At my work we have two systems, one that collects the customers payments automatically every month. And one that manages the memberships of those customers. Sadly our outdated technology doesn’t communicate to each other so we don’t know if a customer actually paid for their membership without manually auditing them.
I’ve been put in charge of this process and boy does it take awhile to do.
I have limited knowledge of C++ and was looking into maybe writing a program to do the comparisons for me.
I have two ideas on how to implement this, and was wondering what you guys thought. If these would be best or if it’s even possible or if there’s a better solution?
Current Setup: We have a list of all members in excel, with how much each should be paying, we then go through the actual money collected and check to make sure everyone’s payment went through and was processed and not declined.
Option 1: have a multi-dimensional array of strings. Read the excel file into this array it would have three Columns, first name, last name, amount they should be paying. This would be put in alphabetical order to help with the searching. I would then export the transactions in css file format and read each line one at a time. When it reads a line it would search the array for the same first and last name. Once found it would take the amount paid confirm it said processed and not declined and if so would subtract it from the customers amount they should be paying. In the end if every customers amount they should be paying is equal to 0 then everyone paid.
Option 2: is similar to option 1 just instead of using a multidimensional array it would use two css files. And not put the items into the array at the start.
Thoughts? Is this a smart way to combat this problem? I’m a newbie programmer so I’m just looking for suggestions/advice.
Your solutions would work, but are suited for small datasets. I don't now what your constraints are, but I think that a more elegant solution would be to setup a database on the first system first(instead of the excel file).
Are you allowed to create a database? How many customers are in the excel file?
Related
I have asked a few questions related to this personal project of mine already on this platform, and this should be the last one since I am so close to finishing. Below is the link to a mock example spreadsheet I've created, which mimics what my actual project does but it contains less sensitive information and is also smaller in size.
Mock Spreadsheet
Basic rundown of the spreadsheet:
Pulls data from a master schedule which is controlled/edited by another party into the Master Schedule tab.
In the columns adjacent to the imported data, an array formula expands the master schedule by classroom in case some of the time slots designate multiple rooms. Additional formulas adjust the date, start time, and end time to be capped within the current day's 24-hour period. The start time of each class is also made to be an hour earlier.
In the Room Schedule tab, an hourly calendar is created based on the room number in the first column, and only corresponds to the current day.
I have tested the spreadsheet extensively with multiple scenarios, and I'm happy with how everything works except for the calculation time. I figured the two volatile functions I use would take some processing time just by themselves, and I certainly didn't expect this to be lightning-fast especially without using a script, but the project that I am actually implementing this method for is much larger and takes a very long time to update. The purpose of this spreadsheet is to allow users to find an open room and "reserve" it by clicking the checkbox next to it (which will consequently color the entire row red) allowing everyone else to know that it is now taken.
I'd like to know if there is any way to optimize / speed up my spreadsheet, or to not update it every time a checkbox is clicked and instead update it "manually", similar to what OP is asking here. I am not familiar with Apps Script nor am I well-versed in writing code overall, but I am willing to learn - I just need a push in the right direction since I am going into this blind. I know the number of formulas in the Room Schedule tab is probably working against me yet I am so close to what I wanted the final product to be, so any help or insight is greatly appreciated!
Feel free to ask any questions if I didn't explain this well enough.
to speed up things you should avoid usage of the same formulae per each row and make use of arrayformulas. for example:
=IF(AND(TEXT(K3,"m/d")<>$A$1,(M3-L3)<0),K3+1,K3+0)
=ARRAYFORMULA(IF(K3:K<>"",
IF((TEXT(K3:K, "m/d")<>$A$1)*((M3:M-L3:L)<0), K3:K+1, K3:K+0), ))
=IF(AND(TEXT(K3,"m/d")=$A$1,(M3-L3)<0),TIMEVALUE("11:59:59 PM"),M3+0)
=ARRAYFORMULA(IF(K3:K<>"",
IF((TEXT(K3,"m/d")=$A$1)*((M3-L3)<0), TIMEVALUE("11:59:59 PM"), M3:M+0), ))
I have data I have to join at the record level. For example data about users is coming in from different source systems but there is not a common primary key or user identifier
Example Data
Source System 1:
{userid = 123, first_name="John", last_name="Smith", many other columns...}
Source System 2:
{userid = EFCBA-09DA0, fname="J.", lname="Smith", many other columns...}
There are about 100 rules I can use to compare one record to another
to see if customer in source system 1 is the same as source system 2.
Some rules may be able to infer record values and add data to a master record about a customer.
Because some rules may infer/add data to any particular record, the rules must be re-applied again when a record changes.
We have millions of records per day we'd have to unify
Apache Beam / Dataflow implementation
Apache beam DAG is by definition acyclic but I could just republish the data through pubsub to the same DAG to make it a cyclic algorithm.
I could create a PCollection of hashmaps that continuously do a self join against all other elements but this seems it's probably an inefficient method
Immutability of a PCollection is a problem if I want to be constantly modifying things as it goes through the rules. This sounds like it would be more efficient with Flink Gelly or Spark GraphX
Is there any way you may know in dataflow to process such a problem efficiently?
Other thoughts
Prolog: I tried running on subset of this data with a subset of the rules but swi-prolog did not seem scalable, and I could not figure out how I would continuously emit the results to other processes.
JDrools/Jess/Rete: Forward chaining would be perfect for the inference and efficient partial application, but this algorithm is more about applying many many rules to individual records, rather than inferring record information from possibly related records.
Graph database: Something like neo4j or datomic would be nice since joins are at the record level rather than row/column scans, but I don't know if it's possible in beam to do something similar
BigQuery or Spanner: Brute forcing these rules in SQL and doing full table scans per record is really slow. It would be much preferred to keep the graph of all records in memory and compute in-memory. We could also try to concat all columns and run multiple compare and update across all columns
Or maybe there's a more standard way to solving these class of problems.
It is hard to say what solution works best for you from what I can read so far. I would try to split the problem further and try to tackle different aspects separately.
From what I understand, the goal is to combine together the matching records that represent the same thing in different sources:
records come from a number of sources:
it is logically the same data but formatted differently;
there are rules to tell if the records represent the same entity:
collection of rules is static;
So, the logic probably roughly goes like:
read a record;
try to find existing matching records;
if matching record found:
update it with new data;
otherwise save the record for future matching;
repeat;
To me this looks very high level and there's probably no single 'correct' solution at this level of detail.
I would probably try to approach this by first understanding it in more detail (maybe you already do), few thoughts:
what are the properties of the data?
are there patterns? E.g. when one system publishes something, do you expect something else from other systems?
what are the requirements in general?
latency, consistency, availability, etc;
how data is read from the sources?
can all the systems publish the records in batches in files, submit them into PubSub, does your solution need to poll them, etc?
can the data be read in parallel or is it a single stream?
then the main question of how can you efficiently match a record in general will probably look different under different assumptions and requirements as well. For example I would think about:
can you fit all data in memory;
are your rules dynamic. Do they change at all, what happens when they do;
can you split the data into categories that can be stored separately and matched efficiently, e.g. if you know you can try to match some things by id field, some other things by hash of something, etc;
do you need to match against all of historical/existing data?
can you have some quick elimination logic to not do expensive checks?
what is the output of the solution? What are the requirements for the output?
I have a task to complete.
There are two types of csv files 4000+ both related to each other.
2 types are:
1. Country2.csv
2. Security_Name.csv
Contents of Country2.csv:
Company Name;Security Name;;;;Final NOS;Final FFR
Contents of Security_Name.csv:
Date;Close Price;Volume
There are multiple countries and for each country multiple security files
Now I need to READ them do some CALCULATION and then WRITE the output in another files
READ
Read both the file Country 2.csv and Security.csv and extract all the data from them.
For example :
Read France 2.csv, extract Security_Name, Final NOS, Final FFR
Then Read Security.csv(which matches the Security_Name) and extract Date, Close Price, Volume
Calculation
Calculations are basically finding Median of the values extracted which is quite simple.
For Example:
Monthly Median Traded Values
Daily Traded Value of a Security ... and so on
Write
Based on the month I need to sort the output in two different file with following formats:
If Month % 3 = 0
Save It as MONTH_NAME.csv in following format:
Security name; 12-month indicator; 3-month indicator; FOT
Else
Save It as MONTH_NAME.csv in following format:
Security Name; Monthly Median Traded Value Ratio; Number of days Volume > 0
My question is how do I design my application in such a way that it is maintainable and the flow of data throughout the execution is seamless?
So first thing. Based on the kind of data you are looking to generate, I would probably be looking at moving this data to a SQL db if at all possible. This is "one SQL query" kind of stuff. And far more maintainable than C++ that generates CSV files from CSV files.
Barring that, I would probably look at using datamash and/or perl. On a Windows platform, you could do this through Cygwin or WSL. Probably less maintainable, but so much easier it's not too much of an issue.
That said, if you're looking for something moderately maintainable, C++ could work. The first thing I would do is design my input classes. Data-centric, but it can work. It sounds like you could have a Country class, a Security class, and a SecurityClose class...or something along those lines. You can think about whether a Security class should contain a collection of SecurityClosees (data), or whether the data should just be "loose" and reference the Security it belongs to. Same with the Country->Security relationship.
Once you've decided how all that's going to look, you want something (likely a function) that can tokenize a CSV line. So "1,2,3" gets turned into a vector<string> with the contents "1" "2" "3". Then, each of your input classes should have a constructor or initializer that takes a vector<string> and populates itself. You might need to pass higher level data along too. Like the filename if you want the security data to know which security it belongs to..
That's basically most of the battle there. Once you've pulled your data into sensibly organized classes, the rest should come more easily. And if you run into bumps, hopefully you can ask specific design or implementation questions from there.
I have a booking system for something where the price can change based on the day. The admins for the site can make these changes. If a booking crosses the boundary of a daily rate, they pay pro-rata for the rates they used.
I'm losing confidence in how this is implemented. There are at least two ways:
Having Rates that specify their validity (start, end fields) and then working out which of those apply. But which overlapping ones take priority? Etc. Nasty. This is what we're trying to do and cannot currently answer sufficiently well.
The same except that there is some form of unique quality to date so that no two rates can overlap. The problem here is we'd need to split existing Rates on insert and rejoin two on delete/edit, etc if they had the same value. We'd need to make sure there were no gaps. It requires some heavy ORM overriding.
Keeping a DayRate table with every day defined. This means keeping a load of extra data around but most bookings are for tens of days, not thousands so I'm not worried about the database bandwidth requirements here. Date would be primary-unique and I'd just do a range filter for grabbing which ones I need to factor in.
The problem is generating these dates ahead of time. I know that as soon as I implement this, somebody will make a booking for 2032. Is there a good way around this or should we limit them?
None of these answers seems great and I have to imagine that I'm not the first guy with a booking system. Is there a better way of keeping track of a rate over a contiguous (possibly infinite) amount of time?
Having implemented an algorithm to recommend products with some success, I'm now looking at ways to calculate the initial input data for this algorithm.
My objective is to calculate a score for each product that a user has some sort of history with.
The data I am currently collecting:
User order history
Product pageview history for both anonymous and registered users
All of this data is timestamped.
What I'm looking for
There are a couple of things I'm looking for suggestions on, and ideally this question should be treated more for discussion rather than aiming for a single 'right' answer.
Any additional data I can collect for a user that can directly imply an interest in a product
Algorithms/equations for turning this data into scores for each product
What I'm NOT looking for
Just to avoid this question being derailed with the wrong kind of answers, here is what I'm doing once I have this data for each user:
Generating a number of user clusters (21 at the moment) using the k-means clustering algorithm, using the pearsons coefficient for the distance score
For each user (on demand) calculating their a graph of similar users by looking for their most and least similar users within their cluster, and repeating for an arbitrary depth.
Calculating a score for each product based on the preferences of other users within the user's graph
Sorting the scores to return a list of recommendations
Basically, I'm not looking for ideas on what to do once I have the input data (I may need further help with that later, but it's not the point of this question), just for ideas on how to generate this input data in the first place
Here's a haymaker of a response:
time spent looking at a product
semantic interpretation of comments left about the product
make a discussion page about a product, brand, or product category and semantically interpret the comments
if they Shared a product page (email, del.icio.us, etc.)
browser (mobile might make them spend less time on the page vis-à-vis laptop while indicating great interest) and connection speed (affects amt. of time spent on the page)
facebook profile similarity
heatmap data (e.g. à la kissmetrics)
What kind of products are you selling? That might help us answer you better. (Since this is an old question, I am addressing both #Andrew Ingram and anyone else who has the same question and found this thread through search.)
You can allow users to explicitly state their preferences, the way netflix allows users to assign stars.
You can assign a positive numeric value for all the stuff they bought, since you say you do have their purchase history. Assign zero for stuff they didn't buy
You could do some sort of weighted value for stuff they bought, adjusted for what's popular. (if nearly everybody bought a product, it doesn't tell you much about a person that they also bought it) See "term frequency–inverse document frequency"
You could also assign some lesser numeric value for items that users looked at but did not buy.