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• Data Quality and Master Data Management Resources

  - by Dejan Sarka

  Many companies or organizations do regular data cleansing. When you cleanse the data, the data quality goes up to some higher level. The data quality level is determined by the amount of work invested in the cleansing. As time passes, the data quality deteriorates, and you need to repeat the cleansing process. If you spend an equal amount of effort as you did with the previous cleansing, you can expect the same level of data quality as you had after the previous cleansing. And then the data quality deteriorates over time again, and the cleansing process starts over and over again. The idea of Data Quality Services is to mitigate the cleansing process. While the amount of time you need to spend on cleansing decreases, you will achieve higher and higher levels of data quality. While cleansing, you learn what types of errors to expect, discover error patterns, find domains of correct values, etc. You don’t throw away this knowledge. You store it and use it to find and correct the same issues automatically during your next cleansing process. The following figure shows this graphically. The idea of master data management, which you can perform with Master Data Services (MDS), is to prevent data quality from deteriorating. Once you reach a particular quality level, the MDS application—together with the defined policies, people, and master data management processes—allow you to maintain this level permanently. This idea is shown in the following picture. OK, now you know what DQS and MDS are about. You can imagine the importance on maintaining the data quality. Here are some resources that help you preparing and executing the data quality (DQ) and master data management (MDM) activities. Books Dejan Sarka and Davide Mauri: Data Quality and Master Data Management with Microsoft SQL Server 2008 R2 – a general introduction to MDM, MDS, and data profiling. Matching explained in depth. Dejan Sarka, Matija Lah and Grega Jerkic: MCTS Self-Paced Training Kit (Exam 70-463): Building Data Warehouses with Microsoft SQL Server 2012 – I wrote quite a few chapters about DQ and MDM, and introduced also SQL Server 2012 DQS. Thomas Redman: Data Quality: The Field Guide – you should start with this book. Thomas Redman is the father of DQ and MDM. Tyler Graham: Microsoft SQL Server 2012 Master Data Services – MDS in depth from a product team mate. Arkady Maydanchik: Data Quality Assessment – data profiling in depth. Tamraparni Dasu, Theodore Johnson: Exploratory Data Mining and Data Cleaning – advanced data profiling with data mining. Forthcoming presentations I am presenting a DQS and MDM seminar at PASS SQL Rally Amsterdam 2013: Wednesday, November 6th, 2013: Enterprise Information Management with SQL Server 2012 – a good kick start to your first DQ and / or MDM project. Courses Data Quality and Master Data Management with SQL Server 2012 – I wrote a 2-day course for SolidQ. If you are interested in this course, which I could also deliver in a shorter seminar way, you can contact your closes SolidQ subsidiary, or, of course, me directly on addresses [email protected] or [email protected]. This course could also complement the existing courseware portfolio of training providers, which are welcome to contact me as well. Start improving the quality of your data now!

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• Deploying Data Mining Models using Model Export and Import

  - by [email protected]

  In this post, we'll take a look at how Oracle Data Mining facilitates model deployment. After building and testing models, a next step is often putting your data mining model into a production system -- referred to as model deployment. The ability to move data mining model(s) easily into a production system can greatly speed model deployment, and reduce the overall cost. Since Oracle Data Mining provides models as first class database objects, models can be manipulated using familiar database techniques and technology. For example, one or more models can be exported to a flat file, similar to a database table dump file (.dmp). This file can be moved to a different instance of Oracle Database EE, and then imported. All methods for exporting and importing models are based on Oracle Data Pump technology and found in the DBMS_DATA_MINING package. Before performing the actual export or import, a directory object must be created. A directory object is a logical name in the database for a physical directory on the host computer. Read/write access to a directory object is necessary to access the host computer file system from within Oracle Database. For our example, we'll work in the DMUSER schema. First, DMUSER requires the privilege to create any directory. This is often granted through the sysdba account. grant create any directory to dmuser; Now, DMUSER can create the directory object specifying the path where the exported model file (.dmp) should be placed. In this case, on a linux machine, we have the directory /scratch/oracle. CREATE OR REPLACE DIRECTORY dmdir AS '/scratch/oracle'; If you aren't sure of the exact name of the model or models to export, you can find the list of models using the following query: select model_name from user_mining_models; There are several options when exporting models. We can export a single model, multiple models, or all models in a schema using the following procedure calls: BEGIN   DBMS_DATA_MINING.EXPORT_MODEL ('MY_MODEL.dmp','dmdir','name =''MY_DT_MODEL'''); END; BEGIN   DBMS_DATA_MINING.EXPORT_MODEL ('MY_MODELS.dmp','dmdir',              'name IN (''MY_DT_MODEL'',''MY_KM_MODEL'')'); END; BEGIN   DBMS_DATA_MINING.EXPORT_MODEL ('ALL_DMUSER_MODELS.dmp','dmdir'); END; A .dmp file can be imported into another schema or database using the following procedure call, for example: BEGIN   DBMS_DATA_MINING.IMPORT_MODEL('MY_MODELS.dmp', 'dmdir'); END; As with models from any data mining tool, when moving a model from one environment to another, care needs to be taken to ensure the transformations that prepare the data for model building are matched (with appropriate parameters and statistics) in the system where the model is deployed. Oracle Data Mining provides automatic data preparation (ADP) and embedded data preparation (EDP) to reduce, or possibly eliminate, the need to explicitly transport transformations with the model. In the case of ADP, ODM automatically prepares the data and includes the necessary transformations in the model itself. In the case of EDP, users can associate their own transformations with attributes of a model. These transformations are automatically applied when applying the model to data, i.e., scoring. Exporting and importing a model with ADP or EDP results in these transformations being immediately available with the model in the production system.

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• Deploying Data Mining Models using Model Export and Import, Part 2

  - by [email protected]

  In my last post, Deploying Data Mining Models using Model Export and Import, we explored using DBMS_DATA_MINING.EXPORT_MODEL and DBMS_DATA_MINING.IMPORT_MODEL to enable moving a model from one system to another. In this post, we'll look at two distributed scenarios that make use of this capability and a tip for easily moving models from one machine to another using only Oracle Database, not an external file transport mechanism, such as FTP. The first scenario, consider a company with geographically distributed business units, each collecting and managing their data locally for the products they sell. Each business unit has in-house data analysts that build models to predict which products to recommend to customers in their space. A central telemarketing business unit also uses these models to score new customers locally using data collected over the phone. Since the models recommend different products, each customer is scored using each model. This is depicted in Figure 1.Figure 1: Target instance importing multiple remote models for local scoring In the second scenario, consider multiple hospitals that collect data on patients with certain types of cancer. The data collection is standardized, so each hospital collects the same patient demographic and other health / tumor data, along with the clinical diagnosis. Instead of each hospital building it's own models, the data is pooled at a central data analysis lab where a predictive model is built. Once completed, the model is distributed to hospitals, clinics, and doctor offices who can score patient data locally.Figure 2: Multiple target instances importing the same model from a source instance for local scoring Since this blog focuses on model export and import, we'll only discuss what is necessary to move a model from one database to another. Here, we use the package DBMS_FILE_TRANSFER, which can move files between Oracle databases. The script is fairly straightforward, but requires setting up a database link and directory objects. We saw how to create directory objects in the previous post. To create a database link to the source database from the target, we can use, for example: create database link SOURCE1_LINK connect to <schema> identified by <password> using 'SOURCE1'; Note that 'SOURCE1' refers to the service name of the remote database entry in your tnsnames.ora file. From SQL*Plus, first connect to the remote database and export the model. Note that the model_file_name does not include the .dmp extension. This is because export_model appends "01" to this name.  Next, connect to the local database and invoke DBMS_FILE_TRANSFER.GET_FILE and import the model. Note that "01" is eliminated in the target system file name.  connect <source_schema>/<password>@SOURCE1_LINK; BEGIN  DBMS_DATA_MINING.EXPORT_MODEL ('EXPORT_FILE_NAME' || '.dmp',                                 'MY_SOURCE_DIR_OBJECT',                                 'name =''MY_MINING_MODEL'''); END; connect <target_schema>/<password>; BEGIN  DBMS_FILE_TRANSFER.GET_FILE ('MY_SOURCE_DIR_OBJECT',                               'EXPORT_FILE_NAME' || '01.dmp',                               'SOURCE1_LINK',                               'MY_TARGET_DIR_OBJECT',                               'EXPORT_FILE_NAME' || '.dmp' );  DBMS_DATA_MINING.IMPORT_MODEL ('EXPORT_FILE_NAME' || '.dmp',                                 'MY_TARGET_DIR_OBJECT'); END; To clean up afterward, you may want to drop the exported .dmp file at the source and the transferred file at the target. For example, utl_file.fremove('&directory_name', '&model_file_name' || '.dmp');

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• Translate report data export from RUEI into HTML for import into OpenOffice Calc Spreadsheets

  - by [email protected]

  A common question of users is, How to import the data from the automated data export of Real User Experience Insight (RUEI) into tools for archiving, dashboarding or combination with other sets of data.XML is well-suited for such a translation via the companion Extensible Stylesheet Language Transformations (XSLT). Basically XSLT utilizes XSL, a template on what to read from your input XML data file and where to place it into the target document. The target document can be anything you like, i.e. XHTML, CSV, or even a OpenOffice Spreadsheet, etc. as long as it is a plain text format.XML 2 OpenOffice.org SpreadsheetFor the XSLT to work as an OpenOffice.org Calc Import Filter:How to add an XML Import Filter to OpenOffice CalcStart OpenOffice.org Calc andselect Tools > XML Filter SettingsNew...Fill in the details as follows:Filter name: RUEI Import filterApplication: OpenOffice.org Calc (.ods)Name of file type: Oracle Real User Experience InsightFile extension: xmlSwitch to the transformation tab and enter/select the following leaving the rest untouchedXSLT for import: ruei_report_data_import_filter.xslPlease see at the end of this blog post for a download of the referenced file.Select RUEI Import filter from list and Test XSLTClick on Browse to selectTransform file: export.php.xmlOpenOffice.org Calc will transform and load the XML file you retrieved from RUEI in a human-readable format.You can now select File > Open... and change the filetype to open your RUEI exports directly in OpenOffice.org Calc, just like any other a native Spreadsheet format.Files of type: Oracle Real User Experience Insight (*.xml)File name: export.php.xml XML 2 XHTMLMost XML-powered browsers provides for inherent XSL Transformation capabilities, you only have to reference the XSLT Stylesheet in the head of your XML file. Then open the file in your favourite Web Browser, Firefox, Opera, Safari or Internet Explorer alike.<?xml version="1.0" encoding="ISO-8859-1"?><!-- inserted line below --> <?xml-stylesheet type="text/xsl" href="ruei_report_data_export_2_xhtml.xsl"?><!-- inserted line above --><report>You can find a patched example export from RUEI plus the above referenced XSL-Stylesheets here: export.php.xml - Example report data export from RUEI ruei_report_data_export_2_xhtml.xsl - RUEI to XHTML XSL Transformation Stylesheetruei_report_data_import_filter.xsl - OpenOffice.org XML import filter for RUEI report export data If you would like to do things like this on the command line you can use either Xalan or xsltproc.The basic command syntax for xsltproc is very simple:xsltproc -o output.file stylesheet.xslt inputfile.xmlYou can use this with the above two stylesheets to translate RUEI Data Exports into XHTML and/or OpenOffice.org Calc ODS-Format. Or you could write your own XSLT to transform into Comma separated Value lists.Please let me know what you think or do with this information in the comments below.Kind regards,Stefan ThiemeReferences used:OpenOffice XML Filter - Create XSLT filters for import and export - http://user.services.openoffice.org/en/forum/viewtopic.php?f=45&t=3490SUN OpenOffice.org XML File Format 1.0 - http://xml.openoffice.org/xml_specification.pdf

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• Big Data – Learning Basics of Big Data in 21 Days – Bookmark

  - by Pinal Dave

  Earlier this month I had a great time to write Bascis of Big Data series. This series received great response and lots of good comments I have received, I am going to follow up this basics series with further in-depth series in near future. Here is the consolidated blog post where you can find all the 21 days blog posts together. Bookmark this page for future reference. Big Data – Beginning Big Data – Day 1 of 21 Big Data – What is Big Data – 3 Vs of Big Data – Volume, Velocity and Variety – Day 2 of 21 Big Data – Evolution of Big Data – Day 3 of 21 Big Data – Basics of Big Data Architecture – Day 4 of 21 Big Data – Buzz Words: What is NoSQL – Day 5 of 21 Big Data – Buzz Words: What is Hadoop – Day 6 of 21 Big Data – Buzz Words: What is MapReduce – Day 7 of 21 Big Data – Buzz Words: What is HDFS – Day 8 of 21 Big Data – Buzz Words: Importance of Relational Database in Big Data World – Day 9 of 21 Big Data – Buzz Words: What is NewSQL – Day 10 of 21 Big Data – Role of Cloud Computing in Big Data – Day 11 of 21 Big Data – Operational Databases Supporting Big Data – RDBMS and NoSQL – Day 12 of 21 Big Data – Operational Databases Supporting Big Data – Key-Value Pair Databases and Document Databases – Day 13 of 21 Big Data – Operational Databases Supporting Big Data – Columnar, Graph and Spatial Database – Day 14 of 21 Big Data – Data Mining with Hive – What is Hive? – What is HiveQL (HQL)? – Day 15 of 21 Big Data – Interacting with Hadoop – What is PIG? – What is PIG Latin? – Day 16 of 21 Big Data – Interacting with Hadoop – What is Sqoop? – What is Zookeeper? – Day 17 of 21 Big Data – Basics of Big Data Analytics – Day 18 of 21 Big Data – How to become a Data Scientist and Learn Data Science? – Day 19 of 21 Big Data – Various Learning Resources – How to Start with Big Data? – Day 20 of 21 Big Data – Final Wrap and What Next – Day 21 of 21 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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• Importing Multiple Schemas to a Model in Oracle SQL Developer Data Modeler

  - by thatjeffsmith

  Your physical data model might stretch across multiple Oracle schemas. Or maybe you just want a single diagram containing tables, views, etc. spanning more than a single user in the database. The process for importing a data dictionary is the same, regardless if you want to suck in objects from one schema, or many schemas. Let’s take a quick look at how to get started with a data dictionary import. I’m using Oracle SQL Developer in this example. The process is nearly identical in Oracle SQL Developer Data Modeler – the only difference being you’ll use the ‘File’ menu to get started versus the ‘File – Data Modeler’ menu in SQL Developer. Remember, the functionality is exactly the same whether you use SQL Developer or SQL Developer Data Modeler when it comes to the data modeling features – you’ll just have a cleaner user interface in SQL Developer Data Modeler. Importing a Data Dictionary to a Model You’ll want to open or create your model first. You can import objects to an existing or new model. The easiest way to get started is to simply open the ‘Browser’ under the View menu. The Browser allows you to navigate your open designs/models You’ll see an ‘Untitled_1′ model by default. I’ve renamed mine to ‘hr_sh_scott_demo.’ Now go back to the File menu, and expand the ‘Data Modeler’ section, and select ‘Import – Data Dictionary.’ This is a fancy way of saying, ‘suck objects out of the database into my model’ Connect! If you haven’t already defined a connection to the database you want to reverse engineer, you’ll need to do that now. I’m going to assume you already have that connection – so select it, and hit the ‘Next’ button. Select the Schema(s) to be imported Select one or more schemas you want to import The schemas selected on this page of the wizard will dictate the lists of tables, views, synonyms, and everything else you can choose from in the next wizard step to import. For brevity, I have selected ALL tables, views, and synonyms from 3 different schemas: HR SCOTT SH Once I hit the ‘Finish’ button in the wizard, SQL Developer will interrogate the database and add the objects to our model. The Big Model and the 3 Little Models I can now see ALL of the objects I just imported in the ‘hr_sh_scott_demo’ relational model in my design tree, and in my relational diagram. Quick Tip: Oracle SQL Developer calls what most folks think of as a ‘Physical Model’ the ‘Relational Model.’ Same difference, mostly. In SQL Developer, a Physical model allows you to define partitioning schemes, advanced storage parameters, and add your PL/SQL code. You can have multiple physical models per relational models. For example I might have a 4 Node RAC in Production that uses partitioning, but in test/dev, only have a single instance with no partitioning. I can have models for both of those physical implementations. The list of tables in my relational model Wouldn’t it be nice if I could segregate the objects based on their schema? Good news, you can! And it’s done by default Several of you might already know where I’m going with this – SUBVIEWS. You can easily create a ‘SubView’ by selecting one or more objects in your model or diagram and add them to a new SubView. SubViews are just mini-models. They contain a subset of objects from the main model. This is very handy when you want to break your model into smaller, more digestible parts. The model information is identical across the model and subviews, so you don’t have to worry about making a change in one place and not having it propagate across your design. SubViews can be used as filters when you create reports and exports as well. So instead of generating a PDF for everything, just show me what’s in my ‘ABC’ subview. But, I don’t want to do any work! Remember, I’m really lazy. More good news – it’s already done by default! The schemas are automatically used to create default SubViews Auto-Navigate to the Object in the Diagram In the subview tree node, right-click on the object you want to navigate to. You can ask to be taken to the main model view or to the SubView location. If you haven’t already opened the SubView in the diagram, it will be automatically opened for you. The SubView diagram only contains the objects from that SubView Your SubView might still be pretty big, many dozens of objects, so don’t forget about the ‘Navigator‘ either! In summary, use the ‘Import’ feature to add existing database objects to your model. If you import from multiple schemas, take advantage of the default schema based SubViews to help you manage your models! Sometimes less is more!

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• Data Quality Through Data Governance

  Data Quality Governance Data quality is very important to every organization, bad data cost an organization time, money, and resources that could be prevented if the proper governance was put in to place.  Data Governance Program Criteria: Support from Executive Management and all Business Units Data Stewardship Program  Cross Functional Team of Data Stewards Data Governance Committee Quality Structured Data It should go without saying but any successful project in today’s business world must get buy in from executive management and all stakeholders involved with the project. If management does not fully support a project because they see it is in there and the company’s best interest then they will remove/eliminate funding, resources and allocated time to work on the project. In essence they can render a project dead until it is official killed by the business. In addition, buy in from stake holders is also very important because they can cause delays increased spending in time, money and resources because they do not support a project. Data Stewardship programs are administered by a data steward manager who primary focus is to support, train and manage a cross functional data stewards team. A cross functional team of data stewards are pulled from various departments act to ensure that all systems work to ensure that an organization’s goals are achieved. Typically, data stewards are subject matter experts that act as mediators between their respective departments and IT. Data Quality Procedures Data Governance Committees are composed of data stewards, Upper management, IT Leadership and various subject matter experts depending on a company. The primary goal of this committee is to define strategic goals, coordinate activities, set data standards and offer data guidelines for the business. Data Quality Policies In 1997, Claudia Imhoff defined a Data Stewardship’s responsibility as to approve business naming standards, develop consistent data definitions, determine data aliases, develop standard calculations and derivations, document the business rules of the corporation, monitor the quality of the data in the data warehouse, define security requirements, and so forth. She further explains data stewards responsible for creating and enforcing polices on the following but not limited to issues. Resolving Data Integration Issues Determining Data Security Documenting Data Definitions, Calculations, Summarizations, etc. Maintaining/Updating Business Rules Analyzing and Improving Data Quality

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• SQLAuthority News – Links to Book On Line – Data Mining Algorithms (Analysis Services – Data Mining)

  - by pinaldave

  I have quite often received request for the Data Mining Algorithms details. Book Online has wonderful resources for the same. I suggest to read them here. Data Mining Algorithms (Analysis Services – Data Mining) The data mining algorithm is the mechanism that creates a data mining model. To create a model, an algorithm first analyzes a set [...]

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• SQL SERVER – Advanced Data Quality Services with Melissa Data – Azure Data Market

  - by pinaldave

  There has been much fanfare over the new SQL Server 2012, and especially around its new companion product Data Quality Services (DQS). Among the many new features is the addition of this integrated knowledge-driven product that enables data stewards everywhere to profile, match, and cleanse data. In addition to the homegrown rules that data stewards can design and implement, there are also connectors to third party providers that are hosted in the Azure Datamarket marketplace.  In this review, I leverage SQL Server 2012 Data Quality Services, and proceed to subscribe to a third party data cleansing product through the Datamarket to showcase this unique capability. Crucial Questions For the purposes of the review, I used a database I had in an Excel spreadsheet with name and address information. Upon a cursory inspection, there are miscellaneous problems with these records; some addresses are missing ZIP codes, others missing a city, and some records are slightly misspelled or have unparsed suites. With DQS, I can easily add a knowledge base to help standardize my values, such as for state abbreviations. But how do I know that my address is correct? And if my address is not correct, what should it be corrected to? The answer lies in a third party knowledge base by the acknowledged USPS certified address accuracy experts at Melissa Data. Reference Data Services Within DQS there is a handy feature to actually add reference data from many different third-party Reference Data Services (RDS) vendors. DQS simplifies the processes of cleansing, standardizing, and enriching data through custom rules and through service providers from the Azure Datamarket. A quick jump over to the Datamarket site shows me that there are a handful of providers that offer data directly through Data Quality Services. Upon subscribing to these services, one can attach a DQS domain or composite domain (fields in a record) to a reference data service provider, and begin using it to cleanse, standardize, and enrich that data. Besides what I am looking for (address correction and enrichment), it is possible to subscribe to a host of other services including geocoding, IP address reference, phone checking and enrichment, as well as name parsing, standardization, and genderization.  These capabilities extend the data quality that DQS has natively by quite a bit. For my current address correction review, I needed to first sign up to a reference data provider on the Azure Data Market site. For this example, I used Melissa Data’s Address Check Service. They offer free one-month trials, so if you wish to follow along, or need to add address quality to your own data, I encourage you to sign up with them. Once I subscribed to the desired Reference Data Provider, I navigated my browser to the Account Keys within My Account to view the generated account key, which I then inserted into the DQS Client – Configuration under the Administration area. Step by Step to Guide That was all it took to hook in the subscribed provider -Melissa Data- directly to my DQS Client. The next step was for me to attach and map in my Reference Data from the newly acquired reference data provider, to a domain in my knowledge base. On the DQS Client home screen, I selected “New Knowledge Base” under Knowledge Base Management on the left-hand side of the home screen. Under New Knowledge Base, I typed a Name and description of my new knowledge base, then proceeded to the Domain Management screen. Here I established a series of domains (fields) and then linked them all together as a composite domain (record set). Using the Create Domain button, I created the following domains according to the fields in my incoming data: Name Address Suite City State Zip I added a Suite column in my domain because Melissa Data has the ability to return missing Suites based on last name or company. And that’s a great benefit of using these third party providers, as they have data that the data steward would not normally have access to. The bottom line is, with these third party data providers, I can actually improve my data. Next, I created a composite domain (fulladdress) and added the (field) domains into the composite domain. This essentially groups our address fields together in a record to facilitate the full address cleansing they perform. I then selected my newly created composite domain and under the Reference Data tab, added my third party reference data provider –Melissa Data’s Address Check- and mapped in each domain that I had to the provider’s Schema. Now that my composite domain has been married to the Reference Data service, I can take the newly published knowledge base and create a project to cleanse and enrich my data. My next task was to create a new Data Quality project, mapping in my data source and matching it to the appropriate domain column, and then kick off the verification process. It took just a few minutes with some progress indicators indicating that it was working. When the process concluded, there was a helpful set of tabs that place the response records into categories: suggested; new; invalid; corrected (automatically); and correct. Accepting the suggestions provided by  Melissa Data allowed me to clean up all the records and flag the invalid ones. It is very apparent that DQS makes address data quality simplistic for any IT professional. Final Note As I have shown, DQS makes data quality very easy. Within minutes I was able to set up a data cleansing and enrichment routine within my data quality project, and ensure that my address data was clean, verified, and standardized against real reference data. As reviewed here, it’s easy to see how both SQL Server 2012 and DQS work to take what used to require a highly skilled developer, and empower an average business or database person to consume external services and clean data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQL Utility, T SQL, Technology Tagged: DQS

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• Problem with apt-get update: failed to fetch error

  - by user171447

  I run an Ubuntu Server 12.04.3 LTS. Today, I wanted to update it, but I did not managed it (yes...), however upgrading worked well. I don't want you to solve my problem but it would be greatful if you could give me some hints. I googled hours, I fould a lot of this kind of errors, but not exactly this. Here is the output of apt-get update: Hit http://filepile.fastit.net precise Release.gpg Hit http://filepile.fastit.net precise Release Hit http://filepile.fastit.net precise/main amd64 Packages Hit http://filepile.fastit.net precise/restricted amd64 Packages Hit http://filepile.fastit.net precise/universe amd64 Packages Hit http://filepile.fastit.net precise/multiverse amd64 Packages Hit http://filepile.fastit.net precise/main i386 Packages Hit http://filepile.fastit.net precise/restricted i386 Packages Hit http://filepile.fastit.net precise/universe i386 Packages Hit http://filepile.fastit.net precise/multiverse i386 Packages Ign http://filepile.fastit.net precise/main TranslationIndex Ign http://filepile.fastit.net precise/multiverse TranslationIndex Ign http://filepile.fastit.net precise/restricted TranslationIndex Ign http://filepile.fastit.net precise/universe TranslationIndex Ign http://filepile.fastit.net precise/main Translation-en_GB Ign http://filepile.fastit.net precise/main Translation-en Ign http://filepile.fastit.net precise/main Translation-en_GB.UTF-8 Hit http://archive.canonical.com precise Release.gpg Ign http://filepile.fastit.net precise/multiverse Translation-en_GB Ign http://filepile.fastit.net precise/multiverse Translation-en Ign http://filepile.fastit.net precise/multiverse Translation-en_GB.UTF-8 Ign http://filepile.fastit.net precise/restricted Translation-en_GB Ign http://filepile.fastit.net precise/restricted Translation-en Ign http://filepile.fastit.net precise/restricted Translation-en_GB.UTF-8 Ign http://filepile.fastit.net precise/universe Translation-en_GB Ign http://filepile.fastit.net precise/universe Translation-en Ign http://filepile.fastit.net precise/universe Translation-en_GB.UTF-8 Hit http://archive.ubuntu.com precise Release.gpg Hit http://archive.canonical.com precise Release Hit http://archive.ubuntu.com precise Release Hit http://archive.ubuntu.com precise/main Sources Hit http://archive.ubuntu.com precise/restricted Sources Hit http://archive.ubuntu.com precise/main i386 Packages Hit http://archive.ubuntu.com precise/restricted i386 Packages Hit http://archive.ubuntu.com precise/multiverse i386 Packages Hit http://archive.ubuntu.com precise/main TranslationIndex Hit http://archive.ubuntu.com precise/multiverse TranslationIndex Hit http://archive.ubuntu.com precise/restricted TranslationIndex Hit http://archive.ubuntu.com precise/main Translation-en_GB Hit http://archive.ubuntu.com precise/main Translation-en Hit http://archive.ubuntu.com precise/multiverse Translation-en_GB Hit http://archive.ubuntu.com precise/multiverse Translation-en Hit http://archive.ubuntu.com precise/restricted Translation-en_GB Hit http://archive.ubuntu.com precise/restricted Translation-en Hit http://fr.archive.ubuntu.com precise-security Release.gpg Hit http://fr.archive.ubuntu.com precise-updates Release.gpg Hit http://fr.archive.ubuntu.com precise-security Release Hit http://fr.archive.ubuntu.com precise-updates Release Hit http://fr.archive.ubuntu.com precise-security/main amd64 Packages Hit http://fr.archive.ubuntu.com precise-security/restricted amd64 Packages Hit http://fr.archive.ubuntu.com precise-security/universe amd64 Packages Hit http://fr.archive.ubuntu.com precise-security/multiverse amd64 Packages :W: Failed to fetch http://archive.canonical.com/dists/precise/Release Unable to find expected entry 'main/binary-amd64/Packages' in Release file (Wrong sources.list entry or malformed file) E: Some index files failed to download. They have been ignored, or old ones used instead. Hit http://fr.archive.ubuntu.com precise-security/main i386 Packages Hit http://fr.archive.ubuntu.com precise-security/restricted i386 Packages Hit http://fr.archive.ubuntu.com precise-security/universe i386 Packages Hit http://fr.archive.ubuntu.com precise-security/multiverse i386 Packages Hit http://fr.archive.ubuntu.com precise-security/main TranslationIndex Hit http://fr.archive.ubuntu.com precise-security/multiverse TranslationIndex Hit http://fr.archive.ubuntu.com precise-security/restricted TranslationIndex Hit http://fr.archive.ubuntu.com precise-security/universe TranslationIndex Hit http://fr.archive.ubuntu.com precise-updates/main amd64 Packages Hit http://fr.archive.ubuntu.com precise-updates/restricted amd64 Packages Hit http://fr.archive.ubuntu.com precise-updates/universe amd64 Packages Hit http://fr.archive.ubuntu.com precise-updates/multiverse amd64 Packages Hit http://fr.archive.ubuntu.com precise-updates/main i386 Packages Hit http://fr.archive.ubuntu.com precise-updates/restricted i386 Packages Hit http://fr.archive.ubuntu.com precise-updates/universe i386 Packages Hit http://fr.archive.ubuntu.com precise-updates/multiverse i386 Packages Hit http://fr.archive.ubuntu.com precise-updates/main TranslationIndex Hit http://fr.archive.ubuntu.com precise-updates/multiverse TranslationIndex Hit http://fr.archive.ubuntu.com precise-updates/restricted TranslationIndex Hit http://fr.archive.ubuntu.com precise-updates/universe TranslationIndex Hit http://fr.archive.ubuntu.com precise-security/main Translation-en Hit http://fr.archive.ubuntu.com precise-security/multiverse Translation-en Hit http://fr.archive.ubuntu.com precise-security/restricted Translation-en Hit http://fr.archive.ubuntu.com precise-security/universe Translation-en Hit http://fr.archive.ubuntu.com precise-updates/main Translation-en_GB Hit http://fr.archive.ubuntu.com precise-updates/main Translation-en Hit http://fr.archive.ubuntu.com precise-updates/multiverse Translation-en_GB Hit http://fr.archive.ubuntu.com precise-updates/multiverse Translation-en Hit http://fr.archive.ubuntu.com precise-updates/restricted Translation-en_GB Hit http://fr.archive.ubuntu.com precise-updates/restricted Translation-en Hit http://fr.archive.ubuntu.com precise-updates/universe Translation-en_GB Hit http://fr.archive.ubuntu.com precise-updates/universe Translation-en And here is my /etc/apt/sources.list: ###### Ubuntu Main Repos deb http://filepile.fastit.net/ubuntu/ precise main restricted universe multiverse # deb http://de.archive.ubuntu.com/ubuntu/ precise main restricted universe multiverse deb http://archive.canonical.com/ precise main restricted universe multiverse ###### Ubuntu Update Repos deb http://fr.archive.ubuntu.com/ubuntu/ precise-security main restricted universe multiverse deb http://fr.archive.ubuntu.com/ubuntu/ precise-updates main restricted universe multiverse deb http://archive.ubuntu.com/ubuntu/ precise main restricted multiverse deb-src http://archive.ubuntu.com/ubuntu precise main restricted multiverse Thanks for your help!

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• E: Sub-process /usr/bin/dpkg returned an error code (1) seems to be choking on kde-runtime-data version issue

  - by BMT

  12.04 LTS, on a dell mini 10. Install stable until about a week ago. Updated about 1x a week, sometimes more often. Several days ago, I booted up and the system was no longer working correctly. All these symptoms occurred simultaneously: Cannot run (exit on opening, every time): Update manager, software center, ubuntuOne, libreOffice. Vinagre autostarts on boot, no explanation, not set to startup with Ubuntu. Using apt-get to fix install results in the following: maura@pandora:~$ sudo apt-get -f install Reading package lists... Done Building dependency tree Reading state information... Done Correcting dependencies... Done The following package was automatically installed and is no longer required: libtelepathy-farstream2 Use 'apt-get autoremove' to remove them. The following extra packages will be installed: gwibber gwibber-service kde-runtime-data software-center Suggested packages: gwibber-service-flickr gwibber-service-digg gwibber-service-statusnet gwibber-service-foursquare gwibber-service-friendfeed gwibber-service-pingfm gwibber-service-qaiku unity-lens-gwibber The following packages will be upgraded: gwibber gwibber-service kde-runtime-data software-center 4 upgraded, 0 newly installed, 0 to remove and 39 not upgraded. 20 not fully installed or removed. Need to get 0 B/5,682 kB of archives. After this operation, 177 kB of additional disk space will be used. Do you want to continue [Y/n]? debconf: Perl may be unconfigured (Can't locate Scalar/Util.pm in @INC (@INC contains: /etc/perl /usr/local/lib/perl/5.14.2 /usr/local/share/perl/5.14.2 /usr/lib/perl5 /usr/share/perl5 /usr/lib/perl/5.14 /usr/share/perl/5.14 /usr/local/lib/site_perl .) at /usr/lib/perl/5.14/Hash/Util.pm line 9. BEGIN failed--compilation aborted at /usr/lib/perl/5.14/Hash/Util.pm line 9. Compilation failed in require at /usr/share/perl/5.14/fields.pm line 122. Compilation failed in require at /usr/share/perl5/Debconf/Log.pm line 10. Compilation failed in require at (eval 1) line 4. BEGIN failed--compilation aborted at (eval 1) line 4. ) -- aborting (Reading database ... 242672 files and directories currently installed.) Preparing to replace gwibber 3.4.1-0ubuntu1 (using .../gwibber_3.4.2-0ubuntu1_i386.deb) ... Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging dpkg: warning: subprocess old pre-removal script returned error exit status 1 dpkg - trying script from the new package instead ... Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging dpkg: error processing /var/cache/apt/archives/gwibber_3.4.2-0ubuntu1_i386.deb (--unpack): subprocess new pre-removal script returned error exit status 1 Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pycompile", line 27, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pycompile", line 27, in <module> import logging ImportError: No module named logging dpkg: error while cleaning up: subprocess installed post-installation script returned error exit status 1 Preparing to replace gwibber-service 3.4.1-0ubuntu1 (using .../gwibber-service_3.4.2-0ubuntu1_all.deb) ... Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging dpkg: warning: subprocess old pre-removal script returned error exit status 1 dpkg - trying script from the new package instead ... Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging dpkg: error processing /var/cache/apt/archives/gwibber-service_3.4.2-0ubuntu1_all.deb (--unpack): subprocess new pre-removal script returned error exit status 1 Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pycompile", line 27, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pycompile", line 27, in <module> import logging ImportError: No module named logging dpkg: error while cleaning up: subprocess installed post-installation script returned error exit status 1 Preparing to replace kde-runtime-data 4:4.8.3-0ubuntu0.1 (using .../kde-runtime-data_4%3a4.8.4-0ubuntu0.1_all.deb) ... Unpacking replacement kde-runtime-data ... dpkg: error processing /var/cache/apt/archives/kde-runtime-data_4%3a4.8.4-0ubuntu0.1_all.deb (--unpack): trying to overwrite '/usr/share/sounds', which is also in package sound-theme-freedesktop 0.7.pristine-2 dpkg-deb (subprocess): subprocess data was killed by signal (Broken pipe) dpkg-deb: error: subprocess <decompress> returned error exit status 2 Preparing to replace python-crypto 2.4.1-1 (using .../python-crypto_2.4.1-1_i386.deb) ... Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging dpkg: warning: subprocess old pre-removal script returned error exit status 1 dpkg - trying script from the new package instead ... Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging dpkg: error processing /var/cache/apt/archives/python-crypto_2.4.1-1_i386.deb (--unpack): subprocess new pre-removal script returned error exit status 1 No apport report written because MaxReports is reached already Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pycompile", line 27, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pycompile", line 27, in <module> import logging ImportError: No module named logging dpkg: error while cleaning up: subprocess installed post-installation script returned error exit status 1 Preparing to replace software-center 5.2.2.2 (using .../software-center_5.2.4_all.deb) ... Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging dpkg: warning: subprocess old pre-removal script returned error exit status 1 dpkg - trying script from the new package instead ... Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging dpkg: error processing /var/cache/apt/archives/software-center_5.2.4_all.deb (--unpack): subprocess new pre-removal script returned error exit status 1 No apport report written because MaxReports is reached already Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pycompile", line 27, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pycompile", line 27, in <module> import logging ImportError: No module named logging dpkg: error while cleaning up: subprocess installed post-installation script returned error exit status 1 Preparing to replace xdiagnose 2.5 (using .../archives/xdiagnose_2.5_all.deb) ... Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging dpkg: warning: subprocess old pre-removal script returned error exit status 1 dpkg - trying script from the new package instead ... Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging dpkg: error processing /var/cache/apt/archives/xdiagnose_2.5_all.deb (--unpack): subprocess new pre-removal script returned error exit status 1 No apport report written because MaxReports is reached already Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pycompile", line 27, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pycompile", line 27, in <module> import logging ImportError: No module named logging dpkg: error while cleaning up: subprocess installed post-installation script returned error exit status 1 Errors were encountered while processing: /var/cache/apt/archives/gwibber_3.4.2-0ubuntu1_i386.deb /var/cache/apt/archives/gwibber-service_3.4.2-0ubuntu1_all.deb /var/cache/apt/archives/kde-runtime-data_4%3a4.8.4-0ubuntu0.1_all.deb /var/cache/apt/archives/python-crypto_2.4.1-1_i386.deb /var/cache/apt/archives/software-center_5.2.4_all.deb /var/cache/apt/archives/xdiagnose_2.5_all.deb E: Sub-process /usr/bin/dpkg returned an error code (1) maura@pandora:~$ ^C maura@pandora:~$

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• Big Data – Is Big Data Relevant to me? – Big Data Questionnaires – Guest Post by Vinod Kumar

  - by Pinal Dave

  This guest post is by Vinod Kumar. Vinod Kumar has worked with SQL Server extensively since joining the industry over a decade ago. Working on various versions of SQL Server 7.0, Oracle 7.3 and other database technologies – he now works with the Microsoft Technology Center (MTC) as a Technology Architect. Let us read the blog post in Vinod’s own voice. I think the series from Pinal is a good one for anyone planning to start on Big Data journey from the basics. In my daily customer interactions this buzz of “Big Data” always comes up, I react generally saying – “Sir, do you really have a ‘Big Data’ problem or do you have a big Data problem?” Generally, there is a silence in the air when I ask this question. Data is everywhere in organizations – be it big data, small data, all data and for few it is bad data which is same as no data :). Wow, don’t discount me as someone who opposes “Big Data”, I am a big supporter as much as I am a critic of the abuse of this term by the people. In this post, I wanted to let my mind flow so that you can also think in the direction I want you to see these concepts. In any case, this is not an exhaustive dump of what is in my mind – but you will surely get the drift how I am going to question Big Data terms from customers!!! Is Big Data Relevant to me? Many of my customers talk to me like blank whiteboard with no idea – “why Big Data”. They want to jump into the bandwagon of technology and they want to decipher insights from their unexplored data a.k.a. unstructured data with structured data. So what are these industry scenario’s that come to mind? Here are some of them: Financials Fraud detection: Banks and Credit cards are monitoring your spending habits on real-time basis. Customer Segmentation: applies in every industry from Banking to Retail to Aviation to Utility and others where they deal with end customer who consume their products and services. Customer Sentiment Analysis: Responding to negative brand perception on social or amplify the positive perception. Sales and Marketing Campaign: Understand the impact and get closer to customer delight. Call Center Analysis: attempt to take unstructured voice recordings and analyze them for content and sentiment. Medical Reduce Re-admissions: How to build a proactive follow-up engagements with patients. Patient Monitoring: How to track Inpatient, Out-Patient, Emergency Visits, Intensive Care Units etc. Preventive Care: Disease identification and Risk stratification is a very crucial business function for medical. Claims fraud detection: There is no precise dollars that one can put here, but this is a big thing for the medical field. Retail Customer Sentiment Analysis, Customer Care Centers, Campaign Management. Supply Chain Analysis: Every sensors and RFID data can be tracked for warehouse space optimization. Location based marketing: Based on where a check-in happens retail stores can be optimize their marketing. Telecom Price optimization and Plans, Finding Customer churn, Customer loyalty programs Call Detail Record (CDR) Analysis, Network optimizations, User Location analysis Customer Behavior Analysis Insurance Fraud Detection & Analysis, Pricing based on customer Sentiment Analysis, Loyalty Management Agents Analysis, Customer Value Management This list can go on to other areas like Utility, Manufacturing, Travel, ITES etc. So as you can see, there are obviously interesting use cases for each of these industry verticals. These are just representative list. Where to start? A lot of times I try to quiz customers on a number of dimensions before starting a Big Data conversation. Are you getting the data you need the way you want it and in a timely manner? Can you get in and analyze the data you need? How quickly is IT to respond to your BI Requests? How easily can you get at the data that you need to run your business/department/project? How are you currently measuring your business? Can you get the data you need to react WITHIN THE QUARTER to impact behaviors to meet your numbers or is it always “rear-view mirror?” How are you measuring: The Brand Customer Sentiment Your Competition Your Pricing Your performance Supply Chain Efficiencies Predictive product / service positioning What are your key challenges of driving collaboration across your global business?  What the challenges in innovation? What challenges are you facing in getting more information out of your data? Note: Garbage-in is Garbage-out. Hold good for all reporting / analytics requirements Big Data POCs? A number of customers get into the realm of setting a small team to work on Big Data – well it is a great start from an understanding point of view, but I tend to ask a number of other questions to such customers. Some of these common questions are: To what degree is your advanced analytics (natural language processing, sentiment analysis, predictive analytics and classification) paired with your Big Data’s efforts? Do you have dedicated resources exploring the possibilities of advanced analytics in Big Data for your business line? Do you plan to employ machine learning technology while doing Advanced Analytics? How is Social Media being monitored in your organization? What is your ability to scale in terms of storage and processing power? Do you have a system in place to sort incoming data in near real time by potential value, data quality, and use frequency? Do you use event-driven architecture to manage incoming data? Do you have specialized data services that can accommodate different formats, security, and the management requirements of multiple data sources? Is your organization currently using or considering in-memory analytics? To what degree are you able to correlate data from your Big Data infrastructure with that from your enterprise data warehouse? Have you extended the role of Data Stewards to include ownership of big data components? Do you prioritize data quality based on the source system (that is Facebook/Twitter data has lower quality thresholds than radio frequency identification (RFID) for a tracking system)? Do your retention policies consider the different legal responsibilities for storing Big Data for a specific amount of time? Do Data Scientists work in close collaboration with Data Stewards to ensure data quality? How is access to attributes of Big Data being given out in the organization? Are roles related to Big Data (Advanced Analyst, Data Scientist) clearly defined? How involved is risk management in the Big Data governance process? Is there a set of documented policies regarding Big Data governance? Is there an enforcement mechanism or approach to ensure that policies are followed? Who is the key sponsor for your Big Data governance program? (The CIO is best) Do you have defined policies surrounding the use of social media data for potential employees and customers, as well as the use of customer Geo-location data? How accessible are complex analytic routines to your user base? What is the level of involvement with outside vendors and third parties in regard to the planning and execution of Big Data projects? What programming technologies are utilized by your data warehouse/BI staff when working with Big Data? These are some of the important questions I ask each customer who is actively evaluating Big Data trends for their organizations. These questions give you a sense of direction where to start, what to use, how to secure, how to analyze and more. Sign off Any Big data is analysis is incomplete without a compelling story. The best way to understand this is to watch Hans Rosling – Gapminder (2:17 to 6:06) videos about the third world myths. Don’t get overwhelmed with the Big Data buzz word, the destination to what your data speaks is important. In this blog post, we did not particularly look at any Big Data technologies. This is a set of questionnaire one needs to keep in mind as they embark their journey of Big Data. I did write some of the basics in my blog: Big Data – Big Hype yet Big Opportunity. Do let me know if these questions make sense?  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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• Big Data – Basics of Big Data Architecture – Day 4 of 21

  - by Pinal Dave

  In yesterday’s blog post we understood how Big Data evolution happened. Today we will understand basics of the Big Data Architecture. Big Data Cycle Just like every other database related applications, bit data project have its development cycle. Though three Vs (link) for sure plays an important role in deciding the architecture of the Big Data projects. Just like every other project Big Data project also goes to similar phases of the data capturing, transforming, integrating, analyzing and building actionable reporting on the top of  the data. While the process looks almost same but due to the nature of the data the architecture is often totally different. Here are few of the question which everyone should ask before going ahead with Big Data architecture. Questions to Ask How big is your total database? What is your requirement of the reporting in terms of time – real time, semi real time or at frequent interval? How important is the data availability and what is the plan for disaster recovery? What are the plans for network and physical security of the data? What platform will be the driving force behind data and what are different service level agreements for the infrastructure? This are just basic questions but based on your application and business need you should come up with the custom list of the question to ask. As I mentioned earlier this question may look quite simple but the answer will not be simple. When we are talking about Big Data implementation there are many other important aspects which we have to consider when we decide to go for the architecture. Building Blocks of Big Data Architecture It is absolutely impossible to discuss and nail down the most optimal architecture for any Big Data Solution in a single blog post, however, we can discuss the basic building blocks of big data architecture. Here is the image which I have built to explain how the building blocks of the Big Data architecture works. Above image gives good overview of how in Big Data Architecture various components are associated with each other. In Big Data various different data sources are part of the architecture hence extract, transform and integration are one of the most essential layers of the architecture. Most of the data is stored in relational as well as non relational data marts and data warehousing solutions. As per the business need various data are processed as well converted to proper reports and visualizations for end users. Just like software the hardware is almost the most important part of the Big Data Architecture. In the big data architecture hardware infrastructure is extremely important and failure over instances as well as redundant physical infrastructure is usually implemented. NoSQL in Data Management NoSQL is a very famous buzz word and it really means Not Relational SQL or Not Only SQL. This is because in Big Data Architecture the data is in any format. It can be unstructured, relational or in any other format or from any other data source. To bring all the data together relational technology is not enough, hence new tools, architecture and other algorithms are invented which takes care of all the kind of data. This is collectively called NoSQL. Tomorrow Next four days we will answer the Buzz Words – Hadoop. 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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• Oracle Database Third Party Model Import and Scoring - Poll

  - by [email protected]

  We in the Oracle Data Mining Technologies group are interested in your needs for third party model import. Are you interested in importing SAS, SPSS, R, etc. analytic models into Oracle for in-database scoring? Thank you for taking a minute to participate in this quick poll.Click here to take the poll.

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• Big Data – Evolution of Big Data – Day 3 of 21

  - by Pinal Dave

  In yesterday’s blog post we answered what is the Big Data. Today we will understand why and how the evolution of Big Data has happened. Though the answer is very simple, I would like to tell it in the form of a history lesson. Data in Flat File In earlier days data was stored in the flat file and there was no structure in the flat file.  If any data has to be retrieved from the flat file it was a project by itself. There was no possibility of retrieving the data efficiently and data integrity has been just a term discussed without any modeling or structure around. Database residing in the flat file had more issues than we would like to discuss in today’s world. It was more like a nightmare when there was any data processing involved in the application. Though, applications developed at that time were also not that advanced the need of the data was always there and there was always need of proper data management. Edgar F Codd and 12 Rules Edgar Frank Codd was a British computer scientist who, while working for IBM, invented the relational model for database management, the theoretical basis for relational databases. He presented 12 rules for the Relational Database and suddenly the chaotic world of the database seems to see discipline in the rules. Relational Database was a promising land for all the unstructured database users. Relational Database brought into the relationship between data as well improved the performance of the data retrieval. Database world had immediately seen a major transformation and every single vendors and database users suddenly started to adopt the relational database models. Relational Database Management Systems Since Edgar F Codd proposed 12 rules for the RBDMS there were many different vendors who started them to build applications and tools to support the relationship between database. This was indeed a learning curve for many of the developer who had never worked before with the modeling of the database. However, as time passed by pretty much everybody accepted the relationship of the database and started to evolve product which performs its best with the boundaries of the RDBMS concepts. This was the best era for the databases and it gave the world extreme experts as well as some of the best products. The Entity Relationship model was also evolved at the same time. In software engineering, an Entity–relationship model (ER model) is a data model for describing a database in an abstract way. Enormous Data Growth Well, everything was going fine with the RDBMS in the database world. As there were no major challenges the adoption of the RDBMS applications and tools was pretty much universal. There was a race at times to make the developer’s life much easier with the RDBMS management tools. Due to the extreme popularity and easy to use system pretty much every data was stored in the RDBMS system. New age applications were built and social media took the world by the storm. Every organizations was feeling pressure to provide the best experience for their users based the data they had with them. While this was all going on at the same time data was growing pretty much every organization and application. Data Warehousing The enormous data growth now presented a big challenge for the organizations who wanted to build intelligent systems based on the data and provide near real time superior user experience to their customers. Various organizations immediately start building data warehousing solutions where the data was stored and processed. The trend of the business intelligence becomes the need of everyday. Data was received from the transaction system and overnight was processed to build intelligent reports from it. Though this is a great solution it has its own set of challenges. The relational database model and data warehousing concepts are all built with keeping traditional relational database modeling in the mind and it still has many challenges when unstructured data was present. Interesting Challenge Every organization had expertise to manage structured data but the world had already changed to unstructured data. There was intelligence in the videos, photos, SMS, text, social media messages and various other data sources. All of these needed to now bring to a single platform and build a uniform system which does what businesses need. The way we do business has also been changed. There was a time when user only got the features what technology supported, however, now users ask for the feature and technology is built to support the same. The need of the real time intelligence from the fast paced data flow is now becoming a necessity. Large amount (Volume) of difference (Variety) of high speed data (Velocity) is the properties of the data. The traditional database system has limits to resolve the challenges this new kind of the data presents. Hence the need of the Big Data Science. We need innovation in how we handle and manage data. We need creative ways to capture data and present to users. Big Data is Reality! Tomorrow In tomorrow’s blog post we will try to answer discuss Basics of Big Data Architecture. 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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• ubuntu/apt-get update said "Failed to Fetch http:// .... 404 not found"

  - by lindenb

  Hi all, I'm trying to run apt-get update on ubuntu 9.10 I've configured my proxy server and I can access the internet without any problem: /etc/apt# wget "http://www.google.com" Resolving (...) Proxy request sent, awaiting response... 200 OK Length: 292 [text/html] Saving to: `index.html' 100%[=================================================================================================================================>] 292 --.-K/s in 0s 2010-04-02 17:20:33 (29.8 MB/s) - `index.html' saved [292/292] But when I tried to use apt-get I got the following message: Ign http://archive.ubuntu.com karmic Release.gpg Ign http://ubuntu.univ-nantes.fr karmic Release.gpg Ign http://ubuntu.univ-nantes.fr karmic/main Translation-en_US Ign http://ubuntu.univ-nantes.fr karmic/restricted Translation-en_US Ign http://archive.ubuntu.com karmic Release Ign http://ubuntu.univ-nantes.fr karmic/multiverse Translation-en_US Ign http://ubuntu.univ-nantes.fr karmic/universe Translation-en_US Ign http://ubuntu.univ-nantes.fr karmic-updates Release.gpg Ign http://archive.ubuntu.com karmic/main Sources Ign http://ubuntu.univ-nantes.fr karmic-updates/main Translation-en_US Ign http://ubuntu.univ-nantes.fr karmic-updates/restricted Translation-en_US Ign http://ubuntu.univ-nantes.fr karmic-updates/multiverse Translation-en_US Ign http://archive.ubuntu.com karmic/restricted Sources Ign http://ubuntu.univ-nantes.fr karmic-updates/universe Translation-en_US Ign http://ubuntu.univ-nantes.fr karmic-security Release.gpg Ign http://archive.ubuntu.com karmic/main Sources Ign http://ubuntu.univ-nantes.fr karmic-security/main Translation-en_US Ign http://ubuntu.univ-nantes.fr karmic-security/restricted Translation-en_US Ign http://ubuntu.univ-nantes.fr karmic-security/multiverse Translation-en_US Ign http://archive.ubuntu.com karmic/restricted Sources Ign http://ubuntu.univ-nantes.fr karmic-security/universe Translation-en_US Ign http://ubuntu.univ-nantes.fr karmic Release Err http://archive.ubuntu.com karmic/main Sources 404 Not Found Ign http://ubuntu.univ-nantes.fr karmic-updates Release Ign http://ubuntu.univ-nantes.fr karmic-security Release Err http://archive.ubuntu.com karmic/restricted Sources 404 Not Found Ign http://ubuntu.univ-nantes.fr karmic/main Packages Ign http://ubuntu.univ-nantes.fr karmic/restricted Packages Ign http://ubuntu.univ-nantes.fr karmic/multiverse Packages Ign http://ubuntu.univ-nantes.fr karmic/restricted Sources Ign http://ubuntu.univ-nantes.fr karmic/main Sources Ign http://ubuntu.univ-nantes.fr karmic/universe Sources Ign http://ubuntu.univ-nantes.fr karmic/universe Packages Ign http://ubuntu.univ-nantes.fr karmic-updates/main Packages Ign http://ubuntu.univ-nantes.fr karmic-updates/restricted Packages Ign http://ubuntu.univ-nantes.fr karmic-updates/multiverse Packages Ign http://ubuntu.univ-nantes.fr karmic-updates/restricted Sources Ign http://ubuntu.univ-nantes.fr karmic-updates/main Sources Ign http://ubuntu.univ-nantes.fr karmic-updates/universe Sources Ign http://ubuntu.univ-nantes.fr karmic-updates/universe Packages Ign http://ubuntu.univ-nantes.fr karmic-security/main Packages Ign http://ubuntu.univ-nantes.fr karmic-security/restricted Packages Ign http://ubuntu.univ-nantes.fr karmic-security/multiverse Packages Ign http://ubuntu.univ-nantes.fr karmic-security/restricted Sources Ign http://ubuntu.univ-nantes.fr karmic-security/main Sources Ign http://ubuntu.univ-nantes.fr karmic-security/universe Sources Ign http://ubuntu.univ-nantes.fr karmic-security/universe Packages Ign http://ubuntu.univ-nantes.fr karmic/main Packages Ign http://ubuntu.univ-nantes.fr karmic/restricted Packages Ign http://ubuntu.univ-nantes.fr karmic/multiverse Packages Ign http://ubuntu.univ-nantes.fr karmic/restricted Sources Ign http://ubuntu.univ-nantes.fr karmic/main Sources Ign http://ubuntu.univ-nantes.fr karmic/universe Sources Ign http://ubuntu.univ-nantes.fr karmic/universe Packages Ign http://ubuntu.univ-nantes.fr karmic-updates/main Packages Ign http://ubuntu.univ-nantes.fr karmic-updates/restricted Packages Ign http://ubuntu.univ-nantes.fr karmic-updates/multiverse Packages Ign http://ubuntu.univ-nantes.fr karmic-updates/restricted Sources Ign http://ubuntu.univ-nantes.fr karmic-updates/main Sources Ign http://ubuntu.univ-nantes.fr karmic-updates/universe Sources Ign http://ubuntu.univ-nantes.fr karmic-updates/universe Packages Ign http://ubuntu.univ-nantes.fr karmic-security/main Packages Ign http://ubuntu.univ-nantes.fr karmic-security/restricted Packages Ign http://ubuntu.univ-nantes.fr karmic-security/multiverse Packages Ign http://ubuntu.univ-nantes.fr karmic-security/restricted Sources Ign http://ubuntu.univ-nantes.fr karmic-security/main Sources Ign http://ubuntu.univ-nantes.fr karmic-security/universe Sources Ign http://ubuntu.univ-nantes.fr karmic-security/universe Packages Err http://ubuntu.univ-nantes.fr karmic/main Packages 404 Not Found Err http://ubuntu.univ-nantes.fr karmic/restricted Packages 404 Not Found Err http://ubuntu.univ-nantes.fr karmic/multiverse Packages 404 Not Found Err http://ubuntu.univ-nantes.fr karmic/restricted Sources 404 Not Found Err http://ubuntu.univ-nantes.fr karmic/main Sources 404 Not Found Err http://ubuntu.univ-nantes.fr karmic/universe Sources 404 Not Found Err http://ubuntu.univ-nantes.fr karmic/universe Packages 404 Not Found Err http://ubuntu.univ-nantes.fr karmic-updates/main Packages 404 Not Found Err http://ubuntu.univ-nantes.fr karmic-updates/restricted Packages 404 Not Found Err http://ubuntu.univ-nantes.fr karmic-updates/multiverse Packages 404 Not Found Err http://ubuntu.univ-nantes.fr karmic-updates/restricted Sources 404 Not Found Err http://ubuntu.univ-nantes.fr karmic-updates/main Sources 404 Not Found Err http://ubuntu.univ-nantes.fr karmic-updates/universe Sources 404 Not Found Err http://ubuntu.univ-nantes.fr karmic-updates/universe Packages 404 Not Found Err http://ubuntu.univ-nantes.fr karmic-security/main Packages 404 Not Found Err http://ubuntu.univ-nantes.fr karmic-security/restricted Packages 404 Not Found Err http://ubuntu.univ-nantes.fr karmic-security/multiverse Packages 404 Not Found Err http://ubuntu.univ-nantes.fr karmic-security/restricted Sources 404 Not Found Err http://ubuntu.univ-nantes.fr karmic-security/main Sources 404 Not Found Err http://ubuntu.univ-nantes.fr karmic-security/universe Sources 404 Not Found Err http://ubuntu.univ-nantes.fr karmic-security/universe Packages 404 Not Found W: Failed to fetch http://archive.ubuntu.com/ubuntu/dists/karmic/main/source/Sources.gz 404 Not Found W: Failed to fetch http://archive.ubuntu.com/ubuntu/dists/karmic/restricted/source/Sources.gz 404 Not Found W: Failed to fetch http://ubuntu.univ-nantes.fr/ubuntu/dists/karmic/main/binary-i386/Packages.gz 404 Not Found W: Failed to fetch http://ubuntu.univ-nantes.fr/ubuntu/dists/karmic/restricted/binary-i386/Packages.gz 404 Not Found W: Failed to fetch http://ubuntu.univ-nantes.fr/ubuntu/dists/karmic/multiverse/binary-i386/Packages.gz 404 Not Found W: Failed to fetch http://ubuntu.univ-nantes.fr/ubuntu/dists/karmic/restricted/source/Sources.gz 404 Not Found W: Failed to fetch http://ubuntu.univ-nantes.fr/ubuntu/dists/karmic/main/source/Sources.gz 404 Not Found W: Failed to fetch http://ubuntu.univ-nantes.fr/ubuntu/dists/karmic/universe/source/Sources.gz 404 Not Found W: Failed to fetch http://ubuntu.univ-nantes.fr/ubuntu/dists/karmic/universe/binary-i386/Packages.gz 404 Not Found W: Failed to fetch http://ubuntu.univ-nantes.fr/ubuntu/dists/karmic-updates/main/binary-i386/Packages.gz 404 Not Found W: Failed to fetch http://ubuntu.univ-nantes.fr/ubuntu/dists/karmic-updates/restricted/binary-i386/Packages.gz 404 Not Found W: Failed to fetch http://ubuntu.univ-nantes.fr/ubuntu/dists/karmic-updates/multiverse/binary-i386/Packages.gz 404 Not Found W: Failed to fetch http://ubuntu.univ-nantes.fr/ubuntu/dists/karmic-updates/restricted/source/Sources.gz 404 Not Found W: Failed to fetch http://ubuntu.univ-nantes.fr/ubuntu/dists/karmic-updates/main/source/Sources.gz 404 Not Found W: Failed to fetch http://ubuntu.univ-nantes.fr/ubuntu/dists/karmic-updates/universe/source/Sources.gz 404 Not Found W: Failed to fetch http://ubuntu.univ-nantes.fr/ubuntu/dists/karmic-updates/universe/binary-i386/Packages.gz 404 Not Found W: Failed to fetch http://ubuntu.univ-nantes.fr/ubuntu/dists/karmic-security/main/binary-i386/Packages.gz 404 Not Found W: Failed to fetch http://ubuntu.univ-nantes.fr/ubuntu/dists/karmic-security/restricted/binary-i386/Packages.gz 404 Not Found W: Failed to fetch http://ubuntu.univ-nantes.fr/ubuntu/dists/karmic-security/multiverse/binary-i386/Packages.gz 404 Not Found W: Failed to fetch http://ubuntu.univ-nantes.fr/ubuntu/dists/karmic-security/restricted/source/Sources.gz 404 Not Found W: Failed to fetch http://ubuntu.univ-nantes.fr/ubuntu/dists/karmic-security/main/source/Sources.gz 404 Not Found W: Failed to fetch http://ubuntu.univ-nantes.fr/ubuntu/dists/karmic-security/universe/source/Sources.gz 404 Not Found W: Failed to fetch http://ubuntu.univ-nantes.fr/ubuntu/dists/karmic-security/universe/binary-i386/Packages.gz 404 Not Found apt.conf However I can 'see' those files with firefox. more /etc/apt/apt.conf Acquire::http::proxy "http://www.myproxyname.fr:3128"; I also tried with port '80', or with a blank /etc/apt/apt.conf source.list grep -v "#" /etc/apt/sources.list deb http://ubuntu.univ-nantes.fr/ubuntu/ karmic main restricted multiverse deb http://ubuntu.univ-nantes.fr/ubuntu/ karmic-updates main restricted multiverse deb http://ubuntu.univ-nantes.fr/ubuntu/ karmic universe deb http://ubuntu.univ-nantes.fr/ubuntu/ karmic-updates universe deb http://ubuntu.univ-nantes.fr/ubuntu/ karmic-security main restricted multiverse deb http://ubuntu.univ-nantes.fr/ubuntu/ karmic-security universe does anyone knows how to fix this ? Thanks Pierre

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• Working with Temporal Data in SQL Server

  - by Dejan Sarka

  My third Pluralsight course, Working with Temporal Data in SQL Server, is published. I am really proud on the second part of the course, where I discuss optimization of temporal queries. This was a nearly impossible task for decades. First solutions appeared only lately. I present all together six solutions (and one more that is not a solution), and I invented four of them. http://pluralsight.com/training/Courses/TableOfContents/working-with-temporal-data-sql-server

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• New Communications Industry Data Model with "Factory Installed" Predictive Analytics using Oracle Da

  - by charlie.berger

  Oracle Introduces Oracle Communications Data Model to Provide Actionable Insight for Communications Service Providers   We've integrated pre-installed analytical methodologies with the new Oracle Communications Data Model to deliver automated, simple, yet powerful predictive analytics solutions for customers.  Churn, sentiment analysis, identifying customer segments - all things that can be anticipated and hence, preconcieved and implemented inside an applications.  Read on for more information! TM Forum Management World, Nice, France - 18 May 2010 News Facts To help communications service providers (CSPs) manage and analyze rapidly growing data volumes cost effectively, Oracle today introduced the Oracle Communications Data Model. With the Oracle Communications Data Model, CSPs can achieve rapid time to value by quickly implementing a standards-based enterprise data warehouse that features communications industry-specific reporting, analytics and data mining. The combination of the Oracle Communications Data Model, Oracle Exadata and the Oracle Business Intelligence (BI) Foundation represents the most comprehensive data warehouse and BI solution for the communications industry. Also announced today, Hong Kong Broadband Network enhanced their data warehouse system, going live on Oracle Communications Data Model in three months. The leading provider increased its subscriber base by 37 percent in six months and reduced customer churn to less than one percent. Product Details Oracle Communications Data Model provides industry-specific schema and embedded analytics that address key areas such as customer management, marketing segmentation, product development and network health. CSPs can efficiently capture and monitor critical data and transform it into actionable information to support development and delivery of next-generation services using: More than 1,300 industry-specific measurements and key performance indicators (KPIs) such as network reliability statistics, provisioning metrics and customer churn propensity. Embedded OLAP cubes for extremely fast dimensional analysis of business information. Embedded data mining models for sophisticated trending and predictive analysis. Support for multiple lines of business, such as cable, mobile, wireline and Internet, which can be easily extended to support future requirements. With Oracle Communications Data Model, CSPs can jump start the implementation of a communications data warehouse in line with communications-industry standards including the TM Forum Information Framework (SID), formerly known as the Shared Information Model. Oracle Communications Data Model is optimized for any Oracle Database 11g platform, including Oracle Exadata, which can improve call data record query performance by 10x or more. Supporting Quotes "Oracle Communications Data Model covers a wide range of business areas that are relevant to modern communications service providers and is a comprehensive solution - with its data model and pre-packaged templates including BI dashboards, KPIs, OLAP cubes and mining models. It helps us save a great deal of time in building and implementing a customized data warehouse and enables us to leverage the advanced analytics quickly and more effectively," said Yasuki Hayashi, executive manager, NTT Comware Corporation. "Data volumes will only continue to grow as communications service providers expand next-generation networks, deploy new services and adopt new business models. They will increasingly need efficient, reliable data warehouses to capture key insights on data such as customer value, network value and churn probability. With the Oracle Communications Data Model, Oracle has demonstrated its commitment to meeting these needs by delivering data warehouse tools designed to fill communications industry-specific needs," said Elisabeth Rainge, program director, Network Software, IDC. "The TM Forum Conformance Mark provides reassurance to customers seeking standards-based, and therefore, cost-effective and flexible solutions. TM Forum is extremely pleased to work with Oracle to certify its Oracle Communications Data Model solution. Upon successful completion, this certification will represent the broadest and most complete implementation of the TM Forum Information Framework to date, with more than 130 aggregate business entities," said Keith Willetts, chairman and chief executive officer, TM Forum. Supporting Resources Oracle Communications Oracle Communications Data Model Data Sheet Oracle Communications Data Model Podcast Oracle Data Warehousing Oracle Communications on YouTube Oracle Communications on Delicious Oracle Communications on Facebook Oracle Communications on Twitter Oracle Communications on LinkedIn Oracle Database on Twitter The Data Warehouse Insider Blog

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• Big Data – How to become a Data Scientist and Learn Data Science? – Day 19 of 21

  - by Pinal Dave

  In yesterday’s blog post we learned the importance of the analytics in Big Data Story. In this article we will understand how to become a Data Scientist for Big Data Story. Data Scientist is a new buzz word, everyone seems to be wanting to become Data Scientist. Let us go over a few key topics related to Data Scientist in this blog post. First of all we will understand what is a Data Scientist. In the new world of Big Data, I see pretty much everyone wants to become Data Scientist and there are lots of people I have already met who claims that they are Data Scientist. When I ask what is their role, I have got a wide variety of answers. What is Data Scientist? Data scientists are the experts who understand various aspects of the business and know how to strategies data to achieve the business goals. They should have a solid foundation of various data algorithms, modeling and statistics methodology. What do Data Scientists do? Data scientists understand the data very well. They just go beyond the regular data algorithms and builds interesting trends from available data. They innovate and resurrect the entire new meaning from the existing data. They are artists in disguise of computer analyst. They look at the data traditionally as well as explore various new ways to look at the data. Data Scientists do not wait to build their solutions from existing data. They think creatively, they think before the data has entered into the system. Data Scientists are visionary experts who understands the business needs and plan ahead of the time, this tremendously help to build solutions at rapid speed. Besides being data expert, the major quality of Data Scientists is “curiosity”. They always wonder about what more they can get from their existing data and how to get maximum out of future incoming data. Data Scientists do wonders with the data, which goes beyond the job descriptions of Data Analysist or Business Analysist. Skills Required for Data Scientists Here are few of the skills a Data Scientist must have. Expert level skills with statistical tools like SAS, Excel, R etc. Understanding Mathematical Models Hands-on with Visualization Tools like Tableau, PowerPivots, D3. j’s etc. Analytical skills to understand business needs Communication skills On the technology front any Data Scientists should know underlying technologies like (Hadoop, Cloudera) as well as their entire ecosystem (programming language, analysis and visualization tools etc.) . Remember that for becoming a successful Data Scientist one require have par excellent skills, just having a degree in a relevant education field will not suffice. Final Note Data Scientists is indeed very exciting job profile. As per research there are not enough Data Scientists in the world to handle the current data explosion. In near future Data is going to expand exponentially, and the need of the Data Scientists will increase along with it. It is indeed the job one should focus if you like data and science of statistics. Courtesy: emc Tomorrow In tomorrow’s blog post we will discuss about various Big Data Learning resources. 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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• Unstructured Data - The future of Data Administration

  Some have claimed that there is a problem with the way data is currently managed using the relational paradigm do to the rise of unstructured data in modern business. PCMag.com defines unstructured data as data that does not reside in a fixed location. They further explain that unstructured data refers to data in a free text form that is not bound to any specific structure. With the rise of unstructured data in the form of emails, spread sheets, images and documents the critics have a right to argue that the relational paradigm is not as effective as the object oriented data paradigm in managing this type of data. The relational paradigm relies heavily on structure and relationships in and between items of data. This type of paradigm works best in a relation database management system like Microsoft SQL, MySQL, and Oracle because data is forced to conform to a structure in the form of tables and relations can be derived from the existence of one or more tables. These critics also claim that database administrators have not kept up with reality because their primary focus in regards to data administration deals with structured data and the relational paradigm. The relational paradigm was developed in the 1970’s as a way to improve data management when compared to standard flat files. Little has changed since then, and modern database administrators need to know more than just how to handle structured data. That is why critics claim that today’s data professionals do not have the proper skills in order to store and maintain data for modern systems when compared to the skills of system designers, programmers , software engineers, and data designers  due to the industry trend of object oriented design and development. I think that they are wrong. I do not disagree that the industry is moving toward an object oriented approach to development with the potential to use more of an object oriented approach to data.   However, I think that it is business itself that is limiting database administrators from changing how data is stored because of the potential costs, and impact that might occur by altering any part of stored data. Furthermore, database administrators like all technology workers constantly are trying to improve their technical skills in order to excel in their job, so I think that accusing data professional is not just when the root cause of the lack of innovation is controlled by business, and it is business that will suffer for their inability to keep up with technology. One way for database professionals to better prepare for the future of database management is start working with data in the form of objects and so that they can extract data from the objects so that the stored information within objects can be used in relation to the data stored in a using the relational paradigm. Furthermore, I think the use of pattern matching will increase with the increased use of unstructured data because object can be selected, filtered and altered based on the existence of a pattern found within an object.

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• Developing an analytics's system processing large amounts of data - where to start

  - by Ryan

  Imagine you're writing some sort of Web Analytics system - you're recording raw page hits along with some extra things like tagging cookies etc and then producing stats such as Which pages got most traffic over a time period Which referers sent most traffic Goals completed (goal being a view of a particular page) And more advanced things like which referers sent the most number of vistors who later hit a goal. The naieve way of approaching this would be to throw it in a relational database and run queries over it - but that won't scale. You could pre-calculate everything (have a queue of incoming 'hits' and use to update report tables) - but what if you later change a goal - how could you efficiently re-calculate just the data that would be effected. Obviously this has been done before ;) so any tips on where to start, methods & examples, architecture, technologies etc.

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• download/export emails from webmail

  - by misterjinx

  hello, I'm just switching my hosts and I want to move the emails from my accounts too. In order to have my current emails on the new host I want to download/export them and to import at the other host. In order to do this I use one of the webmail (squirrelmail, roundcube, horde) clients available on my current host. The problem is that except for roundcube, I don't see any download/export option available. And in roundcube I can only select one email at a time and download it as eml. My question is how do I export/download all the emails from one account and import them at the new host? I know this is possible because I remember doing this some time ago using squirrelmail, but I can't find anything related to this now. Thank you.

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• Big Data – Beginning Big Data – Day 1 of 21

  - by Pinal Dave

  What is Big Data? I want to learn Big Data. I have no clue where and how to start learning about it. Does Big Data really means data is big? What are the tools and software I need to know to learn Big Data? I often receive questions which I mentioned above. They are good questions and honestly when we search online, it is hard to find authoritative and authentic answers. I have been working with Big Data and NoSQL for a while and I have decided that I will attempt to discuss this subject over here in the blog. In the next 21 days we will understand what is so big about Big Data. Big Data – Big Thing! Big Data is becoming one of the most talked about technology trends nowadays. The real challenge with the big organization is to get maximum out of the data already available and predict what kind of data to collect in the future. How to take the existing data and make it meaningful that it provides us accurate insight in the past data is one of the key discussion points in many of the executive meetings in organizations. With the explosion of the data the challenge has gone to the next level and now a Big Data is becoming the reality in many organizations. Big Data – A Rubik’s Cube I like to compare big data with the Rubik’s cube. I believe they have many similarities. Just like a Rubik’s cube it has many different solutions. Let us visualize a Rubik’s cube solving challenge where there are many experts participating. If you take five Rubik’s cube and mix up the same way and give it to five different expert to solve it. It is quite possible that all the five people will solve the Rubik’s cube in fractions of the seconds but if you pay attention to the same closely, you will notice that even though the final outcome is the same, the route taken to solve the Rubik’s cube is not the same. Every expert will start at a different place and will try to resolve it with different methods. Some will solve one color first and others will solve another color first. Even though they follow the same kind of algorithm to solve the puzzle they will start and end at a different place and their moves will be different at many occasions. It is  nearly impossible to have a exact same route taken by two experts. Big Market and Multiple Solutions Big Data is exactly like a Rubik’s cube – even though the goal of every organization and expert is same to get maximum out of the data, the route and the starting point are different for each organization and expert. As organizations are evaluating and architecting big data solutions they are also learning the ways and opportunities which are related to Big Data. There is not a single solution to big data as well there is not a single vendor which can claim to know all about Big Data. Honestly, Big Data is too big a concept and there are many players – different architectures, different vendors and different technology. What is Next? In this 31 days series we will be exploring many essential topics related to big data. I do not claim that you will be master of the subject after 31 days but I claim that I will be covering following topics in easy to understand language. Architecture of Big Data Big Data a Management and Implementation Different Technologies – Hadoop, Mapreduce Real World Conversations Best Practices Tomorrow In tomorrow’s blog post we will try to answer one of the very essential questions – What is Big Data? 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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• Reading data from an Entity Framework data model through a WCF Data Service

  - by nikolaosk

  This is going to be the fourth post of a series of posts regarding ASP.Net and the Entity Framework and how we can use Entity Framework to access our datastore. You can find the first one here , the second one here and the third one here . I have a post regarding ASP.Net and EntityDataSource. You can read it here .I have 3 more posts on Profiling Entity Framework applications. You can have a look at them here , here and here . Microsoft with .Net 3.0 Framework, introduced WCF. WCF is Microsoft's...(read more)

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• Big Data&rsquo;s Killer App&hellip;

  - by jean-pierre.dijcks

  Recently Keith spent  some time talking about the cloud on this blog and I will spare you my thoughts on the whole thing. What I do want to write down is something about the Big Data movement and what I think is the killer app for Big Data... Where is this coming from, ok, I confess... I spent 3 days in cloud land at the Cloud Connect conference in Santa Clara and it was quite a lot of fun. One of the nice things at Cloud Connect was that there was a track dedicated to Big Data, which prompted me to some extend to write this post. What is Big Data anyways? The most valuable point made in the Big Data track was that Big Data in itself is not very cool. Doing something with Big Data is what makes all of this cool and interesting to a business user! The other good insight I got was that a lot of people think Big Data means a single gigantic monolithic system holding gazillions of bytes or documents or log files. Well turns out that most people in the Big Data track are talking about a lot of collections of smaller data sets. So rather than thinking "big = monolithic" you should be thinking "big = many data sets". This is more than just theoretical, it is actually relevant when thinking about big data and how to process it. It is important because it means that the platform that stores data will most likely consist out of multiple solutions. You may be storing logs on something like HDFS, you may store your customer information in Oracle and you may store distilled clickstream information in some distilled form in MySQL. The big question you will need to solve is not what lives where, but how to get it all together and get some value out of all that data. NoSQL and MapReduce Nope, sorry, this is not the killer app... and no I'm not saying this because my business card says Oracle and I'm therefore biased. I think language is important, but as with storage I think pragmatic is better. In other words, some questions can be answered with SQL very efficiently, others can be answered with PERL or TCL others with MR. History should teach us that anyone trying to solve a problem will use any and all tools around. For example, most data warehouses (Big Data 1.0?) get a lot of data in flat files. Everyone then runs a bunch of shell scripts to massage or verify those files and then shoves those files into the database. We've even built shell script support into external tables to allow for this. I think the Big Data projects will do the same. Some people will use MapReduce, although I would argue that things like Cascading are more interesting, some people will use Java. Some data is stored on HDFS making Cascading the way to go, some data is stored in Oracle and SQL does do a good job there. As with storage and with history, be pragmatic and use what fits and neither NoSQL nor MR will be the one and only. Also, a language, while important, does in itself not deliver business value. So while cool it is not a killer app... Vertical Behavioral Analytics This is the killer app! And you are now thinking: "what does that mean?" Let's decompose that heading. First of all, analytics. I would think you had guessed by now that this is really what I'm after, and of course you are right. But not just analytics, which has a very large scope and means many things to many people. I'm not just after Business Intelligence (analytics 1.0?) or data mining (analytics 2.0?) but I'm after something more interesting that you can only do after collecting large volumes of specific data. That all important data is about behavior. What do my customers do? More importantly why do they behave like that? If you can figure that out, you can tailor web sites, stores, products etc. to that behavior and figure out how to be successful. Today's behavior that is somewhat easily tracked is web site clicks, search patterns and all of those things that a web site or web server tracks. that is where the Big Data lives and where these patters are now emerging. Other examples however are emerging, and one of the examples used at the conference was about prediction churn for a telco based on the social network its members are a part of. That social network is not about LinkedIn or Facebook, but about who calls whom. I call you a lot, you switch provider, and I might/will switch too. And that just naturally brings me to the next word, vertical. Vertical in this context means per industry, e.g. communications or retail or government or any other vertical. The reason for being more specific than just behavioral analytics is that each industry has its own data sources, has its own quirky logic and has its own demands and priorities. Of course, the methods and some of the software will be common and some will have both retail and service industry analytics in place (your corner coffee store for example). But the gist of it all is that analytics that can predict customer behavior for a specific focused group of people in a specific industry is what makes Big Data interesting. Building a Vertical Behavioral Analysis System Well, that is going to be interesting. I have not seen much going on in that space and if I had to have some criticism on the cloud connect conference it would be the lack of concrete user cases on big data. The telco example, while a step into the vertical behavioral part is not really on big data. It used a sample of data from the customers' data warehouse. One thing I do think, and this is where I think parts of the NoSQL stuff come from, is that we will be doing this analysis where the data is. Over the past 10 years we at Oracle have called this in-database analytics. I guess we were (too) early? Now the entire market is going there including companies like SAS. In-place btw does not mean "no data movement at all", what it means that you will do this on data's permanent home. For SAS that is kind of the current problem. Most of the inputs live in a data warehouse. So why move it into SAS and back? That all worked with 1 TB data warehouses, but when we are looking at 100TB to 500 TB of distilled data... Comments? As it is still early days with these systems, I'm very interested in seeing reactions and thoughts to some of these thoughts...

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