Few days now I've got new project to do related with a "real world modelling" program.
Here's how it looks like:
A visit to a psychologist (Use queue). Experts provides psychologist's advice, some of them (n) forms therapeutic groups of k people (GrT - duration of group therapy in hours), other experts (m) takes individual patients (InT - duration of individual therapy in hours). Each newly came patient (new patient's appearance probability is p1, recurring patients comes after period of time (h)) can choose to go to a psychologist providing individual therapies, or to group therapies. If group therapy session is full, patients who are wishing to participate in group sessions must wait. Recurring patients wishing to go to group sessions can start a session with smaller group, but can't go to same session with newly came patients. It has been observed that patients who took individual therapy are recovering faster than those, who chose group sessions(they will need less sessions), but there are exceptions - due to social interaction factor, some patients (probability p2) recover h percent faster than those, who choose individual therapy. Individual session costs InC, group session GrC. You need to assess what therapeutic approach patient should choose optimizing with their resources, and how many and what specialists should hire a health care facility.
Here's my approach to this problem:
Read text file containing Names, Surnames, money willing to spend and place everything in queue structure.
Find which group is better for patient by generating random number for p2probability and using it, we'll find if patient recover faster in individual or group therapy. IMO factor sequence here: Money(looking, if patient can afford individual therapy sessions) > p2 (should patient take group sessions if it's better for him).
By looking how many patients there are in queue, we can find how many psychologists we'll need. (Are there any other factors here? What if we are short of experts?)
Problems that I can't understand: how do I implement p1 probability of new patients appearance if I write every patient into a text file and put them in a queue? How many therapy sessions does it take for patient to recover (static number?)?
Am I missing something? Basically it's open question and there could be no right answer. If anyone have any suggestions how to build this program to better one, I'd be glad to take it!
Programming language I'm using: C++
If you want to break up a task, analyse it and prepare it for coding, you could :
Firstly make a Block diagram, representing program flow control.
Followed by Pseudo code implementation.
P.S. update the question following the above and when you reach the "code stage", there, definitely, will be more help.
I've been doing some work for my exams in a few days and I'm going through some past papers but unfortunately there are no corresponding answers. I've answered the question and I was wondering if someone could tell me if I am correct.
My question is
(c) A transactional dataset, T, is given below:
t1: Milk, Chicken, Beer
t2: Chicken, Cheese
t3: Cheese, Boots
t4: Cheese, Chicken, Beer,
t5: Chicken, Beer, Clothes, Cheese, Milk
t6: Clothes, Beer, Milk
t7: Beer, Milk, Clothes
Assume that minimum support is 0.5 (minsup = 0.5).
(i) Find all frequent itemsets.
Here is how I worked it out:
Item : Amount
Milk : 4
Chicken : 4
Beer : 5
Cheese : 4
Boots : 1
Clothes : 3
Now because the minsup is 0.5 you eliminate boots and clothes and make a combo of the remaining giving:
{items} : Amount
{Milk, Chicken} : 2
{Milk, Beer} : 4
{Milk, Cheese} : 1
{Chicken, Beer} : 3
{Chicken, Cheese} : 3
{Beer, Cheese} : 2
Which leaves milk and beer as the only frequent item set then as it is the only one above the minsup?
I agree you should go for the Apriori Algorithm.
The Apriori algorithm is based on the idea that for a pair o items to be frequent, each individual item should also be frequent.
If the hamburguer-ketchup pair is frequent, the hamburger itself must also appear frequently in the baskets. The same can be said about the ketchup.
So for the algorithm, it is established a "threshold X" to define what is or it is not frequent. If an item appears more than X times, it is considered frequent.
The first step of the algorithm is to pass for each item in each basket, and calculate their frequency (count how many time it appears).
This can be done with a hash of size N, where the position y of the hash, refers to the frequency of Y.
If item y has a frequency greater than X, it is said to be frequent.
In the second step of the algorithm, we iterate through the items again, computing the frequency of pairs in the baskets. The catch is that
we compute only for items that are individually frequent. So if item y and item z are frequent on itselves,
we then compute the frequency of the pair. This condition greatly reduces the pairs to compute, and the amount of memory taken.
Once this is calculated, the frequencies greater than the threshold are said frequent itemset.
(http://girlincomputerscience.blogspot.com.br/2013/01/frequent-itemset-problem-for-mapreduce.html)
There are two ways to solve the problem:
using Apriori algorithm
Using FP counting
Assuming that you are using Apriori, the answer you got is correct.
The algorithm is simple:
First you count frequent 1-item sets and exclude the item-sets below minimum support.
Then count frequent 2-item sets by combining frequent items from previous iteration and exclude the item-sets below support threshold.
The algorithm can go on until no item-sets are greater than threshold.
In the problem given to you, you only get 1 set of 2 items greater than threshold so you can't move further.
There is a solved example of further steps on Wikipedia here.
You can refer "Data Mining Concepts and Techniques" by Han and Kamber for more examples.
OK to start, you must first understand, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.
Now, the amount of raw data stored in corporate databases is exploding. From trillions of point-of-sale transactions and credit card purchases to pixel-by-pixel images of galaxies, databases are now measured in gigabytes and terabytes. (One terabyte = one trillion bytes. A terabyte is equivalent to about 2 million books!) For instance, every day, Wal-Mart uploads 20 million point-of-sale transactions to an A&T massively parallel system with 483 processors running a centralized database. Raw data by itself, however, does not provide much information. In today's fiercely competitive business environment, companies need to rapidly turn these terabytes of raw data into significant insights into their customers and markets to guide their marketing, investment, and management strategies.
Now you must understand that association rule mining is an important model in data mining. Its mining algorithms discover all item associations (or rules) in the data that satisfy the user-specified minimum support (minsup) and minimum confidence (minconf) constraints. Minsup controls the minimum number of data cases that a rule must cover. Minconf controls the predictive strength of the rule. Since only one minsup is used for the whole database, the model implicitly assumes that all items in the data are of the same nature and/or have similar frequencies in the data. This is, however, seldom the case in real- life applications. In many applications, some items appear very frequently in the data, while others rarely appear. If minsup is set too high, those rules that involve rare items will not be found. To find rules that involve both frequent and rare items, minsup has to be set very low. This may cause combinatorial explosion because those frequent items will be associated with one another in all possible ways. This dilemma is called the rare item problem. This paper proposes a novel technique to solve this problem. The technique allows the user to specify multiple minimum supports to reflect the natures of the items and their varied frequencies in the database. In rule mining, different rules may need to satisfy different minimum supports depending on what items are in the rules.
Given a set of transactions T (the database), the problem of mining association rules is to discover all association rules that have support and confidence greater than the user-specified minimum support (called minsup) and minimum confidence (called minconf).
I hope that once you understand the very basics of data mining that the answer to this question shall become apparent.
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Recently, I found myself having to write up some concerns I have about race conditions in an application that is in development (not by me). This will likely be brought to the attention of stakeholders who are non-technical and with whom I do not have a direct line of communication, so my explanation needs to be in written form.
I have already made an attempt at this write-up. I gloss over the technical specifics as best I can, give an example of how a race condition would occur in the application, and describe its impact. I feel I did pretty well, but it's far from perfect.
The problem is, as much as I try to shield the reader from computer science, I have still found it difficult to eliminate phrases like "threads of execution" and "mutual exclusion" without losing correctness and substance. The risk is that, with too much hand-waving, these concerns could be dismissed as a made-up boogeyman.
Anyway, my question to you is this: How would you explain race conditions to a non-technical audience? Would you dare to explain CPU scheduling? Would you invoke the dining philosophers?
You don't have to work within the constraints of my situation (but it would be awesomely helpful if you did).
Company X has $1,000 in the bank. X pays a rent of $2,000 and received a payment of $10,000 for services rendered to company Y. However, due to a race condition, X is in deficit of $1,000 and is now applying for bankruptcy. =(
You might want to explain how the bank handles company X's account in this way: Bank staff A takes the current value of $1,000 and adds $10,000 to it. Bank staff B takes the current value of $1,000 and subtracts $2,000 from it. Bank staff A updates the value to $11,000. Bank staff B updates the value to -$1,000.
I think bank transactions might be a good example, both because it's easy to see that an incorrect result is bad and because race conditions are easy to create in such an environment.
I have $500 on my account.
Someone transfers $200 to me at the same time that I withdraw $50.
Now, if the bank doesn't handle race conditions properly, they will do the following (assuming the transactions are handled manually, of course)
Clerk A will see the request to add $200 to my balance, and note that my balance is currently $500.
Clerk B will see the request to subtract $50 from my balance, and note that my balance is currently $500 (clerk A hasn't yet transferred the money).
Clerk A finishes the paperwork and sets my account balance to $700 (500 + the 200 he was supposed to add).
And then, a minute later (because clerk B just had to grab a cup of coffee), clerk B finishes up the other transaction and sets my balance to $450 (the 500 I had when he checked, minus the 50 he was meant to subtract).
My balance is now $450, when it should have been $650, because of a race condition. The outcome depended on the order in which different parts of the two transactions were performed.
That's the general description of how race conditions are bad. Now say that instead of clerks, we have our application processing two separate tasks at the same time (that's your 'threads of execution'), and just like above, they both read a value, modify the value that they read, and then write it back. One of the modifications may then be lost if this happens in the order shown above.
That should relate it to the specific problems in your app.
I would go for a Dining Philosopher's-esque approach, but depending on my audience, I would try to analogize it to the context of my audience. Are you speaking to business executives? Then analogize it to something like allocate a meeting room or a corporate car or booking a hotel room or whatever. Are you talking to average people? Then the dining philosopher's example is fine, or you can think up a similar situation involving caring for farm animals or sitting in chairs or whatever.
Whether you hijack the dining philosopher's example, or make up your own, definitely use a metaphor.
If you are writing to a non technical audience, you'll want to simplify your explanations and relate it to something they can understand. One explanation taken from the paper Analogies for teaching parallel computing to inexperienced programmers (http://portal.acm.org/citation.cfm?doid=1189136.1189172) explains it in terms of a pen game:
We’re going to play a game called the
Pen Game. The rules are simple: I’m
going to hold a pen in my hand, and
then I’ll say “One, two, three, go.”
When I say “go,” take the pen from my
hand. Whoever gets the pen wins.
Ready? One, two, three, go.
You then ask if the outcome of this game can be predicted in advance. If it can't be predicted, can we guarantee a correct outcome? This should lead to the realization that it's possible to get incorrect results for simultaneous writes to the same piece of memory.
I was going to recommend the dining philosophers, but I see you have already found that one. So, as an alternative, how about using gridlock as an analogy?
Imagine normal traffic driving along the four streets next to a single city block (North ave, South ave, East street and West street). When there are only one or two cars on the road, everything moves smoothly. When there is steady traffic, some cars will have to stop and wait for other cars to move past, but this is a manageable problem. One car stops to wait for another car to go by, and then continues on its merry way.
Now, picture rush-hour traffic at the same location. Let's say that one car driving South on West street can't make it all the way through the intersection at the NorthWest corner of our city block. That car now blocks all of the Westbound cross traffic on North ave. It doesn't take long before a Westbound car tries to make it through the NorthEast corner intersection and gets stuck, blocking all of the Northbound traffic on East street. When this situation makes it all the way around the four intersections, no cars can move! Each one is waiting for the cars in front of it to move ahead, but there is no way for the gridlock to be releived without pulling cars out backwards.
The comparison to computing should be straightforward. Cars are threads or processes, streets and avenues are processors, buffers, or cores. The concept of blocking can be described using traffic lights or stop signs, and the whole thing starts to make intuitive sense, even to non-programmers.
Write a program:
Wait for salary.
Go to shop.
Buy food.
Turn on the plate.
Put food on the plate.
Keep plate for 20 minutes.
Eat.
Go to bed.
Now try to have two threads (you, wife) execute it without syncronization.
You: Wait for salary.
Wife: Go to the shop without money, crash
You: Turn on the plate.
You: Keep plate for 20 minutes.
You: Go to bed.
Wife: Eat at someone else's place.
Wife: Go to bed.
Peter wants to pull out of his driveway. He checks that nothing is in the way of his car, then gets in. His son Frank then hides behind the car. Peter cannot see him and runs him over.
The important thing here is that for a computer, "inspect" and "modify" tend to be two separate actions, so an example where you can't check something when you modify it is a good one.
How about the plain obvious?
A race condition is literally a race between two people.
A company is bidding on a project. Two employees working independently on bids submit them to the customer, but one of the employees has outdated information. Neither employee know that the other is in the process of submitting a bid, therefore depending on who is faster, the first bid may be replaced with the slower employee. This will cause confusion as the bid may have changed over time.
There needs to be communication between the two employees to either work together or stop one of them.
One difficulty in explaining the general concept is that race conditions manifest themselves in a wide variety of situations. If your goal is give your non-technical audience the sense that this is a generic problem type, you should try to offer more than one example.
A picture is worth a 1000 words. Its true. If you draw a timeline and put some entity on it, and show its state changes as time progresses you can demonstrate a race-condition pretty easily in one diagram. It may take a few redos to get the picture just right, but I've always found that drawing it out gets my point across must faster than describing it.
I think it's hard to explain this in a simple way, because thinking about concurrency is inherently hard. The basic idea of a financial transaction might be a good place to start, since people will have some familiarity with them from real life.
In any kind of transaction, you need to make simultaneous entries in two places - debits and credits. If the transaction gets interrupted in the middle by someone else trying to perform another transaction, they will see the wrong balance in one or the other of the accounts.
There's a great example in Structured Concurrent Programming With Operating Systems Applications (as I recall)
In the impoverished country of Bezerkistan, two lines merge onto a single track in a tunnel. There have been collisions and the ruling junta needs a solution.
The issue is that it's mountainous and the engineers are blind. There's very little advance warning of two trains about to collide in the tunnel.
Here's the plan.
Put a big bowl at the juncture.
Give each engineer a little brass monkey.
When you're about to enter the tunnel, you stop your train. You pat around in the bowl to see if a brass monkey is in the bowl.
If there's a monkey, someone else is using the tunnel, so you have to wait until their train is entirely in the tunnel, at which time the conductor gets out of the caboose and grabs the monkey from the bowl.
If there's no monkey, no one else is using the tunnel. So, you can grab your monkey from the engine compartment, put it in the bowl and drive through the tunnel, knowing you have acquired exclusive access to the track. Of course, you stop briefly to allow the conductor to retrieve the brass monkey.
Guess what?
They still had collisions!
Why? What's the situation or sequence of actions that causes this to fail?
That's a race condition.
In a written document, you can explain how the race condition leads to an accident.
In a presentation, you can coach the audience through reasoning about concurrency and locking.
i would use a shared memory bank account example of a data race condition.
explain that the computer does something like: load balance; add 1; store balance;. consider two threads that are modifying your bank account balance (you and your wife are both depositing one dollar at the same time).
if both threads get interuupted after the: load balance; and then resume, you can lose one dollar.
see: http://wasp.cs.washington.edu/atomeclipse/handouts.pdf
As you mentioned, you often need to introduce other concepts (mutual exclusion, threads of execution) to accurately describe race conditions, even in a metaphor. So try defining these terms (or at least getting the idea across) first, using metaphor.
As a simple example, let's use a 4-way intersection (set in a country where you drive on the right). Divide the intersection into 4 quadrants: North-West, North-East, South-East, and South-West. Now call each quadrant a resource, and call each car a thread of execution. These cars only respect traffic systems, and since there are no stop signs or traffic lights at this intersection, the cars barrel right on through without slowing or considering traffic.
You can easily show that simultaneous use of one of these quadrants by more than one car is bad, and results in a car crash. One obvious solution is to install a traffic system. The system ensures that no more than one car is passing through a quadrant at the same time. It can do this intricately, without tying up all the resources. For example, letting cars coming from the South make a left turn to head West (using south-east and north-west quadrants), while letting cars coming from the West make a right turn to head South (using the south-west quadrant). The traffic system is providing mutual exclusion, or preventing simultaneous use (by multiple cars) of a common resource (the quadrant of road in the intersection).
This at least provides the ideas behind these definitions, the idea that simultaneously accessing shared resources can be bad, and that mutual exclusion can solve this problem. After this is established, you'll need to map these to a more appropriate metaphor to show what a race condition is and how it's one of those bad things that results from lack of mutual exclusion for a common resource.
It takes a bit longer, but it grants some familiarity with terms and the big picture before drilling down into a more complex metaphor.
Talking about money to your stakeholders might send them into panic mode especially if they assume they are losing actual money because of this, which is not exactly ideal if the problem does not specifically result in a net loss of profits, so here's a less financially oriented story on how you can explain a race condition to anyone.
This story does not address the concept of deadlock, but the more traditional race condition scenario and consequences.
STORY STARTS HERE:
The Setting: There are 3 cities connected by a railway network. The trains do not have any signs on them indicating which city they are coming from and which city they are going to because they are being used between all 3 cities and the railway network didn't want to deal with the hassle of changing signs all the time. Since the network is small there is no concrete schedule on when trains arrive and leave. The station overseers just get a call from the other city station overseers when a train departs, the overseer takes a note of the time when it has left and since all trains are the same models they drive at the same speed, so when the overseer receives a call from the other cities they announce to the people in the station that: "The next train will be heading to city C". So the people who wish to travel to city C await the train, hop on and merrily ride to city C.
The Problem: But one day, as a train was planning its route from A to B to C, it broke down half-way between A and B. Luckily the technicians are very skilled and would be able to repair the train in a short while. However that same day another train was also planning a different route from C to B to A. The overseer at station B received a call from A that a train is coming, and shortly after received another call from C that another train was also coming. The station overseer then announced to the passengers awaiting in the station: "The first train arriving will be heading to station C, and shortly after the train after that will be heading to station A." As the passengers gathered their luggage and went to their respective platforms. The overseer saw a train coming and redirected the rails to the platform where people were planning to head to city C. Little did they know that the train was actually going to city A instead. The other train, after having fixed its' mechanical problems also arrived at the station and the overseer happily directed it to the platform containing passengers wishing to go to city A. Needless to say none of the passengers arrived where they planned to, all because the overseer assumed that they would arrive in order as usual.
The problem with race conditions and many many computer science constructs is that people are not computers. Every time I explain an algorithm to my students they say "but it doesn't make sense to do it that way", to which I reply "computers don't have common sense, all they have are instructions". That aside, you should explain a race condition as a race, and it makes most sense to let people actually try the race, if they can. That way they can see how things go wrong. But... they are not allowed to use common sense.
So let's assume we have a game where 2 persons fill up stacks of colored blocks in order Red, Orange, Yellow. They have many red, orange and yellow blocks. All stacks need to be exactly three blocks high.
In the first game both try to do this as fast as possible, but they only work on their own stacks.
In the second game they try to work together by allowing themselves to also stack blocks on each other's stacks. However they are not allowed to change the block they have in their hand, and they have to place a planned block.
You can imagine a situation like this occurs in stack 1:
player 1 grabs a red block
player 1 places red block - player 2 grabs an orange block
player 1 grabs an orange block - player 2 places an orange block
player 1 places an orange block
So now we have a stack with two orange blocks. It's obvious that with a human game this would never happen, because people have common sense: they see that the orange block is already placed, and revert their decision to also place an orange block.
Also you can show them this video: https://www.youtube.com/watch?v=TcGwNdbsAbc
Let's use a whiteboard to do a trivial accounting task. We've got $100 on hand - write it on the whiteboard.
Alice has dozens of invoices that add up to $100, so she's going to note that $100, go and add up her list and come back in 5 minutes and write $200 on the board.
Bob's been shopping. He's going to take that number from the whiteboard and go and subtract $50 worth of purchases, and then he's going to write $50 on the board.
If Bob gets back first, we'll see $200 after Alice writes her result. If Alice gets back first we'll see $50, also wrong. What we want to see is $150, and we need to add some precautions somewhere to make that happen.
That should be enough to scaffold a discussion of technical solutions with reasonable intuitions.
For example, a mutex means you lock the door to the room with the whiteboard in it, and make them do their work in there. An optimistic solution means you get them both to check and start over if the number changed while they were away. If you want to talk about deadlocks, you can laugh about Bob calling Alice from inside the locked room to ask her to hurry up.
Send them to Race Condition on Wikipedia.
The first part will make some sense, and the rest (not shown below) will make you look smart since they will assume you understand it.
"A race condition or race hazard is a flaw in a system or process whereby the output and/or result of the process is unexpectedly and critically dependent on the sequence or timing of other events. The term originates with the idea of two signals racing each other to influence the output first."
I think the key point to get across is that its most frequently a timing issue that can be unpredictable because the timing something takes differs from time to time.