Data warehouse

Using VLOOKUP in Excel VLOOKUP is one of Excel’s most useful functions, and it’s also one of the least understood. In this article, we demystify VLOOKUP by way of a real-life example. We’ll create a usable Invoice Template for a fictitious company. So what is VLOOKUP? Here’s an example of a list, or database. Usually lists like this have some sort of unique identifier for each item in the list. The hardest part of using VLOOKUP is understanding exactly what it’s for. VLOOKUP retrieves information from a database/list based on a supplied instance of the unique identifier. Put another way, if you put the VLOOKUP function into a cell and pass it one of the unique identifiers from your database, it will return you one of the pieces of information associated with that unique identifier. If all you need is one piece of information from the database, it would be a lot of trouble to go to to construct a formula with a VLOOKUP function in it. First we start Excel… …and we create ourselves a blank invoice: That’s it!

Knowledge extraction Knowledge extraction is the creation of knowledge from structured (relational databases, XML) and unstructured (text, documents, images) sources. The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must represent knowledge in a manner that facilitates inferencing. Although it is methodically similar to information extraction (NLP) and ETL (data warehouse), the main criteria is that the extraction result goes beyond the creation of structured information or the transformation into a relational schema. It requires either the reuse of existing formal knowledge (reusing identifiers or ontologies) or the generation of a schema based on the source data. Overview[edit] After the standardization of knowledge representation languages such as RDF and OWL, much research has been conducted in the area, especially regarding transforming relational databases into RDF, identity resolution, knowledge discovery and ontology learning. Examples[edit] XML[edit]

Is the Relational Database Doomed? Recently, a lot of new non-relational databases have cropped up both inside and outside the cloud. One key message this sends is, "if you want vast, on-demand scalability, you need a non-relational database". If that is true, then is this a sign that the once mighty relational database finally has a chink in its armor? Relational databases have been around for over 30 years. First, Some Background A relational database is essentially a group of tables (entities). Relational databases are facilitated through Relational Database Management Systems (RDBMS). The reasons for the dominance of relational databases are not trivial. However, to offer all of this, relational databases have to be incredibly complex internally. The Problem with Relational Databases Today, we are in a slightly different situation. Relational databases scale well, but usually only when that scaling happens on a single server node. Next page: The New Breed The New Breed No Entity Joins Key/Value Stores: The Good CouchDB

The Case for Data Warehousing | The Data Warehousing Information Center The following is a list of the basic reasons why organizations implement data warehousing. This list was put together because too much of the data warehousing literature confuses "next order" benefits with these basic reasons. For example, spend a little time reading data warehouse trade material and you will read about using a data warehouse to "convert data into business intelligence", "make management decision making based on facts not intuition", "get closer to the customers", and the seemingly ubiquitously used phrase "gain competitive advantage". In probably 99% of the data warehousing implementations, data warehousing is only one step out of many in the long road toward the ultimate goal of accomplishing these highfalutin objectives. The basic reasons organizations implement data warehouses are: To perform server/disk bound tasks associated with querying and reporting on servers/disks not used by transaction processing systems The concern here is security.

Backdoor webserver using MySQL SQL Injection | &GreenSQL By David Maman, GreenSQL CTO MySQL Database is a great product used by thousand of websites. Various web applications use MySQL as their default database. Most people know that SQL injection allows attackers to retrieve database records, pass login screens, and change database content, through the creation of new administrative users. First of all, I will give you a brief description of SQL injection. What is SQL Injection? SQL injection is an attack that allows the attacker to add logical expressions and additional commands to an existing SQL query. For example, the following SQL command is used to validate user login requests: $sql_query = "select * from users where user='$user' and password='$pass'" If the user-submitted data is not properly validated, an attacker can exploit this query and pass through the login screen by simply submitting specially crafted variables. $sql_query = "select * from users where user='admin' or '1'='1' and password='$pass'" Command 1- Writing arbitrary files

Knowledge retrieval Knowledge Retrieval seeks to return information in a structured form, consistent with human cognitive processes as opposed to simple lists of data items. It draws on a range of fields including epistemology (theory of knowledge), cognitive psychology, cognitive neuroscience, logic and inference, machine learning and knowledge discovery, linguistics, and information technology. Overview[edit] In the field of retrieval systems, established approaches include: Data Retrieval Systems (DRS), such as database management systems, are well suitable for the storage and retrieval of structured data.Information Retrieval Systems (IRS), such as web search engines, are very effective in finding the relevant documents or web pages. Both approaches require a user to read and analyze often long lists of data sets or documents in order to extract meaning. The goal of knowledge retrieval systems is to reduce the burden of those processes by improved search and representation. References[edit]

Business intelligence industry trends February 21, 2012 This is one of a series of posts on business intelligence and related analytic technology subjects, keying off the 2011/2012 version of the Gartner Magic Quadrant for Business Intelligence Platforms. The four posts in the series cover: Besides company-specific comments, the 2011/2012 Gartner Magic Quadrant for Business Intelligence (BI) Platforms offered observations on overall BI trends in a “Market Overview” section. Not inconsistently with my comments on departmental analytics, Gartner sees actual BI business users as favoring ease of getting the job done, while IT departments are more concerned about full feature sets, integration, corporate standards, and license costs.However, Gartner says as a separate point that all kinds of users want to relieve some of the complexity of BI, and really of analytics in general. Here’s the forest that I suspect Gartner is missing for the trees: Let me be even more specific. Comments

Six Architectural Styles of Data Hubs by Malcolm Chisholm Data hubs are an important component in information architecture. However, they are rather diverse, and this diversity often means that the term “hub” means quite different things to different people. It also means that a definition of “data hub” is inevitably going to be rather generic. The following definition is used here: A data hub is a database which is populated with data from one or more sources and from which data is taken to one or more destinations. A database that is situated between one source and one destination is more appropriately termed a “staging area”. Why Data Hubs? The more that data is understood to be an enterprise resource that needs to be shared and exchanged, the more likely it is that data hubs will appear in enterprise information architectures. They often needlessly replicate movement of the same data. Data hubs, therefore, may present a better alternative, although we need to be cautious. The Publish-Subscribe Data Hub Figure 2 shows this hub architecture.

Get Started Developing For Android With Eclipse, Reloaded - Smashing Magazine In the first part1 of this tutorial series, we built a simple brew timer application using Android and Eclipse. In this second part, we’ll continue developing the application by adding extra functionality. In doing this, you’ll be introduced to some important and powerful features of the Android SDK, including Persistent data storage, Activities and Intent as well as Shared user preferences. To follow this tutorial, you’ll need the code from the previous article. If you want to get started right away, grab the code from GitHub2 and check out the tutorial_part_1 tag using this: $ git clone $ cd BrewClock $ git checkout tutorial_part_1 Once you’ve checked out the code on GitHub, you’ll need to import the project into Eclipse: After importing the project into Eclipse, you might receive a warning message: Android required .class compatibility set to 5.0. Getting Started With Data Storage Link Abstracting the Database Link Retrieving Data Link Data Binding Link

Data mining Process of extracting and discovering patterns in large data sets Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.[1] Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information (with intelligent methods) from a data set and transforming the information into a comprehensible structure for further use.[1][2][3][4] Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD.[5] Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.[1] Etymology[edit] Background[edit] The manual extraction of patterns from data has occurred for centuries. Process[edit]