{"id":23101170,"url":"https://github.com/carusel02/sequential-data-processing-and-analysis","last_synced_at":"2025-10-26T06:33:07.623Z","repository":{"id":91694808,"uuid":"479460482","full_name":"Carusel02/Sequential-data-processing-and-analysis","owner":"Carusel02","description":"Sequential data processing and analysis using linked-list in 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Sequential data processing and analysis\n## Solution method\n`main functions`\n- as main functions I used adding to the list, deleting\nto a node in the list, copying a list to another list, displaying\nof a list, freeing the nodes of a list\n\n`task1` (e1)\n- the main idea underlying the requirement was the use\na \"flag\" to mark the nodes to be deleted.\n- thus, the list will be scanned once more and they will be deleted\nthe nodes that have an active flag(1).(could also be used\nand changing the timestamp with a negative value for a\nhighlight this procedure)\n- also, I implemented 2 functions that calculate the deviation and\nmediate.\n\n`task2` (e2)\n- for task2, a simple way to solve it would be to add the window of\n5 nodes in an auxiliary list, then sorting it.\n- the value in the middle is then added, and for chaining\norders, I had to give free to the previous list and it\nupdate with the new list formed from these extracted values.\n\n`task3` (e3)\n- similar to task2, it needed a function thatcalculate the average on a window of 5 nodes and add it to a the new list.\n- at the end, the old list is updated.\n\n`task4` (u)\n- the equalization of the frequency consisted of a simple operation which\ngo through the list and modify the nodes that meet the requirements.\n- using a smoothing function, I modified the list accordingly\nrequirement.\n\n`task5` (c)\n- completing an interval consists of adding elements after\na knot.\n- the timestamp of the node changes by +200 to the last value\nand the date of each node is influenced by a parameter C.\n- the left and right sums are weighted averages (constant numbers\nfor an interval in which nodes are added) that take into account\nthe first 3 terms and the last 3 terms from where the addition is made.\n- they are multiplied by some coefficients, the most important (large)\ncoefficients being multiplied by the terms closest to\nthe interval in which it is added.\n- for this task, I used a single function that calculates\neach information of a node and adds it after a node through a\nauxiliary function.\n\n`task6` (st)\n- the part with the extract from the argument was made using operations with\nthe ASCII code, then this number was passed as a parameter\nin a function that sorts and then counts how many nodes the data has\nbelonging to the interval.\n\n## Implementation\n- to implement the code, I used VScode on Linux\nto be able to use the utility valgrind (for memory leaks) and gdb\n(for segmentation faults).\n- *the hardest part wasn't the requirement\nand writing the code, but the debugging part xD*\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcarusel02%2Fsequential-data-processing-and-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcarusel02%2Fsequential-data-processing-and-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcarusel02%2Fsequential-data-processing-and-analysis/lists"}