prostodata

5 ms·

1.
by prostodata 6y ago
> pull everything to my PC and use pandas, to take advantage of its ability to build up results piece by piece. Most difficulties in data processing arise from the need to process multiple tables . It is highly difficult in SQL but it
2.
by prostodata 6y ago
It should be noted that the expression like comp = salary / (age - 18) is (strictly speaking) not part of the relational algebra because it is not using set operations (like join or union). It was added to the relational model
3.
by prostodata 6y ago
Here are some major issues [1]: SQL’s shortcomings can be grouped into these categories: - lack of proper orthogonality — SQL is hard to compose; - lack of compactness — SQL is a large language; - lack of consistency — SQL is inconsistent i
4.
by prostodata 6y ago
Just for comparison, a functional approach is a major alternative to relational and set-oriented models and query languages. The difference is that functions and operations with functions are first class elements of the model. One versio
5.
by prostodata 6y ago
Here is one possible implementation of the concept-oriented model of data for data processing. It heavily relies on functions and operations with functions and is an alternative to purely set-oriented approaches like map-reduce or join-grou
6.
by prostodata 6y ago
For example, I have data from 1900 till 2000. I train ARMA using this data by storing the corresponding coefficients as model parameters. Now I get data from 2010 to 2020. My goal is to use these (AR and MA) coefficients in order to predict
7.
by prostodata 6y ago
In fact, there are two general approaches to unifying ML with time series: o Unifying ML API and patterns so that time series can be analyzed in the same way as normal tabular data. Example: sktime o Preprocessing libraries applying data tr
8.
by prostodata 6y ago
When I see a new forecasting library, my first question is whether it can apply ARIMA in a sklearn manner by training a model using (large) train time series X, storing the model by discarding the train data, and then using this model for p
9.
by prostodata 6y ago
Anomaly detection belongs to unsupervised learning while in time series analysis we normally think about future and future values are viewed as labels. One approach to think in terms of anomaly detection is to train a normal forecasting mod
10.
by prostodata 6y ago
> No, the relational model is beautiful and consistent! SQL is messy... What is beautiful and consistent is the relational algebra. The relation model relies on this formalism to model data by making some rather strong assumptions abou
11.
by prostodata 6y ago
I think both conceptions are important. On one hand, some code is naturally related to certain data and hence we should accordingly design the program. On the other hand, complex and distributed programs have code which must be dynamically
12.
by prostodata 6y ago
> I am currently working on complex (JOIN and aggregates) queries without any SQL knowledge One way to process data without joins and groupy in multiple tables is to use Prosto toolkit: https://github.com/prostodata&#x2
13.
by prostodata 6y ago
Apache Pulsar should not be confused with another quite popular Pulsar: Event driven concurrent framework for Python https://github.com/quantmind/pulsar
14.
by prostodata 6y ago
In the context of computer science, matrices are used as a representation construct with different possible formal interpretations which frequently can be recognized depending on the supported operations. For example, some functions of NumP
15.
by prostodata 7y ago
Here is another project for working with CAN: https://github.com/hardbyte/python-can I used it (as well as Cantools) for importing various CAN-specific data formats.
16.
by prostodata 7y ago
Does it relate somehow to Intel SGX (Software Guard Extensions): https://en.wikipedia.org/wiki/Software_Guard_Extensions
17.
by prostodata 7y ago
PyTorch is simpler, easer to use, consumes less memory and allows for dynamic dynamic computational graphs (dynamic operations during the forward pass).
18.
by prostodata 7y ago
Is there any (significant) difference between sharding and load balancing? It seems that in both cases the idea is to distribute (supposedly independent) requests between workers and one of the main difficulties is that requests might not b