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Microsoft Introduces Microsoft Defender For Azure Cosmos DB

The evolution of databases gives developers and organizations a wide range of database types that can be tailored for their varying needs. In order to protect these sensitive data sets against common threats, customized security measures are required as well because each type has its own unique features.

The use of NoSQL databases has become more prevalent in recent years, as they offer single-digit millisecond response times and can scale automatically with your application’s needs. Azure Cosmos DB is one such service that provides fast access to data without sacrificing flexibility or manageability through its automatic management features.

Microsoft recently announced that users of their cloud service, Microsoft Defender for Cloud, can now access an early preview of Defender for Azure Cosmos DB. 

Defender for Azure Cosmos DB is an ultimate solution to protect your database from various kinds of attacks, such as application layer hacking or SQL injection. It also helps you identify any potential risks before they become dangerous by monitoring all activity on the account and raising alerts when something unusual happens with it to take steps immediately to stop further damages done regarding this situation.

You can get started with a free trial


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Meta Open-Sources A Compositional Deadlock Detector for Android Java

The research team at Meta has developed a new static analyzer that catches deadlocks in Java code for Android without ever running the app. What distinguished this work from past efforts is its ability to analyze revisions within large software libraries with hundreds of millions of lines–enough time and space so problems can be found before they manifest themselves as bugs or crashes. The proposed analyzer is open-sourced and forms part of the Infer static analysis framework.

Using abstract interpretation techniques, the proposed analyzer has been designed to summarize how each method behaves when acquiring locks and releasing them and whether it can run on the main thread or background task. This is done elegantly by compounding all behaviors into one summary that reflects what callers will be affected if their operation depends upon this particular piece of code being fast enough for them not to experience lags while running through various parts within your application’s workflow process.

This tool takes a different approach by not analyzing all source files in an app. Instead, it starts with the revisions’ modified methods first and uses that data for its analysis – which can be scalable because of this heuristic.

The team’s research proves that their analysis is sound and complete for a non-deterministic programming language, which means it can detect all deadlocks without false positives.

The static detection of deadlocks has been very valuable in analyzing and diagnosing. Our approach achieves this goal while also making it sufficiently scalable to deploy analyzers on large codebases.



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Google’s Jigsaw Unit is Releasing An Open-Source Tool Called ‘Harassment Manager’ to Reduce Toxicity Online for Journalists

When covering controversial topics or live under autocratic governments, online harassment is a constant challenge. For women, it can range from nuisances like insults and memes targeting their appearance to matters of life-threatening danger. They’ve been targeted before for reporting on stories that upset powerful groups with negative feedback who feel threatened by what you’re doing coverage-wise.

Google’s Jigsaw’s team of experts has made long-standing investments in user experience research, technology, and other initiatives to help women navigate targeted harassment online. Jigsaw unit releases the code for an open-source anti-harassment tool called Harassment Manager. The program enables journalists and other public figures to manage better abusive comments on social media platforms like Twitter with Jigsaw’s Perspective API, which sorts through potentially harmful messages.

Harassment Manager is a tool that helps users identify and document harmful posts, mute or block perpetrators of harassment on social media. It has an advanced filtering and reporting system that automatically sorts messages into queues, so you can address them all at once rather than individually through the platform’s default tools which are often not effective or well-suited for dealing with harassment issues on their own account. The interface provides insights into how much “toxicity” there was in each message while it’s being processed – this helps users decide whether they want certain replies blurred out before reading them.

Harassment Managers helps users keep track of abusive messages and downloads a standalone report containing the evidence. This creates an easy paper trail for their employer or law enforcement if necessary and gives them access to services like those provided by The Thomson Reuters Foundation which will be released soon.




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Automation & Visualization – The Next Phase of Data and AI

It has become quite evident now that the proclamation of “Data being the new oil” wasn’t hyperbolic in the least. In fact, over the last decade, there has been an unprecedented avalanche of data that is being generated, and the proliferation of connected devices has only fueled this growth.

However, the existence of large mounds of data and the need to analyze it isn’t exactly new. In fact, analytics in its most elementary form has existed for a very long time. Even as far back as in the 1950s, researchers and business organizations were going about in earnest laboriously compiling, streamlining, and analyzing whatever data they could get their hands on. With negligible technology, the majority of time and effort was spent on collecting the data, and the analysis was, more often than not, quite rudimentary.

With the turn of this century, computing power was at its absolute peak, the internet had become ubiquitous in most parts of the world, and big data had found its rightful place in the technology lexicon. As a result, large and complex data was no longer an insurmountable mountain but an exciting resource that could be mined extensively to generate meaningful insights.

The last decade, in particular, has seen an explosion of data from completely new sources such as connected devices enabled by IoT or online clickstream data generated due to the proliferation of mobile devices. However, with Cloud Computing offering speed, scalability, and accessibility, a lot of this data could be effectively analyzed not just by large enterprises but also small businesses that were previously confined to the sidelines.

Many of these small to mid-sized organizations that couldn’t afford to have a dedicated data warehouse benefited greatly from the emergence of open-source software and scripting languages. This turned out to be a boon, especially for organizations looking for a data lake where they can store all their unstructured data. Eventually, organizations are looking to derive intelligence from this unstructured data, and Cognitive Analytics can be tremendously beneficial in addressing the challenges arising from big data and providing accurate insights that actually help decision-makers.

Predictive Analytics is also equally important in developing accurate forecasts or predictions about everything, including people, products, and machines. With a reservoir of historical and current data available, businesses are always looking to extract the greatest value from it to help them predict outcomes with the sort of accuracy that was simply not possible before.

For instance, a business looking to determine customer demand during the holiday season or the probability of a machine breaking down during peak production runs would have to integrate analytical technology with their functional systems by using a micro-services approach. With analytics happening on edge, real-time decisions can be made with minimal human intervention to optimize efficiencies or prevent breakdowns.

Automated Analytics, Data Visualization, and Accelerated Insights

As the technology landscape continues to evolve, there will be certain themes that will emerge as front runners in the world of analytics and data science over the next decade.

Perhaps foremost among them is the larger idea of automation, which has already been embraced to some degree in data science. However, thanks to the progress in AI & ML, this automation will extend across the entire cycle – from data gathering and cleansing to data modeling and deployment.

This organically ties into the second theme of accelerating the process of converting data to insights. Organizations will have to connect the dots between various data ingestion tools that can work seamlessly with their data platform and provide easy to comprehend visual insights that are delivered with lightning speed.

Lastly, I see data visualization emerging as a major theme to address the need to make data analytics more accessible to a wide set of end-users. There is still a sizable gap between the professionals who work on data and the end-users who are the consumers of such data. The right data visualization platform can create immersive and engaging experiences for data consumers by making it more palatable, visual, and interactive.

Data and AI can help businesses attain operational efficiencies, make accurate forecasts, and enable timely decision-making. However, their most enduring impact will be in helping businesses create tailored products and offerings that are specifically aligned to unique customer needs and provide a greater degree of personalization that leads to long-term brand loyalty and customer advocacy.

Author: Ajay Agrawal is the Senior Vice President & Head of CoE – AI/Analytics at Happiest Minds Technologies. He spearheads the CoE for leadership around Artificial Intelligence, Data Science, Big Data, and Data Engineering among other initiatives.