MS Sql Server: Doing Calculations With Functions
1 like934 views
This document discusses mathematical functions in SQL Server that can perform calculations on table columns, such as SUM, AVG, MIN, MAX, and COUNT. It provides examples of using these aggregate functions to find the total booty, average booty, maximum booty, minimum booty, and number of cases in a sample robberies table. The document also notes that these functions cannot be used with a WHERE clause and explains how to instead use HAVING, ANY, or IN. It describes combining aggregate functions with GROUP BY and using multiple aggregates together.
1 of 10
Ad
Recommended
Sql cheat-sheet
Sql cheat-sheetSteve Tran This document provides a cheat sheet on basic and advanced SQL queries. It covers topics such as selecting, filtering, aggregating, ordering data with SELECT statements. It also discusses creating views as virtual tables from query results, joining tables with different join types, updating and modifying data, indexing columns to improve query performance, and using aggregation and utility functions for reporting. Views allow creating virtual tables from complex queries, joins allow combining data from multiple tables, and indexing speeds up queries filtered on indexed columns.
Group functions
Group functionssehrishishaq1 Group functions in SQL can operate on entire tables or groups of rows to return a single result. They include AVG, COUNT, MAX, MIN, and SUM. The MIN and MAX functions return the minimum or maximum value of a column. COUNT returns the number of rows. SUM adds up numeric values in a column. AVG calculates the average of numeric column values. Group functions cannot be used in the WHERE clause.
Unit 6: Functions and Subroutines - Part 2/2
Unit 6: Functions and Subroutines - Part 2/2Matthew Campbell, OCT This document discusses Excel macros and functions/subroutines. It provides examples of using ByRef and ByVal parameters to determine if an argument is passed by reference or by value. When an argument is passed by reference, any changes made to it within the procedure will persist outside the procedure. When passed by value, changes are local to the procedure.
Computer notes - Hashing
Computer notes - Hashingecomputernotes An AVL tree, ordered by key insert: a standard insert; (log n) find: a standard find (without removing, of course); (log n) remove: a standard remove; (log n)
NCCU: Statistics in the Criminal Justice System, R basics and Simulation - Pr...
NCCU: Statistics in the Criminal Justice System, R basics and Simulation - Pr...The Statistical and Applied Mathematical Sciences Institute R is a free software environment for statistical computing and graphics that provides a wide variety of statistical techniques and graphical methods. It includes base functions and packages, and is used through interfaces like RStudio. R represents data using objects like vectors, matrices, and data frames. Common operations include calculations, generating random variables, and visualizing data. R can be used to analyze a glass fragment dataset to visualize compositions and potentially classify an unknown fragment.
computer notes - Data Structures - 35
computer notes - Data Structures - 35ecomputernotes The document discusses various data structures including skip lists, AVL trees, and hashing. It explains that skip lists allow for logarithmic-time operations and are simple to implement. Hashing provides constant-time operations by mapping keys to array indices via a hash function, but collisions must be handled. Common hash functions discussed include summing character codes or converting to a number in a prime base.
MEngine Overview
MEngine Overvieweuming The document provides an overview of the MEngine, which uses CObjects as its fundamental object type. Some key points:
- CObjects avoid memory leaks and provide a convenient, versatile object model through an automatic linked list structure. This is a better paradigm than arrays.
- CObjects forget about how objects are arranged in memory, making it easy to move and track changing objects like in games. CObjects can be referenced across multiple lists simultaneously.
- CObjects are created and managed through factories to simplify memory management and reuse of objects.
- Thinking in terms of CObjects provides a flexible design paradigm that can model more complex relationships than traditional C++ objects and pointers.
R: Apply Functions
R: Apply FunctionsDataminingTools Inc Apply functions allow executing a function repeatedly on each row, column, or element of a matrix, data frame, or list without using loops. Common apply functions include sapply(), lapply(), apply(), mapply(), and tapply(). Apply functions provide a more efficient way to perform operations across data compared to traditional loops. tapply() allows breaking a vector into pieces and applying a function to each piece, similar to sapply() but allowing customization of how the breakdown occurs.
Lesson9
Lesson9Alex Honcharuk This document summarizes key points about date and time data types in C#, including DateTime, DateTimeOffset, and TimeSpan. It also discusses time zones using the TimeZone and TimeZoneInfo classes, equality comparison, overriding the Equals method, and implementing the ICloneable interface. Specifically, it provides details on:
- DateTime and DateTimeOffset for representing dates and times, while TimeSpan represents time intervals.
- TimeZone and TimeZoneInfo classes provide information on time zones, offsets, and daylight saving rules.
- Equality comparison checks value equality versus referential equality.
- Overriding Equals for custom object equality checks.
- The ICloneable interface allows cloning objects by
random forest regression
random forest regressionAkhilesh Joshi This document discusses random forest regression. It imports necessary libraries in Python like pandas, numpy, and matplotlib. It then loads a dataset, splits it into features (X) and target (y). A random forest regressor is created and fit to the data. The model is used to make a prediction and a plot is generated showing the regression line against the true values. Similar steps are performed in R, loading a dataset, creating a random forest regressor, plotting the results and making a prediction.
Mapreduce: Theory and implementation
Mapreduce: Theory and implementationSri Prasanna This document provides an overview of MapReduce theory and implementation:
- MapReduce is a programming model that allows for automatic parallelization and distribution of large-scale data processing across hundreds/thousands of CPUs.
- It borrows concepts from functional programming, requiring users to implement map and reduce functions. Map processes key-value pairs in parallel while reduce combines intermediate outputs.
- The MapReduce framework handles fault tolerance, locality of data and tasks, and other optimizations to make large data processing efficient and scalable.
Excel/R
Excel/RAndrija Djurovic This document provides an example of running an R script from Excel to create plots. It describes setting up an Excel file with buttons to run an R script and open the resulting PDF. The R script generates random data, plots it, and saves the plots to a PDF. Clicking the first button runs the R script, passing cell values as arguments. Clicking the second button opens the PDF if it was created.
multiple linear regression
multiple linear regressionAkhilesh Joshi This document discusses building a multiple linear regression model to predict profit using backward elimination. It imports datasets, encodes categorical variables, splits data into training and test sets, fits a regression model to the training set, predicts results on the test set, and builds an optimal model through iterative backward elimination of insignificant variables. Key steps include encoding state as a factor, sequentially removing variables with high p-values from the model, and evaluating the models.
simple linear regression
simple linear regressionAkhilesh Joshi The document shows how to perform simple linear regression in Python and R to predict salary based on years of experience. It imports data, splits it into training and test sets, builds a linear regression model on the training set, uses it to predict the test set, and visualizes the results. Key steps include importing libraries, loading and preparing data, fitting a linear regression model, making predictions on new data, and plotting actual vs predicted values for both training and test sets.
decision tree regression
decision tree regressionAkhilesh Joshi This document discusses decision tree regression for predicting salary based on position level. It shows how to import data, build a decision tree regression model using scikit-learn in Python and rpart in R, make predictions, and plot the results. It notes that decision trees are discrete models, so the plots need to treat the x-axis as discrete rather than continuous to properly visualize the model's piecewise constant predictions.
PART 6: FROM GEO INTO YOUR REPORT
PART 6: FROM GEO INTO YOUR REPORTAndrea Antonello GEOGRAPHIC SCRIPTING IN GVSIG
HALFWAY BETWEEN USER AND DEVELOPER held at the University of Potsdam in 2016 - PART 6: FROM GEO INTO YOUR REPORT
Sql FUNCTIONS
Sql FUNCTIONSAbrar ali SQL functions allow for the manipulation of submitted data and return values. There are string, numeric, group, and date/time functions. Group functions operate on sets of rows to return a single result per set, including average (AVG), count (COUNT), maximum (MAX), minimum (MIN), sum (SUM), and truncate (TRUNCATE). Examples are provided demonstrating how to use each group function in a SQL query.
c++ programming Unit 4 operators
c++ programming Unit 4 operatorsAAKASH KUMAR This document discusses operators in C++. It describes different types of operators - unary, binary, and ternary - and provides examples of common operators like arithmetic, relational, and logical operators. Specifically, it explains that operators operate on operands to produce results, and classifications of operators are based on the number of operands needed.
Stack linked list
Stack linked listbhargav0077 The document discusses different implementations of stacks and queues using linked lists and arrays. It describes how stacks can be implemented using a linked list, with push and pop operations adding and removing nodes from the head of the list. Queues can also be implemented with linked lists or arrays, but arrays require additional logic to maintain first-in, first-out order efficiently. Common applications of stacks and queues include evaluating expressions, checking for balanced brackets, and managing tasks in operating systems and server requests.
R-Excel Integration
R-Excel IntegrationAndrija Djurovic This document provides an example of integrating R and Excel using the XLConnect package. It summarizes data in R, generates a graph, and writes the results to an Excel template, applying formatting. Key steps include: loading an Excel template, writing an R data frame and summary results to sheets, adding a pre-generated graph image, and saving the updated workbook.
Presentation topic is stick data structure
Presentation topic is stick data structureAizazAli21 This document discusses the stack data structure. It defines a stack as a linear data structure where elements are added and removed from only one end, called the top. Common stack operations like push, pop, and peek are described. Examples are given to illustrate how stacks can be used to perform calculations. The document also explains how stacks can be implemented using both arrays and linked lists. Key points are that arrays maintain a size and add/remove from the first element, while linked lists use a head node and insert/remove as the new head.
polynomial linear regression
polynomial linear regressionAkhilesh Joshi The document shows how to perform polynomial regression in Python and R. It imports data on position level and salary. Linear and polynomial regression models are fit to predict salary from position level. Visualizations are created to compare the linear and polynomial regression fits and predictions. Polynomial regression fits the data better by capturing the nonlinear relationship between position level and salary.
knn classification
knn classificationAkhilesh Joshi This document discusses K-nearest neighbors (K-NN) classification using Python and R. It covers reading and preprocessing a dataset, splitting it into training and test sets, feature scaling, fitting a K-NN classifier to the training set, making predictions on the test set, and evaluating performance using metrics like confusion matrix and classification report. The key steps are importing libraries, preprocessing the data, training a K-NN classifier, predicting on new data, and evaluating results.
PART 3: THE SCRIPTING COMPOSER AND PYTHON
PART 3: THE SCRIPTING COMPOSER AND PYTHONAndrea Antonello GEOGRAPHIC SCRIPTING IN GVSIG
HALFWAY BETWEEN USER AND DEVELOPER held at the University of Potsdam in 2016 - PART 3: THE SCRIPTING COMPOSER AND PYTHON
PART 4: GEOGRAPHIC SCRIPTING
PART 4: GEOGRAPHIC SCRIPTINGAndrea Antonello This document discusses geographic scripting in gvSIG using the Python programming language. It provides examples of building geometries like points, lines, polygons and multi-geometries programmatically using the gvsig module. It also demonstrates using predicates to check relationships between geometries like intersection and containment. Functions for performing geometric operations on layers like intersection, union, difference and buffer are also illustrated.
Java Week6(B) Notepad
Java Week6(B) NotepadChaitanya Rajkumar Limmala This applet program takes an integer input in one text field, calculates its factorial value using a for loop, and displays the result in another text field when the "Compute" button is clicked. It also clears both text fields when the "Clear" button is clicked. The applet contains labels, text fields, and buttons to accept user input, perform the calculation, and display the output.
V22 function-1
V22 function-1Dhirendra Chauhan The document discusses several aggregate functions in SQL including SUM(), MAX(), MIN(), COUNT(), AVG(). It provides the syntax and examples of using each function with and without conditions to get the total sum, largest value, smallest value, number of rows, and average value from columns in tables.
Matlab: Saving And Publishing
Matlab: Saving And PublishingDataminingTools Inc This document discusses saving and publishing in Matlab. It describes how to save variables and images using commands like save and imsave. It also explains how to publish animations by creating an AVI file using functions like avifile and addframe to capture a sequence of graphs and add them to the video file.
R Statistics
R StatisticsDataminingTools Inc This document provides an overview of statistical functions and graphs available in R. It lists common distributions that can be evaluated or simulated in R, including beta, binomial, Cauchy, chi-squared, exponential, F, gamma, geometric, hypergeometric, log-normal, logistic, negative binomial, normal, Poisson, Student's t, uniform, and Weibull. It also discusses functions for calculating median, mean, variance, standard deviation, five number summary, and generating stem-and-leaf plots, histograms, density plots, bar plots, pie charts in R. Categorical data can be grouped using the cut function and summarized using table.
More Related Content
What's hot (19)
Lesson9
Lesson9Alex Honcharuk This document summarizes key points about date and time data types in C#, including DateTime, DateTimeOffset, and TimeSpan. It also discusses time zones using the TimeZone and TimeZoneInfo classes, equality comparison, overriding the Equals method, and implementing the ICloneable interface. Specifically, it provides details on:
- DateTime and DateTimeOffset for representing dates and times, while TimeSpan represents time intervals.
- TimeZone and TimeZoneInfo classes provide information on time zones, offsets, and daylight saving rules.
- Equality comparison checks value equality versus referential equality.
- Overriding Equals for custom object equality checks.
- The ICloneable interface allows cloning objects by
random forest regression
random forest regressionAkhilesh Joshi This document discusses random forest regression. It imports necessary libraries in Python like pandas, numpy, and matplotlib. It then loads a dataset, splits it into features (X) and target (y). A random forest regressor is created and fit to the data. The model is used to make a prediction and a plot is generated showing the regression line against the true values. Similar steps are performed in R, loading a dataset, creating a random forest regressor, plotting the results and making a prediction.
Mapreduce: Theory and implementation
Mapreduce: Theory and implementationSri Prasanna This document provides an overview of MapReduce theory and implementation:
- MapReduce is a programming model that allows for automatic parallelization and distribution of large-scale data processing across hundreds/thousands of CPUs.
- It borrows concepts from functional programming, requiring users to implement map and reduce functions. Map processes key-value pairs in parallel while reduce combines intermediate outputs.
- The MapReduce framework handles fault tolerance, locality of data and tasks, and other optimizations to make large data processing efficient and scalable.
Excel/R
Excel/RAndrija Djurovic This document provides an example of running an R script from Excel to create plots. It describes setting up an Excel file with buttons to run an R script and open the resulting PDF. The R script generates random data, plots it, and saves the plots to a PDF. Clicking the first button runs the R script, passing cell values as arguments. Clicking the second button opens the PDF if it was created.
multiple linear regression
multiple linear regressionAkhilesh Joshi This document discusses building a multiple linear regression model to predict profit using backward elimination. It imports datasets, encodes categorical variables, splits data into training and test sets, fits a regression model to the training set, predicts results on the test set, and builds an optimal model through iterative backward elimination of insignificant variables. Key steps include encoding state as a factor, sequentially removing variables with high p-values from the model, and evaluating the models.
simple linear regression
simple linear regressionAkhilesh Joshi The document shows how to perform simple linear regression in Python and R to predict salary based on years of experience. It imports data, splits it into training and test sets, builds a linear regression model on the training set, uses it to predict the test set, and visualizes the results. Key steps include importing libraries, loading and preparing data, fitting a linear regression model, making predictions on new data, and plotting actual vs predicted values for both training and test sets.
decision tree regression
decision tree regressionAkhilesh Joshi This document discusses decision tree regression for predicting salary based on position level. It shows how to import data, build a decision tree regression model using scikit-learn in Python and rpart in R, make predictions, and plot the results. It notes that decision trees are discrete models, so the plots need to treat the x-axis as discrete rather than continuous to properly visualize the model's piecewise constant predictions.
PART 6: FROM GEO INTO YOUR REPORT
PART 6: FROM GEO INTO YOUR REPORTAndrea Antonello GEOGRAPHIC SCRIPTING IN GVSIG
HALFWAY BETWEEN USER AND DEVELOPER held at the University of Potsdam in 2016 - PART 6: FROM GEO INTO YOUR REPORT
Sql FUNCTIONS
Sql FUNCTIONSAbrar ali SQL functions allow for the manipulation of submitted data and return values. There are string, numeric, group, and date/time functions. Group functions operate on sets of rows to return a single result per set, including average (AVG), count (COUNT), maximum (MAX), minimum (MIN), sum (SUM), and truncate (TRUNCATE). Examples are provided demonstrating how to use each group function in a SQL query.
c++ programming Unit 4 operators
c++ programming Unit 4 operatorsAAKASH KUMAR This document discusses operators in C++. It describes different types of operators - unary, binary, and ternary - and provides examples of common operators like arithmetic, relational, and logical operators. Specifically, it explains that operators operate on operands to produce results, and classifications of operators are based on the number of operands needed.
Stack linked list
Stack linked listbhargav0077 The document discusses different implementations of stacks and queues using linked lists and arrays. It describes how stacks can be implemented using a linked list, with push and pop operations adding and removing nodes from the head of the list. Queues can also be implemented with linked lists or arrays, but arrays require additional logic to maintain first-in, first-out order efficiently. Common applications of stacks and queues include evaluating expressions, checking for balanced brackets, and managing tasks in operating systems and server requests.
R-Excel Integration
R-Excel IntegrationAndrija Djurovic This document provides an example of integrating R and Excel using the XLConnect package. It summarizes data in R, generates a graph, and writes the results to an Excel template, applying formatting. Key steps include: loading an Excel template, writing an R data frame and summary results to sheets, adding a pre-generated graph image, and saving the updated workbook.
Presentation topic is stick data structure
Presentation topic is stick data structureAizazAli21 This document discusses the stack data structure. It defines a stack as a linear data structure where elements are added and removed from only one end, called the top. Common stack operations like push, pop, and peek are described. Examples are given to illustrate how stacks can be used to perform calculations. The document also explains how stacks can be implemented using both arrays and linked lists. Key points are that arrays maintain a size and add/remove from the first element, while linked lists use a head node and insert/remove as the new head.
polynomial linear regression
polynomial linear regressionAkhilesh Joshi The document shows how to perform polynomial regression in Python and R. It imports data on position level and salary. Linear and polynomial regression models are fit to predict salary from position level. Visualizations are created to compare the linear and polynomial regression fits and predictions. Polynomial regression fits the data better by capturing the nonlinear relationship between position level and salary.
knn classification
knn classificationAkhilesh Joshi This document discusses K-nearest neighbors (K-NN) classification using Python and R. It covers reading and preprocessing a dataset, splitting it into training and test sets, feature scaling, fitting a K-NN classifier to the training set, making predictions on the test set, and evaluating performance using metrics like confusion matrix and classification report. The key steps are importing libraries, preprocessing the data, training a K-NN classifier, predicting on new data, and evaluating results.
PART 3: THE SCRIPTING COMPOSER AND PYTHON
PART 3: THE SCRIPTING COMPOSER AND PYTHONAndrea Antonello GEOGRAPHIC SCRIPTING IN GVSIG
HALFWAY BETWEEN USER AND DEVELOPER held at the University of Potsdam in 2016 - PART 3: THE SCRIPTING COMPOSER AND PYTHON
PART 4: GEOGRAPHIC SCRIPTING
PART 4: GEOGRAPHIC SCRIPTINGAndrea Antonello This document discusses geographic scripting in gvSIG using the Python programming language. It provides examples of building geometries like points, lines, polygons and multi-geometries programmatically using the gvsig module. It also demonstrates using predicates to check relationships between geometries like intersection and containment. Functions for performing geometric operations on layers like intersection, union, difference and buffer are also illustrated.
Java Week6(B) Notepad
Java Week6(B) NotepadChaitanya Rajkumar Limmala This applet program takes an integer input in one text field, calculates its factorial value using a for loop, and displays the result in another text field when the "Compute" button is clicked. It also clears both text fields when the "Clear" button is clicked. The applet contains labels, text fields, and buttons to accept user input, perform the calculation, and display the output.
V22 function-1
V22 function-1Dhirendra Chauhan The document discusses several aggregate functions in SQL including SUM(), MAX(), MIN(), COUNT(), AVG(). It provides the syntax and examples of using each function with and without conditions to get the total sum, largest value, smallest value, number of rows, and average value from columns in tables.
Viewers also liked (20)
Matlab: Saving And Publishing
Matlab: Saving And PublishingDataminingTools Inc This document discusses saving and publishing in Matlab. It describes how to save variables and images using commands like save and imsave. It also explains how to publish animations by creating an AVI file using functions like avifile and addframe to capture a sequence of graphs and add them to the video file.
R Statistics
R StatisticsDataminingTools Inc This document provides an overview of statistical functions and graphs available in R. It lists common distributions that can be evaluated or simulated in R, including beta, binomial, Cauchy, chi-squared, exponential, F, gamma, geometric, hypergeometric, log-normal, logistic, negative binomial, normal, Poisson, Student's t, uniform, and Weibull. It also discusses functions for calculating median, mean, variance, standard deviation, five number summary, and generating stem-and-leaf plots, histograms, density plots, bar plots, pie charts in R. Categorical data can be grouped using the cut function and summarized using table.
Quick Look At Clustering
Quick Look At ClusteringDataminingTools Inc Centroid clustering algorithms aim to partition objects into clusters to minimize distances between objects and cluster centroids. K-means clustering assigns objects to the nearest centroid and recalculates centroids until clusters stabilize. Agglomerative hierarchical clustering starts with each object in its own cluster and recursively merges the closest pairs of clusters until all objects are in one cluster, shown as a dendrogram. Distances between clusters can be calculated as single, complete, or average linkages based on distances between members.
LISP: Scope and extent in lisp
LISP: Scope and extent in lispDataminingTools Inc Scope and extent refer to the region and duration in which references to objects can occur in Common Lisp. The scope is determined by factors like the location of the reference, the expression type, and location in the program text. There are different types of scope including lexical scope, where references are only allowed in certain program portions, indefinite scope with anywhere references, and dynamic extent where references are allowed between establishment and disestablishment. Scope and extent are important concepts for understanding variable bindings and references in Common Lisp.
Webmining Overview
Webmining OverviewDataminingTools Inc Web mining involves analyzing textual and link structure data from the world wide web to discover useful information. It deals with petabytes of data generated daily and needs to adapt to evolving usage patterns in real-time. Topics related to web mining include web graph analysis, power laws, structured data extraction, web advertising, user analysis, social networks, and blog analysis. The future will involve very large-scale data mining of datasets too big to fit in memory or even on a single disk.
Kidical Mass Presentation
Kidical Mass PresentationEugene SRTS Kidical Mass is a safe, legal, and fun family bike ride. Started in Eugene, Oregon it now takes place around the country and around the world!
Start one in your community now.
Data Applied: Association
Data Applied: AssociationDataminingTools Inc Association rules can predict combinations of attributes and map relationships between attributes. Rules are selected based on having high coverage (correctly predicting many instances) and meeting a minimum specified accuracy. Association rule generation involves specifying a minimum coverage and accuracy, generating item sets of attribute combinations, and permuting the item sets to create rules that meet the thresholds.
MS Sql Server: Manipulating Database
MS Sql Server: Manipulating DatabaseDataminingTools Inc To manipulate a database, you can:
1) Add, modify, or delete fields using ALTER TABLE commands like ADD, ALTER COLUMN, and DROP COLUMN.
2) Add new records by inserting rows and delete existing records using a DELETE FROM table WHERE condition statement.
3) Take care with data type conversions when modifying fields to ensure compatibility.
Quick Look At Classification
Quick Look At ClassificationDataminingTools Inc Classification assigns objects to classes based on their attributes. Naive Bayes uses probabilities to determine the most likely class, while k-nearest neighbor (k-NN) compares attributes of unknown instances to training instances to classify based on the most common class of the nearest k instances. Normalization ensures different attribute units do not impact neighbor selection, and algorithms like naive Bayes use eager learning by generalizing training data into a model beforehand, whereas k-NN uses lazy learning by only generalizing when classifying new instances.
Continuous Random Variables
Continuous Random VariablesDataminingTools Inc 1. Continuous random variables are defined over intervals rather than discrete points. The probability that a continuous random variable takes on a value in an interval from a to b is given by an integral of the probability density function f(x) over that interval.
2. The probability density function f(x) defines the probabilities of intervals of the continuous random variable rather than individual points. It has the properties that it is always nonnegative and its integral over all x is 1.
3. The cumulative distribution function F(x) gives the probability that the random variable takes on a value less than or equal to x. It is defined as the integral of the probability density function from negative infinity to x.
MS SQL SERVER: Microsoft sequence clustering and association rules
MS SQL SERVER: Microsoft sequence clustering and association rulesDataminingTools Inc 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.
C,C++ In Matlab
C,C++ In MatlabDataminingTools Inc This document discusses integrating C/C++ code with MATLAB using MEX files. MEX files allow running C/C++ code from MATLAB for increased computation speed compared to MATLAB code. MEX files contain a gateway routine called mexFunction that interacts with MATLAB and subroutines containing the C/C++ code. The document explains how to write MEX files, use MATLAB data structures like mxArrays, call built-in MATLAB functions, and compile and use the resulting MEX binary file.
Procedures And Functions in Matlab
Procedures And Functions in MatlabDataminingTools Inc Script files, also called M-files, make MATLAB programming more efficient than entering individual commands. M-files are text files that contain MATLAB commands to perform specific tasks. They can be created and edited in any plain text editor or the built-in MATLAB editor. User defined functions are a special type of M-file that take inputs and produce outputs, with the function name and arguments specified in the first line. Strings in MATLAB are matrices of character elements that can be manipulated using various functions like converting between data types and extracting substrings.
LISP: Declarations In Lisp
LISP: Declarations In LispDataminingTools Inc Declarations allow users to provide extra information to Lisp about variables, functions, and forms. Declarations are optional but can affect interpretation. The declare construct embeds declarations and they are valid at the start of certain special forms and lambda expressions. Common declaration specifiers include type, special, ftype, inline, and notinline to provide type or implementation information.
Matlab Text Files
Matlab Text FilesDataminingTools Inc To write to a file in Matlab, use the fopen function to open a file with write permission and the fprintf function to write text to the file. To read from a file, use the fopen function to open the file with read permission and the fscanf function to read data from the file. Matlab has functions like circshift to circularly shift matrix elements, sort to sort arrays in ascending or descending order, and issorted to check if elements are already sorted.
Matlab: Discrete Linear Systems
Matlab: Discrete Linear SystemsDataminingTools Inc Discrete linear systems are digital implementations of linear time-invariant systems. They can be represented using various models including transfer functions, state-space models, and convolution matrices. Common transfer function representations include zero-pole-gain form and partial fraction expansion. Filters can also be modeled using second-order sections or lattice/ladder structures.
Mysql:Operators
Mysql:OperatorsDataminingTools Inc This document discusses MySQL operators including:
- Precedence of operators and type conversion
- Arithmetic, bitwise, logical, and comparison operators
- Mathematical, string, and date functions
It provides examples of using various operators and functions to perform calculations on values, with applications including calculations on stored data, text search/editing, and electrical applications.
Ad
Similar to MS Sql Server: Doing Calculations With Functions (20)
Aggregate Function - Database
Aggregate Function - DatabaseShahadat153031 Shahadat Hossain presented on aggregate functions in SQL. Aggregate functions take a collection of values as input and return a single value. Common aggregate functions include MAX(), MIN(), AVG(), SUM(), and COUNT(). Each function operates on a single column and returns a single value. SUM() and AVG() operate on numeric data, while MIN(), MAX(), and COUNT() can operate on numeric or non-numeric data. Examples demonstrated how to use each function to return values like the total salary, average salary, minimum salary, and number of records that meet a condition.
SQL-AGG-FUN.pdfiiiijuyyttfffgyyuyyyyyhhh
SQL-AGG-FUN.pdfiiiijuyyttfffgyyuyyyyyhhhNaveeN547338 Aggregate functions operate on a collection of values from a column and return a single value. The main aggregate functions are SUM, AVG, MIN, MAX, and COUNT. COUNT returns the number of rows, SUM adds values, AVG calculates the average, MIN returns the minimum value, and MAX returns the maximum value. The GROUP BY clause groups data by one or more columns and the HAVING clause allows filtering groups based on aggregate functions.
PHP mysql Aggregate functions
PHP mysql Aggregate functionsMudasir Syed Aggregate functions summarize data from multiple rows into a single value. They operate on a single column and return a single value. Common aggregate functions include SUM, AVG, MIN, MAX, and COUNT. SUM returns the sum of numeric column values. AVG returns the average of numeric column values. MIN and MAX return the minimum and maximum values in a column. COUNT returns the number of rows.
AGGREGATE FUNCTION.pptx
AGGREGATE FUNCTION.pptxAnusha sivakumar This document discusses SQL aggregation functions such as COUNT, SUM, AVG, MAX, and MIN. It provides examples of using each function to aggregate data from a sample table containing employee names and page counts. The COUNT function returns the number of rows, SUM adds values, AVG calculates the average, MAX returns the largest value, and MIN returns the smallest value. Syntax examples are given for each function applied to the sample data.
Database Query Using SQL_ip.docx
Database Query Using SQL_ip.docxVandanaGoyal21 The document provides information on various SQL functions. It discusses functions for sorting query results, performing calculations on aggregate data, grouping data, and filtering groups. Date and string functions are also covered, along with numeric and mathematical functions. Common functions include ORDER BY for sorting, SUM, AVG, COUNT for aggregates, GROUP BY for grouping, and HAVING for filtering groups. NOW() and SYSDATE() are described as functions for returning the current date and time.
03 qmds2005 session03
03 qmds2005 session03Niit Care This document discusses querying and managing data using SQL Server 2005. It covers using string, date, mathematical, ranking, and system functions to customize result sets. It also covers summarizing data using aggregate functions, and grouping data using clauses like GROUP BY, HAVING, COMPUTE, and PIVOT. Examples are provided to demonstrate string, date, ranking, and grouping functions.
Sql functions
Sql functionsilias ahmed SQL has built-in functions for performing calculations on data, including aggregate functions that return a single value calculated from column values (e.g. AVG, COUNT, MAX) and scalar functions that return a single value based on an input (e.g. UCASE, LEN, ROUND). The document provides examples of using functions like COUNT, SUM, AVG with the Orders table and explains that aggregate functions often require a GROUP BY statement to group the results properly.
Aggregate Functions,Final
Aggregate Functions,Finalmukesh24pandey The document discusses various aggregate functions used in SQL such as SUM, COUNT, AVG, MIN, and MAX. It provides examples of how to use these functions to retrieve aggregated data from tables, including the use of GROUP BY and HAVING clauses. Aggregate functions summarize data over multiple rows, returning a single value. COUNT(*) counts all rows while other functions like COUNT ignore NULL values.
Introduction to Oracle Functions--(SQL)--Abhishek Sharma
Introduction to Oracle Functions--(SQL)--Abhishek Sharmaअभिषेक शर्मा Functions make query results easier to understand and manipulate data values. There are two categories of functions: single row/scalar functions that return one value per row, and group/aggregate functions that operate on sets of values to return a single result. The GROUP BY clause groups rows based on columns and is used with aggregate functions to return summary results for each group.
Sql wksht-3
Sql wksht-3Mukesh Tekwani This document discusses summary queries in SQL. It explains that summary queries are used to retrieve aggregate or summary information rather than details of individual records. It describes SQL column functions such as SUM, AVG, MIN, MAX, COUNT that can be used to summarize data. It also discusses GROUP BY and HAVING clauses that allow grouping and filtering of aggregated data. Subqueries and the CASE statement for conditional logic in SQL queries are also briefly covered.
SQL.pptx
SQL.pptxMrHello6 The document discusses aggregate functions in SQL such as SUM, AVG, COUNT, MAX, MIN. It provides examples of using these functions to calculate totals, averages, counts and find maximum and minimum values from columns in tables. It also covers the use of the GROUP BY clause to perform aggregate calculations on grouped data and the HAVING clause to filter groups.
DBMS: Week 07 - Advanced SQL Queries in MySQL
DBMS: Week 07 - Advanced SQL Queries in MySQLRashidFaridChishti DBMS: Week 07 - Advanced SQL Queries in MySQL
Introduction to oracle functions
Introduction to oracle functionsNitesh Singh Functions in Oracle can be used to manipulate data values and are categorized as single-row/scalar functions and group/aggregate functions. Single-row functions operate on each row and return one value per row, while group functions operate on sets of values to return one result. The GROUP BY clause is used to group or categorize data and can be used with aggregate functions to return summary results for each group.
database_set_operations_&_function.pptx
database_set_operations_&_function.pptxtanvirkhanfahim This document discusses various SQL functions including set operations, aggregate functions, and other SQL functions. It provides examples of using union, intersect, and except to combine result sets from multiple queries and eliminate or retain duplicates. It also demonstrates the use of aggregate functions like avg, min, max, sum, and count on columns. Finally, it mentions other SQL functions like abs, power, round, sqrt, and truncate along with examples.
MySQL-commands.pdf
MySQL-commands.pdfssuserc5aa74 The document discusses various SQL commands used for managing databases and querying data. It covers commands for creating and deleting databases and tables, inserting, updating, and deleting records, modifying table structures, grouping and filtering query results using aggregate functions and conditions, and ordering query results. Key SQL commands covered include CREATE, DROP, INSERT, UPDATE, DELETE, ALTER, SELECT, GROUP BY, HAVING, and ORDER BY.
Sql group functions
Sql group functionsSumit Tambe This document introduces SQL group functions. It discusses how to use aggregate functions like SUM, AVG, COUNT, MIN, MAX to perform calculations across multiple rows. It explains how to use the GROUP BY clause to group data and the HAVING clause as the WHERE clause for grouped data. It also covers nesting functions, order of clause evaluation, and statistical functions like STDDEV and VARIANCE.
Sql group functions(2)
Sql group functions(2)Sumit Tambe This document introduces SQL group functions. It discusses how to use aggregate functions like SUM, AVG, COUNT, MIN, MAX to perform calculations across multiple rows. It explains how to use the GROUP BY clause to group data and the HAVING clause as the WHERE clause for grouped data. It also covers nesting functions, order of clause evaluation, and statistical functions like STDDEV and VARIANCE.
Aggregate function
Aggregate functionRayhan Chowdhury Aggregate functions are functions that take a collection of values as input and return a single value.The ISO standard defines five (5) aggregate functions namely :-
1) COUNT
2) SUM
3) AVG
4) MIN
5) MAX
1.COUNT Function
The COUNT function returns the total number of values in the specified field. It works on both numeric and non-numeric data types. All aggregate functions by default exclude nulls values before working on the data.
MIN function
The MIN function returns the smallest value in the specified table field.
2.MAX function
Just as the name suggests, the MAX function is the opposite of the MIN function. It returns the largest value from the specified table field.
3.SUM function
Suppose we want a report that gives total amount of payments made so far. We can use the MySQL SUM function which returns the sum of all the values in the specified column. SUM works on numeric fields only. Null values are excluded from the result returned.
4.AVG function
MySQL AVG function returns the average of the values in a specified column. Just like the SUM function, it works only on numeric data types.
5.MIN function
The MIN function returns the smallest value in the specified table field.
Ad
More from DataminingTools Inc (20)
Terminology Machine Learning
Terminology Machine LearningDataminingTools Inc The document defines several key machine learning and neural network terminology including:
- Activation level - The output value of a neuron in an artificial neural network.
- Activation function - The function that determines the output value of a neuron based on its net input.
- Attributes - Properties of an instance that can be used to determine its classification in machine learning tasks.
- Axon - The output part of a biological neuron that transmits signals to other neurons.
Techniques Machine Learning
Techniques Machine LearningDataminingTools Inc Machine learning techniques can be used to enable computers to learn from data and perform tasks. Some key techniques discussed in the document include decision tree learning, artificial neural networks, Bayesian learning, support vector machines, genetic algorithms, graph-based learning, reinforcement learning, and pattern recognition. Each technique has its own strengths and applications.
Machine learning Introduction
Machine learning IntroductionDataminingTools Inc Machine learning is the ability of machines to learn from experience and improve their performance on tasks over time without being explicitly programmed. It involves the development of algorithms that allow computers to learn from large amounts of data. There are different types of machine learning including supervised learning, unsupervised learning, and semi-supervised learning. The history of machine learning began in the 1950s with research into neural networks, pattern recognition, and knowledge systems. Significant developments occurred in each subsequent decade, including decision trees, connectionism, reinforcement learning, and support vector machines. Machine learning continues to progress and find new applications in areas like data mining, language processing, and robotics.
Areas of machine leanring
Areas of machine leanringDataminingTools Inc This document provides an overview of machine learning applications across several domains:
- Financial applications including trading strategies, forecasting, and portfolio management utilize techniques like reinforcement learning and neural networks.
- Weather forecasting uses neural networks, support vector machines, and time series analysis to predict temperature and rainfall.
- Speech recognition and natural language processing apply machine learning to tasks like document classification, tagging, and parsing using probabilistic and neural network models.
- Other applications include smart environments using predictive models, computer games using reinforcement learning, robotics combining mechanics and software, and medical decision support analyzing clinical and biological data.
AI: Planning and AI
AI: Planning and AIDataminingTools Inc A situated planning agent treats planning and acting as a single process rather than separate processes. It uses conditional planning to construct plans that account for possible contingencies by including sensing actions. The agent resolves any flaws in the conditional plan before executing actions when their conditions are met. When facing uncertainty, the agent must have preferences between outcomes to make decisions using utility theory and represent probabilities using a joint probability distribution over variables in the domain.
AI: Logic in AI 2
AI: Logic in AI 2DataminingTools Inc A simple planning agent uses percepts from the environment to build a model of the current state and calls a planning algorithm to generate a plan to achieve its goal. Practical planning involves restricting the language used to define problems, using specialized planners rather than general theorem provers, and adopting hierarchical decomposition to store and retrieve abstract plans from a library. A solution is a fully instantiated, totally ordered plan that guarantees achieving the goal.
AI: Logic in AI
AI: Logic in AIDataminingTools Inc The document discusses different types of logical reasoning systems used in artificial intelligence, including knowledge-based agents, first-order logic, higher-order logic, goal-based agents, knowledge engineering, and description logics. It provides examples of objects, properties, relations, and functions that can be represented and reasoned about logically. It also compares different approaches to logical indexing and outlines the key components and inference tasks involved in description logics.
AI: Learning in AI 2
AI: Learning in AI 2DataminingTools Inc Bayesian learning views hypotheses as intermediaries between data and predictions. Belief networks can represent learning problems with known or unknown structures and fully or partially observable variables. Belief networks use localized representations, whereas neural networks use distributed representations. Reinforcement learning uses rewards to learn successful agent functions, such as Q-learning which learns action-value functions. Active learning agents consider actions, outcomes, and how actions affect rewards received. Genetic algorithms evolve individuals to successful solutions measured by fitness functions. Explanation-based learning speeds up programs by reusing results of prior computations.
AI: Learning in AI 
AI: Learning in AI DataminingTools Inc Neural networks can be used for machine learning tasks like classification. They consist of interconnected nodes that update their weight values during a training process using examples. Neural networks have been applied successfully to tasks like handwritten character recognition, autonomous vehicle control by observing human drivers, and text-to-speech pronunciation generation. Their architecture is inspired by the human brain but neural networks are trained using computational methods while the brain uses biological processes.
AI: Introduction to artificial intelligence
AI: Introduction to artificial intelligenceDataminingTools Inc This document provides an introduction to artificial intelligence including definitions of AI, categories of AI systems, requirements for an artificially intelligent system, a brief history of AI, examples of AI in the real world, definitions of intelligent agents and different types of agent programs. It defines AI as the study of intelligent behavior in computational processes and how to make computers capable of tasks that humans currently perform better. It outlines categories of systems that think like humans rationally, or act like humans rationally. It also describes the requirements for a system to exhibit intelligent behavior through natural language processing, knowledge representation, reasoning, and machine learning.
AI: Belief Networks
AI: Belief NetworksDataminingTools Inc Conditional planning deals with incomplete information by constructing conditional plans that account for possible contingencies. The agent includes sensing actions to determine which part of the plan to execute based on conditions. Belief networks are constructed by choosing relevant variables, ordering them, and adding nodes while satisfying conditional independence properties. Inference in multi-connected belief networks can use clustering, conditioning, or stochastic simulation methods. Knowledge engineering for probabilistic reasoning first decides on topics and variables, then encodes general and problem-specific dependencies and relationships to answer queries.
AI: AI & Searching
AI: AI & SearchingDataminingTools Inc 1. There are three main ways to avoid repeated states during search: do not return to the previous state, avoid paths with cycles, and do not re-generate any previously generated state.
2. Constraint satisfaction problems have additional structural properties beyond basic problem requirements, including satisfying constraints.
3. Best first search orders nodes so the best evaluation is expanded first, making it an informed search method.
AI: AI & Problem Solving
AI: AI & Problem SolvingDataminingTools Inc The document discusses various problem solving techniques in artificial intelligence, including different types of problems, components of well-defined problems, measuring problem solving performance, and different search strategies. It describes single-state and multiple-state problems, and defines the key components of a problem including the data type, operators, goal test, and path cost. It also explains different search strategies such as breadth-first search, uniform cost search, depth-first search, depth-limited search, iterative deepening search, and bidirectional search.
Data Mining: Text and web mining
Data Mining: Text and web miningDataminingTools Inc This document discusses text and web mining. It defines text mining as analyzing huge amounts of text data to extract information. It discusses measures for text retrieval like precision and recall. It also covers text retrieval and indexing methods like inverted indices and signature files. Query processing techniques and ways to reduce dimensionality like latent semantic indexing are explained. The document also discusses challenges in mining the world wide web due to its size and dynamic nature. It defines web usage mining as collecting web access information to analyze paths to accessed web pages.
Data Mining: Outlier analysis
Data Mining: Outlier analysisDataminingTools Inc Outlier analysis is used to identify outliers, which are data objects that are inconsistent with the general behavior or model of the data. There are two main types of outlier detection - statistical distribution-based detection, which identifies outliers based on how far they are from the average statistical distribution, and distance-based detection, which finds outliers based on how far they are from other data objects. Outlier analysis is useful for tasks like fraud detection, where outliers may indicate fraudulent activity that is different from normal patterns in the data.
Data Mining: Mining stream time series and sequence data
Data Mining: Mining stream time series and sequence dataDataminingTools Inc This document discusses various methodologies for processing and analyzing stream data, time series data, and sequence data. It covers topics such as random sampling and sketches/synopses for stream data, data stream management systems, the Hoeffding tree and VFDT algorithms for stream data classification, concept-adapting algorithms, ensemble approaches, clustering of evolving data streams, time series databases, Markov chains for sequence analysis, and algorithms like the forward algorithm, Viterbi algorithm, and Baum-Welch algorithm for hidden Markov models.
Data Mining: Mining ,associations, and correlations
Data Mining: Mining ,associations, and correlationsDataminingTools Inc Market basket analysis examines customer purchasing patterns to determine which items are commonly bought together. This can help retailers with marketing strategies like product bundling and complementary product placement. Association rule mining is a two-step process that first finds frequent item sets that occur together above a minimum support threshold, and then generates strong association rules from these frequent item sets that satisfy minimum support and confidence. Various techniques can improve the efficiency of the Apriori algorithm for mining association rules, such as hashing, transaction reduction, partitioning, sampling, and dynamic item-set counting. Pruning strategies like item merging, sub-item-set pruning, and item skipping can also enhance efficiency. Constraint-based mining allows users to specify constraints on the type of
Data Mining: Graph mining and social network analysis
Data Mining: Graph mining and social network analysisDataminingTools Inc Graph mining analyzes structured data like social networks and the web through graph search algorithms. It aims to find frequent subgraphs using Apriori-based or pattern growth approaches. Social networks exhibit characteristics like densification and heavy-tailed degree distributions. Link mining analyzes heterogeneous, multi-relational social network data through tasks like link prediction and group detection, facing challenges of logical vs statistical dependencies and collective classification. Multi-relational data mining searches for patterns across multiple database tables, including multi-relational clustering that utilizes information across relations.
Data warehouse and olap technology
Data warehouse and olap technologyDataminingTools Inc This document discusses data warehousing and online analytical processing (OLAP) technology. It defines a data warehouse, compares it to operational databases, and explains how OLAP systems organize and present data for analysis. The document also describes multidimensional data models, common OLAP operations, and the steps to design and construct a data warehouse. Finally, it discusses applications of data warehouses and efficient processing of OLAP queries.
Data Mining: Data processing
Data Mining: Data processingDataminingTools Inc Data processing involves cleaning, integrating, transforming, reducing, and summarizing data from various sources into a coherent and useful format. It aims to handle issues like missing values, noise, inconsistencies, and volume to produce an accurate and compact representation of the original data without losing information. Some key techniques involved are data cleaning through binning, regression, and clustering to smooth or detect outliers; data integration to combine multiple sources; data transformation through smoothing, aggregation, generalization and normalization; and data reduction using cube aggregation, attribute selection, dimensionality reduction, and discretization.
Recently uploaded (20)
AI Agents in Logistics and Supply Chain Applications Benefits and Implementation
AI Agents in Logistics and Supply Chain Applications Benefits and ImplementationChristine Shepherd AI agents are reshaping logistics and supply chain operations by enabling automation, predictive insights, and real-time decision-making across key functions such as demand forecasting, inventory management, procurement, transportation, and warehouse operations. Powered by technologies like machine learning, NLP, computer vision, and robotic process automation, these agents deliver significant benefits including cost reduction, improved efficiency, greater visibility, and enhanced adaptability to market changes. While practical use cases show measurable gains in areas like dynamic routing and real-time inventory tracking, successful implementation requires careful integration with existing systems, quality data, and strategic scaling. Despite challenges such as data integration and change management, AI agents offer a strong competitive edge, with widespread industry adoption expected by 2025.
Scaling GenAI Inference From Prototype to Production: Real-World Lessons in S...
Scaling GenAI Inference From Prototype to Production: Real-World Lessons in S...Anish Kumar Presented by: Anish Kumar
LinkedIn: https://www.linkedin.com/in/anishkumar/
This lightning talk dives into real-world GenAI projects that scaled from prototype to production using Databricks’ fully managed tools. Facing cost and time constraints, we leveraged four key Databricks features—Workflows, Model Serving, Serverless Compute, and Notebooks—to build an AI inference pipeline processing millions of documents (text and audiobooks).
This approach enables rapid experimentation, easy tuning of GenAI prompts and compute settings, seamless data iteration and efficient quality testing—allowing Data Scientists and Engineers to collaborate effectively. Learn how to design modular, parameterized notebooks that run concurrently, manage dependencies and accelerate AI-driven insights.
Whether you're optimizing AI inference, automating complex data workflows or architecting next-gen serverless AI systems, this session delivers actionable strategies to maximize performance while keeping costs low.
cnc-drilling-dowel-inserting-machine-drillteq-d-510-english.pdf
cnc-drilling-dowel-inserting-machine-drillteq-d-510-english.pdfAmirStern2 CNC מכונות קידוח drillteq d-510
Oracle Cloud Infrastructure Generative AI Professional
Oracle Cloud Infrastructure Generative AI ProfessionalVICTOR MAESTRE RAMIREZ Oracle Cloud Infrastructure Generative AI Professional
Viral>Wondershare Filmora 14.5.18.12900 Crack Free Download
Viral>Wondershare Filmora 14.5.18.12900 Crack Free DownloadPuppy jhon ➡ 🌍📱👉COPY & PASTE LINK👉👉👉 ➤ ➤➤ https://drfiles.net/
Wondershare Filmora Crack is a user-friendly video editing software designed for both beginners and experienced users.
Ben Blair - Operating Safely in a Vibe Coding World
Ben Blair - Operating Safely in a Vibe Coding WorldAWS Chicago AWS Community Day Midwest 2025
Ben Blair
Operating Safely in a Vibe Coding World
“State-space Models vs. Transformers for Ultra-low-power Edge AI,” a Presenta...
“State-space Models vs. Transformers for Ultra-low-power Edge AI,” a Presenta...Edge AI and Vision Alliance For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2025/06/state-space-models-vs-transformers-for-ultra-low-power-edge-ai-a-presentation-from-brainchip/
Tony Lewis, Chief Technology Officer at BrainChip, presents the “State-space Models vs. Transformers for Ultra-low-power Edge AI” tutorial at the May 2025 Embedded Vision Summit.
At the embedded edge, choices of language model architectures have profound implications on the ability to meet demanding performance, latency and energy efficiency requirements. In this presentation, Lewis contrasts state-space models (SSMs) with transformers for use in this constrained regime. While transformers rely on a read-write key-value cache, SSMs can be constructed as read-only architectures, enabling the use of novel memory types and reducing power consumption. Furthermore, SSMs require significantly fewer multiply-accumulate units—drastically reducing compute energy and chip area.
New techniques enable distillation-based migration from transformer models such as Llama to SSMs without major performance loss. In latency-sensitive applications, techniques such as precomputing input sequences allow SSMs to achieve sub-100 ms time-to-first-token, enabling real-time interactivity. Lewis presents a detailed side-by-side comparison of these architectures, outlining their trade-offs and opportunities at the extreme edge.
Azure vs AWS Which Cloud Platform Is Best for Your Business in 2025
Azure vs AWS Which Cloud Platform Is Best for Your Business in 2025Infrassist Technologies Pvt. Ltd. Azure vs. AWS is a common comparison when businesses evaluate cloud platforms for performance, flexibility, and cost-efficiency.
If You Use Databricks, You Definitely Need FME
If You Use Databricks, You Definitely Need FMESafe Software DataBricks makes it easy to use Apache Spark. It provides a platform with the potential to analyze and process huge volumes of data. Sounds awesome. The sales brochure reads as if it is a can-do-all data integration platform. Does it replace our beloved FME platform or does it provide opportunities for FME to shine? Challenge accepted
Can We Use Rust to Develop Extensions for PostgreSQL? (POSETTE: An Event for ...
Can We Use Rust to Develop Extensions for PostgreSQL? (POSETTE: An Event for ...NTT DATA Technology & Innovation Can We Use Rust to Develop Extensions for PostgreSQL?
(POSETTE: An Event for Postgres 2025)
June 11, 2025
Shinya Kato
NTT DATA Japan Corporation
Domino IQ – What to Expect, First Steps and Use Cases
Domino IQ – What to Expect, First Steps and Use Casespanagenda Webinar Recording: https://www.panagenda.com/webinars/domino-iq-what-to-expect-first-steps-and-use-cases/
HCL Domino iQ Server – From Ideas Portal to implemented Feature. Discover what it is, what it isn’t, and explore the opportunities and challenges it presents.
Key Takeaways
- What are Large Language Models (LLMs) and how do they relate to Domino iQ
- Essential prerequisites for deploying Domino iQ Server
- Step-by-step instructions on setting up your Domino iQ Server
- Share and discuss thoughts and ideas to maximize the potential of Domino iQ
Crypto Super 500 - 14th Report - June2025.pdf
Crypto Super 500 - 14th Report - June2025.pdfStephen Perrenod This OrionX's 14th semi-annual report on the state of the cryptocurrency mining market. The report focuses on Proof-of-Work cryptocurrencies since those use substantial supercomputer power to mint new coins and encode transactions on their blockchains. Only two make the cut this time, Bitcoin with $18 billion of annual economic value produced and Dogecoin with $1 billion. Bitcoin has now reached the Zettascale with typical hash rates of 0.9 Zettahashes per second. Bitcoin is powered by the world's largest decentralized supercomputer in a continuous winner take all lottery incentive network.
Your startup on AWS - How to architect and maintain a Lean and Mean account
Your startup on AWS - How to architect and maintain a Lean and Mean accountangelo60207 Prevent infrastructure costs from becoming a significant line item on your startup’s budget! Serial entrepreneur and software architect Angelo Mandato will share his experience with AWS Activate (startup credits from AWS) and knowledge on how to architect a lean and mean AWS account ideal for budget minded and bootstrapped startups. In this session you will learn how to manage a production ready AWS account capable of scaling as your startup grows for less than $100/month before credits. We will discuss AWS Budgets, Cost Explorer, architect priorities, and the importance of having flexible, optimized Infrastructure as Code. We will wrap everything up discussing opportunities where to save with AWS services such as S3, EC2, Load Balancers, Lambda Functions, RDS, and many others.
Down the Rabbit Hole – Solving 5 Training Roadblocks
Down the Rabbit Hole – Solving 5 Training RoadblocksRustici Software Feeling stuck in the Matrix of your training technologies? You’re not alone. Managing your training catalog, wrangling LMSs and delivering content across different tools and audiences can feel like dodging digital bullets. At some point, you hit a fork in the road: Keep patching things up as issues pop up… or follow the rabbit hole to the root of the problems.
Good news, we’ve already been down that rabbit hole. Peter Overton and Cameron Gray of Rustici Software are here to share what we found. In this webinar, we’ll break down 5 training roadblocks in delivery and management and show you how they’re easier to fix than you might think.
Developing Schemas with FME and Excel - Peak of Data & AI 2025
Developing Schemas with FME and Excel - Peak of Data & AI 2025Safe Software When working with other team members who may not know the Esri GIS platform or may not be database professionals; discussing schema development or changes can be difficult. I have been using Excel to help illustrate and discuss schema design/changes during meetings and it has proven a useful tool to help illustrate how a schema will be built. With just a few extra columns, that Excel file can be sent to FME to create new feature classes/tables. This presentation will go thru the steps needed to accomplish this task and provide some lessons learned and tips/tricks that I use to speed the process.
Bridging the divide: A conversation on tariffs today in the book industry - T...
Bridging the divide: A conversation on tariffs today in the book industry - T...BookNet Canada A collaboration-focused conversation on the recently imposed US and Canadian tariffs where speakers shared insights into the current legislative landscape, ongoing advocacy efforts, and recommended next steps. This event was presented in partnership with the Book Industry Study Group.
Link to accompanying resource: https://bnctechforum.ca/sessions/bridging-the-divide-a-conversation-on-tariffs-today-in-the-book-industry/
Presented by BookNet Canada and the Book Industry Study Group on May 29, 2025 with support from the Department of Canadian Heritage.
vertical-cnc-processing-centers-drillteq-v-200-en.pdf
vertical-cnc-processing-centers-drillteq-v-200-en.pdfAmirStern2 מכונות CNC קידוח אנכיות הן הבחירה הנכונה והטובה ביותר לקידוח ארונות וארגזים לייצור רהיטים. החלק נוסע לאורך ציר ה-x באמצעות ציר דיגיטלי מדויק, ותפוס ע"י צבת מכנית, כך שאין צורך לבצע setup (התאמות) לגדלים שונים של חלקים.
“State-space Models vs. Transformers for Ultra-low-power Edge AI,” a Presenta...
“State-space Models vs. Transformers for Ultra-low-power Edge AI,” a Presenta...Edge AI and Vision Alliance
Azure vs AWS Which Cloud Platform Is Best for Your Business in 2025
Azure vs AWS Which Cloud Platform Is Best for Your Business in 2025Infrassist Technologies Pvt. Ltd.
Can We Use Rust to Develop Extensions for PostgreSQL? (POSETTE: An Event for ...
Can We Use Rust to Develop Extensions for PostgreSQL? (POSETTE: An Event for ...NTT DATA Technology & Innovation
MS Sql Server: Doing Calculations With Functions
- 1. 10SQL SERVER: DOINGCALCULATIONS WITH FUNCTIONS
- 2. Mathematical FunctionsCan mathematical functions be used on my tables? Yes. Microsoft SQL Server 2008 provides several mathematical functions such as sum(col), avg(col), min(col), max(col), count(col) etc. In SQL, they are referred to as aggregate functions as they work upon aggregates of rows.Functions Explained:Sum(fieldName): Find the sum of field values of all recordsAvg(fieldName): Find the average of field values of all recordsMin(fieldName): Find the minimum of the field values of all recordsMax(fieldName): Find the maximum of the field values of all recordsCount(fieldName): Find the number of field values in the table records
- 3. Mathematical FunctionsConsider an Interpol database which contains a table of the biggest robberies that took place this year, all around the world.Now, lets look into the application of math functions over this table.
- 4. Mathematical Functions1. Find the TOTAL booty of all the robberies:Select sum(booty) from robberies;2. Find the Average booty of all the robberies:Select avg(booty) from robberies;3. Find the robbery with the maximal bootySelect max(booty) from robberies;4. Find the robbery with the minimal bootySelect min(booty) from robberies;5. Find the number of robbery cases:Select count(booty) from robberies;
- 5. Using as conditionHEY??? I CANNOT USE THESE FUNCTIONS WITH MY ‘WHERE’ CONDITION???Microsoft SQL Server 2008 restricts the user to use these functions with ‘WHERE’ conditions. Therefore, to solve our problem three keywords: ‘having’, ‘any’ and ‘in’select * from tablename having <condition>;select * from tablename where colname=any(cond);select * from tablename where colname in (condition);
- 6. Advanced AggregatesIS THERE ANY OTHER MODIFICATION TO THESE FUNCTIONS? We can combine these functions with ‘group by’ function for better results.select sum(col1),col2 from tablename group by col2;The above command will find out the sum of each group from column 2 More than one aggregate functions can be used simultaneously, seperated by commas.Eg: select sum(col1), count(col1) from tablename;Consider the example in the next slide.
- 7. Advanced AggregatesConsider an employee table:Find the Number of Employees working in each department:Select count(empid), depid from employee;Result:
- 8. Additional Functions upper (fieldName)Converts the value of fieldName to upper case. Can be used with strings. lower (fieldName)Converts the value of fieldName to lowercase. Can be used with strings.
- 9. Summary10. Doing Calculations with functions Sum
- 10. Avg
- 11. Count
- 12. Min
- 13. Max
- 14. Count
- 15. Advanced groupingVisit 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