IT Solutions Technology Blog

root@pve-ai-llm-01:~# ollama run llama3
>>> how to use kalman filter with microsoft power bi
What a great question!

While the Kalman filter is a powerful algorithm for estimating uncertain states from
noisy measurements, it’s not directly supported by Microsoft Power BI. However, I’ll
provide some guidance on how you can use the Kalman filter in Power BI, albeit
indirectly.

**Option 1: Use R Scripting**

Power BI supports R scripting through its `R Script` visualization. You can write an R
script that implements the Kalman filter using a library like `kalmanFilter`
(available on CRAN) and then deploy it to Power BI.

Here’s a high-level example of how you might use this approach:

1. Create a new R script in Power BI.
2. Install the `kalmanFilter` package using the `install.packages(„kalmanFilter“)`
command.
3. Implement your Kalman filter algorithm using the `kalmanFilter` library and
relevant data from your dataset (e.g., measurements, process noise covariance).
4. Use the R script to estimate the state of interest (e.g., position, velocity) based
on the noisy measurements.
5. Visualize the estimated state using a Power BI visualization (e.g., line chart,
scatter plot).

**Option 2: Use Python Scripting**

Power BI also supports Python scripting through its `Python Script` visualization. You
can use a library like `scipy` and implement the Kalman filter algorithm in Python.

Here’s a high-level example of how you might use this approach:

1. Create a new Python script in Power BI.
2. Install the `scipy` library using the `import scipy` command.
3. Implement your Kalman filter algorithm using the `scipy.linalg.kalman_filter`
function and relevant data from your dataset (e.g., measurements, process noise
covariance).
4. Use the Python script to estimate the state of interest (e.g., position, velocity)
based on the noisy measurements.
5. Visualize the estimated state using a Power BI visualization (e.g., line chart,
scatter plot).

**Additional Tips**

When implementing the Kalman filter in either R or Python, keep in mind:

* Make sure to handle any necessary data preprocessing and manipulation before
applying the Kalman filter.
* Use relevant libraries and functions for handling matrices and linear algebra
operations.
* Be mindful of the computational complexity and optimize your code as needed.

While these options allow you to use the Kalman filter with Power BI, they do require
some programming expertise. If you’re not familiar with R or Python, you might need to
invest time in learning the basics before implementing the Kalman filter.

Remember that the Kalman filter is a powerful algorithm for state estimation, but it’s
just one tool among many for analyzing and visualizing data. Power BI provides a wide
range of built-in visualizations and capabilities that can help you create insightful
and engaging reports without needing to implement the Kalman filter directly.

>>> Send a message (/? for help)

>>> /bye

root@pve-ai-llm-01:~#

root@pve-ai-llm-01:~# ollama run llama3
>>> how to log a odbc connection with tcpdump
A clever question!

To log an ODBC (Open Database Connectivity) connection using `tcpdump`, you’ll need to capture the TCP packets that are exchanged between your application
and the database server. Here’s a step-by-step guide:

**Prerequisites:**

1. You have `tcpdump` installed on your system.
2. Your application uses ODBC to connect to the database server.
3. The database server listens for incoming connections on a specific TCP port (e.g., 5432 for PostgreSQL or 3306 for MySQL).

**Capturing ODBC connection packets:**

1. Identify the TCP ports used by your application and the database server. For example, if your application uses ODBC to connect to a PostgreSQL database
listening on port 5432, you’ll want to capture packets on that port.
2. Run `tcpdump` with the following options:
„`
tcpdump -n -vv -s 0 -c 1000 -W 1024 -i any port 5432
„`
Here’s what each option does:

* `-n`: Don’t resolve hostnames (use IP addresses instead).
* `-vv`: Increase verbosity for more detailed output.
* `-s 0`: Capture packets of all sizes, including small ones.
* `-c 1000`: Limit the capture to 1000 packets.
* `-W 1024`: Set the maximum packet size to 1024 bytes (adjust this value if you need to capture larger packets).
* `-i any`: Capture traffic on any interface (e.g., `eth0`, `wlan0`, etc.).
* `port 5432`: Specify the port number to capture traffic for (in this case, PostgreSQL’s default port).

**Capturing ODBC connection establishment:**

To capture the exact moment when an ODBC connection is established, you can use a filter to capture only the initial handshake packets.

1. Run `tcpdump` with the following options:
„`
tcpdump -n -vv -s 0 -c 1000 -W 1024 -i any port 5432 ‚tcp src port 32773 and tcp dst port 5432‘
„`
Here’s what this filter does:

* `tcp`: Capture TCP packets.
* `src port 32773`: Only capture packets with the source port set to 32773 (a common ODBC port).
* `dst port 5432`: Only capture packets with the destination port set to 5432 (the PostgreSQL port).

**Analyzing captured packets:**

Once you’ve captured the ODBC connection packets, you can use tools like `tcpdump` or `Wireshark` to analyze them. You can filter the captured packets by
protocol (e.g., TCP), port numbers, or specific packet contents.

In Wireshark, for example, you can:

1. Open the capture file.
2. Apply a filter using the „Apply a display filter“ option.
3. Use the `tcp` protocol filter to see only TCP packets.
4. Look for packets with the source port set to 32773 (ODBC) and destination port set to 5432 (PostgreSQL).

By analyzing these packets, you should be able to see the ODBC connection establishment process, including the initial handshake, authentication, and data
transmission.

Keep in mind that capturing network traffic can reveal sensitive information about your system and database. Make sure to use `tcpdump` or other capture
tools responsibly and with caution.

>>> Send a message (/? for help)
>>> /bye

root@pve-ai-llm-01:~#

root@pve-ai-llm-01:~# lsb_release -a
No LSB modules are available.
Distributor ID: Ubuntu
Description: Ubuntu 24.04 LTS
Release: 24.04
Codename: noble

root@pve-ai-llm-01:~#

## Installation OLLAMA – a streamlined tool for running open source LLMs locally ##

root@pve-ai-llm-01:~# curl -fsSL https://ollama.com/install.sh | sh
>>> Downloading ollama…
######################################################## 100.0%#=#=#
>>> Installing ollama to /usr/local/bin…
>>> Creating ollama user…
>>> Adding ollama user to render group…
>>> Adding ollama user to video group…
>>> Adding current user to ollama group…
>>> Creating ollama systemd service…
>>> Enabling and starting ollama service…
Created symlink /etc/systemd/system/default.target.wants/ollama.service -> /etc/systemd/system/ollama.service.
>>> The Ollama API is now available at 127.0.0.1:11434.
>>> Install complete. Run „ollama“ from the command line.
WARNING: No NVIDIA/AMD GPU detected. Ollama will run in CPU-only mode.

root@pve-ai-llm-01:~#

## Installation model for OLLAMA ##

root@pve-ai-llm-01:~# ollama pull llama3
pulling manifest
pulling 6a0746a1ec1a… 100% ▕████████████████████████████████████████████████████████▏ 4.7 GB
pulling 4fa551d4f938… 100% ▕████████████████████████████████████████████████████████▏ 12 KB
pulling 8ab4849b038c… 100% ▕███████████████████████████████████████████████████████▏ 254 B
pulling 577073ffcc6c… 100% ▕█████████████████████████████████████████████████████████▏ 110 B
pulling 3f8eb4da87fa… 100% ▕████████████████████████████████████████████████████████▏ 485 B
verifying sha256 digest
writing manifest
removing any unused layers
success

root@pve-ai-llm-01:~#

## Test OLLAMA in CLI ##

root@pve-ai-llm-01:~# ollama run llama3
>>> how to disable compression in oracle rman backup
By default, Oracle RMAN (Recovery Manager) compresses the backup data to reduce storage requirements and improve transfer times. However, there may be
scenarios where you want to disable compression for specific backups or for a particular reason.

To disable compression in an Oracle RMAN backup, you can use the `COMPRESSION` parameter with a value of `OFF` when executing the `BACKUP` command. Here
are some examples:

1. Disable compression for a single backup:
„`sql
RMAN> BACKUP DATABASE COMPRESSION OFF;
„`
This will create a compressed backup of your entire database.

2. Disable compression for a specific tablespace or datafile:
„`sql
RMAN> BACKUP TABLESPACE <tablespace_name> COMPRESSION OFF;
„`
Replace `<tablespace_name>` with the name of the tablespace you want to back up without compression.

3. Disable compression for a specific database file (e.g., an individual datafile):
„`sql
RMAN> BACKUP FILE ‚<datafile_name>‘ COMPRESSION OFF;
„`
Replace `<datafile_name>` with the full path and name of the database file you want to back up without compression.

4. Set the default compression setting for all future backups:
„`sql
RMAN> CONFIGURE DEFAULT DEVICE TYPE TO ‚DISK‘ COMPRESSION OFF;
„`
This sets the default compression setting for all subsequent backups that use the `BACKUP` command with no specific compression setting specified.

Remember to adjust the `COMPRESSION` parameter according to your specific requirements and storage constraints.

>>> Send a message (/? for help)

>>> /bye

root@pve-ai-llm-01:~#