Search Results

Search found 11617 results on 465 pages for 'big blue'.

Page 5/465 | < Previous Page | 1 2 3 4 5 6 7 8 9 10 11 12  | Next Page >

  • 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.

    Read the article

  • 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

    Read the article

  • 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.

    Read the article

  • 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 { ??? }

    Read the article

  • 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

    Read the article

  • 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.

    Read the article

  • 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

    Read the article

  • 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.

    Read the article

  • 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.

    Read the article

  • 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?

    Read the article

  • 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.

    Read the article

  • 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!

    Read the article

  • 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!

    Read the article

  • 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! :(

    Read the article

  • 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...

    Read the article

  • 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!

    Read the article

  • 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?

    Read the article

  • 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.

    Read the article

  • 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!

    Read the article

  • Word 2010 & 2007 Blue Background on screen as default

    - by poor1
    Default blue background and white text in Microsoft Word. I have just moved to Word 2010 (Student Version now released) and although it is possible to create individual documents with a blue background it is not possible to set the program with a blue background as a default. I understand this was discontinued with Office 2007. The only way I can open a document with a blue background is to create a Template with a blue background and use that for each document I wish to create. I'm sure there must be a method of hacking the registry to accomplish this. Can you assist. There must be countless people who who they knew how.

    Read the article

  • 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.

    Read the article

  • 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!

    Read the article

  • 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.

    Read the article

  • Troubleshooting Windows Blue Screen Errors

    The so-called ‘Blue Screen of Death’ has inspired fear in the hearts of mere mortals, but Systems Administrators are expected be capable of casually beating back this sinister beast. So imagine Ben Lye’s distress when he discovered that many aspiring SysAdmins had no structured approach to tackling the root of the problem. Setting out to remedy the situation, Ben lays out a simple 3-step plan, and dispenses some good advice.

    Read the article

< Previous Page | 1 2 3 4 5 6 7 8 9 10 11 12  | Next Page >