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  • Programmaticaly finding the Landau notation (Big O or Theta notation) of an algorithm?

    - by Julien L
    I'm used to search for the Landau (Big O, Theta...) notation of my algorithms by hand to make sure they are as optimized as they can be, but when the functions are getting really big and complex, it's taking way too much time to do it by hand. it's also prone to human errors. I spent some time on Codility (coding/algo exercises), and noticed they will give you the Landau notation for your submitted solution (both in Time and Memory usage). I was wondering how they do that... How would you do it? Is there another way besides Lexical Analysis or parsing of the code? PS: This question concerns mainly PHP and or JavaScript, but I'm opened to any language and theory.

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  • Big-O complexity of c^n + n*(logn)^2 + (10*n)^c

    - by zebraman
    I need to derive the Big-O complexity of this expression: c^n + n*(log(n))^2 + (10*n)^c where c is a constant and n is a variable. I'm pretty sure I understand how to derive the Big-O complexity of each term individually, I just don't know how the Big-O complexity changes when the terms are combined like this. Ideas? Any help would be great, thanks.

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  • TDWI World Conference Features Oracle and Big Data

    - by Mandy Ho
    Oracle is a Gold Sponsor at this year's TDWI World Conference Series, held at the Manchester Grand Hyatt in San Diego, California - July 31 to Aug 1. The theme of this event is Big Data Tipping Point: BI Strategies in the Era of Big Data. The conference features an educational look at how data is now being generated so quickly that organizations across all industries need new technologies to stay ahead - to understand customer behavior, detect fraud, improve processes and accelerate performance. Attendees will hear how the internet, social media and streaming data are fundamentally changing business intelligence and data warehousing. Big data is reaching critical mass - the tipping point. Oracle will be conducting the following Evening Workshop. To reserve your space, call 1.800.820.5592 ext 10775. Title...:    Integrating Big Data into Your Data Center (or A Big Data Reference Architecture) Date.:    Wed., August 1, 2012, at 7:00 p.m Venue:: Manchester Grand Hyatt, San Diego, Room Weblogs, Social Media, smart meters, senors and other devices generate high volumes of low density information that isn't readily accessible in enterprise data warehouses and business intelligence applications today. But, this data can have relevant business value, especially when analyzed alongside traditional information sources. In this session, we will outline a reference architecture for big data that will help you maximize the value of your big data implementation. You will learn: The key technologies in a big architecture, and their specific use case The integration point of the various technologies and how they fit into your existing IT environment How effectively leverage analytical sandboxes for data discovery and agile development of data driven solutions   At the end of this session you will understand the reference architecture and have the tools to implement this architecture at your company. Presenter: Jean-Pierre Dijcks, Senior Principal Product Manager Don't miss our booth and the chance to meet with our Big data experts on the exhibition floor at booth #306. 

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  • Becoming A Great Developer

    - by Lee Brandt
    Image via Wikipedia I’ve been doing the whole programming thing for awhile and reading and watching some of the best in the business. I have come to notice that the really great developers do a few things that (I think) makes them great. Now don’t get me wrong, I am not saying that I am one of these few. I still struggle with doing some of the things that makes one great at development. Coincidently, many of these things also make you a better person period. Believe That Guidance Is Better Than Answers This is one I have no problem with. I prefer guidance any time I am learning from another developer. Answers may get you going, but guidance will leave you stranded. At some point, you will come across a problem that can only be solved by thinking for yourself and this is where that guidance will really come in handy. You can use that guidance and extrapolate whatever technology to salve that problem (if it’s the right tool for solving that problem). The problem is, lots of developers simply want someone to tell them, “Do this, then this, then set that, and write this.” Favor thinking and learn the guidance of doing X and don’t ask someone to show you how to do X, if that makes sense. Read, Read and Read If you don’t like reading, you’re probably NOT going to make it into the Great Developer group. Great developers read books, they read magazines and they read code. Open source playgrounds like SourceForge, CodePlex and GitHub, have made it extremely easy to download code from developers you admire and see how they do stuff. Chances are, if you read their blog too, they’ll even explain WHY they did what they did (see “Guidance” above). MSDN and Code Magazine have not only code samples, but explanations of how to use certain technologies and sometimes even when NOT to use that same technology. Books are also out on just about every topic. I still favor the less technology centric books. For instance, I generally don’t buy books like, “Getting Started with Jiminy Jappets”. I look for titles like, “How To Write More Effective Code” (again, see guidance). The Addison-Wesley Signature Series is a great example of these types of books. They teach technology-agnostic concepts. Head First Design Patterns is another great guidance book. It teaches the "Gang Of Four" Design Patterns in a very easy-to-understand, picture-heavy way (I LIKE pictures). Hang Your Balls Out There Even though the advice came from a 3rd-shift Kinko’s attendant, doesn’t mean it’s not sound advice. Write some code and put it out for others to read, criticize and castigate you for. Understand that there are some real jerks out there who are absolute geniuses. Don’t be afraid to get some great advice wrapped in some really nasty language. Try to take what’s good about it and leave what’s not. I have a tough time with this myself. I don’t really have any code out there that is available for review (other than my demo code). It takes some guts to do, but in the end, there is no substitute for getting a community of developers to critique your code and give you ways to improve. Get Involved Speaking of community, the local and online user groups and discussion forums are a great place to hear about technologies and techniques you might never come across otherwise. Mostly because you might not know to look. But, once you sit down with a bunch of other developers and start discussing what you’re interested in, you may open up a whole new perspective on it. Don’t just go to the UG meetings and watch the presentations either, get out there and talk, socialize. I realize geeks weren’t meant to necessarily be social creatures, but if you’re amongst other geeks, it’s much easier. I’ve learned more in the last 3-4 years that I have been involved in the community that I did in my previous 8 years of coding without it. Socializing works, even if socialism doesn’t. Continuous Improvement Lean proponents might call this “Kaizen”, but I call it progress. We all know, especially in the technology realm, if you’re not moving ahead, you’re falling behind. It may seem like drinking from a fire hose, but step back and pick out the technologies that speak to you. The ones that may you’re little heart go pitter-patter. Concentrate on those. If you’re still overloaded, pick the best of the best. Just know that if you’re not looking at the code you wrote last week or at least last year with some embarrassment, you’re probably stagnating. That’s about all I can say about that, cause I am all out of clichés to throw at it. :0) Write Code Great painters paint, great writers write, and great developers write code. The most sure-fire way to improve your coding ability is to continue writing code. Don’t just write code that your work throws on you, pick that technology you love or are curious to know more about and walk through some blog demo examples. Take the language you use everyday and try to get it to do something crazy. Who knows, you might create the next Google search algorithm! All in all, being a great developer is about finding yourself in all this code. If it is just a job to you, you will probably never be one of the “Great Developers”, but you’re probably okay with that. If, on the other hand, you do aspire to greatness, get out there and GET it. No one’s going hand it to you.

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  • Al abrir archivo desde navegador se abre el directorio

    - by user67662
    al descargar un archivo a través de cualquier navegador (chrome, firefox, etc) e intentar abrirlo directamente, en vez de abrirse el archivo se abre el directorio en que se descargó. lo mismo me sucedió al intentar abrir un archivo desde el dash de gnome-shell. Esto sólo me sucede con los accesos directos a los archivos, cuando estoy dentro de nautilus se abre el archivo sin problemas. he intentado en distintos entornos de escritorio, el que uso más constantemente es Gnome-Shell, bajo Ubuntu 12.04 ¿cómo lo puedo solucionar? Gracias!

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  • Big Data: Size isn’t everything

    - by Simon Elliston Ball
    Big Data has a big problem; it’s the word “Big”. These days, a quick Google search will uncover terabytes of negative opinion about the futility of relying on huge volumes of data to produce magical, meaningful insight. There are also many clichéd but correct assertions about the difficulties of correlation versus causation, in massive data sets. In reading some of these pieces, I begin to understand how climatologists must feel when people complain ironically about “global warming” during snowfall. Big Data has a name problem. There is a lot more to it than size. Shape, Speed, and…err…Veracity are also key elements (now I understand why Gartner and the gang went with V’s instead of S’s). The need to handle data of different shapes (Variety) is not new. Data developers have always had to mold strange-shaped data into our reporting systems, integrating with semi-structured sources, and even straying into full-text searching. However, what we lacked was an easy way to add semi-structured and unstructured data to our arsenal. New “Big Data” tools such as MongoDB, and other NoSQL (Not Only SQL) databases, or a graph database like Neo4J, fill this gap. Still, to many, they simply introduce noise to the clean signal that is their sensibly normalized data structures. What about speed (Velocity)? It’s not just high frequency trading that generates data faster than a single system can handle. Many other applications need to make trade-offs that traditional databases won’t, in order to cope with high data insert speeds, or to extract quickly the required information from data streams. Unfortunately, many people equate Big Data with the Hadoop platform, whose batch driven queries and job processing queues have little to do with “velocity”. StreamInsight, Esper and Tibco BusinessEvents are examples of Big Data tools designed to handle high-velocity data streams. Again, the name doesn’t do the discipline of Big Data any favors. Ultimately, though, does analyzing fast moving data produce insights as useful as the ones we get through a more considered approach, enabled by traditional BI? Finally, we have Veracity and Value. In many ways, these additions to the classic Volume, Velocity and Variety trio acknowledge the criticism that without high-quality data and genuinely valuable outputs then data, big or otherwise, is worthless. As a discipline, Big Data has recognized this, and data quality and cleaning tools are starting to appear to support it. Rather than simply decrying the irrelevance of Volume, we need as a profession to focus how to improve Veracity and Value. Perhaps we should just declare the ‘Big’ silent, embrace these new data tools and help develop better practices for their use, just as we did the good old RDBMS? What does Big Data mean to you? Which V gives your business the most pain, or the most value? Do you see these new tools as a useful addition to the BI toolbox, or are they just enabling a dangerous trend to find ghosts in the noise?

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  • When is BIG, big enough for a database?

    - by David ???
    I'm developing a Java application that has performance at its core. I have a list of some 40,000 "final" objects, i.e., I have an initialization input data of 40,000 vectors. This data is unchanged throughout the program's run. I am always preforming lookups against a single ID property to retrieve the proper vectors. Currently I am using a HashMap over a sub-sample of a 1,000 vectors, but I'm not sure it will scale to production. When is BIG, actually big enough for a use of DB? One more thing, an SQLite DB is a viable option as no concurrency is involved, so I guess the "threshold" for db use, is perhaps lower.

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  • Proving that a function f(n) belongs to a Big-Theta(g(n))

    - by PLS
    Its a exercise that ask to indicate the class Big-Theta(g(n)) the functions belongs to and to prove the assertion. In this case f(n) = (n^2+1)^10 By definition f(n) E Big-Theta(g(n)) <= c1*g(n) < f(n) < c2*g(n), where c1 and c2 are two constants. I know that for this specific f(n) the Big-Theta is g(n^20) but I don't know who to prove it properly. I guess I need to manipulate this inequality but I don't know how

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  • Running a simple integration scenario using the Oracle Big Data Connectors on Hadoop/HDFS cluster

    - by hamsun
    Between the elephant ( the tradional image of the Hadoop framework) and the Oracle Iron Man (Big Data..) an english setter could be seen as the link to the right data Data, Data, Data, we are living in a world where data technology based on popular applications , search engines, Webservers, rich sms messages, email clients, weather forecasts and so on, have a predominant role in our life. More and more technologies are used to analyze/track our behavior, try to detect patterns, to propose us "the best/right user experience" from the Google Ad services, to Telco companies or large consumer sites (like Amazon:) ). The more we use all these technologies, the more we generate data, and thus there is a need of huge data marts and specific hardware/software servers (as the Exadata servers) in order to treat/analyze/understand the trends and offer new services to the users. Some of these "data feeds" are raw, unstructured data, and cannot be processed effectively by normal SQL queries. Large scale distributed processing was an emerging infrastructure need and the solution seemed to be the "collocation of compute nodes with the data", which in turn leaded to MapReduce parallel patterns and the development of the Hadoop framework, which is based on MapReduce and a distributed file system (HDFS) that runs on larger clusters of rather inexpensive servers. Several Oracle products are using the distributed / aggregation pattern for data calculation ( Coherence, NoSql, times ten ) so once that you are familiar with one of these technologies, lets says with coherence aggregators, you will find the whole Hadoop, MapReduce concept very similar. Oracle Big Data Appliance is based on the Cloudera Distribution (CDH), and the Oracle Big Data Connectors can be plugged on a Hadoop cluster running the CDH distribution or equivalent Hadoop clusters. In this paper, a "lab like" implementation of this concept is done on a single Linux X64 server, running an Oracle Database 11g Enterprise Edition Release 11.2.0.4.0, and a single node Apache hadoop-1.2.1 HDFS cluster, using the SQL connector for HDFS. The whole setup is fairly simple: Install on a Linux x64 server ( or virtual box appliance) an Oracle Database 11g Enterprise Edition Release 11.2.0.4.0 server Get the Apache Hadoop distribution from: http://mir2.ovh.net/ftp.apache.org/dist/hadoop/common/hadoop-1.2.1. Get the Oracle Big Data Connectors from: http://www.oracle.com/technetwork/bdc/big-data-connectors/downloads/index.html?ssSourceSiteId=ocomen. Check the java version of your Linux server with the command: java -version java version "1.7.0_40" Java(TM) SE Runtime Environment (build 1.7.0_40-b43) Java HotSpot(TM) 64-Bit Server VM (build 24.0-b56, mixed mode) Decompress the hadoop hadoop-1.2.1.tar.gz file to /u01/hadoop-1.2.1 Modify your .bash_profile export HADOOP_HOME=/u01/hadoop-1.2.1 export PATH=$PATH:$HADOOP_HOME/bin export HIVE_HOME=/u01/hive-0.11.0 export PATH=$PATH:$HADOOP_HOME/bin:$HIVE_HOME/bin (also see my sample .bash_profile) Set up ssh trust for Hadoop process, this is a mandatory step, in our case we have to establish a "local trust" as will are using a single node configuration copy the new public keys to the list of authorized keys connect and test the ssh setup to your localhost: We will run a "pseudo-Hadoop cluster", in what is called "local standalone mode", all the Hadoop java components are running in one Java process, this is enough for our demo purposes. We need to "fine tune" some Hadoop configuration files, we have to go at our $HADOOP_HOME/conf, and modify the files: core-site.xml hdfs-site.xml mapred-site.xml check that the hadoop binaries are referenced correctly from the command line by executing: hadoop -version As Hadoop is managing our "clustered HDFS" file system we have to create "the mount point" and format it , the mount point will be declared to core-site.xml as: The layout under the /u01/hadoop-1.2.1/data will be created and used by other hadoop components (MapReduce = /mapred/...) HDFS is using the /dfs/... layout structure format the HDFS hadoop file system: Start the java components for the HDFS system As an additional check, you can use the GUI Hadoop browsers to check the content of your HDFS configurations: Once our HDFS Hadoop setup is done you can use the HDFS file system to store data ( big data : )), and plug them back and forth to Oracle Databases by the means of the Big Data Connectors ( which is the next configuration step). You can create / use a Hive db, but in our case we will make a simple integration of "raw data" , through the creation of an External Table to a local Oracle instance ( on the same Linux box, we run the Hadoop HDFS one node cluster and one Oracle DB). Download some public "big data", I use the site: http://france.meteofrance.com/france/observations, from where I can get *.csv files for my big data simulations :). Here is the data layout of my example file: Download the Big Data Connector from the OTN (oraosch-2.2.0.zip), unzip it to your local file system (see picture below) Modify your environment in order to access the connector libraries , and make the following test: [oracle@dg1 bin]$./hdfs_stream Usage: hdfs_stream locationFile [oracle@dg1 bin]$ Load the data to the Hadoop hdfs file system: hadoop fs -mkdir bgtest_data hadoop fs -put obsFrance.txt bgtest_data/obsFrance.txt hadoop fs -ls /user/oracle/bgtest_data/obsFrance.txt [oracle@dg1 bg-data-raw]$ hadoop fs -ls /user/oracle/bgtest_data/obsFrance.txt Found 1 items -rw-r--r-- 1 oracle supergroup 54103 2013-10-22 06:10 /user/oracle/bgtest_data/obsFrance.txt [oracle@dg1 bg-data-raw]$hadoop fs -ls hdfs:///user/oracle/bgtest_data/obsFrance.txt Found 1 items -rw-r--r-- 1 oracle supergroup 54103 2013-10-22 06:10 /user/oracle/bgtest_data/obsFrance.txt Check the content of the HDFS with the browser UI: Start the Oracle database, and run the following script in order to create the Oracle database user, the Oracle directories for the Oracle Big Data Connector (dg1 it’s my own db id replace accordingly yours): #!/bin/bash export ORAENV_ASK=NO export ORACLE_SID=dg1 . oraenv sqlplus /nolog <<EOF CONNECT / AS sysdba; CREATE OR REPLACE DIRECTORY osch_bin_path AS '/u01/orahdfs-2.2.0/bin'; CREATE USER BGUSER IDENTIFIED BY oracle; GRANT CREATE SESSION, CREATE TABLE TO BGUSER; GRANT EXECUTE ON sys.utl_file TO BGUSER; GRANT READ, EXECUTE ON DIRECTORY osch_bin_path TO BGUSER; CREATE OR REPLACE DIRECTORY BGT_LOG_DIR as '/u01/BG_TEST/logs'; GRANT READ, WRITE ON DIRECTORY BGT_LOG_DIR to BGUSER; CREATE OR REPLACE DIRECTORY BGT_DATA_DIR as '/u01/BG_TEST/data'; GRANT READ, WRITE ON DIRECTORY BGT_DATA_DIR to BGUSER; EOF Put the following in a file named t3.sh and make it executable, hadoop jar $OSCH_HOME/jlib/orahdfs.jar \ oracle.hadoop.exttab.ExternalTable \ -D oracle.hadoop.exttab.tableName=BGTEST_DP_XTAB \ -D oracle.hadoop.exttab.defaultDirectory=BGT_DATA_DIR \ -D oracle.hadoop.exttab.dataPaths="hdfs:///user/oracle/bgtest_data/obsFrance.txt" \ -D oracle.hadoop.exttab.columnCount=7 \ -D oracle.hadoop.connection.url=jdbc:oracle:thin:@//localhost:1521/dg1 \ -D oracle.hadoop.connection.user=BGUSER \ -D oracle.hadoop.exttab.printStackTrace=true \ -createTable --noexecute then test the creation fo the external table with it: [oracle@dg1 samples]$ ./t3.sh ./t3.sh: line 2: /u01/orahdfs-2.2.0: Is a directory Oracle SQL Connector for HDFS Release 2.2.0 - Production Copyright (c) 2011, 2013, Oracle and/or its affiliates. All rights reserved. Enter Database Password:] The create table command was not executed. The following table would be created. CREATE TABLE "BGUSER"."BGTEST_DP_XTAB" ( "C1" VARCHAR2(4000), "C2" VARCHAR2(4000), "C3" VARCHAR2(4000), "C4" VARCHAR2(4000), "C5" VARCHAR2(4000), "C6" VARCHAR2(4000), "C7" VARCHAR2(4000) ) ORGANIZATION EXTERNAL ( TYPE ORACLE_LOADER DEFAULT DIRECTORY "BGT_DATA_DIR" ACCESS PARAMETERS ( RECORDS DELIMITED BY 0X'0A' CHARACTERSET AL32UTF8 STRING SIZES ARE IN CHARACTERS PREPROCESSOR "OSCH_BIN_PATH":'hdfs_stream' FIELDS TERMINATED BY 0X'2C' MISSING FIELD VALUES ARE NULL ( "C1" CHAR(4000), "C2" CHAR(4000), "C3" CHAR(4000), "C4" CHAR(4000), "C5" CHAR(4000), "C6" CHAR(4000), "C7" CHAR(4000) ) ) LOCATION ( 'osch-20131022081035-74-1' ) ) PARALLEL REJECT LIMIT UNLIMITED; The following location files would be created. osch-20131022081035-74-1 contains 1 URI, 54103 bytes 54103 hdfs://localhost:19000/user/oracle/bgtest_data/obsFrance.txt Then remove the --noexecute flag and create the external Oracle table for the Hadoop data. Check the results: The create table command succeeded. CREATE TABLE "BGUSER"."BGTEST_DP_XTAB" ( "C1" VARCHAR2(4000), "C2" VARCHAR2(4000), "C3" VARCHAR2(4000), "C4" VARCHAR2(4000), "C5" VARCHAR2(4000), "C6" VARCHAR2(4000), "C7" VARCHAR2(4000) ) ORGANIZATION EXTERNAL ( TYPE ORACLE_LOADER DEFAULT DIRECTORY "BGT_DATA_DIR" ACCESS PARAMETERS ( RECORDS DELIMITED BY 0X'0A' CHARACTERSET AL32UTF8 STRING SIZES ARE IN CHARACTERS PREPROCESSOR "OSCH_BIN_PATH":'hdfs_stream' FIELDS TERMINATED BY 0X'2C' MISSING FIELD VALUES ARE NULL ( "C1" CHAR(4000), "C2" CHAR(4000), "C3" CHAR(4000), "C4" CHAR(4000), "C5" CHAR(4000), "C6" CHAR(4000), "C7" CHAR(4000) ) ) LOCATION ( 'osch-20131022081719-3239-1' ) ) PARALLEL REJECT LIMIT UNLIMITED; The following location files were created. osch-20131022081719-3239-1 contains 1 URI, 54103 bytes 54103 hdfs://localhost:19000/user/oracle/bgtest_data/obsFrance.txt This is the view from the SQL Developer: and finally the number of lines in the oracle table, imported from our Hadoop HDFS cluster SQL select count(*) from "BGUSER"."BGTEST_DP_XTAB"; COUNT(*) ---------- 1151 In a next post we will integrate data from a Hive database, and try some ODI integrations with the ODI Big Data connector. Our simplistic approach is just a step to show you how these unstructured data world can be integrated to Oracle infrastructure. Hadoop, BigData, NoSql are great technologies, they are widely used and Oracle is offering a large integration infrastructure based on these services. Oracle University presents a complete curriculum on all the Oracle related technologies: NoSQL: Introduction to Oracle NoSQL Database Using Oracle NoSQL Database Big Data: Introduction to Big Data Oracle Big Data Essentials Oracle Big Data Overview Oracle Data Integrator: Oracle Data Integrator 12c: New Features Oracle Data Integrator 11g: Integration and Administration Oracle Data Integrator: Administration and Development Oracle Data Integrator 11g: Advanced Integration and Development Oracle Coherence 12c: Oracle Coherence 12c: New Features Oracle Coherence 12c: Share and Manage Data in Clusters Oracle Coherence 12c: Oracle GoldenGate 11g: Fundamentals for Oracle Oracle GoldenGate 11g: Fundamentals for SQL Server Oracle GoldenGate 11g Fundamentals for Oracle Oracle GoldenGate 11g Fundamentals for DB2 Oracle GoldenGate 11g Fundamentals for Teradata Oracle GoldenGate 11g Fundamentals for HP NonStop Oracle GoldenGate 11g Management Pack: Overview Oracle GoldenGate 11g Troubleshooting and Tuning Oracle GoldenGate 11g: Advanced Configuration for Oracle Other Resources: Apache Hadoop : http://hadoop.apache.org/ is the homepage for these technologies. "Hadoop Definitive Guide 3rdEdition" by Tom White is a classical lecture for people who want to know more about Hadoop , and some active "googling " will also give you some more references. About the author: Eugene Simos is based in France and joined Oracle through the BEA-Weblogic Acquisition, where he worked for the Professional Service, Support, end Education for major accounts across the EMEA Region. He worked in the banking sector, ATT, Telco companies giving him extensive experience on production environments. Eugen currently specializes in Oracle Fusion Middleware teaching an array of courses on Weblogic/Webcenter, Content,BPM /SOA/Identity-Security/GoldenGate/Virtualisation/Unified Comm Suite) throughout the EMEA region.

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  • How meaningful is the Big-O time complexity of an algorithm?

    - by james creasy
    Programmers often talk about the time complexity of an algorithm, e.g. O(log n) or O(n^2). Time complexity classifications are made as the input size goes to infinity, but ironically infinite input size in computation is not used. Put another way, the classification of an algorithm is based on a situation that algorithm will never be in: where n = infinity. Also, consider that a polynomial time algorithm where the exponent is huge is just as useless as an exponential time algorithm with tiny base (e.g., 1.00000001^n) is useful. Given this, how much can I rely on the Big-O time complexity to advise choice of an algorithm?

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  • Big-O of PHP functions?

    - by Kendall Hopkins
    After using PHP for a while now, I've noticed that not all PHP built in functions as fast as expected. Consider the below two possible implementations of a function that finds if a number is prime using a cached array of primes. //very slow for large $prime_array $prime_array = array( 2, 3, 5, 7, 11, 13, .... 104729, ... ); $result_array = array(); foreach( $array_of_number => $number ) { $result_array[$number] = in_array( $number, $large_prime_array ); } //still decent performance for large $prime_array $prime_array => array( 2 => NULL, 3 => NULL, 5 => NULL, 7 => NULL, 11 => NULL, 13 => NULL, .... 104729 => NULL, ... ); foreach( $array_of_number => $number ) { $result_array[$number] = array_key_exists( $number, $large_prime_array ); } This is because in_array is implemented with a linear search O(n) which will linearly slow down as $prime_array grows. Where the array_key_exists function is implemented with a hash lookup O(1) which will not slow down unless the hash table gets extremely populated (in which case it's only O(logn)). So far I've had to discover the big-O's via trial and error, and occasionally looking at the source code. Now for the question... I was wondering if there was a list of the theoretical (or practical) big O times for all* the PHP built in functions. *or at least the interesting ones For example find it very hard to predict what the big O of functions listed because the possible implementation depends on unknown core data structures of PHP: array_merge, array_merge_recursive, array_reverse, array_intersect, array_combine, str_replace (with array inputs), etc.

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  • List of Big-O for PHP functions?

    - by Kendall Hopkins
    After using PHP for a while now, I've noticed that not all PHP built in functions as fast as expected. Consider the below two possible implementations of a function that finds if a number is prime using a cached array of primes. //very slow for large $prime_array $prime_array = array( 2, 3, 5, 7, 11, 13, .... 104729, ... ); $result_array = array(); foreach( $array_of_number => $number ) { $result_array[$number] = in_array( $number, $large_prime_array ); } //still decent performance for large $prime_array $prime_array => array( 2 => NULL, 3 => NULL, 5 => NULL, 7 => NULL, 11 => NULL, 13 => NULL, .... 104729 => NULL, ... ); foreach( $array_of_number => $number ) { $result_array[$number] = array_key_exists( $number, $large_prime_array ); } This is because in_array is implemented with a linear search O(n) which will linearly slow down as $prime_array grows. Where the array_key_exists function is implemented with a hash lookup O(1) which will not slow down unless the hash table gets extremely populated (in which case it's only O(logn)). So far I've had to discover the big-O's via trial and error, and occasionally looking at the source code. Now for the question... I was wondering if there was a list of the theoretical (or practical) big O times for all* the PHP built in functions. *or at least the interesting ones For example find it very hard to predict what the big O of functions listed because the possible implementation depends on unknown core data structures of PHP: array_merge, array_merge_recursive, array_reverse, array_intersect, array_combine, str_replace (with array inputs), etc.

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  • Using Emacs for big big projects

    - by ignatius
    Hello, Maybe is a often repeated question here, but i can't find anything similar with the search. The point is that i like to use Emacs for my personal projects, usually very small applications using C or python, but i was wondering how to use it also for my work, in which we have project with about 10k files of source code, so is veeeery big (actually i am using source insight, that is very nice tool, but only for windows), questions are: Searching: Which is the most convenient way to search a string within the whole project? Navigating throught the function: I mean something like putting the cursor over a function, define, var, and going to the definition Refactoring Also if you have any experience with this and want to share your thoughts i will consider it highly interesting. Br

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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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  • <= 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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