is a good step towards a data-driven culture. An implementation with new SaaS services from the cloud and approaches such as ELT instead of ETL also accelerates the development.

In my opinion, this approach has been around for some time, especially in the area of Cloud Data Warehousing and Data Lakes. Here, these two technologies have long been combined with each other in a hybrid approach (Read here more about it). In my opinion, the new trend term Data Lakehouse simply describes this established approach.

How to build up a Data Lakehouse?

To be a bit more concrete, we can take a look at how and with which technologies and services such Data Lakehouses can be built. In the figure below, an architecture is shown that was realised in the Google Cloud. Here, Cloud Storage and BigQuery are used as storage. Due to the good connectivity in Google Cloud, the services can easily exchange data with each other and thus be used for analysis, machine learning and other topics.

Data Lakehouse on GCP -Source Google

The Data Mart

The Data Mart contains data focused on a specific line of business. Normally they dependent on the Data Warehouse, but also could be indpenedent that is often when it’s originating from an operational database or system. They are therefore a subset of a Data Warehouse or a system. Data marts offer the advantage of faster data retrieval. Since they usually contain only a subset of the Data Warehouse, the amount of data in a data mart is consequently considerably smaller. This smaller amount of data provides a much faster result when queries are made [3].

Summary

Data Lakehouses combine the Data Lake with a Data Warehouse to enable unified governance and ease of data movement [4]. From the Data Warehouse, the data can then be distributed to BI layers, ML services or even Data Marts. Read here more about Data Lakehouses.

Sources and Further Readings

Talend, Data Lake vs. Data Warehouse

IBM, Charting the data lake: Using the data models with schema-on-read and schema-on-write (2017)

Datenbanken Verstehen, Data Mart (2022)

AWS, What is a Lake House approach? (2021)