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• The Business case for Big Data

  - by jasonw

  The Business Case for Big Data Part 1 What's the Big Deal Okay, so a new buzz word is emerging. It's gone beyond just a buzzword now, and I think it is going to change the landscape of retail, financial services, healthcare....everything. Let me spend a moment to talk about what i'm going to talk about. Massive amounts of data are being collected every second, more than ever imaginable, and the size of this data is more than can be practically managed by today’s current strategies and technologies. There is a revolution at hand centering on this groundswell of data and it will change how we execute our businesses through greater efficiencies, new revenue discovery and even enable innovation. It is the revolution of Big Data. This is more than just a new buzzword is being tossed around technology circles.This blog series for Big Data will explain this new wave of technology and provide a roadmap for businesses to take advantage of this growing trend. Cases for Big Data There is a growing list of use cases for big data. We naturally think of Marketing as the low hanging fruit. Many projects look to analyze twitter feeds to find new ways to do marketing. I think of a great example from a TED speech that I recently saw on data visualization from Facebook from my masters studies at University of Virginia. We can see when the most likely time for breaks-ups occurs by looking at status changes and updates on users Walls. This is the intersection of Big Data, Analytics and traditional structured data. Ted Video Marketers can use this to sell more stuff. I really like the following piece on looking at twitter feeds to measure mood. The following company was bought by a hedge fund. They could predict how the S&P was going to do within three days at an 85% accuracy. Link to the article Here we see a convergence of predictive analytics and Big Data. So, we'll look at a lot of these business cases and start talking about what this means for the business. It's more than just finding ways to use Hadoop + NoSql and we'll talk about that too. How do I start in Big Data? That's what is coming next post.

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• Forbes Article on Big Data and Java Embedded Technology

  - by hinkmond

  Whoa, cool! Forbes magazine has an online article about what I've been blogging about all this time: Big Data and Java Embedded Technology, tying it all together with a big bow, connecting small devices to the data center. See: Billions of Java Embedded Devices Here's a quote: By the end of the decade we could see tens of billions of new Internet-connected devices... with billions of Internet- connected devices generating Big Data, are the next big thing. ... That’s why Oracle has put together an ecosystem of solutions for this new, Big Data-oriented device-to-data center world: secure, powerful, and adaptable embedded Java for intelligent devices, integrated middleware... This is the next big thing. Java SE Embedded Technology is something to watch for in the new year. Start developing for it now to get a head-start... Hinkmond

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• Unlock the Value of Big Data

  - by Mike.Hallett(at)Oracle-BI&EPM

  Partners should read this comprehensive new e-book to get advice from Oracle and industry leaders on how you can use big data to generate new business insights and make better decisions for your customers. “Big data represents an opportunity averaging 14% of current revenue.” —From the Oracle big data e-book, Meeting the Challenge of Big Data You’ll gain instant access to: Straightforward approaches for acquiring, organizing, and analyzing data Architectures and tools needed to integrate new data with your existing investments Survey data revealing how leading companies are using big data, so you can benchmark your progress Expert resources such as white papers, analyst videos, 3-D demos, and more If you want to be ready for the data deluge, Meeting the Challenge of Big Data is a must-read. Register today for the e-book and read it on your computer or Apple iPad.  

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• Big Data – Data Mining with Hive – What is Hive? – What is HiveQL (HQL)? – Day 15 of 21

  - by Pinal Dave

  In yesterday’s blog post we learned the importance of the operational database in Big Data Story. In this article we will understand what is Hive and HQL in Big Data Story. Yahoo started working on PIG (we will understand that in the next blog post) for their application deployment on Hadoop. The goal of Yahoo to manage their unstructured data. Similarly Facebook started deploying their warehouse solutions on Hadoop which has resulted in HIVE. The reason for going with HIVE is because the traditional warehousing solutions are getting very expensive. What is HIVE? Hive is a datawarehouseing infrastructure for Hadoop. The primary responsibility is to provide data summarization, query and analysis. It  supports analysis of large datasets stored in Hadoop’s HDFS as well as on the Amazon S3 filesystem. The best part of HIVE is that it supports SQL-Like access to structured data which is known as HiveQL (or HQL) as well as big data analysis with the help of MapReduce. Hive is not built to get a quick response to queries but it it is built for data mining applications. Data mining applications can take from several minutes to several hours to analysis the data and HIVE is primarily used there. HIVE Organization The data are organized in three different formats in HIVE. Tables: They are very similar to RDBMS tables and contains rows and tables. Hive is just layered over the Hadoop File System (HDFS), hence tables are directly mapped to directories of the filesystems. It also supports tables stored in other native file systems. Partitions: Hive tables can have more than one partition. They are mapped to subdirectories and file systems as well. Buckets: In Hive data may be divided into buckets. Buckets are stored as files in partition in the underlying file system. Hive also has metastore which stores all the metadata. It is a relational database containing various information related to Hive Schema (column types, owners, key-value data, statistics etc.). We can use MySQL database over here. What is HiveSQL (HQL)? Hive query language provides the basic SQL like operations. Here are few of the tasks which HQL can do easily. Create and manage tables and partitions Support various Relational, Arithmetic and Logical Operators Evaluate functions Download the contents of a table to a local directory or result of queries to HDFS directory Here is the example of the HQL Query: SELECT upper(name), salesprice FROM sales; SELECT category, count(1) FROM products GROUP BY category; When you look at the above query, you can see they are very similar to SQL like queries. Tomorrow In tomorrow’s blog post we will discuss about very important components of the Big Data Ecosystem – Pig. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• SQL Server binary(128) convert from little endian to big endian

  - by bogdanbrudiu

  how to convert a binary(128) from little endian to big endian in SQL Server?

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• <= vs < when proving big-o notation

  - by user600197

  We just started learning big-o in class. I understand the general concept that f(x) is big-o of g(x) if there exists two constants c,k such that for all xk |f(x)|<=c|g(x)|. I had a question whether or not it is required that we include the <= to sign or whether it is just sufficient to put the < sign? For example: suppose f(x)=17x+11 and we are to prove that this is O(x^2). Then if we take c=28 and xk=1 we know that 17x+11<=28x^2. So since we know that x will always be greater than 1 this implies that 28x^2 will always be greater than 17x+11. So, do we really need to include the equal sign (<=) or is it okay if we just write (<)? Thanks in advance.

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• Simple Big O with lg(n) proof

  - by halohunter

  I'm attempting to guess and prove the Big O for: f(n) = n^3 - 7n^2 + nlg(n) + 10 I guess that big O is n^3 as it is the term with the largest order of growth However, I'm having trouble proving it. My unsuccesful attempt follows: f(n) <= cg(n) f(n) <= n^3 - 7n^2 + nlg(n) + 10 <= cn^3 f(n) <= n^3 + (n^3)*lg(n) + 10n^3 <= cn^3 f(n) <= N^3(11 + lg(n)) <= cn^3 so 11 + lg(n) = c But this can't be right because c must be constant. What am I doing wrong?

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• Principles of Big Data By Jules J Berman, O&rsquo;Reilly Media Book Review

  - by Compudicted

  Originally posted on: http://geekswithblogs.net/Compudicted/archive/2013/11/04/principles-of-big-data-by-jules-j-berman-orsquoreilly-media.aspx A fantastic book! Must be part, if not yet, of the fundamentals of the Big Data as a field of science. Highly recommend to those who are into the Big Data practice. Yet, I confess this book is one of my best reads this year and for a number of reasons: The book is full of wisdom, intimate insight, historical facts and real life examples to how Big Data projects get conceived, operate and sadly, yes, sometimes die. But not only that, the book is most importantly is filled with valuable advice, accurate and even overwhelming amount of reference (from the positive side), and the author does not event stop there: there are numerous technical excerpts, links and examples allowing to quickly accomplish many daunting tasks or make you aware of what one needs to perform as a data practitioner (excuse my use of the word practitioner, I just did not find a better substitute to it to trying to reference all who face Big Data). Be aware that Jules Berman’s background is in medicine, naturally, this book discusses this subject a lot as it is very dear to the author’s heart I believe, this does not make this book any less significant however, quite the opposite, I trust if there is an area in science or practice where the biggest benefits can be ripped from Big Data projects it is indeed the medical science, let’s make Cancer history! On a personal note, for me as a database, BI professional it has helped to understand better the motives behind Big Data initiatives, their underwater rivers and high altitude winds that divert or propel them forward. Additionally, I was impressed by the depth and number of mining algorithms covered in it. I must tell this made me very curious and tempting to find out more about these indispensable attributes of Big Data so sure I will be trying stretching my wallet to acquire several books that go more in depth on several most popular of them. My favorite parts of the book, well, all of them actually, but especially chapter 9: Analysis, it is just very close to my heart. But the real reason is it let me see what I do with data from a different angle. And then the next - “Special Considerations”, they are just two logical parts. The writing language is of this book is very acceptable for all levels, I had no technical problem reading it in ebook format on my 8” tablet or a large screen monitor. If I would be asked to say at least something negative I have to state I had a feeling initially that the book’s first part reads like an academic material relaxing the reader as the book progresses forward. I admit I am impressed with Jules’ abilities to use several programming languages and OSS tools, bravo! And I agree, it is not too, too hard to grasp at least the principals of a modern programming language, which seems becomes a defacto knowledge standard item for any modern human being. So grab a copy of this book, read it end to end and make yourself shielded from making mistakes at any stage of your Big Data initiative, by the way this book also helps build better future Big Data projects. Disclaimer: I received a free electronic copy of this book as part of the O'Reilly Blogger Program.

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• Oracle Big Data Software Downloads

  - by Mike.Hallett(at)Oracle-BI&EPM

  Companies have been making business decisions for decades based on transactional data stored in relational databases. Beyond that critical data, is a potential treasure trove of less structured data: weblogs, social media, email, sensors, and photographs that can be mined for useful information. Oracle offers a broad 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 Big Data Connectors Downloads here, includes: Oracle SQL Connector for Hadoop Distributed File System Release 2.1.0 Oracle Loader for Hadoop Release 2.1.0 Oracle Data Integrator Companion 11g Oracle R Connector for Hadoop v 2.1 Oracle Big Data Documentation The Oracle Big Data solution offers an integrated portfolio of products to help you organize and analyze your diverse data sources alongside your existing data to find new insights and capitalize on hidden relationships. Oracle Big Data, Release 2.2.0 - E41604_01 zip (27.4 MB) Integrated Software and Big Data Connectors User's Guide HTML PDF Oracle Data Integrator (ODI) Application Adapter for Hadoop Apache Hadoop is designed to handle and process data that is typically from data sources that are non-relational and data volumes that are beyond what is handled by relational databases. Typical processing in Hadoop includes data validation and transformations that are programmed as MapReduce jobs. Designing and implementing a MapReduce job usually requires expert programming knowledge. However, when you use Oracle Data Integrator with the Application Adapter for Hadoop, you do not need to write MapReduce jobs. Oracle Data Integrator uses Hive and the Hive Query Language (HiveQL), a SQL-like language for implementing MapReduce jobs. Employing familiar and easy-to-use tools and pre-configured knowledge modules (KMs), the application adapter provides the following capabilities: Loading data into Hadoop from the local file system and HDFS Performing validation and transformation of data within Hadoop Loading processed data from Hadoop to an Oracle database for further processing and generating reports Oracle Database Loader for Hadoop Oracle Loader for Hadoop is an efficient and high-performance loader for fast movement of data from a Hadoop cluster into a table in an Oracle database. It pre-partitions the data if necessary and transforms it into a database-ready format. Oracle Loader for Hadoop is a Java MapReduce application that balances the data across reducers to help maximize performance. Oracle R Connector for Hadoop Oracle R Connector for Hadoop is a collection of R packages that provide: Interfaces to work with Hive tables, the Apache Hadoop compute infrastructure, the local R environment, and Oracle database tables Predictive analytic techniques, written in R or Java as Hadoop MapReduce jobs, that can be applied to data in HDFS files You install and load this package as you would any other R package. Using simple R functions, you can perform tasks such as: Access and transform HDFS data using a Hive-enabled transparency layer Use the R language for writing mappers and reducers Copy data between R memory, the local file system, HDFS, Hive, and Oracle databases Schedule R programs to execute as Hadoop MapReduce jobs and return the results to any of those locations Oracle SQL Connector for Hadoop Distributed File System Using Oracle SQL Connector for HDFS, you can use an Oracle Database to access and analyze data residing in Hadoop in these formats: Data Pump files in HDFS Delimited text files in HDFS Hive tables For other file formats, such as JSON files, you can stage the input in Hive tables before using Oracle SQL Connector for HDFS. Oracle SQL Connector for HDFS uses external tables to provide Oracle Database with read access to Hive tables, and to delimited text files and Data Pump files in HDFS. Related Documentation Cloudera's Distribution Including Apache Hadoop Library HTML Oracle R Enterprise HTML Oracle NoSQL Database HTML Recent Blog Posts Big Data Appliance vs. DIY Price Comparison Big Data: Architecture Overview Big Data: Achieve the Impossible in Real-Time Big Data: Vertical Behavioral Analytics Big Data: In-Memory MapReduce Flume and Hive for Log Analytics Building Workflows in Oozie

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• Master Data Management – A Foundation for Big Data Analysis

  - by Manouj Tahiliani

  While Master Data Management has crossed the proverbial chasm and is on its way to becoming mainstream, businesses are being hammered by a new megatrend called Big Data. Big Data is characterized by massive volumes, its high frequency, the variety of less structured data sources such as email, sensors, smart meters, social networks, and Weblogs, and the need to analyze vast amounts of data to determine value to improve upon management decisions. Businesses that have embraced MDM to get a single, enriched and unified view of Master data by resolving semantic discrepancies and augmenting the explicit master data information from within the enterprise with implicit data from outside the enterprise like social profiles will have a leg up in embracing Big Data solutions. This is especially true for large and medium-sized businesses in industries like Retail, Communications, Financial Services, etc that would find it very challenging to get comprehensive analytical coverage and derive long-term success without resolving the limitations of the heterogeneous topology that leads to disparate, fragmented and incomplete master data. For analytical success from Big Data or in other words ROI from Big Data Investments, businesses need to acquire, organize and analyze the deluge of data to make better decisions. There will need to be a coexistence of structured and unstructured data and to maintain a tight link between the two to extract maximum insights. MDM is the catalyst that helps maintain that tight linkage by providing an understanding about the identity, characteristics of Persons, Companies, Products, Suppliers, etc. associated with the Big Data and thereby help accelerate ROI. In my next post I will discuss about patterns for co-existing Big Data Solutions and MDM. Feel free to provide comments and thoughts on above as well as Integration or Architectural patterns.

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• Marshalling a big-endian byte collection into a struct in order to pull out values

  - by Pat

  There is an insightful question about reading a C/C++ data structure in C# from a byte array, but I cannot get the code to work for my collection of big-endian (network byte order) bytes. (EDIT: Note that my real struct has more than just one field.) Is there a way to marshal the bytes into a big-endian version of the structure and then pull out the values in the endianness of the framework (that of the host, which is usually little-endian)? This should summarize what I'm looking for (LE=LittleEndian, BE=BigEndian): void Main() { var leBytes = new byte[] {1, 0}; var beBytes = new byte[] {0, 1}; Foo fooLe = ByteArrayToStructure<Foo>(leBytes); Foo fooBe = ByteArrayToStructureBigEndian<Foo>(beBytes); Assert.AreEqual(fooLe, fooBe); } [StructLayout(LayoutKind.Explicit, Size=2)] public struct Foo { [FieldOffset(0)] public ushort firstUshort; } T ByteArrayToStructure<T>(byte[] bytes) where T: struct { GCHandle handle = GCHandle.Alloc(bytes, GCHandleType.Pinned); T stuff = (T)Marshal.PtrToStructure(handle.AddrOfPinnedObject(),typeof(T)); handle.Free(); return stuff; } T ByteArrayToStructureBigEndian<T>(byte[] bytes) where T: struct { ??? }

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• Convert a raw string to an array of big-endian words with Ruby

  - by Zag zag..

  Hello, I would like to convert a raw string to an array of big-endian words. As example, here is a JavaScript function that do it well (by Paul Johnston): /* * Convert a raw string to an array of big-endian words * Characters >255 have their high-byte silently ignored. */ function rstr2binb(input) { var output = Array(input.length >> 2); for(var i = 0; i < output.length; i++) output[i] = 0; for(var i = 0; i < input.length * 8; i += 8) output[i>>5] |= (input.charCodeAt(i / 8) & 0xFF) << (24 - i % 32); return output; } I believe the Ruby equivalent can be String#unpack(format). However, I don't know what should be the correct format parameter. Thank you for any help. Regards

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• Big-Oh running time of code in Java (are my answers accurate

  - by Terry Frederick

  the Method hasTwoTrueValues returns true if at least two values in an array of booleans are true. Provide the Big-Oh running time for all three implementations proposed. // Version 1 public boolean has TwoTrueValues( boolean [ ] arr ) { int count = 0; for( int i = 0; i < arr. length; i++ ) if( arr[ i ] ) count++; return count >= 2; } // Version 2 public boolean hasTwoTrueValues( boolean [ ] arr ) { for( int i = 0; i < arr.length; i++ ) for( int j = i + 1; j < arr.length; j++ ) if( arr[ i ] && arr[ j ] ) return true; } // Version 3 public boolean hasTwoTrueValues( boolean [ ] arr ) { for( int i = 0; i < arr.length; i++ if( arr[ i ] ) for( int j = i + 1; j < arr.length; j++ ) if( arr[ j ] ) return true; return false; } For Version 1 I say the running time is O(n) Version 2 I say O(n^2) Version 3 I say O(n^2) I am really new to this Big Oh Notation so if my answers are incorrect could you please explain and help.

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• Suggested Web Application Framework and Database for Enterprise, “Big-Data” App?

  - by willOEM

  I have a web application that I have been developing for a small group within my company over the past few years, using Pipeline Pilot (plus jQuery and Python scripting) for web development and back-end computation, and Oracle 10g for my RDBMS. Users upload experimental genomic data, which is parsed into a database, and made available for querying, transformation, and reporting. Experimental data sets are large and have many layers of metadata. A given experimental data record might have a foreign key relationship with a table that describes this data point's assay. Assays can cover multiple genes, which can have multiple transcript, which can have multiple mutations, which can affect multiple signaling pathways, etc. Users need to approach this data from any point in those layers in the metadata. Since all data sets for a given data type can run over a billion rows, this results in some large, dynamic queries that are hard to predict. New data sets are added on a weekly basis (~1GB per set). Experimental data is never updated, but the associated metadata can be updated weekly for a few records and yearly for most others. For every data set insert the system sees, there will be between 10 and 100 selects run against it and associated data. It is okay for updates and inserts to run slow, so long as queries run quick and are as up-to-date as possible. The application continues to grow in size and scope and is already starting to run slower than I like. I am worried that we have about outgrown Pipeline Pilot, and perhaps Oracle (as the sole database). Would a NoSQL database or an OLAP system be appropriate here? What web application frameworks work well with systems like this? I'd like the solution to be something scalable, portable and supportable X-years down the road. Here is the current state of the application: Web Server/Data Processing: Pipeline Pilot on Windows Server + IIS Database: Oracle 10g, ~1TB of data, ~180 tables with several billion-plus row tables Network Storage: Isilon, ~50TB of low-priority raw data

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• Indicates the Big-Theta g(n) and prove it [closed]

  - by PLS

  Indicates the Big-Theta g(n) for the function below and prove it (n^2 + 1)^10 I don not know how to prove it.

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• Big numbers in C

  - by teehoo

  I need help working with very big numbers. According to Windows calc, the exponent 174^55 = 1.6990597648061509725749329578093e+123. How would I store this using C (c99 standard). int main(){ long long int x = 174^55; //result is 153 printf("%lld\n", x); } For those curious, it is for a school project where we are implementing the RSA cryptographic algorithm, which deals with exponentiating large numbers with large powers for encryption/decryption.

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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 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 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 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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