This is a new project, I’m working on from early last year. The motivation behind this project is to build a programing language that allows users to analyze private data without exposing sensitive information. Many data analysis languages (R, Python, MATLAB etc.) in the current market assume direct access to data. PRIVATE, on the other hand, performs a privacy calculation that will make sure only non-sensitive information is released to the user.
This is the tutorial series by Simon Dennis, Founder of PRIVATE
Most people got huge water bills after some time due to COVID-19. I wanted to double-check the calculation because the amount was somewhat big. Unlucky I didn’t find any online calculator that get the job done (there was a one in waterboard, but it has a maximum limit of 60 days). So I went with the default option, Excel. I thought of sharing the excel workbook I used as It might be helpful to others. I really don’t know how the VAT calculation is done, so I used 8%.
I recently had to change my car battery. This is after running 4.5 years with my original battery. I replaced the original with an Amaron battery for 13000 rupees (checked the images at the end). I thought someone might find this information helpful.
In times of the recent tragic Easter attacks and the insurgency in our home country Sri Lanka, The Sri Lankan Graduates’ Society, The University of Melbourne, organized a silent candlelight vigil in paying respect to the lives lost and to commemorate the much-needed unity among us to walk through these difficult times together.
Recently I wanted to run the JOB benchmark for an experiment. This benchmark uses an IMDB dataset, published in 2013. Initially, I had some trouble running the benchmark as it was designed for a PostgreSQL database. And the dataset was created in a UNIX system which can create issues when used in a Windows system. So I decided to share the exact steps you need to take to take in order to create a Microsoft SQL Server database with IMDB dataset. All the scripts used in the project can be found in this Git repo.
Jaro–Winkler Similarity is a widely used similarity measure for checking the similarity between two strings. Being a similarity measure (not a distance measure), a higher value means more similar strings. You can read on basics and how it works on Wikipedia. It’s available in many places and I’m not going into that. However, none of these sites talks about how to correctly count the number of transpositions in complex situations.
Transposition is defined as “matches which are not in the same position”. For a simple example like ‘cart’ vs ‘cratec’ it is obvious with 4 matches and 2 transpositions (‘r’ and ‘a’ are in not in the same position). But for 'xabcdxxxxxx' vs 'yaybycydyyyyyy' in the first look, all letters seem to be out of position but there are no transpositions (4 matches). For very similar 'xabcdxxxxxx' vs 'ydyaybycyyyyyy', there are 4 transpositions (4 matches). With these examples, it might not be trivial to count the number of transpositions.
If you are a Database administrator or a developer working with a transaction database, you might have come across this problem
“Is it worthy to build that index?”
Exact answer for that question is only known once you build it. However, luckily SQL server provides you with functionality to check the workload performance under hypothetical indexes (without actually creating them)
You can find more information about hypothetical indexes here.
I will just provide you with a simple python code that will help you with the hypothetical index creation. Example code will compose of 3 parts
Enabling the index (unlike the normal indexes you need to enable them before using)