MS SQL SERVER: Introduction To Datamining Suing Sql Server
2 likes400 views
Data mining involves analyzing large datasets to discover patterns. It can be used to better understand systems by studying trends and patterns in vast amounts of data. Data mining uses classification, clustering, association, and regression algorithms to organize data and discover patterns. The data mining process involves data collection, cleaning, transformation, modeling, and assessment. Examples of data mining applications include customer relationship management, enterprise resource planning, and web log analysis.
1 of 15
Ad
Recommended
Research trends in data warehousing and data mining
Research trends in data warehousing and data miningEr. Nawaraj Bhandari This document discusses classification and prediction in data analysis. It defines classification as predicting categorical class labels, such as predicting if a loan applicant is risky or safe. Prediction predicts continuous numeric values, such as predicting how much a customer will spend. The document provides examples of classification, including a bank predicting loan risk and a company predicting computer purchases. It also provides an example of prediction, where a company predicts customer spending. It then discusses how classification works, including building a classifier model from training data and using the model to classify new data. Finally, it discusses decision tree induction for classification and the k-means algorithm.
Ghhh
Ghhhagammya This document discusses various data mining functionalities including classification, clustering, association rule mining, and numeric prediction. It provides examples of each functionality using sample datasets. Classification techniques discussed include decision trees, rules, neural networks, naive Bayes, and support vector machines. Clustering is described as an unsupervised technique to group similar instances. Association rule mining is used to find frequent patterns and correlations in transactional data. Numeric prediction extends classification to predict numeric rather than categorical targets.
Data Cleaning Techniques
Data Cleaning TechniquesAmir Masoud Sefidian Description of four techniques for Data Cleaning:
1.DWCLEANER Framework
2.Data Mining Techniques include Association Rule and Functional Dependecies
,...
Data Mining: Key definitions
Data Mining: Key definitionsDataminingTools Inc Data mining is the process of analyzing large amounts of data to discover patterns and extract useful information. It involves collecting data, removing noise, focusing on relevant portions, analyzing patterns, and obtaining required information. Key components of a data mining system include a data warehouse server, information repository, user interface, data mining engine, and pattern evaluation module. A data warehouse is a centralized repository that stores integrated data from multiple sources in a unified format.
Data preprocessing
Data preprocessingsuganmca14 The document discusses various techniques for preprocessing raw data prior to data mining or machine learning. It describes how data cleaning is used to fill in missing values, smooth noisy data by identifying outliers, and resolve inconsistencies. It also covers data integration, which combines data from multiple sources, and data transformation techniques that transform data into appropriate forms for analysis. The key steps in data preprocessing include data cleaning, integration, transformation, and reduction to handle issues like missing values, noise, inconsistencies and redundancies in raw data.
Introduction to data pre-processing and cleaning 
Introduction to data pre-processing and cleaning Matteo Manca This document discusses data preparation and cleaning. It begins by explaining why data cleaning is important, as raw data is often incomplete, noisy, inconsistent, or not in a format suitable for analysis. The main steps of data cleaning are then outlined, including handling missing values, identifying outliers, resolving inconsistencies, and transforming data. Best practices for data cleaning like using pipelines to document the cleaning process and saving clean data files are also presented. Finally, the document introduces R and RStudio as tools that can be used for data cleaning.
Data mining tasks
Data mining tasksKhwaja Aamer This document discusses data mining and different types of data mining techniques. It defines data mining as the process of analyzing large amounts of data to discover patterns and relationships. The document describes predictive data mining, which makes predictions based on historical data, and descriptive data mining, which identifies patterns and relationships. It also discusses classification, clustering, time-series analysis, and data summarization as specific data mining techniques.
Data Mining and Knowledge
Data Mining and KnowledgeKartik Kalpande Patil Data mining is the process of discovering patterns in large data sets and is a core part of the knowledge discovery process. It involves preprocessing, transforming, and mining data to extract useful patterns. Main data mining tasks include classification, association rule mining, clustering, sequential pattern mining, and deviation detection. The goal is to extract valid, novel, useful, and understandable patterns that can be interpreted into knowledge through an iterative and interactive process.
Data pre processing
Data pre processingpommurajopt Data preprocessing involves cleaning, transforming, and reducing raw data to prepare it for data mining and analysis. It addresses issues like missing values, inconsistent data, and reducing data size. The key goals of data preprocessing are to handle data problems, integrate multiple data sources, and reduce data size while maintaining the same analytical results. Major tasks involve data cleaning, integration, transformation, and reduction.
data mining
data miningmanasa polu This document provides an introduction to data mining. It defines data mining as the process of extracting knowledge from large amounts of data. The document outlines the typical steps in the knowledge discovery process including data cleaning, transformation, mining, and evaluation. It also describes some common challenges in data mining like dealing with large, high-dimensional, heterogeneous and distributed data. Finally, it summarizes several common data mining tasks like classification, association analysis, clustering, and anomaly detection.
Data mining nouman javed
Data mining nouman javednouman javed This document discusses data mining and knowledge discovery in databases (KDD). It defines data mining as the process of extracting knowledge from large amounts of data. The document outlines the six steps of data mining: data selection, data cleaning, data transformation, data mining, evaluation of mined patterns, and knowledge presentation. Examples of data mining applications include recommendation systems on Amazon and YouTube, call detail records for detecting fraud, and targeted advertising on Facebook. Infographic models are also included to illustrate the data mining process which transforms raw data into useful knowledge.
Data Mining Technniques
Data Mining TechnniquesLivares Technologies Pvt Ltd Presented by,
Mr. Blesson Joseph
Senior Software Engineer, Livares Technologies
What is data mining?
Data mining is the process of analyzing data and summarizing it to produce useful information.
Queries based on SQL, a database programming language, are used to answer basic questions about data.
But, as the collection of data grows in a database, the amount of data can easily become overwhelming.
A data warehouse is a collection of databases that work together. A data warehouse makes it possible to integrate data from multiple databases, which can give new insights into the data.
The ultimate goal of a database is not just to store data, but to help businesses make decisions based on that data.
Goal of Data Mining
The overall goal of the data mining
process is to extract information from a
data set and transform it into an
understandable structure for further use.
Data mining is the computing process of
discovering patterns in large data sets
involving methods.
These patterns can be used for predictive
analytics.
Data reduction
Data reductionkalavathisugan This document discusses techniques for data reduction to reduce the size of large datasets for analysis. It describes five main strategies for data reduction: data cube aggregation, dimensionality reduction, data compression, numerosity reduction, and discretization. Data cube aggregation involves aggregating data at higher conceptual levels, such as aggregating quarterly sales data to annual totals. Dimensionality reduction removes redundant attributes. The document then focuses on attribute subset selection techniques, including stepwise forward selection, stepwise backward elimination, and combinations of the two, to select a minimal set of relevant attributes. Decision trees can also be used for attribute selection by removing attributes not used in the tree.
Manage your Datasets
Manage your DatasetsEng Teong Cheah Managing Datasets shows how to upload various types of data to Azure Machine Learning, and how to explore and visualize the uploaded data.
Data mining
Data miningsnegacmr The document discusses three main issues in data integration: schema integration, redundancy, and detection and resolution of data value conflicts. It also discusses data preprocessing techniques used to transform raw data into a useful format, including handling missing and noisy data. The key steps of data preprocessing are data cleaning, handling missing data by ignoring tuples or filling missing values, and resolving noisy data.
Data preprocessing
Data preprocessingankur bhalla Data preprocessing involves transforming raw data into an understandable and consistent format. It includes data cleaning, integration, transformation, and reduction. Data cleaning aims to fill missing values, smooth noise, and resolve inconsistencies. Data integration combines data from multiple sources. Data transformation handles tasks like normalization and aggregation to prepare the data for mining. Data reduction techniques obtain a reduced representation of data that maintains analytical results but reduces volume, such as through aggregation, dimensionality reduction, discretization, and sampling.
XL-MINER: Associations
XL-MINER: AssociationsDataminingTools Inc This document discusses association rules and charts in XLMiner. Association rules are used to find relationships between data that occur frequently and can be formulated into if-then rules. Charts allow data to be viewed visually and XLMiner provides box plots, histograms, and matrix plots. Box plots display five data metrics, histograms show frequency of values, and matrix plots enable viewing pairwise variable relationships. Support, confidence, and lift are discussed as properties of association rules.
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.
Elementary data organisation
Elementary data organisationMuzamil Hussain This document discusses elementary data organization, including primitive and non-primitive data types, data structures, and common data structure operations. It defines data as values assigned to entities, and information as meaningful, processed data. Primitive data types directly supported by machines are listed. Non-primitive data types require additional processing. Data structures arrange data in memory and include common examples like arrays and linked lists. Operations on data structures include traversing, searching, inserting, deleting, sorting, and merging. Data structures are classified as linear or non-linear based on how elements are arranged.
Knowledge Discovery & Representation
Knowledge Discovery & RepresentationDarshan Patil The document discusses knowledge discovery and data mining. It describes knowledge discovery as automatically searching large volumes of data for patterns that can be considered knowledge. The document outlines the five steps of the knowledge discovery process and notes it is closely related to data mining. It then discusses data mining, describing the purpose, preference, and search techniques used in data mining algorithms. The document also categorizes data mining and describes how it provides links between transactional and analytical systems to analyze relationships and patterns in stored data.
Exploratory data analysis with Python
Exploratory data analysis with PythonDavis David Introduction to Exploratory Data Analysis.To access the source code click here https://github.com/Davisy/Exploratory-Data-Analysis-
A random decision tree frameworkfor privacy preserving data mining
A random decision tree frameworkfor privacy preserving data miningVenkat Projects This document summarizes a framework for privacy-preserving data mining using a random decision tree algorithm. Multiple parties like banks, insurance companies, and credit card companies share data but need to keep certain attributes private. The random decision tree algorithm partitions data based on each party's needs, encrypts the data using homomorphic encryption, builds a decision tree model on the encrypted data, and allows parties to classify new instances while preserving privacy. It compares the accuracy of random decision trees to traditional ID3 decision trees.
Data Dictionary in System Analysis and Design
Data Dictionary in System Analysis and DesignArafat Hossan Data Dictionary
Use of Data Dictionary
Elements of Data Dictionary
Data Dictionary Analysis
Importance of Data Dictionary
Data warehouse logical design
Data warehouse logical designEr. Nawaraj Bhandari This document discusses various concepts in data warehouse logical design including data marts, types of data marts (dependent, independent, hybrid), star schemas, snowflake schemas, and fact constellation schemas. It defines each concept and provides examples to illustrate them. Dependent data marts are created from an existing data warehouse, independent data marts are stand-alone without a data warehouse, and hybrid data marts combine data from a warehouse and other sources. Star schemas have one table for each dimension that joins to a central fact table, while snowflake schemas have normalized dimension tables. Fact constellation schemas have multiple fact tables that share dimension tables.
Data Mining: Classification and analysis
Data Mining: Classification and analysisDataminingTools Inc Data mining involves classification, cluster analysis, outlier mining, and evolution analysis. Classification models data to distinguish classes using techniques like decision trees or neural networks. Cluster analysis groups similar objects without labels, while outlier mining finds irregular objects. Evolution analysis models changes over time. Data mining performance considers algorithm efficiency, scalability, and handling diverse and complex data types from multiple sources.
Commands of DML in SQL
Commands of DML in SQLAshish Gaurkhede This document discusses SQL commands used for data manipulation. It describes four key SQL commands - SELECT, INSERT, UPDATE, and DELETE. SELECT is used to view data from a table. INSERT adds new rows to a table. UPDATE modifies existing data in a table. DELETE removes rows from a table. Syntax and examples are provided for each command.
SQL for interview
SQL for interviewAditya Kumar Tripathy This document discusses SQL fundamentals including what is data, databases, database management systems, and relational databases. It defines key concepts like tables, rows, columns, and relationships. It describes different types of DBMS like hierarchical, network, relational, and object oriented. The document also covers SQL commands like SELECT, INSERT, UPDATE, DELETE, constraints, functions and more. It provides examples of SQL queries and functions.
Database Systems - SQL - DDL Statements (Chapter 3/2)
Database Systems - SQL - DDL Statements (Chapter 3/2)Vidyasagar Mundroy This document discusses various data definition language (DDL) statements in SQL. It describes DDL statements to create, modify, and delete database objects like schemas, tables, views, and indexes. Specifically, it covers the CREATE, ALTER, and DROP statements for schemas, tables, views, indexes, and constraints. Examples are provided for each statement type.
MS Sql Server: Introduction To Database Concepts
MS Sql Server: Introduction To Database ConceptsDataminingTools Inc A database is a collection of related data organized into tables. Data is any raw fact or statistic, and is important because all decisions depend on underlying data. A database management system (DBMS) is used to organize data into tables to avoid problems with file-based storage like inconsistency, redundancy, integrity issues, and security problems. It allows for concurrent access. DBMS are widely used in real-world applications like movie theaters, prisons, and banks to manage related information. A table in a database contains records organized into rows with attributes or fields forming the columns. A key uniquely identifies each record.
Ad
More Related Content
What's hot (17)
Data pre processing
Data pre processingpommurajopt Data preprocessing involves cleaning, transforming, and reducing raw data to prepare it for data mining and analysis. It addresses issues like missing values, inconsistent data, and reducing data size. The key goals of data preprocessing are to handle data problems, integrate multiple data sources, and reduce data size while maintaining the same analytical results. Major tasks involve data cleaning, integration, transformation, and reduction.
data mining
data miningmanasa polu This document provides an introduction to data mining. It defines data mining as the process of extracting knowledge from large amounts of data. The document outlines the typical steps in the knowledge discovery process including data cleaning, transformation, mining, and evaluation. It also describes some common challenges in data mining like dealing with large, high-dimensional, heterogeneous and distributed data. Finally, it summarizes several common data mining tasks like classification, association analysis, clustering, and anomaly detection.
Data mining nouman javed
Data mining nouman javednouman javed This document discusses data mining and knowledge discovery in databases (KDD). It defines data mining as the process of extracting knowledge from large amounts of data. The document outlines the six steps of data mining: data selection, data cleaning, data transformation, data mining, evaluation of mined patterns, and knowledge presentation. Examples of data mining applications include recommendation systems on Amazon and YouTube, call detail records for detecting fraud, and targeted advertising on Facebook. Infographic models are also included to illustrate the data mining process which transforms raw data into useful knowledge.
Data Mining Technniques
Data Mining TechnniquesLivares Technologies Pvt Ltd Presented by,
Mr. Blesson Joseph
Senior Software Engineer, Livares Technologies
What is data mining?
Data mining is the process of analyzing data and summarizing it to produce useful information.
Queries based on SQL, a database programming language, are used to answer basic questions about data.
But, as the collection of data grows in a database, the amount of data can easily become overwhelming.
A data warehouse is a collection of databases that work together. A data warehouse makes it possible to integrate data from multiple databases, which can give new insights into the data.
The ultimate goal of a database is not just to store data, but to help businesses make decisions based on that data.
Goal of Data Mining
The overall goal of the data mining
process is to extract information from a
data set and transform it into an
understandable structure for further use.
Data mining is the computing process of
discovering patterns in large data sets
involving methods.
These patterns can be used for predictive
analytics.
Data reduction
Data reductionkalavathisugan This document discusses techniques for data reduction to reduce the size of large datasets for analysis. It describes five main strategies for data reduction: data cube aggregation, dimensionality reduction, data compression, numerosity reduction, and discretization. Data cube aggregation involves aggregating data at higher conceptual levels, such as aggregating quarterly sales data to annual totals. Dimensionality reduction removes redundant attributes. The document then focuses on attribute subset selection techniques, including stepwise forward selection, stepwise backward elimination, and combinations of the two, to select a minimal set of relevant attributes. Decision trees can also be used for attribute selection by removing attributes not used in the tree.
Manage your Datasets
Manage your DatasetsEng Teong Cheah Managing Datasets shows how to upload various types of data to Azure Machine Learning, and how to explore and visualize the uploaded data.
Data mining
Data miningsnegacmr The document discusses three main issues in data integration: schema integration, redundancy, and detection and resolution of data value conflicts. It also discusses data preprocessing techniques used to transform raw data into a useful format, including handling missing and noisy data. The key steps of data preprocessing are data cleaning, handling missing data by ignoring tuples or filling missing values, and resolving noisy data.
Data preprocessing
Data preprocessingankur bhalla Data preprocessing involves transforming raw data into an understandable and consistent format. It includes data cleaning, integration, transformation, and reduction. Data cleaning aims to fill missing values, smooth noise, and resolve inconsistencies. Data integration combines data from multiple sources. Data transformation handles tasks like normalization and aggregation to prepare the data for mining. Data reduction techniques obtain a reduced representation of data that maintains analytical results but reduces volume, such as through aggregation, dimensionality reduction, discretization, and sampling.
XL-MINER: Associations
XL-MINER: AssociationsDataminingTools Inc This document discusses association rules and charts in XLMiner. Association rules are used to find relationships between data that occur frequently and can be formulated into if-then rules. Charts allow data to be viewed visually and XLMiner provides box plots, histograms, and matrix plots. Box plots display five data metrics, histograms show frequency of values, and matrix plots enable viewing pairwise variable relationships. Support, confidence, and lift are discussed as properties of association rules.
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.
Elementary data organisation
Elementary data organisationMuzamil Hussain This document discusses elementary data organization, including primitive and non-primitive data types, data structures, and common data structure operations. It defines data as values assigned to entities, and information as meaningful, processed data. Primitive data types directly supported by machines are listed. Non-primitive data types require additional processing. Data structures arrange data in memory and include common examples like arrays and linked lists. Operations on data structures include traversing, searching, inserting, deleting, sorting, and merging. Data structures are classified as linear or non-linear based on how elements are arranged.
Knowledge Discovery & Representation
Knowledge Discovery & RepresentationDarshan Patil The document discusses knowledge discovery and data mining. It describes knowledge discovery as automatically searching large volumes of data for patterns that can be considered knowledge. The document outlines the five steps of the knowledge discovery process and notes it is closely related to data mining. It then discusses data mining, describing the purpose, preference, and search techniques used in data mining algorithms. The document also categorizes data mining and describes how it provides links between transactional and analytical systems to analyze relationships and patterns in stored data.
Exploratory data analysis with Python
Exploratory data analysis with PythonDavis David Introduction to Exploratory Data Analysis.To access the source code click here https://github.com/Davisy/Exploratory-Data-Analysis-
A random decision tree frameworkfor privacy preserving data mining
A random decision tree frameworkfor privacy preserving data miningVenkat Projects This document summarizes a framework for privacy-preserving data mining using a random decision tree algorithm. Multiple parties like banks, insurance companies, and credit card companies share data but need to keep certain attributes private. The random decision tree algorithm partitions data based on each party's needs, encrypts the data using homomorphic encryption, builds a decision tree model on the encrypted data, and allows parties to classify new instances while preserving privacy. It compares the accuracy of random decision trees to traditional ID3 decision trees.
Data Dictionary in System Analysis and Design
Data Dictionary in System Analysis and DesignArafat Hossan Data Dictionary
Use of Data Dictionary
Elements of Data Dictionary
Data Dictionary Analysis
Importance of Data Dictionary
Data warehouse logical design
Data warehouse logical designEr. Nawaraj Bhandari This document discusses various concepts in data warehouse logical design including data marts, types of data marts (dependent, independent, hybrid), star schemas, snowflake schemas, and fact constellation schemas. It defines each concept and provides examples to illustrate them. Dependent data marts are created from an existing data warehouse, independent data marts are stand-alone without a data warehouse, and hybrid data marts combine data from a warehouse and other sources. Star schemas have one table for each dimension that joins to a central fact table, while snowflake schemas have normalized dimension tables. Fact constellation schemas have multiple fact tables that share dimension tables.
Data Mining: Classification and analysis
Data Mining: Classification and analysisDataminingTools Inc Data mining involves classification, cluster analysis, outlier mining, and evolution analysis. Classification models data to distinguish classes using techniques like decision trees or neural networks. Cluster analysis groups similar objects without labels, while outlier mining finds irregular objects. Evolution analysis models changes over time. Data mining performance considers algorithm efficiency, scalability, and handling diverse and complex data types from multiple sources.
Viewers also liked (7)
Commands of DML in SQL
Commands of DML in SQLAshish Gaurkhede This document discusses SQL commands used for data manipulation. It describes four key SQL commands - SELECT, INSERT, UPDATE, and DELETE. SELECT is used to view data from a table. INSERT adds new rows to a table. UPDATE modifies existing data in a table. DELETE removes rows from a table. Syntax and examples are provided for each command.
SQL for interview
SQL for interviewAditya Kumar Tripathy This document discusses SQL fundamentals including what is data, databases, database management systems, and relational databases. It defines key concepts like tables, rows, columns, and relationships. It describes different types of DBMS like hierarchical, network, relational, and object oriented. The document also covers SQL commands like SELECT, INSERT, UPDATE, DELETE, constraints, functions and more. It provides examples of SQL queries and functions.
Database Systems - SQL - DDL Statements (Chapter 3/2)
Database Systems - SQL - DDL Statements (Chapter 3/2)Vidyasagar Mundroy This document discusses various data definition language (DDL) statements in SQL. It describes DDL statements to create, modify, and delete database objects like schemas, tables, views, and indexes. Specifically, it covers the CREATE, ALTER, and DROP statements for schemas, tables, views, indexes, and constraints. Examples are provided for each statement type.
MS Sql Server: Introduction To Database Concepts
MS Sql Server: Introduction To Database ConceptsDataminingTools Inc A database is a collection of related data organized into tables. Data is any raw fact or statistic, and is important because all decisions depend on underlying data. A database management system (DBMS) is used to organize data into tables to avoid problems with file-based storage like inconsistency, redundancy, integrity issues, and security problems. It allows for concurrent access. DBMS are widely used in real-world applications like movie theaters, prisons, and banks to manage related information. A table in a database contains records organized into rows with attributes or fields forming the columns. A key uniquely identifies each record.
DML, DDL, DCL ,DRL/DQL and TCL Statements in SQL with Examples
DML, DDL, DCL ,DRL/DQL and TCL Statements in SQL with ExamplesLGS, GBHS&IC, University Of South-Asia, TARA-Technologies SQL language includes four primary statement types: DML, DDL, DCL, and TCL. DML statements manipulate data within tables using operations like SELECT, INSERT, UPDATE, and DELETE. DDL statements define and modify database schema using commands like CREATE, ALTER, and DROP. DCL statements control user access privileges with GRANT and REVOKE. TCL statements manage transactions with COMMIT, ROLLBACK, and SAVEPOINT to maintain data integrity.
Sql Server Basics
Sql Server Basicsrainynovember12 The document provides an overview of basic concepts related to SQL server databases including database objects, file systems, storage structures, and query processing. It discusses topics like SQL server databases, storage files and file groups, data pages and extents, data organization in heaps vs indexed tables, and how queries are processed through either full table scans or using indexes.
DML, DDL, DCL ,DRL/DQL and TCL Statements in SQL with Examples
DML, DDL, DCL ,DRL/DQL and TCL Statements in SQL with ExamplesLGS, GBHS&IC, University Of South-Asia, TARA-Technologies
Ad
Similar to MS SQL SERVER: Introduction To Datamining Suing Sql Server (20)
Data Warehousing AWS 12345
Data Warehousing AWS 12345AkhilSinghal21 This document provides an overview of data warehousing and data mining. It begins by defining a data warehouse as a system that contains historical and cumulative data from single or multiple sources for simplifying reporting, analysis, and decision making. It describes three common data warehouse architectures and the key components of a data warehouse, including the database, ETL tools, metadata, query tools, and data marts. The document then defines data mining as extracting usable data from raw data using software to analyze patterns. It outlines descriptive and predictive data mining tasks and techniques like clustering, associations, summarization, prediction, and classification. Finally, it provides examples of data mining applications and discusses how AWS services like Amazon Redshift can provide scalable data warehousing
Top 30 Data Analyst Interview Questions.pdf
Top 30 Data Analyst Interview Questions.pdfShaikSikindar1 Data Analytics has emerged has one of the central aspects of business operations. Consequently, the quest to grab professional positions within the Data Analytics domain has assumed unimaginable proportions. So if you too happen to be someone who is desirous of making through a Data Analyst .
UNIT 2: Part 2: Data Warehousing and Data Mining
UNIT 2: Part 2: Data Warehousing and Data MiningNandakumar P This document provides an overview of data pre-processing techniques used in data mining. It discusses common steps in data pre-processing including data cleaning, integration, transformation, reduction, and discretization. Specific techniques covered include handling missing and noisy data, data normalization, attribute selection, dimensionality reduction, and the Apriori and FP-Growth algorithms for frequent pattern mining. The goals of data pre-processing are to improve data quality, handle inconsistencies, and prepare the data for analysis.
Unit-V-Introduction to Data Mining.pptx
Unit-V-Introduction to Data Mining.pptxHarsha Patel Data mining involves extracting useful patterns from large data sets to help businesses make informed decisions. It allows organizations to obtain knowledge from data, make improvements, and aid decision making in a cost-effective manner. However, data mining tools can be difficult to use and may not always provide precise results. Knowledge discovery is the overall process of discovering useful information from data, which includes steps like data cleaning, integration, selection, transformation, and mining followed by pattern evaluation and presentation of knowledge.
Seminar Presentation
Seminar PresentationVaibhav Dhattarwal This document discusses various data mining techniques, including artificial neural networks. It provides an overview of the knowledge discovery in databases process and the cross-industry standard process for data mining. It also describes techniques such as classification, clustering, regression, association rules, and neural networks. Specifically, it discusses how neural networks are inspired by biological neural networks and can be used to model complex relationships in data.
Data mining , Knowledge Discovery Process, Classification
Data mining , Knowledge Discovery Process, ClassificationDr. Abdul Ahad Abro The document provides an overview of data mining techniques and processes. It discusses data mining as the process of extracting knowledge from large amounts of data. It describes common data mining tasks like classification, regression, clustering, and association rule learning. It also outlines popular data mining processes like CRISP-DM and SEMMA that involve steps of business understanding, data preparation, modeling, evaluation and deployment. Decision trees are presented as a popular classification technique that uses a tree structure to split data into nodes and leaves to classify examples.
Lecture2 (1).ppt
Lecture2 (1).pptMinakshee Patil Data mining is the process of extracting patterns from large data sets to identify useful information. It involves applying machine learning algorithms to detect patterns in sample data and then using the learned patterns to predict future behaviors or outcomes. Data mining utilizes techniques from machine learning, statistics, databases, and visualization to analyze large datasets and discover hidden patterns. The goal of data mining is to extract useful information from large datasets and transform it into an understandable structure for further use.
Unit i
Unit iAishwaryaLakshmiA This document discusses data mining and related topics. It begins by defining data mining as the process of discovering patterns in large datasets using methods from machine learning, statistics, and database systems. The document then discusses data warehouses, how they work, and their role in data mining. It describes different data mining functionalities and tasks such as classification, prediction, and clustering. The document outlines some common data mining applications and issues related to methodology, performance, and diverse data types. Finally, it discusses some social implications of data mining involving privacy, profiling, and unauthorized use of data.
International Refereed Journal of Engineering and Science (IRJES) 
International Refereed Journal of Engineering and Science (IRJES) irjes International Refereed Journal of Engineering and Science (IRJES)
Ad hoc & sensor networks, Adaptive applications, Aeronautical Engineering, Aerospace Engineering
Agricultural Engineering, AI and Image Recognition, Allied engineering materials, Applied mechanics,
Architecture & Planning, Artificial intelligence, Audio Engineering, Automation and Mobile Robots
Automotive Engineering….
Unit II.pdf
Unit II.pdfKennyPratheepKumar The document discusses the components of a data mining system and data discretization. The key components of a data mining system are a data source, data mining engine, data warehouse server, pattern evaluation module, graphical user interface, and knowledge base. The data source provides the raw data, which is then cleaned, integrated and stored on the data warehouse server. The data mining engine applies various algorithms to the data to discover patterns. Pattern evaluation and the knowledge base help assess the quality of patterns found. Data discretization involves converting continuous attribute values into a finite set of intervals to simplify analysis and management of data.
Prescriptive Analytics-1.pptx
Prescriptive Analytics-1.pptxKarthik132344 Prescriptive analytics is the process of analyzing data to provide recommendations on how to optimize business practices based on multiple predicted outcomes. It is the third and final tier of modern data processing, after descriptive analytics which analyzes current data, and predictive analytics which predicts future behavior based on models. Prescriptive analytics utilizes machine learning, business rules, AI and algorithms to simulate various approaches to numerous outcomes and suggest the best possible actions. Data mining is the process of analyzing raw data to identify patterns and extract useful information that can help companies improve marketing strategies and sales. Process mining involves analyzing event logs from enterprise systems to understand processes and identify inefficiencies.
Business Intelligence and Analytics Unit-2 part-A .pptx
Business Intelligence and Analytics Unit-2 part-A .pptxRupaRani28 This document provides an overview of data mining, including its definition, process, applications, and challenges. Data mining involves analyzing large datasets to extract useful patterns and trends. It has several key steps: data is collected and loaded into warehouses, analysts determine how to organize it, software sorts and organizes the data, and it is presented to end users. Data mining is used by organizations in retail, finance, marketing and other industries to determine customer preferences and behaviors to help with decisions. While powerful, data mining also faces challenges to do with performance, data issues, and selecting the right techniques.
Data mining
Data mininghardavishah56 Data mining involves analyzing large datasets to discover patterns and extract useful information. It has evolved from early methods like regression analysis and involves techniques from machine learning, statistics, and databases. Data mining is used for applications like market analysis, fraud detection, customer retention, and science exploration by performing descriptive tasks like frequent pattern mining and associations or classification/prediction tasks. It involves preprocessing data, extracting patterns, and evaluating and presenting results.
Data mining
Data miningpradeepa n my topic is data mining technique.. what is data mining,and its type,application are included in this presentation..
Introduction to Data Mining.pptx
Introduction to Data Mining.pptxOliverBrown75 Students usually face a lot of issues completing their data mining assignments. Assignment Achievers are here to help you release your burden and guide you with the best tips.
Introduction to Data Mining 
Introduction to Data Mining Sushil Kulkarni This lecture gives various definitions of Data Mining. It also gives why Data Mining is required. Various examples on Classification , Cluster and Association rules are given.
Data mining concepts and work
Data mining concepts and workAmr Abd El Latief presentation on recent data mining Techniques ,and future directions of research from the recent research papers made in Pre-master ,in Cairo University under supervision of Dr. Rabie
Part1
Part1sumit621 The document provides an overview of data mining techniques and related concepts. It defines data mining and compares it to knowledge discovery in databases (KDD). It discusses the basic data mining tasks of classification, clustering, association rule mining, and summarization. It also covers related areas like databases, statistics, machine learning, and visualization techniques used in data mining. Finally, it provides an overview of common data mining techniques including decision trees, neural networks, genetic algorithms, and others.
Data processing
Data processingAnupamSingh211 Python software development provides ease of programming to the developers and gives quick results for any kind of projects. Suma Soft is an expert company providing complete Python software development services for small, mid and big level companies. It holds an expertise for 19 years and is backed up by a strong patronage. To know more- https://www.sumasoft.com/python-software-development
Data Mining: Data mining classification and analysis
Data Mining: Data mining classification and analysisDatamining Tools Data mining involves classification, cluster analysis, outlier mining, and evolution analysis. Classification models data to distinguish classes using techniques like decision trees or neural networks. Cluster analysis groups similar objects without labels, while outlier mining finds irregular objects. Evolution analysis models changes over time. Data mining performance depends on algorithm efficiency and scalability for large datasets across diverse database types.
Ad
More from sqlserver content (20)
MS SQL SERVER: Using the data mining tools
MS SQL SERVER: Using the data mining toolssqlserver content 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 algorithms, data, and columns.
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 servers using SQL Server
MS SQL SERVER: SSIS and data mining
MS SQL SERVER: SSIS and data miningsqlserver content SQL Server Integration Services (SSIS) is a platform for data integration and workflow applications used for extracting, transforming, and loading (ETL) data. SSIS packages contain control flows and data flows to organize tasks for data migration. SSIS provides tools for loading data, transforming data types, and splitting data into training and testing sets for data mining models. It includes data mining transformations in the control flow and data flow environments to prepare and analyze text data for classification, clustering, and association models.
MS SQL SERVER: Programming sql server data mining
MS SQL SERVER: Programming sql server data miningsqlserver content 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 covers how to create, execute, and debug stored procedures for adding business logic, including registering assemblies, setting permissions, and attaching to processes for debugging. The document provides an overview of major data mining APIs and programming techniques.
MS SQL SERVER: Olap cubes and data mining
MS SQL SERVER: Olap cubes and data miningsqlserver content OLAP cubes allow analysts to query aggregated data for decision making. Cubes are multidimensional databases containing dimensions like customer, product, and time. Processing a cube involves building dimension hierarchies and precalculating aggregations. Cubes can then be queried using MDX and browsed in tools like Excel. Data mining uses statistical techniques from fields like statistics and machine learning to analyze data, similar to how OLAP analyzes aggregated data. The data mining designer in SQL Server Analysis Services allows modifying mining structures and models, while the data mining wizard helps define new mining structures and models.
MS SQL SERVER: Microsoft time series algorithm
MS SQL SERVER: Microsoft time series algorithmsqlserver content The document provides an overview of Microsoft Time Series algorithms for forecasting continuous values like sales over time. It discusses creating time series models in SQL Server that use a key time column to order data and make predictions on predictable attributes. The document demonstrates creating a time series mining model called "Forecasting_MIXED" using the Microsoft Time Series algorithm to predict quantity and amount based on reporting date and region. It shows how to create, alter, and execute the DMX query to generate the model and structure.
MS SQL SERVER: Microsoft sequence clustering and association rules
MS SQL SERVER: Microsoft sequence clustering and association rulessqlserver content This document provides an overview of Microsoft Sequence Clustering and Association Rules algorithms in SQL Server Analysis Services. It describes how sequence clustering models group sequences into clusters based on identical transitions between states. It also explains how association rule mining analyzes frequent patterns in transactional data to generate rules and make recommendations. Key parameters for each algorithm like minimum support, cluster count, and maximum itemset size are also outlined.
MS SQL SERVER: Neural network and logistic regression
MS SQL SERVER: Neural network and logistic regressionsqlserver content The document provides an overview of Microsoft Neural Network and Logistic Regression algorithms. It discusses how the Neural Network algorithm works using multiple layers of neurons to detect nonlinear relationships between inputs and outputs. It also describes parameters that control the behavior of the Neural Network model, including the maximum number of inputs/outputs, hidden node ratio, and holdout percentage for validation. Sample DMX queries are provided to create and train Neural Network models on employee data to predict attributes.
MS SQL SERVER: Microsoft naive bayes algorithm
MS SQL SERVER: Microsoft naive bayes algorithmsqlserver content The Naive Bayes algorithm is a classification algorithm in SQL Server that uses Bayes' theorem with the assumption of independence between attributes. It is less computationally intensive than other algorithms. A Naive Bayes model can be explored using different views to understand attribute relationships and feature importance. DMX queries can retrieve model metadata, content, and predictions. Parameters like maximum input attributes control the algorithm behavior.
MS SQL SERVER: Decision trees algorithm
MS SQL SERVER: Decision trees algorithmsqlserver content The document discusses Microsoft's decision trees algorithm and its use for classification, regression, and association mining. It provides examples of DMX queries for classification models predicting school plans based on student attributes, regression models predicting parent income, and association models for customer dance preferences. It also covers interpreting decision tree model content, parameters for controlling tree growth and shape, and stored procedures for viewing and manipulating decision tree models.
MS SQL Server: Data mining concepts and dmx
MS SQL Server: Data mining concepts and dmxsqlserver content Data Mining Extensions (DMX) is a query language used to create, manage, and query data mining models. DMX was introduced in 1999 to define common concepts for data mining. It includes objects like mining structures and models. Mining structures define columns and hold cached data, while models perform machine learning on structures. DMX statements are used for creation, prediction, and training. Prediction joins apply model patterns to estimate unknown values in source data.
MS Sql Server: Reporting models
MS Sql Server: Reporting modelssqlserver content SQL Server reporting models provide abstractions of report data sources to make report creation easy for business users. Reporting models define metadata like entities, attributes, and relationships. Model Designer is used to create reporting models by connecting to a data source, defining the model, and publishing. Report Builder then uses these reporting models to allow users to easily create ad hoc reports by selecting a data source and report type, building the layout, and publishing. Grouping, sorting, and filtering functionality allow customizing reports.
MS Sql Server: Reporting manipulating data
MS Sql Server: Reporting manipulating datasqlserver content SQL Server allows for the manipulation of data in tables, lists, and charts. Reports can include sub-reports stored on the report server with their own data source. Report parameters allow values to pass into queries to filter data and alter reports. Parameters are defined with a name, data type, and options, and their values can be linked between queries and reports.
MS Sql Server: Reporting introduction
MS Sql Server: Reporting introductionsqlserver content SQL Server 2008 Reporting Services allows users to create reports with any structure using a unique data format and benefits from enhanced performance and scalability. Reports can be integrated with Microsoft Office SharePoint Services for central delivery and management. The reporting lifecycle involves authoring reports using Report Designer, Report Builder, or custom tools, delivering reports through the Report Server or custom applications, and managing reports using the Report Manager or custom management tools.
MS Sql Server: Reporting basics
MS Sql Server: Reporting basicssqlserver content SQL Server provides tools for creating reports from data sources using Report Definition Language and Report Builder. Reports can be authored using the Report Designer development tool or the web-based Report Builder and can access data from data sources and data sets. Reports allow for customization of page options, inclusion of headers, footers, and interactive features, and use of aggregate functions to summarize data within groupings.
MS Sql Server: Datamining Introduction
MS Sql Server: Datamining Introductionsqlserver content This document discusses data mining in SQL Server 2008. It provides an overview of different types of data analysis including ad-hoc querying, reporting, OLAP cubes, and data mining. It then describes how data mining algorithms like classification, regression, and association analysis are used to explore data, find patterns, and make predictions. It also outlines the typical data mining process and highlights new features and algorithms in SQL Server 2008 for time series analysis, market basket analysis, churn analysis, and more.
MS Sql Server: Business Intelligence
MS Sql Server: Business Intelligencesqlserver content SQL Server provides business intelligence capabilities including data warehousing, data marts, cubes, and UI tools. It supports core BI processes like integration, analysis, and reporting. Key features include Analysis Services for cubes, Integration Services for ETL, Data Mining, Reporting Services, and scalability. It also enables KPIs, one-click cubes, proactive caching, and integration with Microsoft Office.
MS SQLSERVER:Feeding Data Into Database
MS SQLSERVER:Feeding Data Into Databasesqlserver content ETL is the process of extracting data from various sources, transforming it to fit operational needs, and loading it into a data warehouse or database. It involves extracting data from sources like DVDs, books, and friends, transforming it by cleaning and consolidating the data, and loading it into a data warehouse for storage and analysis. Migrating a database transfers data from one platform or format to another, such as moving from a Foxpro database to SQL Server. New data is inserted into databases using INSERT statements specifying the table and values, ensuring data types match. Updates are made using UPDATE statements specifying the table, set value, and WHERE condition to identify the record to change.
MS SQLSERVER:Doing Calculations With Functions
MS SQLSERVER:Doing Calculations With Functionssqlserver content This document discusses mathematical functions in SQL Server that can perform calculations on columns in tables. It explains aggregate functions like SUM, AVG, MIN, MAX, and COUNT that calculate the total, average, minimum, maximum, and count of values. These functions can be used to find calculations on a single column or combined with GROUP BY to calculate aggregates for each group. Additional functions like UPPER and LOWER can manipulate string values. Examples demonstrate finding total booty, average booty, highest/lowest booty, and robbery case count from a robberies table using these functions.
MS SQLSERVER:Deleting A Database
MS SQLSERVER:Deleting A Databasesqlserver content This document discusses Data Control Language (DCL) commands in SQL Server for deleting databases and tables, as well as saving data using save points and rollbacks. It explains that DCL is used to control data and can delete tables using the DROP command or save data using save points and rollbacks. Save points act as markers to which the database state can be reverted using the ROLLBACK command, allowing users to undo changes made before a COMMIT.
MS SQLSERVER:Customizing Your D Base Design
MS SQLSERVER:Customizing Your D Base Designsqlserver content This document discusses various ways to customize databases in SQL Server, including managing data integrity through primary keys, foreign keys, constraints, and other methods. It defines primary keys as uniquely identifying records, and describes how to add primary keys when creating or altering tables. Foreign keys are defined as fields that match a primary key in another table. The document also covers adding not null, unique, and check constraints to customize data validation and integrity rules.
Recently uploaded (20)
Challenges in Migrating Imperative Deep Learning Programs to Graph Execution:...
Challenges in Migrating Imperative Deep Learning Programs to Graph Execution:...Raffi Khatchadourian Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code that supports symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development tends to produce DL code that is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, less error-prone imperative DL frameworks encouraging eager execution have emerged at the expense of run-time performance. While hybrid approaches aim for the "best of both worlds," the challenges in applying them in the real world are largely unknown. We conduct a data-driven analysis of challenges---and resultant bugs---involved in writing reliable yet performant imperative DL code by studying 250 open-source projects, consisting of 19.7 MLOC, along with 470 and 446 manually examined code patches and bug reports, respectively. The results indicate that hybridization: (i) is prone to API misuse, (ii) can result in performance degradation---the opposite of its intention, and (iii) has limited application due to execution mode incompatibility. We put forth several recommendations, best practices, and anti-patterns for effectively hybridizing imperative DL code, potentially benefiting DL practitioners, API designers, tool developers, and educators.
Transcript: Canadian book publishing: Insights from the latest salary survey ...
Transcript: Canadian book publishing: Insights from the latest salary survey ...BookNet Canada Join us for a presentation in partnership with the Association of Canadian Publishers (ACP) as they share results from the recently conducted Canadian Book Publishing Industry Salary Survey. This comprehensive survey provides key insights into average salaries across departments, roles, and demographic metrics. Members of ACP’s Diversity and Inclusion Committee will join us to unpack what the findings mean in the context of justice, equity, diversity, and inclusion in the industry.
Results of the 2024 Canadian Book Publishing Industry Salary Survey: https://publishers.ca/wp-content/uploads/2025/04/ACP_Salary_Survey_FINAL-2.pdf
Link to presentation slides and transcript: https://bnctechforum.ca/sessions/canadian-book-publishing-insights-from-the-latest-salary-survey/
Presented by BookNet Canada and the Association of Canadian Publishers on May 1, 2025 with support from the Department of Canadian Heritage.
Canadian book publishing: Insights from the latest salary survey - Tech Forum...
Canadian book publishing: Insights from the latest salary survey - Tech Forum...BookNet Canada Join us for a presentation in partnership with the Association of Canadian Publishers (ACP) as they share results from the recently conducted Canadian Book Publishing Industry Salary Survey. This comprehensive survey provides key insights into average salaries across departments, roles, and demographic metrics. Members of ACP’s Diversity and Inclusion Committee will join us to unpack what the findings mean in the context of justice, equity, diversity, and inclusion in the industry.
Results of the 2024 Canadian Book Publishing Industry Salary Survey: https://publishers.ca/wp-content/uploads/2025/04/ACP_Salary_Survey_FINAL-2.pdf
Link to presentation recording and transcript: https://bnctechforum.ca/sessions/canadian-book-publishing-insights-from-the-latest-salary-survey/
Presented by BookNet Canada and the Association of Canadian Publishers on May 1, 2025 with support from the Department of Canadian Heritage.
How to Install & Activate ListGrabber - eGrabber
How to Install & Activate ListGrabber - eGrabbereGrabber ListGrabber: Build Targeted Lists Online in Minutes
TrsLabs - AI Agents for All - Chatbots to Multi-Agents Systems
TrsLabs - AI Agents for All - Chatbots to Multi-Agents SystemsTrs Labs AI Adoption for Your Business
AI applications have evolved from chatbots
into sophisticated AI agents capable of
handling complex workflows. Multi-agent
systems are the next phase of evolution.
Build 3D Animated Safety Induction - Tech EHS
Build 3D Animated Safety Induction - Tech EHSTECH EHS Solution Train Smarter, Not Harder – Let 3D Animation Lead the Way!
Discover how 3D animation makes inductions more engaging, effective, and cost-efficient.
Check out the slides to see how you can transform your safety training process!
Slide 1: Why 3D animation changes the game
Slide 2: Site-specific induction isn’t optional—it’s essential
Slide 3: Visitors are most at risk. Keep them safe
Slide 4: Videos beat text—especially when safety is on the line
Slide 5: TechEHS makes safety engaging and consistent
Slide 6: Better retention, lower costs, safer sites
Slide 7: Ready to elevate your induction process?
Can an animated video make a difference to your site's safety? Let's talk.
TrsLabs - Fintech Product & Business Consulting
TrsLabs - Fintech Product & Business ConsultingTrs Labs Hybrid Growth Mandate Model with TrsLabs
Strategic Investments, Inorganic Growth, Business Model Pivoting are critical activities that business don't do/change everyday. In cases like this, it may benefit your business to choose a temporary external consultant.
An unbiased plan driven by clearcut deliverables, market dynamics and without the influence of your internal office equations empower business leaders to make right choices.
Getting things done within a budget within a timeframe is key to Growing Business - No matter whether you are a start-up or a big company
Talk to us & Unlock the competitive advantage
GyrusAI - Broadcasting & Streaming Applications Driven by AI and ML
GyrusAI - Broadcasting & Streaming Applications Driven by AI and MLGyrus AI Gyrus AI: AI/ML for Broadcasting & Streaming
Gyrus is a Vision Al company developing Neural Network Accelerators and ready to deploy AI/ML Models for Video Processing and Video Analytics.
Our Solutions:
Intelligent Media Search
Semantic & contextual search for faster, smarter content discovery.
In-Scene Ad Placement
AI-powered ad insertion to maximize monetization and user experience.
Video Anonymization
Automatically masks sensitive content to ensure privacy compliance.
Vision Analytics
Real-time object detection and engagement tracking.
Why Gyrus AI?
We help media companies streamline operations, enhance media discovery, and stay competitive in the rapidly evolving broadcasting & streaming landscape.
🚀 Ready to Transform Your Media Workflow?
🔗 Visit Us: https://gyrus.ai/
📅 Book a Demo: https://gyrus.ai/contact
📝 Read More: https://gyrus.ai/blog/
🔗 Follow Us:
LinkedIn - https://www.linkedin.com/company/gyrusai/
Twitter/X - https://twitter.com/GyrusAI
YouTube - https://www.youtube.com/channel/UCk2GzLj6xp0A6Wqix1GWSkw
Facebook - https://www.facebook.com/GyrusAI
Vaibhav Gupta BAML: AI work flows without Hallucinations
Vaibhav Gupta BAML: AI work flows without Hallucinationsjohn409870 Shipping Agents
Vaibhav Gupta
Cofounder @ Boundary
in/vaigup
boundaryml/baml
Imagine if every API call you made
failed only 5% of the time
boundaryml/baml
Imagine if every LLM call you made
failed only 5% of the time
boundaryml/baml
Imagine if every LLM call you made
failed only 5% of the time
boundaryml/baml
Fault tolerant systems are hard
but now everything must be
fault tolerant
boundaryml/baml
We need to change how we
think about these systems
Aaron Villalpando
Cofounder @ Boundary
Boundary
Combinator
boundaryml/baml
We used to write websites like this:
boundaryml/baml
But now we do this:
boundaryml/baml
Problems web dev had:
boundaryml/baml
Problems web dev had:
Strings. Strings everywhere.
boundaryml/baml
Problems web dev had:
Strings. Strings everywhere.
State management was impossible.
boundaryml/baml
Problems web dev had:
Strings. Strings everywhere.
State management was impossible.
Dynamic components? forget about it.
boundaryml/baml
Problems web dev had:
Strings. Strings everywhere.
State management was impossible.
Dynamic components? forget about it.
Reuse components? Good luck.
boundaryml/baml
Problems web dev had:
Strings. Strings everywhere.
State management was impossible.
Dynamic components? forget about it.
Reuse components? Good luck.
Iteration loops took minutes.
boundaryml/baml
Problems web dev had:
Strings. Strings everywhere.
State management was impossible.
Dynamic components? forget about it.
Reuse components? Good luck.
Iteration loops took minutes.
Low engineering rigor
boundaryml/baml
React added engineering rigor
boundaryml/baml
The syntax we use changes how we
think about problems
boundaryml/baml
We used to write agents like this:
boundaryml/baml
Problems agents have:
boundaryml/baml
Problems agents have:
Strings. Strings everywhere.
Context management is impossible.
Changing one thing breaks another.
New models come out all the time.
Iteration loops take minutes.
boundaryml/baml
Problems agents have:
Strings. Strings everywhere.
Context management is impossible.
Changing one thing breaks another.
New models come out all the time.
Iteration loops take minutes.
Low engineering rigor
boundaryml/baml
Agents need
the expressiveness of English,
but the structure of code
F*** You, Show Me The Prompt.
boundaryml/baml
<show don’t tell>
Less prompting +
More engineering
=
Reliability +
Maintainability
BAML
Sam
Greg Antonio
Chris
turned down
openai to join
ex-founder, one
of the earliest
BAML users
MIT PhD
20+ years in
compilers
made his own
database, 400k+
youtube views
Vaibhav Gupta
in/vaigup
[email protected]
boundaryml/baml
Thank you!
Transcript: #StandardsGoals for 2025: Standards & certification roundup - Tec...
Transcript: #StandardsGoals for 2025: Standards & certification roundup - Tec...BookNet Canada Book industry standards are evolving rapidly. In the first part of this session, we’ll share an overview of key developments from 2024 and the early months of 2025. Then, BookNet’s resident standards expert, Tom Richardson, and CEO, Lauren Stewart, have a forward-looking conversation about what’s next.
Link to recording, presentation slides, and accompanying resource: https://bnctechforum.ca/sessions/standardsgoals-for-2025-standards-certification-roundup/
Presented by BookNet Canada on May 6, 2025 with support from the Department of Canadian Heritage.
Bepents tech services - a premier cybersecurity consulting firm
Bepents tech services - a premier cybersecurity consulting firmBenard76 Introduction
Bepents Tech Services is a premier cybersecurity consulting firm dedicated to protecting digital infrastructure, data, and business continuity. We partner with organizations of all sizes to defend against today’s evolving cyber threats through expert testing, strategic advisory, and managed services.
🔎 Why You Need us
Cyberattacks are no longer a question of “if”—they are a question of “when.” Businesses of all sizes are under constant threat from ransomware, data breaches, phishing attacks, insider threats, and targeted exploits. While most companies focus on growth and operations, security is often overlooked—until it’s too late.
At Bepents Tech, we bridge that gap by being your trusted cybersecurity partner.
🚨 Real-World Threats. Real-Time Defense.
Sophisticated Attackers: Hackers now use advanced tools and techniques to evade detection. Off-the-shelf antivirus isn’t enough.
Human Error: Over 90% of breaches involve employee mistakes. We help build a "human firewall" through training and simulations.
Exposed APIs & Apps: Modern businesses rely heavily on web and mobile apps. We find hidden vulnerabilities before attackers do.
Cloud Misconfigurations: Cloud platforms like AWS and Azure are powerful but complex—and one misstep can expose your entire infrastructure.
💡 What Sets Us Apart
Hands-On Experts: Our team includes certified ethical hackers (OSCP, CEH), cloud architects, red teamers, and security engineers with real-world breach response experience.
Custom, Not Cookie-Cutter: We don’t offer generic solutions. Every engagement is tailored to your environment, risk profile, and industry.
End-to-End Support: From proactive testing to incident response, we support your full cybersecurity lifecycle.
Business-Aligned Security: We help you balance protection with performance—so security becomes a business enabler, not a roadblock.
📊 Risk is Expensive. Prevention is Profitable.
A single data breach costs businesses an average of $4.45 million (IBM, 2023).
Regulatory fines, loss of trust, downtime, and legal exposure can cripple your reputation.
Investing in cybersecurity isn’t just a technical decision—it’s a business strategy.
🔐 When You Choose Bepents Tech, You Get:
Peace of Mind – We monitor, detect, and respond before damage occurs.
Resilience – Your systems, apps, cloud, and team will be ready to withstand real attacks.
Confidence – You’ll meet compliance mandates and pass audits without stress.
Expert Guidance – Our team becomes an extension of yours, keeping you ahead of the threat curve.
Security isn’t a product. It’s a partnership.
Let Bepents tech be your shield in a world full of cyber threats.
🌍 Our Clientele
At Bepents Tech Services, we’ve earned the trust of organizations across industries by delivering high-impact cybersecurity, performance engineering, and strategic consulting. From regulatory bodies to tech startups, law firms, and global consultancies, we tailor our solutions to each client's unique needs.
TrsLabs - Leverage the Power of UPI Payments
TrsLabs - Leverage the Power of UPI PaymentsTrs Labs Revolutionize your Fintech growth with UPI Payments
"Riding the UPI strategy" refers to leveraging the Unified Payments Interface (UPI) to drive digital payments in India and beyond. This involves understanding UPI's features, benefits, and potential, and developing strategies to maximize its usage and impact. Essentially, it's about strategically utilizing UPI to promote digital payments, financial inclusion, and economic growth.
Cybersecurity Identity and Access Solutions using Azure AD
Cybersecurity Identity and Access Solutions using Azure ADVICTOR MAESTRE RAMIREZ Cybersecurity Identity and Access Solutions using Azure AD
How analogue intelligence complements AI
How analogue intelligence complements AIPaul Rowe
Artificial Intelligence is providing benefits in many areas of work within the heritage sector, from image analysis, to ideas generation, and new research tools. However, it is more critical than ever for people, with analogue intelligence, to ensure the integrity and ethical use of AI. Including real people can improve the use of AI by identifying potential biases, cross-checking results, refining workflows, and providing contextual relevance to AI-driven results.
News about the impact of AI often paints a rosy picture. In practice, there are many potential pitfalls. This presentation discusses these issues and looks at the role of analogue intelligence and analogue interfaces in providing the best results to our audiences. How do we deal with factually incorrect results? How do we get content generated that better reflects the diversity of our communities? What roles are there for physical, in-person experiences in the digital world?
Designing Low-Latency Systems with Rust and ScyllaDB: An Architectural Deep Dive
Designing Low-Latency Systems with Rust and ScyllaDB: An Architectural Deep DiveScyllaDB Want to learn practical tips for designing systems that can scale efficiently without compromising speed?
Join us for a workshop where we’ll address these challenges head-on and explore how to architect low-latency systems using Rust. During this free interactive workshop oriented for developers, engineers, and architects, we’ll cover how Rust’s unique language features and the Tokio async runtime enable high-performance application development.
As you explore key principles of designing low-latency systems with Rust, you will learn how to:
- Create and compile a real-world app with Rust
- Connect the application to ScyllaDB (NoSQL data store)
- Negotiate tradeoffs related to data modeling and querying
- Manage and monitor the database for consistently low latencies
HCL Nomad Web – Best Practices and Managing Multiuser Environments
HCL Nomad Web – Best Practices and Managing Multiuser Environmentspanagenda Webinar Recording: https://www.panagenda.com/webinars/hcl-nomad-web-best-practices-and-managing-multiuser-environments/
HCL Nomad Web is heralded as the next generation of the HCL Notes client, offering numerous advantages such as eliminating the need for packaging, distribution, and installation. Nomad Web client upgrades will be installed “automatically” in the background. This significantly reduces the administrative footprint compared to traditional HCL Notes clients. However, troubleshooting issues in Nomad Web present unique challenges compared to the Notes client.
Join Christoph and Marc as they demonstrate how to simplify the troubleshooting process in HCL Nomad Web, ensuring a smoother and more efficient user experience.
In this webinar, we will explore effective strategies for diagnosing and resolving common problems in HCL Nomad Web, including
- Accessing the console
- Locating and interpreting log files
- Accessing the data folder within the browser’s cache (using OPFS)
- Understand the difference between single- and multi-user scenarios
- Utilizing Client Clocking
#StandardsGoals for 2025: Standards & certification roundup - Tech Forum 2025
#StandardsGoals for 2025: Standards & certification roundup - Tech Forum 2025BookNet Canada Book industry standards are evolving rapidly. In the first part of this session, we’ll share an overview of key developments from 2024 and the early months of 2025. Then, BookNet’s resident standards expert, Tom Richardson, and CEO, Lauren Stewart, have a forward-looking conversation about what’s next.
Link to recording, transcript, and accompanying resource: https://bnctechforum.ca/sessions/standardsgoals-for-2025-standards-certification-roundup/
Presented by BookNet Canada on May 6, 2025 with support from the Department of Canadian Heritage.
Challenges in Migrating Imperative Deep Learning Programs to Graph Execution:...
Challenges in Migrating Imperative Deep Learning Programs to Graph Execution:...Raffi Khatchadourian
MS SQL SERVER: Introduction To Datamining Suing Sql Server
- 1. 14SQL SERVER: INTRODUCTION TO DATA MINING USING SQL SERVER
- 2. What is a Data Mining?Data mining is the process of analyzing a data set to find patternsData mining can also defined as deriving of knowledge from raw-data
- 3. AliasesData mining is also known by the following terms:
- 4. Importance of Data miningThe Amount of data in the contemporary world is humungous. By studying this data and understanding the trend and patterns, one can understand the system better. Due to data mining, conclusions which are profitable for an organization or decisions which may help a librarian manage books better: may be arrived at. Pervasiveness of data:CRM(Customer Relationship Management)ERP(Enterprise Resource Planning)Database serversData PoolWeb Server Logs
- 5. Data MiningThe traditional SQL queries that we learnt till now follow the method of ‘querying’ and based upon the response, ‘explore’ the system more. Query and Exploration MethodData Mining MethodThe Data mining methodology hence takes the opposite direction as that of query methodsHere, the important attribute on which the analysis is based is the ‘name’. Hence, it is called as the class
- 6. ApplicationsThe Application of data mining covers a wide domain. Any place where data is involved can be operated upon using data mining. Some of the real world applications of data mining are as follows:
- 7. Algorithms for Data miningThe Data mining systems utilize a wide variety of algorithms. The Four common algorithm types are:
- 8. Tasks involved in Data MiningThe Process of data mining is divided into various steps as follows: Classification
- 9. Clustering
- 10. Association
- 11. Regression
- 12. ForecastingLet us have a look at them
- 13. ClassificationClassification is the process of grouping items into meaningful groups. The Groups are later treated as a single element and the relation between the groups are analyzed. Simply put, it is the task of assigning a group to each case.Example:Data Set
- 14. ClusteringClustering is the process of grouping data items based on some attributesExample:Data SetClustered based on nearness
- 15. Data mining algorithmsData Mining is a complex methodology which needs advanced algorithms operating on useful data.The Data mining algorithms are mainly divided into 2 types:Supervising algorithmUnsupervising algorithmIn a supervising algorithm, the system needs a target(may be a set of attributes) to learn againstWhereas the Unsupervising algorithm, iterates till the boundaries of the problem are reached
- 16. Regression and ForecastingREGRESSION:In some problems, the analysis, instead of looking for patterns that describe prime attributes (classes), we look for patterns in numerical valuesThere are 2 types of regression: 1.Linear regression 2. Logostic RegressionRegression is used to solve many business problems like predicting sea-wave patterns, temperature, air pressure, and humidity.FORECASTING:As the name suggests, it is the fore telling of data from that which currently exists.Eg: Election results forecast
- 17. Steps to takeThe Process of data mining consists of various steps which are listed below:Data Collection: Collect dataData Cleaning: Eliminate unwanted, irrelevant and wrong dataData Transformation: Change data into a word that can be used for data mining. The Types of data transformations are:Numerical TransformationGroupingAggregation: Form groups of minute data items and handle them as aggregates. It makes the process much easier.Missing Value handling: Predict missing values or eliminate all such valuesRemoving Outliers: Remove invalid dataModel Building: Build the data mining model.Model Assessment Test with a large amount of data. If a model needs change, make it immediately.
- 18. What to do next?The Microsoft Office 2007 supports a wide variety of data mining tools. Visit the site www.sqlserverdatamining.com and download the MS Access 2007 Add-on for data mining. Install the add-on.Working with the Access 07 Data mining tools will be handled in the next set of presentations.Summary Data mining
- 19. Applications
- 20. Classification
- 21. Clustering
- 22. Algorithms
- 23. Regressions
- 24. Steps involvedVisit 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