A data warehouse is a relational database that is designed for query and analysis rather than for transaction processing. It usually contains historical data derived from transaction data, but it can include data from other sources. It separates analysis workload from transaction workload and enables an organization to consolidate data from several sources.
In addition to a relational database, a data warehouse environment includes an extraction, transportation, transformation, and loading (ETL) solution, an online analytical processing (OLAP) engine, client analysis tools, and other applications that manage the process of gathering data and delivering it to business users.
Professional dataware-housing-training-in-mumbaiUnmesh Baile
Vibrant Technologies is headquarted in Mumbai,India.We are the best Data Warehousing training provider in Navi Mumbai who provides Live Projects to students.We provide Corporate Training also.We are Best Data Warehousing classes in Mumbai according to our students and corporators
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD Editor
This document summarizes and compares various multi-relational decision tree learning algorithms. It first introduces the MRDTL algorithm, which constructs decision trees from relational databases by using selection graphs to represent patterns at nodes. The document then discusses limitations of MRDTL related to running time and handling missing values. It proposes an improved MRDTL-2 algorithm that uses a naive Bayesian classifier for preprocessing to address missing values, and tuple ID propagation instead of complex data structures to improve running time. Finally, it describes the RDC algorithm, which uses tuple ID propagation and information gain to build decision trees from relational databases.
The document discusses Structured Query Language (SQL). It introduces SQL and provides information on its architecture, commands, data types, and use for data warehousing. SQL is described as a language for storing, manipulating, and retrieving data in relational database management systems. Common SQL commands are listed as CREATE, SELECT, INSERT, UPDATE, DELETE, and DROP.
The document discusses recursion in computer programming. It defines recursion as a function that calls itself or is called by another function. It categorizes different types of recursion such as direct, indirect, linear, tree, and tail recursion. It explains how recursive functions work by using a stack to store information from each function call. Examples of recursively-defined problems like factorials and Fibonacci numbers are provided. Guidelines for writing recursive functions and solving problems recursively are also outlined.
This document discusses abstract data types (ADTs) and general linear lists. It begins by defining an ADT as a data type packaged with meaningful operations for that data type, where the data and operations are encapsulated and hidden from the user. The document then discusses general linear lists as a type of list that allows insertion and deletion anywhere in the list. Six common operations for general linear lists are defined: list, insert, delete, retrieve, traverse, and empty. Examples are given showing how these operations can be used in algorithms operating on a general linear list representing student records. The document concludes by noting general linear lists can be implemented using arrays or linked lists.
Data preprocessing techniques
See my Paris applied psychology conference paper here
https://www.slideshare.net/jasonrodrigues/paris-conference-on-applied-psychology
or
https://prezi.com/view/KBP8JnekVH9LkLOiKY3w/
1. Fundamental Concept - Data Structures using C++ by Varsha Patilwidespreadpromotion
This document provides an overview of key concepts related to data structures and algorithms using C++. It discusses fundamental topics like data types, data objects, abstract data types, and data structures. It also covers algorithms, including their characteristics, design tools like pseudocode and flowcharts, and complexity analysis using Big O notation. Finally, it introduces software engineering concepts like the software development life cycle and its main phases of analysis, design, implementation, testing and verification.
The document discusses arrays and ArrayLists in C#. It notes that arrays are indexed collections that derive from System.Array, allowing usage of its methods. Arrays are declared with syntax like type[] name and initialized with new. Multidimensional arrays can be declared with commas to specify dimensions. The document also introduces ArrayList as an alternative that dynamically resizes itself as needed.
Professional dataware-housing-training-in-mumbaiUnmesh Baile
Vibrant Technologies is headquarted in Mumbai,India.We are the best Data Warehousing training provider in Navi Mumbai who provides Live Projects to students.We provide Corporate Training also.We are Best Data Warehousing classes in Mumbai according to our students and corporators
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD Editor
This document summarizes and compares various multi-relational decision tree learning algorithms. It first introduces the MRDTL algorithm, which constructs decision trees from relational databases by using selection graphs to represent patterns at nodes. The document then discusses limitations of MRDTL related to running time and handling missing values. It proposes an improved MRDTL-2 algorithm that uses a naive Bayesian classifier for preprocessing to address missing values, and tuple ID propagation instead of complex data structures to improve running time. Finally, it describes the RDC algorithm, which uses tuple ID propagation and information gain to build decision trees from relational databases.
The document discusses Structured Query Language (SQL). It introduces SQL and provides information on its architecture, commands, data types, and use for data warehousing. SQL is described as a language for storing, manipulating, and retrieving data in relational database management systems. Common SQL commands are listed as CREATE, SELECT, INSERT, UPDATE, DELETE, and DROP.
The document discusses recursion in computer programming. It defines recursion as a function that calls itself or is called by another function. It categorizes different types of recursion such as direct, indirect, linear, tree, and tail recursion. It explains how recursive functions work by using a stack to store information from each function call. Examples of recursively-defined problems like factorials and Fibonacci numbers are provided. Guidelines for writing recursive functions and solving problems recursively are also outlined.
This document discusses abstract data types (ADTs) and general linear lists. It begins by defining an ADT as a data type packaged with meaningful operations for that data type, where the data and operations are encapsulated and hidden from the user. The document then discusses general linear lists as a type of list that allows insertion and deletion anywhere in the list. Six common operations for general linear lists are defined: list, insert, delete, retrieve, traverse, and empty. Examples are given showing how these operations can be used in algorithms operating on a general linear list representing student records. The document concludes by noting general linear lists can be implemented using arrays or linked lists.
Data preprocessing techniques
See my Paris applied psychology conference paper here
https://www.slideshare.net/jasonrodrigues/paris-conference-on-applied-psychology
or
https://prezi.com/view/KBP8JnekVH9LkLOiKY3w/
1. Fundamental Concept - Data Structures using C++ by Varsha Patilwidespreadpromotion
This document provides an overview of key concepts related to data structures and algorithms using C++. It discusses fundamental topics like data types, data objects, abstract data types, and data structures. It also covers algorithms, including their characteristics, design tools like pseudocode and flowcharts, and complexity analysis using Big O notation. Finally, it introduces software engineering concepts like the software development life cycle and its main phases of analysis, design, implementation, testing and verification.
The document discusses arrays and ArrayLists in C#. It notes that arrays are indexed collections that derive from System.Array, allowing usage of its methods. Arrays are declared with syntax like type[] name and initialized with new. Multidimensional arrays can be declared with commas to specify dimensions. The document also introduces ArrayList as an alternative that dynamically resizes itself as needed.
6. Linked list - Data Structures using C++ by Varsha Patilwidespreadpromotion
The document discusses linked lists as a dynamic data structure. It defines a linked list as a collection of data elements called nodes that together represent a sequence. Each node contains a data field for the element and a link to the next node. This allows elements to be added or removed without reorganizing the entire structure. The document covers different types of linked lists including singly linked, doubly linked, circular, and their applications for storing polynomials and implementing stacks. It also discusses operations like traversal, insertion, and deletion of nodes.
The document discusses algorithm analysis and complexity analysis. It introduces the concept of analyzing an algorithm's runtime by examining the number of key operations like comparisons and assignments, rather than just measuring execution time. This is known as complexity analysis. The document uses an example of summing the rows and values of a matrix to illustrate how complexity analysis can identify the most efficient of multiple algorithms for the same problem. It determines that two example algorithms for summing a matrix have the same asymptotic runtime of O(n^2). The document then introduces Big-O notation for describing an algorithm's asymptotic worst-case runtime.
This document discusses queues and their implementation using data structures in C++. It covers:
1) Defining queues and their operations of insertion at the rear and deletion at the front.
2) Implementing queues using arrays and avoiding their drawbacks using circular queues.
3) Other applications that use queues like simulation, job scheduling, and priority queues.
4) Different queue implementations like multi-queue, deque, and priority queue data structures.
(1) The document discusses I-Extended Databases, which contain generalizations about data in addition to the data. (2) I-Extended Databases can be used in the knowledge discovery process by modeling steps as queries. (3) Patterns discovered in data mining can be represented in I-Extended Databases based on their frequency, confidence, and support values.
The document discusses different file organization methods for storing data on external storage devices. It describes sequential, direct access, and indexed sequential file organization. Sequential files store records in the order they are entered, requiring searching through all preceding records to access a non-sequential record. Direct access files allow direct retrieval of any record through its logical address. Indexed sequential files store records sequentially but have an index file to allow direct access by key. The document compares advantages and disadvantages of each method.
The document discusses the bubble sort algorithm. It begins by explaining how bubble sort works by repeatedly stepping through a list and swapping adjacent elements that are out of order until the list is fully sorted. It then provides a step-by-step example showing the application of bubble sort to sort an array from lowest to highest. The document concludes by presenting pseudocode for a bubble sort implementation.
Introduction to database-Formal Query language and Relational calculusAjit Nayak
The document provides an introduction to relational databases and formal relational query languages. It discusses relational algebra and relational calculus as the two formal query languages that form the mathematical foundation for commercial relational query languages. Relational algebra is a procedural query language that supports operations like select, project, union, set difference, cartesian product and rename. Example queries are provided for each operation to demonstrate their usage. Relational calculus is described as a non-procedural query language with examples of queries written using its syntax.
This document provides an introduction to databases and SQL. It discusses the relational model and how data is stored in tables with rows and columns. It also describes the different languages used in SQL - DDL for data definition, DML for data manipulation, and DCL for data control. Examples are provided of creating tables, inserting data, and using select statements to query and retrieve data. Group functions like COUNT, AVG, MAX etc. are also introduced. The document emphasizes the importance of transactions and using COMMIT to save changes permanently to the database.
The document discusses key concepts related to data structures and algorithms. It defines data as values or sets of values that can be organized hierarchically into fields, records, and files. Entities have attributes that can be assigned values. Related entities form entity sets. Data structures organize data through fields, records, and files while supporting operations like searching, insertion, and deletion. Algorithms are step-by-step processes to solve problems in a finite number of steps. The efficiency of algorithms is measured by time and space complexity.
The document discusses key concepts of relational databases and relational algebra. It defines what a relation is as a set of tuples with attributes, and covers attribute types, keys, relations schemas and instances. It also summarizes the core relational algebra operations of selection, projection, join, union, difference and Cartesian product and how they are used to manipulate and query relations.
10. Search Tree - Data Structures using C++ by Varsha Patilwidespreadpromotion
The document discusses binary search trees and their variants. It explains that search trees are important for algorithm design and it is desirable to minimize the search time of each node. There are static and dynamic binary search trees, with the latter adjusting its structure during access. AVL trees are a type of self-balancing binary search tree where rotations are used to rebalance the tree after insertions or deletions and ensure the heights of subtrees differ by at most one. Compilers use symbol tables implemented as search trees to track variables in source code.
1) An abstract data type (ADT) defines the operations that can be performed on a certain type of data but does not define how those operations are implemented.
2) Common examples of ADTs include lists, stacks, queues, and trees. Each ADT has multiple possible concrete data type implementations.
3) To define an ADT, one identifies the essential data fields and operations without specifying how they are stored or computed. This provides an abstract model of the problem domain.
The document provides an overview of database systems and concepts. It discusses how database management systems (DBMS) are used to store and manage data in an organized way. A DBMS contains data about an enterprise, programs to access the data, and provides a convenient environment. It also describes common data models like relational and object-oriented models, database design approaches, and core components of a database system like storage management, query processing, and transaction management.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
help.mbaassignments@gmail.com
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call us at : 08263069601
Semi-automatic Discovery of Mappings Between Heterogeneous Data Warehouse Dim...IDES Editor
Data Warehousing is the main Business
Intelligence instrument for the analysis of large amounts of
data. It permits the extraction of relevant information for
decision making processes inside organizations. Given the
great diffusion of Data Warehouses, there is an increasing
need to integrate information coming from independent
Data Warehouses or from independently developed data
marts in the same Data Warehouse. In this paper, we
provide a method for the semi-automatic discovery of
common topological properties of dimensions that can be
used to automatically map elements of different dimensions
in heterogeneous Data Warehouses. The method uses
techniques from the Data Integration research area and
combines topological properties of dimensions in a
multidimensional model.
The document discusses C# strings and string manipulation. It begins by explaining that a C# string is a sequential collection of Unicode characters represented by a String object which is a collection of Char objects. It then covers key properties of strings like being immutable and reference types. The document also discusses string literals, common string operators, and methods to manipulate strings, as well as the StringBuilder class as a mutable alternative to strings.
The document discusses stacks and their applications. It describes stacks as last-in, first-out data structures and covers stack operations like push and pop. Common uses of stacks include expression evaluation, recursion, reversing data structures, and printing job queues. The document also discusses time and space complexity analysis of algorithms, conversion between infix, postfix and prefix notation, and software engineering principles like the software development life cycle.
9. Searching & Sorting - Data Structures using C++ by Varsha Patilwidespreadpromotion
The document discusses various searching and sorting algorithms. It covers sequential search, binary search, Fibonacci search, hashed search, indexed sequential search and their time complexities. Sorting algorithms like bubble sort, insertion sort, selection sort are explained along with their analysis. Internal sorting techniques like quicksort, heapsort, radix sort and bucket sort are also mentioned. The document provides details on sorting methods, order, stability and efficiency.
This document summarizes key concepts from Chapter 4 of the textbook "Database System Concepts". It discusses join operations between relations, including outer joins. It also covers views, transactions, integrity constraints, SQL data types, indexes, and authorization. Specific topics covered include view definitions and updates, materialized views, referential integrity constraints, and built-in data types like date and timestamp.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
The document discusses data warehousing and materialization approaches for data integration. It describes:
1) The data warehouse approach, where data is transformed, cleaned and stored in a central database optimized for queries. Extract-Transform-Load tools are used to periodically load and update the warehouse.
2) Data exchange, which takes a declarative approach using schema mappings to materialize a target warehouse instance from source data, handling unknown values.
3) Hybrid approaches involving caching query results or partially materializing views to trade off between virtual and fully materialized integration.
This document provides an overview of Hibernate, an object-relational mapping framework for Java. It discusses what Hibernate is, why it is useful for developers, and some of its main alternatives. The document then covers object-relational mapping challenges like identity, granularity, associations, inheritance, and data types that Hibernate aims to address. It provides a simple example of using Hibernate and describes its basic architecture, configuration, and object lifecycle. Finally, it discusses advanced Hibernate features like association mapping.
6. Linked list - Data Structures using C++ by Varsha Patilwidespreadpromotion
The document discusses linked lists as a dynamic data structure. It defines a linked list as a collection of data elements called nodes that together represent a sequence. Each node contains a data field for the element and a link to the next node. This allows elements to be added or removed without reorganizing the entire structure. The document covers different types of linked lists including singly linked, doubly linked, circular, and their applications for storing polynomials and implementing stacks. It also discusses operations like traversal, insertion, and deletion of nodes.
The document discusses algorithm analysis and complexity analysis. It introduces the concept of analyzing an algorithm's runtime by examining the number of key operations like comparisons and assignments, rather than just measuring execution time. This is known as complexity analysis. The document uses an example of summing the rows and values of a matrix to illustrate how complexity analysis can identify the most efficient of multiple algorithms for the same problem. It determines that two example algorithms for summing a matrix have the same asymptotic runtime of O(n^2). The document then introduces Big-O notation for describing an algorithm's asymptotic worst-case runtime.
This document discusses queues and their implementation using data structures in C++. It covers:
1) Defining queues and their operations of insertion at the rear and deletion at the front.
2) Implementing queues using arrays and avoiding their drawbacks using circular queues.
3) Other applications that use queues like simulation, job scheduling, and priority queues.
4) Different queue implementations like multi-queue, deque, and priority queue data structures.
(1) The document discusses I-Extended Databases, which contain generalizations about data in addition to the data. (2) I-Extended Databases can be used in the knowledge discovery process by modeling steps as queries. (3) Patterns discovered in data mining can be represented in I-Extended Databases based on their frequency, confidence, and support values.
The document discusses different file organization methods for storing data on external storage devices. It describes sequential, direct access, and indexed sequential file organization. Sequential files store records in the order they are entered, requiring searching through all preceding records to access a non-sequential record. Direct access files allow direct retrieval of any record through its logical address. Indexed sequential files store records sequentially but have an index file to allow direct access by key. The document compares advantages and disadvantages of each method.
The document discusses the bubble sort algorithm. It begins by explaining how bubble sort works by repeatedly stepping through a list and swapping adjacent elements that are out of order until the list is fully sorted. It then provides a step-by-step example showing the application of bubble sort to sort an array from lowest to highest. The document concludes by presenting pseudocode for a bubble sort implementation.
Introduction to database-Formal Query language and Relational calculusAjit Nayak
The document provides an introduction to relational databases and formal relational query languages. It discusses relational algebra and relational calculus as the two formal query languages that form the mathematical foundation for commercial relational query languages. Relational algebra is a procedural query language that supports operations like select, project, union, set difference, cartesian product and rename. Example queries are provided for each operation to demonstrate their usage. Relational calculus is described as a non-procedural query language with examples of queries written using its syntax.
This document provides an introduction to databases and SQL. It discusses the relational model and how data is stored in tables with rows and columns. It also describes the different languages used in SQL - DDL for data definition, DML for data manipulation, and DCL for data control. Examples are provided of creating tables, inserting data, and using select statements to query and retrieve data. Group functions like COUNT, AVG, MAX etc. are also introduced. The document emphasizes the importance of transactions and using COMMIT to save changes permanently to the database.
The document discusses key concepts related to data structures and algorithms. It defines data as values or sets of values that can be organized hierarchically into fields, records, and files. Entities have attributes that can be assigned values. Related entities form entity sets. Data structures organize data through fields, records, and files while supporting operations like searching, insertion, and deletion. Algorithms are step-by-step processes to solve problems in a finite number of steps. The efficiency of algorithms is measured by time and space complexity.
The document discusses key concepts of relational databases and relational algebra. It defines what a relation is as a set of tuples with attributes, and covers attribute types, keys, relations schemas and instances. It also summarizes the core relational algebra operations of selection, projection, join, union, difference and Cartesian product and how they are used to manipulate and query relations.
10. Search Tree - Data Structures using C++ by Varsha Patilwidespreadpromotion
The document discusses binary search trees and their variants. It explains that search trees are important for algorithm design and it is desirable to minimize the search time of each node. There are static and dynamic binary search trees, with the latter adjusting its structure during access. AVL trees are a type of self-balancing binary search tree where rotations are used to rebalance the tree after insertions or deletions and ensure the heights of subtrees differ by at most one. Compilers use symbol tables implemented as search trees to track variables in source code.
1) An abstract data type (ADT) defines the operations that can be performed on a certain type of data but does not define how those operations are implemented.
2) Common examples of ADTs include lists, stacks, queues, and trees. Each ADT has multiple possible concrete data type implementations.
3) To define an ADT, one identifies the essential data fields and operations without specifying how they are stored or computed. This provides an abstract model of the problem domain.
The document provides an overview of database systems and concepts. It discusses how database management systems (DBMS) are used to store and manage data in an organized way. A DBMS contains data about an enterprise, programs to access the data, and provides a convenient environment. It also describes common data models like relational and object-oriented models, database design approaches, and core components of a database system like storage management, query processing, and transaction management.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
help.mbaassignments@gmail.com
or
call us at : 08263069601
Semi-automatic Discovery of Mappings Between Heterogeneous Data Warehouse Dim...IDES Editor
Data Warehousing is the main Business
Intelligence instrument for the analysis of large amounts of
data. It permits the extraction of relevant information for
decision making processes inside organizations. Given the
great diffusion of Data Warehouses, there is an increasing
need to integrate information coming from independent
Data Warehouses or from independently developed data
marts in the same Data Warehouse. In this paper, we
provide a method for the semi-automatic discovery of
common topological properties of dimensions that can be
used to automatically map elements of different dimensions
in heterogeneous Data Warehouses. The method uses
techniques from the Data Integration research area and
combines topological properties of dimensions in a
multidimensional model.
The document discusses C# strings and string manipulation. It begins by explaining that a C# string is a sequential collection of Unicode characters represented by a String object which is a collection of Char objects. It then covers key properties of strings like being immutable and reference types. The document also discusses string literals, common string operators, and methods to manipulate strings, as well as the StringBuilder class as a mutable alternative to strings.
The document discusses stacks and their applications. It describes stacks as last-in, first-out data structures and covers stack operations like push and pop. Common uses of stacks include expression evaluation, recursion, reversing data structures, and printing job queues. The document also discusses time and space complexity analysis of algorithms, conversion between infix, postfix and prefix notation, and software engineering principles like the software development life cycle.
9. Searching & Sorting - Data Structures using C++ by Varsha Patilwidespreadpromotion
The document discusses various searching and sorting algorithms. It covers sequential search, binary search, Fibonacci search, hashed search, indexed sequential search and their time complexities. Sorting algorithms like bubble sort, insertion sort, selection sort are explained along with their analysis. Internal sorting techniques like quicksort, heapsort, radix sort and bucket sort are also mentioned. The document provides details on sorting methods, order, stability and efficiency.
This document summarizes key concepts from Chapter 4 of the textbook "Database System Concepts". It discusses join operations between relations, including outer joins. It also covers views, transactions, integrity constraints, SQL data types, indexes, and authorization. Specific topics covered include view definitions and updates, materialized views, referential integrity constraints, and built-in data types like date and timestamp.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
The document discusses data warehousing and materialization approaches for data integration. It describes:
1) The data warehouse approach, where data is transformed, cleaned and stored in a central database optimized for queries. Extract-Transform-Load tools are used to periodically load and update the warehouse.
2) Data exchange, which takes a declarative approach using schema mappings to materialize a target warehouse instance from source data, handling unknown values.
3) Hybrid approaches involving caching query results or partially materializing views to trade off between virtual and fully materialized integration.
This document provides an overview of Hibernate, an object-relational mapping framework for Java. It discusses what Hibernate is, why it is useful for developers, and some of its main alternatives. The document then covers object-relational mapping challenges like identity, granularity, associations, inheritance, and data types that Hibernate aims to address. It provides a simple example of using Hibernate and describes its basic architecture, configuration, and object lifecycle. Finally, it discusses advanced Hibernate features like association mapping.
The document discusses what a data warehouse is and why schools are setting them up. It provides key concepts like OLTP, OLAP, ETL, star schemas, and data marts. A data warehouse extracts data from transactional systems, transforms and loads it into a dimensional data store to support analysis. It is updated via periodic ETL jobs and presents data in simplified, denormalized schemas to support decision making. Implementing a data warehouse requires defining requirements and priorities through collaboration between decision makers and technologists.
This document provides an overview of a data analytics session covering big data architecture, connecting and extracting data from storage, traditional processing with a bank use case, Hadoop-HDFS solutions, and HDFS working. The key topics covered include big data architecture layers, structured and unstructured data extraction, comparisons of storage media, traditional versus Hadoop approaches, HDFS basics including blocks and replication across nodes. The session aims to help learners understand efficient analytics systems for handling large and diverse data sources.
The ETL process contains 3 main steps: extraction, transformation, and loading. Data is extracted from source databases, transformed by applying business rules, and loaded into the target database. A full load populates data warehouse tables for the first time by loading all records, while an incremental load applies dynamic changes over time. A three-tier data warehouse has a source layer to land data, an integration layer to store transformed data, and a dimension layer as the presentation layer. Snapshots are read-only copies of master tables refreshed periodically, while materialized views are pre-computed aggregate tables created from fact and dimension tables with associated materialized view logs. PowerCenter processes large volumes of data including from ERP sources and allows session partitioning
This document discusses various data warehousing concepts. It begins by explaining that fact tables can share dimension tables and that typically multiple dimension tables are associated with a single fact table. It then defines ROLAP, MOLAP, and DOLAP architectures for OLAP and discusses how data is stored in each. An MDDB is described as a multidimensional database that stores data in multidimensional arrays, whereas an RDBMS stores data in tables and columns. The differences between OLTP and OLAP systems are outlined. Transformations in ETL are explained as manipulating data from its source form into a simplified form for the data warehouse. Filter transformations are briefly described. Finally, supported default source types for Informatica Power
The document provides an introduction and tutorial on Oracle and PL/SQL. It discusses key database concepts like tables, schemas, tablespaces, and normalization. It also covers installing Oracle Database 10g, SQL and DML commands, database security, and differences between DBMS and RDBMS. Frequent interview questions on topics like data, information, database components, and SQL are presented along with answers.
This document provides an overview of data warehousing concepts including dimensional modeling, online analytical processing (OLAP), and indexing techniques. It discusses the evolution of data warehousing, definitions of data warehouses, architectures, and common applications. Dimensional modeling concepts such as star schemas, snowflake schemas, and slowly changing dimensions are explained. The presentation concludes with references for further reading.
Implementation of multidimensional databases with document-oriented NoSQL
Implémentation des entrepôts de données NoSQL dans les bases de données NoSQL orienté documents.
The document provides an overview of data warehousing and decision support systems. It discusses how data warehouses evolved from databases used for transaction processing to integrated databases designed for analysis and decision making. Key points include:
- Data warehouses store historical data from multiple sources to support analysis and decision making.
- They address limitations of transactional databases that are optimized for real-time queries rather than complex analysis.
- Effective data warehousing requires resolving data conflicts, documenting assumptions, and learning from mistakes in the implementation process.
The document summarizes the history and evolution of non-relational databases, known as NoSQL databases. It discusses early database systems like MUMPS and IMS, the development of the relational model in the 1970s, and more recent NoSQL databases developed by companies like Google, Amazon, Facebook to handle large, dynamic datasets across many servers. Pioneering systems like Google's Bigtable and Amazon's Dynamo used techniques like distributed indexing, versioning, and eventual consistency that influenced many open-source NoSQL databases today.
The document discusses various concepts related to database design and data warehousing. It describes how DBMS minimize problems like data redundancy, isolation, and inconsistency through techniques like normalization, indexing, and using data dictionaries. It then discusses data warehousing concepts like the need for data warehouses, their key characteristics of being subject-oriented, integrated, and time-variant. Common data warehouse architectures and components like the ETL process, OLAP, and decision support systems are also summarized.
This document provides an overview of topics to be covered in a database management systems course, including parallel and distributed databases, NoSQL databases, and MapReduce. It discusses parallel databases and different architectures for distributed databases. It introduces several NoSQL databases like Amazon SimpleDB, Google BigTable, and HBase and describes their data models and implementations. It also provides details about MapReduce, including its programming model, implementation, optimizations, and statistics on its usage at Google. The next class meetings will include a mid-term exam, student presentations on assigned topics, and a proposal for each student's final project.
This document provides a summary of 20 interview questions related to Informatica. It discusses concepts like the components of Informatica, what a repository is and how to add one, different types of transformations used in mappings and their purposes, how to make transformations reusable, how to import source and target definitions, and what a session is and how to create it. The document is a training resource that provides answers to common Informatica interview questions.
Data science involves extracting knowledge from data to solve business problems. The data science life cycle includes defining the problem, collecting and preparing data, exploring the data, building models, and communicating results. Data preparation is an essential step that can consume 60% of a project's time. It involves cleaning, transforming, handling outliers, integrating, and reducing data. Models are built using machine learning algorithms like regression for continuous variables and classification for discrete variables. Results are visualized and communicated effectively to clients.
The document defines conceptual, logical, and physical data models and compares their key features. A conceptual model shows entities and relationships without attributes or keys. A logical model adds attributes, primary keys, and foreign keys. A physical model specifies tables, columns, data types, and other implementation details.
This document provides an overview of key topics from BTM 382 Database Management including:
- The structure and content of the course including chapters on data models, database design, programming, and management.
- Descriptions of the relational, entity-relationship, object-oriented, and NoSQL data models and how they have evolved over time.
- An explanation of how Big Data challenges are addressed through NoSQL databases which sacrifice consistency for speed.
- Guidance on which data model to use based on factors like data complexity, performance needs, and organizational objectives.
This document provides an overview of different data models discussed in chapters 2, 12, and 14 of a database management course. It describes the evolution of data models from hierarchical and network models to the relational model and entity-relationship model. The document also discusses the object-oriented data model, big data challenges, and how NoSQL databases help address those challenges. Key tradeoffs between consistency and speed are explained in the context of CAP theorem and ACID versus BASE properties. The document concludes with guidance on selecting an appropriate data model based on data structure complexity and performance needs.
Reimagining Your Library Space: How to Increase the Vibes in Your Library No ...Diana Rendina
Librarians are leading the way in creating future-ready citizens – now we need to update our spaces to match. In this session, attendees will get inspiration for transforming their library spaces. You’ll learn how to survey students and patrons, create a focus group, and use design thinking to brainstorm ideas for your space. We’ll discuss budget friendly ways to change your space as well as how to find funding. No matter where you’re at, you’ll find ideas for reimagining your space in this session.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
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তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
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Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...
Warehousing
1. ANHAI DOAN ALON HALEVY ZACHARY IVES
CHAPTER 10: DATA
WAREHOUSING & CACHING
PRINCIPLES OF
DATA INTEGRATION
2. Data Warehousing and
Materialization
We have mostly focused on techniques for virtual
data integration (see Ch. 1)
Queries are composed with mappings on the fly and data
is fetched on demand
This represents one extreme point
In this chapter, we consider cases where data is
transformed and materialized “in advance” of the
queries
The main scenario: the data warehouse
3. What Is a Data Warehouse?
In many organizations, we want a central “store” of
all of our entities, concepts, metadata, and historical
information
For doing data validation, complex mining, analysis,
prediction, …
This is the data warehouse
To this point we’ve focused on scenarios where the
data “lives” in the sources – here we may have a
“master” version (and archival version) in a central
database
For performance reasons, availability reasons, archival
reasons, …
4. In the Rest of this Chapter…
The data warehouse as the master data instance
Data warehouse architectures, design, loading
Data exchange: declarative data warehousing
Hybrid models: caching and partial materialization
Querying externally archived data
5. Outline
The data warehouse
Motivation: Master data management
Physical design
Extract/transform/load
Data exchange
Caching & partial materialization
Operating on external data
6. Master Data Management
One of the “modern” uses of the data warehouse is
not only to support analytics but to serve as a
reference to all of the entities in the organization
A cleaned, validated repository of what we know
… which can be linked to by data sources
… which may help with data cleaning
… and which may be the basis of data governance
(processes by which data is created and modified in a
systematic way, e.g., to comply with gov’t regulations)
There is an emerging field called master data
management out the process of creating these
7. Data Warehouse Architecture
At the top – a
centralized database
Generally configured for
queries and appends –
not transactions
Many indices,
materialized views, etc.
Data is loaded and
periodically updated via
Extract/Transform/Load
(ETL) tools
Data Warehouse
ETL ETL ETL ETL
RDBMS1 RDBMS2
HTML1 XML1
ETL pipeline
outputs
ETL
8. ETL Tools
ETL tools are the equivalent of schema mappings in
virtual integration, but are more powerful
Arbitrary pieces of code to take data from a source,
convert it into data for the warehouse:
import filters – read and convert from data sources
data transformations – join, aggregate, filter, convert
data
de-duplication – finds multiple records referring to the
same entity, merges them
profiling – builds tables, histograms, etc. to summarize
data
quality management – test against master values, known
9. Example ETL Tool Chain
This is an example for e-commerce loading
Note multiple stages of filtering (using selection or
join-like operations), logging bad records, before we
group and load
Invoice
line items
Split
Date-
time
Filter
invalid
Join
Filter
invalid
Invalid
dates/times
Invalid
items
Item
records
Filter
non -
match
Invalid
customers
Group by
customer
Customer
balance
Customer
records
10. Basic Data Warehouse – Summary
Two aspects:
A central DBMS optimized for appends and querying
The “master data” instance
Or the instance for doing mining, analytics, and prediction
A set of procedural ETL “pipelines” to fetch, transform,
filter, clean, and load data
Often these tools are more expressive than standard conjunctive
queries (as in Chapters 2-3)
… But not always!
This raises a question – can we do warehousing with declarative
mappings?
12. Data Exchange
Intuitively, a declarative setup for data warehousing
Declarative schema mappings as in Ch. 2-3
Materialized database as in the previous section
Also allow for unknown values when we map from
source to target (warehouse) instance
If we know a professor teaches a student, then there must
exist a course C that the student took and the professor
taught – but we may not know which…
13. Data Exchange Formulation
A data exchange setting (S,T,M,CT) has:
S, source schema representing all of the source tables
jointly
T, target schema
A set of mappings or tuple-generating dependencies
relating S and T
A set of constraints (equality-generating dependencies)
(∀X)s1(X1), ..., sm (Xm ) → (∃Y) t1(Y1), ..., tk (Yk )
)Y(Y)Y(t...,,)Y()tY( jill11 =→∃
14. An Example
Source S has
Teaches(prof, student)
Adviser(adviser, student)
Target T has
Advise(adviser, student)
TeachesCourse(prof, course)
Takes(course, student)
),(),,(.,),(:
),(),(:
),(),,(.),(:
),(.),(:
4
3
2
1
studCTakesCDrseTeachesCouDCstudprofAdviserr
studprofAdvisestudprofAdviserr
studCTakesCprofrseTeachesCouCstudprofTeachesr
studDAdviseDstudprofTeachesr
∃→
→
∃→
∃→
existential variables represent unknowns
15. The Data Exchange Solution
The goal of data exchange is to compute an instance
of the target schema, given a data exchange setting
D = (S,T,M,CT) and an instance I(S)
An instance J of Schema T is a data exchange
solution for D and I if
1. the pair (I,J) satisfies schema mapping M, and
2. J satisfies constraints CT
16. Instance I(S) has
Teaches
Adviser
Back to the Example, Now with Data
prof student
Ann Bob
Chloe David
Instance J(T) has
Advise
TeachesCourse
Takes
adviser student
Ellen Bob
Felicia David
adviser student
Ellen Bob
Felicia David
course student
C1 Bob
C2 David
prof course
Ann C1
Chloe C2
variables or labeled nulls
represent unknown values
17. Instance I(S) has
Teaches
Adviser
This Is also a Solution
prof student
Ann Bob
Chloe David
Instance J(T) has
Advise
TeachesCourse
Takes
adviser student
Ellen Bob
Felicia David
adviser student
Ellen Bob
Felicia David
course student
C1 Bob
C1 David
prof course
Ann C1
Chloe C1
this time the labeled
nulls are all the same!
18. Universal Solutions
Intuitively, the first solution should be better than
the second
The first solution uses the same variable for the course
taught by Ann and by Chloe – they are the same course
But this was not specified in the original schema!
We formalize that through the notion of the
universal solution, which must not lose any
information
19. Formalizing the Universal Solution
First we define instance homomorphism:
Let J1, J2 be two instances of schema T
A mapping h: J1 J2 is a homomorphism from J1 to J2 if
h(c) = c for every c ∈ C,
for every tuple R(a1,…,an) ∈ J1 the tuple R(h(a1),…,h(an)) ∈ J2
J1, J2 are homomorphically equivalent if there are
homomorphisms h: J1 J2 and h’: J2 J1
Def: Universal solution for data exchange setting
D = (S,T,M,CT), where I is an instance of S.
A data exchange solution J for D and I is a universal
solution if, for every other data exchange solution J’ for D
and I, there exists a homomorphism h: J J’
20. Computing Universal Solutions
The standard process is to use a procedure called
the chase
Informally:
Consider every formula r of M in turn:
If there is a variable substitution for the left-hand side (lhs) of r
where the right-hand side (rhs) is not in the solution – add it
If we create a new tuple, for every existential variable in the rhs,
substitute a new fresh variable
See Chapter 10 Algorithm 10 for full pseudocode
21. Core Universal Solutions
Universal solutions may be of arbitrary size
The core universal solution is the minimal universal
solution
22. Data Exchange and Querying
As with the data warehouse, all queries are directly
posed over the target database – no reformulation
necessary
However, we typically assume certain answers
semantics
To get the certain answers (which are the same as in the
virtual integration setting with GLAV/TGD mappings) –
compute the query answers and then drop any tuples
with labeled nulls (variables)
23. Data Exchange vs. Warehousing
From an external perspective, exchange and
warehousing are essentially equivalent
But there are different trade-offs in procedural vs.
declarative mappings
Procedural – more expressive
Declarative – easier to reason about, compose, invert,
create matieralized views for, etc. (see Chapter 6)
25. The Spectrum of Materialization
Many real EII systems compute and maintain
materialized views, or cache results
A “hybrid” point between the fully virtual and fully
materialized approaches
Virtual integration
(EII)
Data exchange /
data warehouse
sources materialized all mediated relations
materialized
caching or partial materialization –
some views materialized
26. Possible Techniques for Choosing
What to Materialize
Cache results of prior queries
Take the results of each query, materialize them
Use answering queries using views to reuse
Expire using time-to-live… May not always be fresh!
Administrator-selected views
Someone manually specifies views to compute and
maintain, as with a relational DBMS
System automatically maintains
Automatic view selection
Using query workload, update frequencies – a view
materialization wizard chooses what to materialize
28. Many “Integration-Like” Scenarios
over Historical Data
Many Web scenarios where we have large logs of
data accesses, created by the server
Goal: put these together and query them!
Looks like a very simple data integration scenario –
external data, but single schema
A common approach: use programming
environments like MapReduce (or SQL layers above)
to query the data on a cluster
MapReduce reliably runs large jobs across 100s or 1000s
of “shared nothing” nodes in a cluster
29. MapReduce Basics
MapReduce is essentially a template for writing
distributed programs – corresponding to a single SQL
SELECT..FROM..WHERE..GROUP BY..HAVING block
with user-defined functions
The MapReduce runtime calls a set of functions:
map is given a tuple, outputs 0 or more tuples in response
roughly like the WHERE clause
shuffle is a stage for doing sort-based grouping on a key
(specified by the map)
reduce is an aggregate function called over the set of
tuples with the same grouping key
31. MapReduce as ETL
Some people use MapReduce to take data,
transform it, and load it into a warehouse
… which is basically what ETL tools do!
The dividing line between DBMSs, EII, MapReduce is
blurring as of the development of this book
SQL MapReduce
MapReduce over SQL engines
Shared-nothing DBMSs
NoSQL
32. Warehousing & Materialization Wrap-
up
There are benefits to centralizing & materializing data
Performance, especially for analytics / mining
Archival
Standardization / canonicalization
Data warehouses typically use procedural ETL tools to
extract, transform, load (and clean) data
Data exchange replaces ETL with declarative
mappings (where feasible)
Hybrid schemes exist for partial materialization
Increasingly we are integrating via MapReduce and its
cousins