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  • Big O, how do you calculate/approximate it?

    - by Sven
    Most people with a degree in CS will certainly know what Big O stands for. It helps us to measure how (in)efficient an algorithm really is and if you know in what category the problem you are trying to solve lays in you can figure out if it is still possible to squeeze out that little extra performance.* But I'm curious, how do you calculate or approximate the complexity of your algorithms? *: but as they say, don't overdo it, premature optimization is the root of all evil, and optimization without a justified cause should deserve that name as well.

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  • How big is too big (for NTFS)

    - by BCS
    I have a program and as it's done now, it has a data directory with something like 10-30K files in it and it's starting to cause problems. Should I expect that to cause problems and my only solution to tweak my file structure or does that indicate other problems?

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  • Big O and Little o

    - by hyperdude
    If algorithm A has complexity O(n) and algorithm B has complexity o(n^2), what, if anything, can we say about the relationship between A and B? Note: the complexity of A is expressed using big-Oh, and the complexity of B is expressed using little-Oh.

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  • Big Data Sessions at Openworld 2012

    - by Jean-Pierre Dijcks
    If you are coming to San Francisco, and you are interested in all the aspects to big data, this Focus On Big Data is a must have document.  Some (other) highlights: A performance demo of a full rack Big Data Appliance in the engineered systems showcase A set of handson labs on how to go from a NoSQL DB to an effective analytics play on big data Much, much more See you all in a few weeks in SF!

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  • BIG DATA eBook - Now Available

    - by Javier Puerta
    The Big Data interactive e-book “Meeting the Challenge of Big Data: Part One” has just been released. It’s your “one-stop shop” for info about Big Data and the Oracle offering around it.The new e-book (available on your computer or iPad) is packed with multi-media resources to educate Oracle staff, customers, prospects and partners on the value of Big Data. It features videos, tutorials, podcasts, reports, white papers, datasheets, blogs, web links, a 3-D demo, and more. Go and get it here!

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  • Big Oh Notation - formal definition.

    - by aloh
    I'm reading a textbook right now for my Java III class. We're reading about Big-Oh and I'm a little confused by its formal definition. Formal Definition: "A function f(n) is of order at most g(n) - that is, f(n) = O(g(n)) - if a positive real number c and positive integer N exist such that f(n) <= c g(n) for all n = N. That is, c g(n) is an upper bound on f(n) when n is sufficiently large." Ok, that makes sense. But hold on, keep reading...the book gave me this example: "In segment 9.14, we said that an algorithm that uses 5n + 3 operations is O(n). We now can show that 5n + 3 = O(n) by using the formal definition of Big Oh. When n = 3, 5n + 3 <= 5n + n = 6n. Thus, if we let f(n) = 5n + 3, g(n) = n, c = 6, N = 3, we have shown that f(n) <= 6 g(n) for n = 3, or 5n + 3 = O(n). That is, if an algorithm requires time directly proportional to 5n + 3, it is O(n)." Ok, this kind of makes sense to me. They're saying that if n = 3 or greater, 5n + 3 takes less time than if n was less than 3 - thus 5n + n = 6n - right? Makes sense, since if n was 2, 5n + 3 = 13 while 6n = 12 but when n is 3 or greater 5n + 3 will always be less than or equal to 6n. Here's where I get confused. They give me another example: Example 2: "Let's show that 4n^2 + 50n - 10 = O(n^2). It is easy to see that: 4n^2 + 50n - 10 <= 4n^2 + 50n for any n. Since 50n <= 50n^2 for n = 50, 4n^2 + 50n - 10 <= 4n^2 + 50n^2 = 54n^2 for n = 50. Thus, with c = 54 and N = 50, we have shown that 4n^2 + 50n - 10 = O(n^2)." This statement doesn't make sense: 50n <= 50n^2 for n = 50. Isn't any n going to make the 50n less than 50n^2? Not just greater than or equal to 50? Why did they even mention that 50n <= 50n^2? What does that have to do with the problem? Also, 4n^2 + 50n - 10 <= 4n^2 + 50n^2 = 54n^2 for n = 50 is going to be true no matter what n is. And how in the world does picking numbers show that f(n) = O(g(n))? Please help me understand! :(

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  • Big O Complexity of a method

    - by timeNomad
    I have this method: public static int what(String str, char start, char end) { int count=0; for(int i=0;i<str.length(); i++) { if(str.charAt(i) == start) { for(int j=i+1;j<str.length(); j++) { if(str.charAt(j) == end) count++; } } } return count; } What I need to find is: 1) What is it doing? Answer: counting the total number of end occurrences after EACH (or is it? Not specified in the assignment, point 3 depends on this) start. 2) What is its complexity? Answer: the first loops iterates over the string completely, so it's at least O(n), the second loop executes only if start char is found and even then partially (index at which start was found + 1). Although, big O is all about worst case no? So in the worst case, start is the 1st char & the inner iteration iterates over the string n-1 times, the -1 is a constant so it's n. But, the inner loop won't be executed every outer iteration pass, statistically, but since big O is about worst case, is it correct to say the complexity of it is O(n^2)? Ignoring any constants and the fact that in 99.99% of times the inner loop won't execute every outer loop pass. 3) Rewrite it so that complexity is lower. What I'm not sure of is whether start occurs at most once or more, if once at most, then method can be rewritten using one loop (having a flag indicating whether start has been encountered and from there on incrementing count at each end occurrence), yielding a complexity of O(n). In case though, that start can appear multiple times, which most likely it is, because assignment is of a Java course and I don't think they would make such ambiguity. Solving, in this case, is not possible using one loop... WAIT! Yes it is..! Just have a variable, say, inc to be incremented each time start is encountered & used to increment count each time end is encountered after the 1st start was found: inc = 0, count = 0 if (current char == start) inc++ if (inc > 0 && current char == end) count += inc This would also yield a complexity of O(n)? Because there is only 1 loop. Yes I realize I wrote a lot hehe, but what I also realized is that I understand a lot better by forming my thoughts into words...

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  • big O notation algorithm

    - by niggersak
    Use big-O notation to classify the traditional grade school algorithms for addition and multiplication. That is, if asked to add two numbers each having N digits, how many individual additions must be performed? If asked to multiply two N-digit numbers, how many individual multiplications are required? . Suppose f is a function that returns the result of reversing the string of symbols given as its input, and g is a function that returns the concatenation of the two strings given as its input. If x is the string hrwa, what is returned by g(f(x),x)? Explain your answer - don't just provide the result!

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  • Tricky Big-O complexity

    - by timeNomad
    public void foo (int n, int m) { int i = m; while (i > 100) i = i/3; for (int k=i ; k>=0; k--) { for (int j=1; j<n; j*=2) System.out.print(k + "\t" + j); System.out.println(); } } I figured the complexity would be O(logn). That is as a product of the inner loop, the outer loop -- will never be executed more than 100 times, so it can be omitted. What I'm not sure about is the while clause, should it be incorporated into the Big-O complexity? For very large i values it could make an impact, or arithmetic operations, doesn't matter on what scale, count as basic operations and can be omitted?

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  • Database indexes and their Big-O notation

    - by miket2e
    I'm trying to understand the performance of database indexes in terms of Big-O notation. Without knowing much about it, I would guess that: Querying on a primary key or unique index will give you a O(1) lookup time. Querying on a non-unique index will also give a O(1) time, albeit maybe the '1' is slower than for the unique index (?) Querying on a column without an index will give a O(N) lookup time (full table scan). Is this generally correct ? Will querying on a primary key ever give worse performance than O(1) ? My specific concern is for SQLite, but I'd be interested in knowing to what extent this varies between different databases too.

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  • Can someone help with big O notation?

    - by Dann
    void printScientificNotation(double value, int powerOfTen) { if (value >= 1.0 && value < 10.0) { System.out.println(value + " x 10^" + powerOfTen); } else if (value < 1.0) { printScientificNotation(value * 10, powerOfTen - 1); } else // value >= 10.0 { printScientificNotation(value / 10, powerOfTen + 1); } } I understand how the method goes but I cannot figure out a way to represent the method. For example, if value was 0.00000009 or 9e-8, the method will call on printScientificNotation(value * 10, powerOfTen - 1); eight times and System.out.println(value + " x 10^" + powerOfTen); once. So the it is called recursively by the exponent for e. But how do I represent this by big O notation? Thanks!

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  • Big Data for Retail

    - by David Dorf
    Right up there with mobile, social, and cloud is the term "big data," which seems to be popping up lots in the press these days.  Companies like Google, Yahoo, and Facebook have popularized a new class of data technologies meant to solve the problem of processing large amounts of data quickly.  I first mentioned this in a posting back in March 2009.  Put simply, big data implies datasets so large they can't normally be processed using a standard transactional database.  The term "noSQL" is often used in this context as well. Actually, using parallel processing within the Oracle database combined with Exadata can achieve impressive results.  Look for more from Oracle at OpenWorld as hinted by Jean-Pierre Dijcks. McKinsey recently released a report on big data in which retail was specifically mentioned as an industry that can benefit from the new technologies.  I won't rehash that report because my friend Rama already did such a good job in his posting, Impact of "Big Data" on Retail. The presentation below does a pretty good job of framing the problem, although it doesn't really get into the available technologies (e.g. Exadata, Hadoop, Cassandra, etc.) and isn't retail specific. Determine the Right Analytic Database: A Survey of New Data Technologies So when a retailer asks me about big data, here's what I say:  Big data refers to a set of technologies for processing large volumes of structured and unstructured data.  Imagine collecting everything uttered by your customers on Facebook and Twitter and combining it with all the data you can find about the products you sell (e.g. reviews, images, demonstration videos), including competitive data.  Assuming you could process all that data, you could then personalize offers to specific customers based on their tastes, ensure prices are competitive, and implement better local assortments.  It's really not that far off.

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  • Recurrence relation solution

    - by Travis
    I'm revising past midterms for a final exam this week and am trying to make sense of a solution my professor posted for one of past exams. (You can see the original pdf here, question #6). I'm given the original recurrence relation T(m)=3T(n/2) + n and am told T(1) = 1. I'm pretty sure the solution I've been given is wrong in a few places. The solution is as follows: Let n=2^m T(2^m) = 3T(2^(m-1)) + 2^m 3T(2^(m-1)) = 3^2*T(2^(m-2)) + 2^(m-1)*3 ... 3^(m-1)T(2) = T(1) + 2*3^(m-1) I'm pretty sure this last line is incorrect and they forgot to multiply T(1) by 3^m. He then (tries to) sum the expressions: T(2^m) = 1 + (2^m + 2^(m-1)*3 + ... + 2*3(m-1)) = 1 + 2^m(1 + (3/2)^1 + (3/2)^2 + ... + (3/2)^(m-1)) = 1 + 2^m((3/2)^m-1)*(1/2) = 1 + 3^m - 2^(m-1) = 1 + n^log 3 - n/2 Thus the algorithm is big Theta of (n^log 3). I'm pretty sure that he also got the summation wrong here. By my calculations this should be as follows: T(2^m) = 2^m + 3 * 2^(m-1) + 3^2 * 2^(m-2) + ... + 3^m (3^m because 3^m*T(1) = 3^m should be added, not 1) = 2^m * ((3/2)^1 + (3/2)^2 + ... + (3/2)^m) = 2^m * sum of (3/2)^i from i=0 to m = 2^m * ((3/2)^(m+1) - 1)/(3/2 - 1) = 2^m * ((3/2)^(m+1) - 1)/(1/2) = 2^(m+1) * 3^(m+1)/2^(m+1) - 2^(m+1) = 3^(m+1) - 2 * 2^m Replacing n = 2^m, and from that m = log n T(n) = 3*3^(log n) - 2*n n is O(3^log n), thus the runtime is big Theta of (3^log n) Does this seem right? Thanks for your help!

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  • Fast Data - Big Data's achilles heel

    - by thegreeneman
    At OOW 2013 in Mark Hurd and Thomas Kurian's keynote, they discussed Oracle's Fast Data software solution stack and discussed a number of customers deploying Oracle's Big Data / Fast Data solutions and in particular Oracle's NoSQL Database.  Since that time, there have been a large number of request seeking clarification on how the Fast Data software stack works together to deliver on the promise of real-time Big Data solutions.   Fast Data is a software solution stack that deals with one aspect of Big Data, high velocity.   The software in the Fast Data solution stack involves 3 key pieces and their integration:  Oracle Event Processing, Oracle Coherence, Oracle NoSQL Database.   All three of these technologies address a high throughput, low latency data management requirement.   Oracle Event Processing enables continuous query to filter the Big Data fire hose, enable intelligent chained events to real-time service invocation and augments the data stream to provide Big Data enrichment. Extended SQL syntax allows the definition of sliding windows of time to allow SQL statements to look for triggers on events like breach of weighted moving average on a real-time data stream.    Oracle Coherence is a distributed, grid caching solution which is used to provide very low latency access to cached data when the data is too big to fit into a single process, so it is spread around in a grid architecture to provide memory latency speed access.  It also has some special capabilities to deploy remote behavioral execution for "near data" processing.   The Oracle NoSQL Database is designed to ingest simple key-value data at a controlled throughput rate while providing data redundancy in a cluster to facilitate highly concurrent low latency reads.  For example, when large sensor networks are generating data that need to be captured while analysts are simultaneously extracting the data using range based queries for upstream analytics.  Another example might be storing cookies from user web sessions for ultra low latency user profile management, also leveraging that data using holistic MapReduce operations with your Hadoop cluster to do segmented site analysis.  Understand how NoSQL plays a critical role in Big Data capture and enrichment while simultaneously providing a low latency and scalable data management infrastructure thru clustered, always on, parallel processing in a shared nothing architecture. Learn how easily a NoSQL cluster can be deployed to provide essential services in industry specific Fast Data solutions. See these technologies work together in a demonstration highlighting the salient features of these Fast Data enabling technologies in a location based personalization service. The question then becomes how do these things work together to deliver an end to end Fast Data solution.  The answer is that while different applications will exhibit unique requirements that may drive the need for one or the other of these technologies, often when it comes to Big Data you may need to use them together.   You may have the need for the memory latencies of the Coherence cache, but just have too much data to cache, so you use a combination of Coherence and Oracle NoSQL to handle extreme speed cache overflow and retrieval.   Here is a great reference to how these two technologies are integrated and work together.  Coherence & Oracle NoSQL Database.   On the stream processing side, it is similar as with the Coherence case.  As your sliding windows get larger, holding all the data in the stream can become difficult and out of band data may need to be offloaded into persistent storage.  OEP needs an extreme speed database like Oracle NoSQL Database to help it continue to perform for the real time loop while dealing with persistent spill in the data stream.  Here is a great resource to learn more about how OEP and Oracle NoSQL Database are integrated and work together.  OEP & Oracle NoSQL Database.

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  • Reporting a WCF application's status to F5's Big IP products

    - by ng5000
    In a Windows Server 2003 environment with a self hosted .Net 3.5/WCF application, how can an application report its status to a BigIP Local Traffic Manager? Example: One of my services errors. My custom WCF application hosting software (written because Windows Server 2008 is not yet available and I'm using WCF TCP bindings) detects this and wants to report itself as down until it can recover the errant service. It needs to report itself as down to the BigIP LTM so that it is no longer sent client originated requests.

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  • analysis Big Oh notation psuedocode

    - by tesshu
    I'm having trouble getting my head around algorithm analysis. I seem to be okay identifying linear or squared algorithms but am totally lost with nlogn or logn algorithms, these seem to stem mainly from while loops? Heres an example I was looking at: Algorithm Calculate(A,n) Input: Array A of size n t?0 for i?0 to n-1 do if A[i] is an odd number then Q.enqueue(A[i]) else while Q is not empty do t?t+Q.dequeue() while Q is not empty do t?t+Q.dequeue() return t My best guess is the for loop is executed n times, its nested while loop q times making NQ and the final while loop also Q times resulting in O(NQ +Q) which is linear? I am probably totally wrong. Any help would be much appreciated. thanks

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  • Big-O for calculating all routes from GPS data

    - by HH
    A non-critical GPS module use lists because it needs to be modifiable, new routes added, new distances calculated, continuos comparisons. Well so I thought but my team member wrote something I am very hard to get into. His pseudo code int k =0; a[][] <- create mapModuleNearbyDotList -array //CPU O(n) for(j = 1 to n) // O(nlog(m)) for(i =1 to n) for(k = 1 to n) if(dot is nearby) adj[i][j]=min(adj[i][j], adj[i][k] + adj[k][j]); His ideas transformations of lists to tables His worst case time complexity is O(n^3), where n is number of elements in his so-called table. Exception to the last point with Finite structure: O(mlog(n)) where n is number of vertices and m is the amount of neighbour vertices. Questions about his ideas why to waste resources to transform constantly-modified lists to table? Fast? only point where I to some extent agree but cannot understand the same upper limits n for each for-loops -- perhaps he supposed it circular why does the code take O(mlog(n)) to proceed in time as finite structure? The term finite may be wrong, explicit?

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  • Big-O for Eight Year Olds?

    - by Jason Baker
    I'm asking more about what this means to my code. I understand the concepts mathematically, I just have a hard time wrapping my head around what they mean conceptually. For example, if one were to perform an O(1) operation on a data structure, I understand that the amount of operations it has to perform won't grow because there are more items. And an O(n) operation would mean that you would perform a set of operations on each element. Could somebody fill in the blanks here? Like what exactly would an O(n^2) operation do? And what the heck does it mean if an operation is O(n log(n))? And does somebody have to smoke crack to write an O(x!)?

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  • Big-O for GPS data

    - by HH
    A non-critical GPS module use lists because it needs to be modifiable, new routes added, new distances calculated, continuos comparisons. Well so I thought but my team member wrote something I am very hard to get into. His pseudo code int k =0; a[][] <- create mapModuleNearbyDotList -array //CPU O(n) for(j = 1 to n) // O(nlog(m)) for(i =1 to n) for(k = 1 to n) if(dot is nearby) adj[i][j]=min(adj[i][j], adj[i][k] + adj[k][j]); His ideas transformations of lists to tables His worst case time complexity is O(n^3), where n is number of elements in his so-called table. Exception to the last point with Finite structure: O(mlog(n)) where n is number of vertices and m is an arbitrary constants Questions about his ideas why to waste resources to transform constantly-modified lists to table? Fast? only point where I to some extent agree but cannot understand the same upper limits n for each for-loops -- perhaps he supposed it circular why does the code take O(mlog(n)) to proceed in time as finite structure? The term finite may be wrong, explicit?

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  • big background without scrolls

    - by mkoso
    I have layout that has wider background picture than the content area. I have made 970ppx wrapper where the content is. And in body I have backgroud image but I need to have anothen background image above of tht body background image so I have made class bgimg. So basically the markup is like this: But the bgimg is about 1050px wide and thus it gives scrolls when users browser is 1024x768. Is there way of getting rid the scrolls? I mean I want to have have scrollbars if users browrser is narrower thant the 970x wrapper of course. So can I put something like overflow hidden for bgimg class? Hopefully you did understannd what I mean.

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  • The importance of Design Patterns with Javascript, NodeJs et al

    - by Lewis
    With Javascript appearing to be the ubiquitous programming language of the web over the next few years, new frameworks popping up every five minutes and event driven programming taking a lead both server and client side: Do you as a Javascript developer consider the traditional Design Patterns as important or less important than they have been with other languages / environments?. Please name the top three design patterns you, as a Javascript developer use regularly and give an example of how they have helped in your Javascript development.

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  • Convert DVD to MKV (et al) without transcoding/recompression

    - by Oli
    Like a lot of people, I have a lot of DVDs. But we also have a stupid amount of disk space and a media centre (Boxee) so the DVDs are getting less and less use. It would be nice to convert our DVDs into something more relevant to our needs. I've dabbled with DVD ripping before but whereas I'd usually transcode down to a smaller picture size with a better video compression algorithm, this takes a silly amount of time. I don't have a couple of hours available for each disk. (Sidebar: is there dedicated, Linux-friendly hardware to improve h264 encoding performance?) So I was wondering if there's anything that take the DVD filesystem, De-CSS it, and then stitch together any the VOBs that make up the main part of the film and package that up in a wrapping format like MKV. A bonus would be if it could grab the subtitles and stick them in too but that's not a requirement as Boxee can grab the subtitles online if it needs to.

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  • Convert DVD to MKV (et al) without transcoding/recompression

    - by Oli
    Like a lot of people, I have a lot of DVDs. But we also have a stupid amount of disk space and a media centre (Boxee) so the DVDs are getting less and less use. It would be nice to convert our DVDs into something more relevant to our needs. I've dabbled with DVD ripping before but whereas I'd usually transcode down to a smaller picture size with a better video compression algorithm, this takes a silly amount of time. I don't have a couple of hours available for each disk. (Sidebar: is there dedicated, Linux-friendly hardware to improve h264 encoding performance?) So I was wondering if there's anything that take the DVD filesystem, De-CSS it, and then stitch together any the VOBs that make up the main part of the film and package that up in a wrapping format like MKV. A bonus would be if it could grab the subtitles and stick them in too but that's not a requirement as Boxee can grab the subtitles online if it needs to.

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  • Tackling Big Data Analytics with Oracle Data Integrator

    - by Irem Radzik
    v\:* {behavior:url(#default#VML);} o\:* {behavior:url(#default#VML);} w\:* {behavior:url(#default#VML);} .shape {behavior:url(#default#VML);} Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman","serif"; mso-fareast-font-family:"Times New Roman";}  By Mike Eisterer  The term big data draws a lot of attention, but behind the hype there's a simple story. For decades, companies have been making business decisions based on transactional data stored in relational databases. Beyond that critical data, however, is a potential treasure trove of less structured data: weblogs, social media, email, sensors, and documents that can be mined for useful information.  Companies are facing emerging technologies, increasing data volumes, numerous data varieties and the processing power needed to efficiently analyze data which changes with high velocity. Oracle offers the broadest and most integrated portfolio of products to help you acquire and organize these diverse data sources and analyze them alongside your existing data to find new insights and capitalize on hidden relationships Oracle Data Integrator Enterprise Edition(ODI) is critical to any enterprise big data strategy. ODI and the Oracle Data Connectors provide native access to Hadoop, leveraging such technologies as MapReduce, HDFS and Hive. Alongside with ODI’s metadata driven approach for extracting, loading and transforming data; companies may now integrate their existing data with big data technologies and deliver timely and trusted data to their analytic and decision support platforms. In this session, you’ll learn about ODI and Oracle Big Data Connectors and how, coupled together, they provide the critical integration with multiple big data platforms. Tackling Big Data Analytics with Oracle Data Integrator October 1, 2012 12:15 PM at MOSCONE WEST – 3005 For other data integration sessions at OpenWorld, please check our Focus-On document.  If you are not able to attend OpenWorld, please check out our latest resources for Data Integration.

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