This document discusses programming SQL Server data mining with Analysis Management Objects (AMO) and stored procedures. It describes how to create mining structures and models using AMO, including defining columns, updating objects, and processing models. It also explains how to create, execute, and debug stored procedures for adding business logic, including registering assemblies, setting permissions, and attaching to processes for debugging. The goal is to provide an overview of programming options for data mining with SQL Server.
This document provides an overview of data flow basics in SQL Server Integration Services (SSIS). It discusses the data flow task, pipeline architecture, various data sources including ADO.NET, Excel, flat file, OLE DB, XML, and raw file sources. It also covers data destinations such as OLE DB, DataReader, Excel, flat file, and SQL Server destinations. Finally, it reviews Analysis Services destinations for dimension processing and partition processing and includes demos of various sources and destinations.
This document provides an overview and demonstrations of data flow transformations in SQL Server Integration Services (SSIS). It begins with a question and answer section and an overview of split and join transformations such as the conditional split, multicast, union all, merge, and lookup transformations. Business intelligence transformations like the slowly changing dimension and term extraction transformations are also covered. The document concludes with demonstrations of the transformations in SSIS packages.
ADO.NET is a set of libraries included with the .NET Framework that help communicate with various data stores from .NET applications. The ADO.NET libraries include classes for connecting to a data source, submitting queries, and processing results. ADO.NET also allows for disconnected data access using objects like the DataSet which allows data to be cached and edited offline. The core ADO.NET objects include connections, commands, data readers, data adapters and data sets which provide functionality similar to but also improvements over ADO.
ADO.NET is a set of classes that provides access to data sources for .NET applications. It includes classes like SqlConnection, SqlCommand, and SqlDataReader for connected data access and SqlDataAdapter and DataSet for disconnected data access. The SqlDataAdapter acts as a bridge between a DataSet and SQL Server for retrieving and saving data. Datasets store copies of data that can be modified locally before updating the database. The DataGridView displays data in a customizable grid and allows sorting and paging of records.
ADO.NET provides a set of classes for working with data in .NET applications. It offers improvements over ADO such as support for disconnected data access, XML transport of data, and a programming model designed for modern applications. The core classes of ADO.NET include the Connection class for establishing a connection to a data source, the Command class for executing queries and stored procedures, the DataReader class for sequential access to query results, and the DataAdapter class for populating a DataSet and updating data in the data source. Developers use ADO.NET to connect to databases, retrieve data using DataAdapters, generate DataSets to store and manipulate the data, and display it using list-bound controls like DropDownLists and
This document provides an overview of ADO.NET compared to ADO and describes the main objects used in ADO.NET for data access like the Connection, Command, DataReader, DataAdapter, DataSet and DataView objects. It discusses how ADO.NET uses a disconnected model with the DataSet object to cache and manage data across tiers compared to ADO's coupled model. The document also includes code examples of creating a DataReader and populating a DataSet using a DataAdapter.
This document summarizes new features in SQL Server 2008 for .NET developers, including spatial data support, BLOB storage using Filestream, enhancements to T-SQL, new date/time types, improved integration with Visual Studio, and business intelligence tools like Analysis Services, Integration Services, and Reporting Services.
This document summarizes new features in SQL Server 2008 for .NET developers, including spatial data support, BLOB storage using Filestream, enhancements to T-SQL, new date/time types, improved integration with Visual Studio, and business intelligence tools like Analysis Services, Integration Services, and Reporting Services.
The document provides information about ADO.NET, which is a data access technology that enables applications to connect to data stores and manipulate data. It discusses key ADO.NET concepts like the object model, different classes like DataSet, DataAdapter, and DataReader. It also covers how to work with ADO.NET in a connected or disconnected manner, use parameters, and perform basic data operations like selecting, inserting, updating and deleting data.
ASP.NET 08 - Data Binding And RepresentationRandy Connolly
This document discusses different ways to represent and bind data in ASP.NET applications. It covers data binding controls to data sources, using .NET collections like ArrayList and Dictionary to store data, using generic collections, populating and using DataTable and DataSet objects to store and manipulate tabular data, and integrating data with XML.
This document discusses various data flow transformations in SQL Server Integration Services (SSIS). It begins with an introduction to the different types of transformations, including row transformations and rowset transformations. It then provides examples and demonstrations of specific transformations like Character Map, Derived Column, Aggregate, Pivot, and Percentage Sampling. The document aims to explain how each transformation works and how it can be used to modify or aggregate data in an SSIS data flow.
The document discusses various aspects of using the GridView control in ASP.NET such as binding data to the GridView, handling paging, sorting and editing. It describes properties like AllowPaging and events like PageIndexChanging. It provides code examples for binding data, handling sorting and paging. The document also discusses different field types that can be used in a GridView like BoundField, TemplateField and HyperLinkField.
- The document discusses setting up Microsoft Access databases and connecting them to a Visual Basic project to display data in forms using DataGridView controls.
- It provides steps for adding a database file to a project, configuring a data connection, selecting tables and columns as data sources, and formatting DataGridView controls to display the bound data.
- Two forms are created - one to display course data and another for student data by dragging DataGridView controls and configuring them to show records from tables in the Access database file.
Web based database application design using vb.net and sql serverAmmara Arooj
The document discusses database concepts like the backend database, frontend design, connectivity, and SQL queries. It provides steps to design the backend database using SQL Server tools. It describes how to design the frontend using ASP.NET controls and write event procedures. It also explains how to connect the frontend and backend using ADO.NET classes like SqlConnection and SqlCommand to execute queries and retrieve data.
The document discusses data access in .NET applications. It describes how earlier models like DAO and ADO had issues around performance and connectivity. ADO.NET improved on ADO by using a disconnected data access model where connections are opened briefly to perform operations then closed. ADO.NET relies on datasets, which hold in-memory representations of data, and data providers like SQL Client that maintain connections to databases.
The document provides an overview of ADO.NET, which is Microsoft's data access technology for .NET applications to connect to and manipulate data in various data stores. It discusses key ADO.NET concepts like connections, commands, data readers, data adapters, datasets and how they are used to work with different data providers like SQL Server, OLE DB, and ODBC. It also covers data binding using data grids and filtering data views.
The document provides an overview of U-SQL, highlighting some differences from traditional SQL like C# keywords overlapping with SQL keywords, the ability to write C# expressions for data transformations, and supporting windowing functions, joins, and analytics capabilities. It also briefly covers topics like sorting, constant rowsets, inserts, and additional resources for learning more about U-SQL.
This document discusses ADO.NET, which is a set of classes that allows .NET applications to communicate with databases. It provides advantages over classic ADO such as supporting both connected and disconnected data access. The key components of ADO.NET are data providers, which act as bridges between applications and databases, and the DataSet, which allows storing and manipulating relational data in memory disconnected from the database.
This document provides an overview of SQL Server database development concepts including SQL Server objects, tables, data types, relationships, constraints, indexes, views, queries, joins, stored procedures and more. It begins with introductory content on SQL Server and databases and then covers these topics through detailed explanations and examples in a structured outline.
This document discusses data mining classification and decision trees. It defines classification, provides examples, and discusses techniques like decision trees. It covers decision tree induction processes like determining the best split, measures of impurity, and stopping criteria. It also addresses issues like overfitting, model evaluation methods, and comparing model performance.
This document provides an overview of the SPSS Data Editor and its capabilities for changing data values, formats, and building a data dictionary. The SPSS Data Editor allows users to cut, copy, and paste data values, add or delete cases and variables, and change the order of variables. It also enables changing data formats between string and numeric, and changing date formats. The data dictionary can be built and displayed. Additional self-help tutorials are available on the website.
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.
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.
This document summarizes different control statements in Matlab including conditional statements like if-elseif-else that allow executing code based on conditions being met, while loops that repeatedly execute code as long as a condition is true, and for loops that iterate over a range of values for a variable. It provides examples of the syntax for each statement type and explains their basic functions.
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.
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.
The document provides instructions for a school project where students will work in groups to research the origins, customs, food, music, clothing, and other aspects of various festivals celebrated in Britain. It lists websites for the students to use to research festivals like Halloween, Christmas, New Year's Eve, Valentine's Day, Easter, Earth Day, and America's Independence Day. The students will fill out a board with their findings and present their research in a PowerPoint with pictures.
Stefan & Irene photo with team at Cinnamon hotel Saigon. They send the Thank you letter to Cinnamon Hotel Saigon for their beautiful, enjoyable stay in December 2012. They appreciate the nice atmosphere, delicious breakfast, the organic attention to client . Also they enjoy the lovely rooms and especially the exceptional friendliness of Cinnamon Hotel team. Visitor at Cinnamon Hotel feel entirely welcome. They adore als the piece of craft art the team made to them. Stefan and Iren wish Cinnamon Hotel a Happy New Year 2013 and all the best for the team and their family of Cinnamon Hotel.
The document discusses the basic components of a computer, including the motherboard, hard disk drive, CD-ROM drive, floppy drive, monitor, keyboard, and mouse. It then explains the basic process of input, processing, and output when performing tasks on a computer. Finally, it provides an overview of different levels of programming languages from machine language to high-level languages like Python and Java.
Mysql is an open source relational database management system that can be downloaded for free from mysql.com. It allows users to define, construct, manipulate and access databases through SQL queries. The document provides an overview of mysql and databases, instructions for downloading and starting mysql, descriptions of basic SQL queries like SELECT, INSERT, UPDATE and DELETE, and examples of creating a sample employee table and running queries on it.
Cross validation is a method to estimate the true error of a model by building models from subsets of the training data and testing them on the remaining subsets. It provides a better estimate of how the model will generalize to new, unseen data compared to just using the error on the training data. Cross validation can also help evaluate which learning algorithm or parameters work best. Nested sub-processes in RapidMiner allow operators to contain additional processes that can be viewed by double clicking the operator icon.
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.
This work summarizes the fabrication and characterization of sub-micron InGaAs Esaki tunnel diodes with record high peak current densities. Two types of diodes were fabricated with different doping levels. Extensive SEM analysis was required to accurately measure the small junction areas down to 0.015 mm2. The diodes exhibited peak current densities as high as 9.75 mA/mm2 without degradation, even at small sizes. Series resistance effects were minimized and characterized for the sub-micron devices.
Skyline is a secure e-commerce web application developed using .NET framework and Visual Studio 2013. It allows administrators to add, update, and view product categories and customer inquiries while users can view products, add items to a cart, and provide feedback. The application implements security features like SQL injection prevention and password hashing to protect against attacks. It also uses design patterns like factory and composite patterns. While some intended features were not fully implemented, the application provides a secure online shopping platform for businesses and customers.
MCS,BCS-7(A,B) Visual programming Syllabus for Final exams @ ISPAli Shah
Exception handling in C# uses four keywords: try, catch, finally, and throw. The try block identifies code that might cause exceptions. The catch block handles exceptions, while finally ensures code is always executed. Exceptions are represented by classes derived from System.Exception, and common exceptions include NullReferenceException and DivideByZeroException. ADO.NET provides objects like SqlConnection and SqlCommand to connect C# applications to SQL Server databases using connection strings. Data can be queried, inserted, and read from databases through these objects.
This document discusses moving existing websites with security issues to the ASP.NET MVC framework using Entity Framework. It provides an overview of MVC and EF, how to set them up in Visual Studio, and examples of using them to improve security by removing direct SQL queries and moving more logic to the server. Key benefits highlighted include built-in features for validation and preventing cross-site request forgery attacks. Examples demonstrate querying databases and validating models without writing direct SQL or adding additional code.
This document provides an overview of ASP.NET, including the different development models (Web Pages, Web Forms, and MVC), layers of a web application, types of architectures (single-tier, two-tier, three-tier), and components of MVC (Model, View, Controller). It describes key aspects of each component, such as how controllers handle requests and render views with data from models. It also covers Razor syntax, passing data between MVC components, and using HTML helpers to generate HTML markup in views.
The document provides an overview of MVC 4.0 and various MVC concepts and techniques including:
- Unit testing in MVC using interfaces to create loosely coupled components and mocking frameworks.
- Exception handling in MVC using the OnException method and HandleError attribute to catch exceptions.
- Routing in MVC using the default route and custom routes added to the route table.
- Navigation structures in MVC using the MVC Site Map Provider which allows specifying controllers and actions in site maps.
- Styling MVC applications using layouts to define common templates applied to views.
- Implementing AJAX in MVC to update individual sections of a page without reloading using
Asp.net mvc presentation by Nitin SawantNitin Sawant
The document provides an introduction to ASP.NET MVC, including definitions of MVC and its components. It discusses the pros and cons of traditional ASP.NET WebForms compared to MVC. Key aspects of MVC like models, views, controllers, routing and HTML helpers are described at a high level. Popular MVC frameworks for different programming languages are also listed.
Microsoft Entity Framework is an object-relational mapper that allows developers to work with relational data as domain-specific objects, and provides automated CRUD operations. It supports various databases and provides a rich query capability through LINQ. Compared to LINQ to SQL, Entity Framework has a full provider model, supports multiple modeling techniques, and continuous support. The Entity Framework architecture includes components like the entity data model, LINQ to Entities, Entity SQL, and ADO.NET data providers. Code First allows defining models and mapping directly through code.
This article describe entity framework code first migration steps in a simple way .Code first migrations commands and how to deployed to the Azure cloud .
ASP.NET MVC is a framework from Microsoft that uses the Model-View-Controller pattern to build dynamic web applications. It provides separation of concerns, testability, and full control over HTML and JavaScript. Key features include test-driven development, friendly URLs through routing, and no view state or server-based forms. The MVC pattern divides applications into separate modules for the model, the view, and the controller.
An assembly in .NET is a collection of types and resources that form a logical unit. Assemblies can contain metadata about types using attributes. Attributes provide additional information that can be attached to classes, methods, and other members. There are built-in attributes in .NET and custom attributes can be created by deriving from the Attribute base class. Built-in attributes like Required and StringLength are used to validate model data in ASP.NET MVC. A custom MyLicenseAttribute was created to require a license key by applying the attribute to assemblies. Attributes help add metadata and customize behavior.
This document provides an overview of Asp.Net MVC and how it compares to traditional Asp.Net web forms. Some key points:
- Asp.Net MVC follows the MVC pattern, separating concerns into models, views, and controllers, allowing for cleaner code and easier testing compared to Asp.Net web forms.
- In Asp.Net MVC, controllers handle requests and return action results, views are responsible for the UI, and models represent application data. This separation of concerns is more aligned with HTTP concepts.
- Asp.Net MVC aims to be more flexible, maintainable, and testable than web forms. It allows for tighter control over HTML and adheres to conventions over configurations
.NET is designed to solve problems that have plagued programmers in the past like incompatibilities between programming languages and technologies. It provides a common language runtime and type system that allows different languages to work together. The .NET Framework handles many common programming tasks like serialization automatically through metadata and provides a large class library for common functions.
The document provides information on ASP.NET MVC, including:
- ASP.NET MVC is a framework for building web apps in .NET using C# or VB.NET that follows the MVC pattern.
- MVC stands for Model-View-Controller, with models containing data, views presenting user interfaces, and controllers coordinating data retrieval and user input.
- ASP.NET MVC provides separation of concerns, testability, and more control over HTML compared to ASP.NET Web Forms.
The document provides an overview of ASP.NET MVC, including:
- ASP.NET MVC is a framework for building web apps using Model-View-Controller (MVC) pattern on the .NET platform.
- MVC separates an app into three main components: Models for data, Views for presentation, and Controllers for logic/app flow.
- Key advantages include easier management of complexity, testability, and control over HTML.
The document discusses implementing MVC architecture in ASP.Net using C# and the Microsoft Data Access Application block. It describes creating class libraries for the abstract, business and data layers. The abstract layer defines a customer class. The data layer implements data access interfaces and uses the application block. The business layer calls the data layer. A web application is created that references the business layer and allows inserting and viewing customers by calling its methods. Implementing MVC in this way separates concerns and improves maintainability.
This document discusses microservices using Node.js and JavaScript. It covers building an HTTP microservice with Express including routing, structure, database integration, logging and testing. It also discusses building command-based microservices with Seneca including patterns, plugins, and queueing. Finally, it discusses containerization with Docker, API gateways, testing, process management with PM2, and some considerations around when microservices may not be the best solution.
CyberLab Training Division :
The .NET Framework is Microsoft's Managed Code programming model for building applications on Windows clients, servers, and mobile or embedded devices. Microsoft's .NET Framework is a software technology that is available with several Microsoft Windows operating systems. In the following sections describes , the basics of Microsoft .Net Frame work Technology and its related programming models.
What is Microsoft .Net Framework
what are the functions of microsoft .net framework?
Common Language Runtime in .Net Framework
How to Common Language Runtime
What is .Net Framework Class Library
What is Common Language Specification
What is Common Type System
What is Microsoft Intermediate Language
What is Portable Executable (PE) File Format
What is Microsoft Just In Time Compiler
How to Managed Code - Microsoft .Net Framework
What is .Net Framework Metadata
what is .Net Framework Assembly
What is Assembly Manifest
What is Global Assembly Cache
What is a .Net Satellite Assembly?
What are the contents of an Assembly?
How to Private Assembly and Shared Assembly
What is Microsoft .Net Strong Name
What is .Net Namespaces
What is Application Domain
What is Code Access Security
What is Garbage Collection
.Net Threads
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Con MongoDB 3.6, podrá avanzar al ritmo que marcan sus datos. Los plazos de lanzamiento de las nuevas aplicaciones se acelerarán, y estas funcionarán de forma segura y fiable en entornos de cualquier tamaño, además de aportar información útil en tiempo real. https://www.mongodb.com/mongodb-3.6
The document provides an overview of the ASP.NET MVC framework. It describes the core components of MVC - Models, Views, and Controllers. Models represent the application's data, Views display the UI, and Controllers handle user input and interaction. It also discusses when to use MVC vs Web Forms, the advantages of each, and new features in MVC 3. The standard project structure for MVC is also outlined.
This document provides an overview of using various Microsoft tools for data mining, including:
1. Business Intelligence Development Studio (BI Dev Studio) which is used to develop data mining models and contains tools like Solution Explorer and Designers.
2. Creating data sources and data source views (DSVs) to connect to and organize data for modeling.
3. Using the Data Mining Wizard to create mining structures and models by selecting data, algorithms, and parameters.
4. Refining models using the Data Mining Designer and tools like the Mining Structure Editor.
5. Generating reports on model results using SQL Server Reporting Services.
6. Managing databases and models using SQL Server
Similar to MS SQL SERVER: Programming sql server data mining (20)
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.
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 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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 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.
UiPath Community Day Amsterdam: Code, Collaborate, ConnectUiPathCommunity
Welcome to our third live UiPath Community Day Amsterdam! Come join us for a half-day of networking and UiPath Platform deep-dives, for devs and non-devs alike, in the middle of summer ☀.
📕 Agenda:
12:30 Welcome Coffee/Light Lunch ☕
13:00 Event opening speech
Ebert Knol, Managing Partner, Tacstone Technology
Jonathan Smith, UiPath MVP, RPA Lead, Ciphix
Cristina Vidu, Senior Marketing Manager, UiPath Community EMEA
Dion Mes, Principal Sales Engineer, UiPath
13:15 ASML: RPA as Tactical Automation
Tactical robotic process automation for solving short-term challenges, while establishing standard and re-usable interfaces that fit IT's long-term goals and objectives.
Yannic Suurmeijer, System Architect, ASML
13:30 PostNL: an insight into RPA at PostNL
Showcasing the solutions our automations have provided, the challenges we’ve faced, and the best practices we’ve developed to support our logistics operations.
Leonard Renne, RPA Developer, PostNL
13:45 Break (30')
14:15 Breakout Sessions: Round 1
Modern Document Understanding in the cloud platform: AI-driven UiPath Document Understanding
Mike Bos, Senior Automation Developer, Tacstone Technology
Process Orchestration: scale up and have your Robots work in harmony
Jon Smith, UiPath MVP, RPA Lead, Ciphix
UiPath Integration Service: connect applications, leverage prebuilt connectors, and set up customer connectors
Johans Brink, CTO, MvR digital workforce
15:00 Breakout Sessions: Round 2
Automation, and GenAI: practical use cases for value generation
Thomas Janssen, UiPath MVP, Senior Automation Developer, Automation Heroes
Human in the Loop/Action Center
Dion Mes, Principal Sales Engineer @UiPath
Improving development with coded workflows
Idris Janszen, Technical Consultant, Ilionx
15:45 End remarks
16:00 Community fun games, sharing knowledge, drinks, and bites 🍻
The Challenge of Interpretability in Generative AI Models.pdfSara Kroft
Navigating the intricacies of generative AI models reveals a pressing challenge: interpretability. Our blog delves into the complexities of understanding how these advanced models make decisions, shedding light on the mechanisms behind their outputs. Explore the latest research, practical implications, and ethical considerations, as we unravel the opaque processes that drive generative AI. Join us in this insightful journey to demystify the black box of artificial intelligence.
Dive into the complexities of generative AI with our blog on interpretability. Find out why making AI models understandable is key to trust and ethical use and discover current efforts to tackle this big challenge.
The History of Embeddings & Multimodal EmbeddingsZilliz
Frank Liu will walk through the history of embeddings and how we got to the cool embedding models used today. He'll end with a demo on how multimodal RAG is used.
Top 12 AI Technology Trends For 2024.pdfMarrie Morris
Technology has become an irreplaceable component of our daily lives. The role of AI in technology revolutionizes our lives for the betterment of the future. In this article, we will learn about the top 12 AI technology trends for 2024.
Finetuning GenAI For Hacking and DefendingPriyanka Aash
Generative AI, particularly through the lens of large language models (LLMs), represents a transformative leap in artificial intelligence. With advancements that have fundamentally altered our approach to AI, understanding and leveraging these technologies is crucial for innovators and practitioners alike. This comprehensive exploration delves into the intricacies of GenAI, from its foundational principles and historical evolution to its practical applications in security and beyond.
"Building Future-Ready Apps with .NET 8 and Azure Serverless Ecosystem", Stan...Fwdays
.NET 8 brought a lot of improvements for developers and maturity to the Azure serverless container ecosystem. So, this talk will cover these changes and explain how you can apply them to your projects. Another reason for this talk is the re-invention of Serverless from a DevOps perspective as a Platform Engineering trend with Backstage and the recent Radius project from Microsoft. So now is the perfect time to look at developer productivity tooling and serverless apps from Microsoft's perspective.
Self-Healing Test Automation Framework - HealeniumKnoldus Inc.
Revolutionize your test automation with Healenium's self-healing framework. Automate test maintenance, reduce flakes, and increase efficiency. Learn how to build a robust test automation foundation. Discover the power of self-healing tests. Transform your testing experience.
Keynote : Presentation on SASE TechnologyPriyanka Aash
Secure Access Service Edge (SASE) solutions are revolutionizing enterprise networks by integrating SD-WAN with comprehensive security services. Traditionally, enterprises managed multiple point solutions for network and security needs, leading to complexity and resource-intensive operations. SASE, as defined by Gartner, consolidates these functions into a unified cloud-based service, offering SD-WAN capabilities alongside advanced security features like secure web gateways, CASB, and remote browser isolation. This convergence not only simplifies management but also enhances security posture and application performance across global networks and cloud environments. Discover how adopting SASE can streamline operations and fortify your enterprise's digital transformation strategy.
Increase Quality with User Access Policies - July 2024Peter Caitens
⭐️ Increase Quality with User Access Policies ⭐️, presented by Peter Caitens and Adam Best of Salesforce. View the slides from this session to hear all about “User Access Policies” and how they can help you onboard users faster with greater quality.
The Zaitechno Handheld Raman Spectrometer is a powerful and portable tool for rapid, non-destructive chemical analysis. It utilizes Raman spectroscopy, a technique that analyzes the vibrational fingerprint of molecules to identify their chemical composition. This handheld instrument allows for on-site analysis of materials, making it ideal for a variety of applications, including:
Material identification: Identify unknown materials, minerals, and contaminants.
Quality control: Ensure the quality and consistency of raw materials and finished products.
Pharmaceutical analysis: Verify the identity and purity of pharmaceutical compounds.
Food safety testing: Detect contaminants and adulterants in food products.
Field analysis: Analyze materials in the field, such as during environmental monitoring or forensic investigations.
The Zaitechno Handheld Raman Spectrometer is easy to use and features a user-friendly interface. It is compact and lightweight, making it ideal for field applications. With its rapid analysis capabilities, the Zaitechno Handheld Raman Spectrometer can help you improve efficiency and productivity in your research or quality control workflows.
Garbage In, Garbage Out: Why poor data curation is killing your AI models (an...Zilliz
Enterprises have traditionally prioritized data quantity, assuming more is better for AI performance. However, a new reality is setting in: high-quality data, not just volume, is the key. This shift exposes a critical gap – many organizations struggle to understand their existing data and lack effective curation strategies and tools. This talk dives into these data challenges and explores the methods of automating data curation.
Generative AI technology is a fascinating field that focuses on creating comp...Nohoax Kanont
Generative AI technology is a fascinating field that focuses on creating computer models capable of generating new, original content. It leverages the power of large language models, neural networks, and machine learning to produce content that can mimic human creativity. This technology has seen a surge in innovation and adoption since the introduction of ChatGPT in 2022, leading to significant productivity benefits across various industries. With its ability to generate text, images, video, and audio, generative AI is transforming how we interact with technology and the types of tasks that can be automated.
6. Programming AMO Data Mining Objects steps in programming data mining objects by using AMO create the data structure model.
7. create the data mining model that supports the mining algorithm you want to use in order to predict or to find the relationships underlying your data.
8. process the mining models to obtain the trained models that you will use later when querying and predicting from the client application.Note: AMO is not for querying; AMO is for managing and administering your mining structures and models. To query your data, use ADOMD.NET
9. Mining Structure Objects A mining structure contains a binding to a data source view that is defined in the database, and contains definitions for all columns participating in the mining models Steps followed to Creating a MiningStructure object are:Create the MiningStructure object and populate the basic attributes
10. Create columns for the model. Each column needs a name and internal ID, a type, a content definition, and a binding.
11. Update the MiningStructure object to the server, by using the Update method of the object.MiningModel ObjectsSteps to create a MiningModel object :Create the MiningModel object and populate the basic attributes. (object name, object ID, and mining algorithm specification)
12. Add the columns of the mining model. One of the columns must be defined as the case key.Update the MiningModel object to the server, by using the Update method of the object.MiningModel objects can be processed independently of other models in the parent MiningStructure.Stored ProceduresStored procedures can be used to call external routines from Microsoft SQL Server Analysis Services
13. You can write an external routines called by a stored procedure in any common language runtime (CLR) language, such as C, C++, C#, Visual Basic, or Visual Basic .NET.
14. Stored procedures can be used to add business functionality to your applications that is not provided by the native functionality of MDXCreating Stored ProceduresAll stored procedures must be associated with a common language runtime (CLR) or Component Object Model (COM) class in order to be used. The class must be installed on the server — usually in the form of a Microsoft ActiveX® dynamic link library (DLL) — and registered as an assembly on the server or in an Analysis Services database.Server stored procedures can be called from any query context. Database stored procedures can only be accessed if the database context is the database under which the stored procedure is defined. For a server or a deployed Microsoft SQL Server Analysis Services database on a server, you can use SQL Server Management Studio to register an assembly. For an Analysis Services project, you can use Analysis Services Designer to register an assembly in the project.
15. Executing Stored ProceduresServer ADOMD.NET allows you to execute DMX queries using the same objects that you would use with ADOMD.NET.The only exception is that you do not have to specify a connection, because you are already connected. You can copy the results from the query into a DataTable, or you can simply return the DataReader returned by ExecuteReader.
16. Deploying and Debugging Stored Procedure AssembliesAfter Compiling and building the stored procedure, you must deploy the procedure to your Analysis Server in order to call it from DMX. To add a .NET assembly to your Analysis Services project, right-click the Assemblies folder in Solution Explorer and select New Assembly Reference.select some security-related options, such as Permissions and Impersonation information. The Permissions property specifies the code access permissions that are granted to the assembly when it’s loaded by Analysis Services. The recommended (and default) value is Safe.
17. Deploying and Debugging Stored Procedure AssembliesTo debug the assembly in Visual Studio, select Attach to Process from the Debug menu. Select the executable msmdsrv.exe from the list, and ensure that the dialog box displays CLR as the Attach To option. you will be able to set breakpoints in your stored procedures at the end.
18. Major Data Mining APIsProgramming AMO Data Mining ObjectsStored Procedures basicsDeploying and Debugging Stored Procedure AssembliesSummary
19. Visit 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