MS SQLSERVER:Advanced Query Concepts Copy
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Nested queries allow queries to be nested within other queries to derive more complex results. The ANY and IN operators can be used to compare a value to multiple results from a subquery. HAVING allows using aggregate functions in a having clause when grouping results, which is not possible in a where clause. Advanced query concepts also include nested select queries, usefulness of the HAVING clause, and operators like ANY and IN.
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MS SQL SERVER: Microsoft sequence clustering and association rulessqlserver content This document provides an overview of Microsoft Sequence Clustering and Association Rules algorithms in SQL Server Analysis Services. It describes how sequence clustering models group sequences into clusters based on identical transitions between states. It also explains how association rule mining analyzes frequent patterns in transactional data to generate rules and make recommendations. Key parameters for each algorithm like minimum support, cluster count, and maximum itemset size are also outlined.
MS SQL SERVER: Neural network and logistic regression
MS SQL SERVER: Neural network and logistic regressionsqlserver content The document provides an overview of Microsoft Neural Network and Logistic Regression algorithms. It discusses how the Neural Network algorithm works using multiple layers of neurons to detect nonlinear relationships between inputs and outputs. It also describes parameters that control the behavior of the Neural Network model, including the maximum number of inputs/outputs, hidden node ratio, and holdout percentage for validation. Sample DMX queries are provided to create and train Neural Network models on employee data to predict attributes.
MS SQL SERVER: Microsoft naive bayes algorithm
MS SQL SERVER: Microsoft naive bayes algorithmsqlserver content The Naive Bayes algorithm is a classification algorithm in SQL Server that uses Bayes' theorem with the assumption of independence between attributes. It is less computationally intensive than other algorithms. A Naive Bayes model can be explored using different views to understand attribute relationships and feature importance. DMX queries can retrieve model metadata, content, and predictions. Parameters like maximum input attributes control the algorithm behavior.
MS SQL SERVER: Decision trees algorithm
MS SQL SERVER: Decision trees algorithmsqlserver content The document discusses Microsoft's decision trees algorithm and its use for classification, regression, and association mining. It provides examples of DMX queries for classification models predicting school plans based on student attributes, regression models predicting parent income, and association models for customer dance preferences. It also covers interpreting decision tree model content, parameters for controlling tree growth and shape, and stored procedures for viewing and manipulating decision tree models.
MS SQL Server: Data mining concepts and dmx
MS SQL Server: Data mining concepts and dmxsqlserver content Data Mining Extensions (DMX) is a query language used to create, manage, and query data mining models. DMX was introduced in 1999 to define common concepts for data mining. It includes objects like mining structures and models. Mining structures define columns and hold cached data, while models perform machine learning on structures. DMX statements are used for creation, prediction, and training. Prediction joins apply model patterns to estimate unknown values in source data.
MS Sql Server: Reporting models
MS Sql Server: Reporting modelssqlserver content SQL Server reporting models provide abstractions of report data sources to make report creation easy for business users. Reporting models define metadata like entities, attributes, and relationships. Model Designer is used to create reporting models by connecting to a data source, defining the model, and publishing. Report Builder then uses these reporting models to allow users to easily create ad hoc reports by selecting a data source and report type, building the layout, and publishing. Grouping, sorting, and filtering functionality allow customizing reports.
MS Sql Server: Reporting manipulating data
MS Sql Server: Reporting manipulating datasqlserver content SQL Server allows for the manipulation of data in tables, lists, and charts. Reports can include sub-reports stored on the report server with their own data source. Report parameters allow values to pass into queries to filter data and alter reports. Parameters are defined with a name, data type, and options, and their values can be linked between queries and reports.
MS Sql Server: Reporting introduction
MS Sql Server: Reporting introductionsqlserver content SQL Server 2008 Reporting Services allows users to create reports with any structure using a unique data format and benefits from enhanced performance and scalability. Reports can be integrated with Microsoft Office SharePoint Services for central delivery and management. The reporting lifecycle involves authoring reports using Report Designer, Report Builder, or custom tools, delivering reports through the Report Server or custom applications, and managing reports using the Report Manager or custom management tools.
MS Sql Server: Reporting basics
MS Sql Server: Reporting basicssqlserver content SQL Server provides tools for creating reports from data sources using Report Definition Language and Report Builder. Reports can be authored using the Report Designer development tool or the web-based Report Builder and can access data from data sources and data sets. Reports allow for customization of page options, inclusion of headers, footers, and interactive features, and use of aggregate functions to summarize data within groupings.
MS Sql Server: Datamining Introduction
MS Sql Server: Datamining Introductionsqlserver content This document discusses data mining in SQL Server 2008. It provides an overview of different types of data analysis including ad-hoc querying, reporting, OLAP cubes, and data mining. It then describes how data mining algorithms like classification, regression, and association analysis are used to explore data, find patterns, and make predictions. It also outlines the typical data mining process and highlights new features and algorithms in SQL Server 2008 for time series analysis, market basket analysis, churn analysis, and more.
MS Sql Server: Business Intelligence
MS Sql Server: Business Intelligencesqlserver content SQL Server provides business intelligence capabilities including data warehousing, data marts, cubes, and UI tools. It supports core BI processes like integration, analysis, and reporting. Key features include Analysis Services for cubes, Integration Services for ETL, Data Mining, Reporting Services, and scalability. It also enables KPIs, one-click cubes, proactive caching, and integration with Microsoft Office.
MS SQLSERVER:Feeding Data Into Database
MS SQLSERVER:Feeding Data Into Databasesqlserver content ETL is the process of extracting data from various sources, transforming it to fit operational needs, and loading it into a data warehouse or database. It involves extracting data from sources like DVDs, books, and friends, transforming it by cleaning and consolidating the data, and loading it into a data warehouse for storage and analysis. Migrating a database transfers data from one platform or format to another, such as moving from a Foxpro database to SQL Server. New data is inserted into databases using INSERT statements specifying the table and values, ensuring data types match. Updates are made using UPDATE statements specifying the table, set value, and WHERE condition to identify the record to change.
MS SQLSERVER:Doing Calculations With Functions
MS SQLSERVER:Doing Calculations With Functionssqlserver content This document discusses mathematical functions in SQL Server that can perform calculations on columns in tables. It explains aggregate functions like SUM, AVG, MIN, MAX, and COUNT that calculate the total, average, minimum, maximum, and count of values. These functions can be used to find calculations on a single column or combined with GROUP BY to calculate aggregates for each group. Additional functions like UPPER and LOWER can manipulate string values. Examples demonstrate finding total booty, average booty, highest/lowest booty, and robbery case count from a robberies table using these functions.
MS SQLSERVER:Deleting A Database
MS SQLSERVER:Deleting A Databasesqlserver content This document discusses Data Control Language (DCL) commands in SQL Server for deleting databases and tables, as well as saving data using save points and rollbacks. It explains that DCL is used to control data and can delete tables using the DROP command or save data using save points and rollbacks. Save points act as markers to which the database state can be reverted using the ROLLBACK command, allowing users to undo changes made before a COMMIT.
MS SQLSERVER:Customizing Your D Base Design
MS SQLSERVER:Customizing Your D Base Designsqlserver content This document discusses various ways to customize databases in SQL Server, including managing data integrity through primary keys, foreign keys, constraints, and other methods. It defines primary keys as uniquely identifying records, and describes how to add primary keys when creating or altering tables. Foreign keys are defined as fields that match a primary key in another table. The document also covers adding not null, unique, and check constraints to customize data validation and integrity rules.
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- 1. 11 SQL SERVER: ADVANCEDQUERY CONCEPTS
- 2. Advanced QueriesHaving seen the basic concepts of SQL, let us divulge into the advanced query concepts.The Presentation consists of the following portions: Nested Queries
- 3. Any
- 4. In
- 5. HavingNested QueriesNested Queries literally mean ‘a query within another query’. This is exactly what we are about to see. In some cases, the data derived using a select statement becomes inadequate. Here, we pipe the output of this statement into another select statement, till we get the results that we needed. Illustration:Select x1Select x2Select x3t2t3t1Select x4 from t1,t2,t3
- 6. Nested Select QueriesConsider the following tables of the Fisherman database:FishermanFishCatch Table:Now, I wish to find the names of the fishermen who caught a bass (any type). The SQL query would be:select name from fisherman where fishermanid = any(selectfishermanID from FishCatch where fish like ‘%Bass%’)As you can see, any is used to compare a particular record’s field to that of multiple records.
- 7. Nested Select QueriesThe previous query can be rewritten using in as follows:Using ANY:select name from fisherman where fishermanid= any(selectfishermanID from FishCatch where fish like ‘%Bass%’)Using IN:select name from fisherman where fishermanidin(selectfishermanID from FishCatch where fish like ‘%Bass%’)NOTE: When using in remember NOT TO PUT THE ‘=‘ (Equal to ) operator
- 8. Usefulness of having ‘Having’We have seen that there exists a restriction that the aggregate functions cannot be used in the right side of where clause. This syntactical restriction may lead to several inconveniences while programming.To understand this, see the following problem:Find the Fisherman who has caught 2 fishes:We might think of this solution:Select FishermanID from fishTable where count(fish) =2But this doesn’t work as aggregate functions are not allowed on the right-side of where clause. So, how do we solve the problem? ‘Having’ is our answer.
- 9. HavingFind the Fisherman who has caught 2 fishes:Solving with having:Select fishermanID from fishtable group by fishermanidhaving count(fish)=2
- 10. Summary11. Advanced Query Concepts: Nested Select SQL
- 11. In
- 12. Any
- 13. HavingVisit more self help tutorialsPick a tutorial of your choice and browse through it at your own pace.The tutorials section is free, self-guiding and will not involve any additional support.Visit us at www.dataminingtools.net