The COVID-19 is one of the most dangerous problems we as a society struggle with today, and to make matters worse the disease is highly contagious and spreading rapidly around the world. As there are many people who are unaware of their health situation and don't find it necessary to get tested, and furthermore aren't enough test kits readily available for every single person, it's essential we use our resources and historical data to track the virus so we can begin to stop it in its tracks.
By preparing travel, social, and contact networks, we may effectively be able to track to a certain degree where the virus is, isn't, and may potentially be. A travel network specifies a single, series, or pattern of travel activities by a node [individual] or group of nodes [group of individuals] by any mode to any location. A social network is defined as a network of known social interactions between family, friends, co-workers, and those you are relatively familiar with. Meanwhile, a contact network tracks the time and proximity one node may have to another at any given time, but isn't specifically limited to others known by the individual; contact networks include interactions with a cashier when buying a coffee or perhaps passing someone nearby on local transportation. By combining the three types of networks, we effectively can understand each node's travel, social, and contact patterns and compare them to COVID-19's own pattern of travel, something we can denote as contact tracing.
Using the data collected from the COVID-19 outbreak as well as by those who have been tested for exposure, we have the opportunity to track the precise whereabouts of the pandemic and fight it before the next wave of it or a future pandemic begins. The first of our two key assumptions for this methodology is we have enough readily available data to use for tracking where COVID-19 has been and currently is so we can also predict where it is likely to go. The second key assumption is that we find a way to track those we don't have data on, as the contact network isn't limited to interactions with known nodes, but unknown ones as well. Nevertheless, this is a rare opportunity we have to begin our fight back against COVID-19 and other future pandemics, and we should take any advantage we can to prepare for it.
Happy New Year from us at ExactData! With each new year the aspiration to evolve technology even further grows exponentially, and the once thought to be improbable becomes possible right before our very eyes through both sustainable and disruptive innovations.
2020 promises to bring massive changes to the tech world, which includes but isn't limited to:
The terms "database" and "database management system" are typically used interchangeably despite the fact the two mean completely separate things. Additionally, both are important terms that those in the technology industry should clearly know how to distinct between, but it seems many people either don't or can't. Very quickly, below are definitions for the two vocabulary terms.
A database is a logically modeled cluster of information [data] that is typically stored on a computer or other type of hardware that is easily accessible in various ways.
A database management system is a computer program or other piece of software that allows one to access, interact with, and manipulate a database.
Additionally, there are many types of database management systems that exist in the world today. Historically, relational database management systems (RDBMS) are the most popular approach for managing data due to their accessibility and performance result capabilities. Examples of RDBMS's include the Amazon RDS, Oracle, and MySQL which all utilize Structured Query Language (SQL) to manipulate the different databases they interact with. All RDBMS's are ACID compliant and typically implement an OLTP system.
To combat the limitations of relational database management systems, NoSQL databases became more popular over the years. The term "NoSQL" was coined by Carlo Strozzi in 1998 as the term for his first database which didn't utilize SQL for managing data, hence the label "NoSQL." Examples of popular NoSQL databases include key-value pair databases, document databases, graph databases, and columnar databases, all of which while are similar in concept are different in theory, as there are advantages and disadvantages to using each in different scenarios.
As we continue to move forward in the technology world, we constantly search for the most optimal solution for all of our data needs. These optimal solutions begin with which database management system or systems we choose to utilize to solve our data-related problems. Some database management systems are more equipped for certain scenarios than others, and figuring out which type works best for you is essential when working with big data.
On Thursday, November 7th the Institute for Robotic Process Information & Artificial Intelligence (IRPA AI) New York Chapter Launch Party will be taking place in Manhattan, New York on 25 West 39th Street on Floor 14. The launch party will include a pre-launch networking event for the new chapter beginning at 5:15pm which will serve as an opportunity to plan and discuss future programs for the chapter. The launch party will also inform guests on how they can be involved with the chapter. Drinks will be served at the launch party during the pre-launch networking happy hour!
For more information or to RSVP to the event, please follow the link here.
For questions or general inquiries about IRPA AI or the NY Chapter Launch Party, please contact Molly Alexander at Molly.Alexander@irpanetwork.com.
You can also learn more about IRPA AI by going to their website and or their LinkedIn!