Three way join in one round on hadoop
•Download as PPTX, PDF•
1 like•855 views
This document discusses performing a three-way join of relations R, S, and T in a single MapReduce job. It presents two algorithms for performing the join: a nested loop join and a sort-based join. It also discusses how to determine the number of reducers to use, giving an example where using a non-square matrix of reducers can lead to data replication or reducer inefficiency. Experimental results show the three-way join took 37 seconds to complete.
Report
Share
Report
Share
Recommended
Group p1
This document provides instructions for a group assignment on data structures and algorithms. It contains 4 questions:
1. Determine the output of sample C++ code that performs a binary search on an array to find a given input value.
2. Represent -8, 8, and 0.3125 in their corresponding 4-bit signed integer and 16-bit floating point binary formats or explain why it is not possible.
3. Circle properties that describe arrays as a data structure and explain the selection. The properties are basic/compound, static/dynamic, and linear/non-linear.
4. For a 3D array stored in column-wise format: a) calculate the address of a given element, and b
4.4 external hashing
This document discusses extendible hashing, which is a hashing technique for dynamic files that allows efficient insertion and deletion of records. It works by using a directory to map hash values to buckets, and dynamically expanding the directory size and number of buckets as needed to accommodate new records. When a bucket overflows, it is split into two buckets, and the directory is expanded to distinguish them. The directory size can also be contracted when buckets can be combined due to deletions. Alternative approaches like dynamic hashing and linear hashing that address the same problem of dynamic files are also overviewed.
Introduction to R r.nabati - iausdj.ac.ir
R is an object-oriented programming language commonly used for statistical analysis and visualization. The document provides an introduction to installing and using R, including how to install R and RStudio, load packages, and work with different data types like vectors, matrices, and data frames. It also covers basic functions, control structures like if/else statements and for loops, plotting graphs, and resources for learning more about R.
Hashing notes data structures (HASHING AND HASH FUNCTIONS)
A Hash table is a data structure used for storing and retrieving data very quickly. Insertion of data in the hash table is based on the key value. Hence every entry in the hash table is associated with some key.
HASHING AND HASH FUNCTIONS, HASH TABLE REPRESENTATION, HASH FUNCTION, TYPES OF HASH FUNCTIONS, COLLISION, COLLISION RESOLUTION, CHAINING, OPEN ADDRESSING – LINEAR PROBING, QUADRATIC PROBING, DOUBLE HASHING
Data structure using c bcse 3102 pcs 1002
This document contains a data structures exam from 2008 with 8 pages. It includes 10 multiple choice questions covering topics like data structures, binary trees, queues, stacks, graphs, and sorting algorithms. It also contains 8 additional problems requiring algorithms and programs related to linked lists, binary trees, heaps, graphs, searching, and polynomial representations. Students are instructed to answer question 1 and 5 of the additional problems.
Hashing In Data Structure
This document provides an introduction to hashing and hash tables. It defines hashing as a data structure that uses a hash function to map values to keys for fast retrieval. It gives an example of mapping list values to array indices using modulo. The document discusses hash tables and their operations of search, insert and delete in O(1) time. It describes collisions that occur during hash function mapping and resolution techniques like separate chaining and linear probing.
R language introduction
- R is a free software environment for statistical computing and graphics. It has an active user community and supports graphical capabilities.
- R can import and export data, perform data manipulation and summaries. It provides various plotting functions and control structures to control program flow.
- Debugging tools in R include traceback, debug, browser and trace which help identify and fix issues in functions.
HCR's Series (Expansion of factorial of any natural number)
This is a finite series which is the sum of first „n‟ natural numbers multiplied by their own respective
factorials. The series has been derived from HCR‟s Rank formula which was proposed by the author. It is
extremely useful in case studies & computations. Although HCR‟s Series is different from the Arithmetic,
Geometric, Harmonic & Taylor‟s Series of simple functions, it is the expansion of factorial of any natural
number in form of discrete summation thus it is also named as HCR‟s divergence series.
Recommended
Group p1
This document provides instructions for a group assignment on data structures and algorithms. It contains 4 questions:
1. Determine the output of sample C++ code that performs a binary search on an array to find a given input value.
2. Represent -8, 8, and 0.3125 in their corresponding 4-bit signed integer and 16-bit floating point binary formats or explain why it is not possible.
3. Circle properties that describe arrays as a data structure and explain the selection. The properties are basic/compound, static/dynamic, and linear/non-linear.
4. For a 3D array stored in column-wise format: a) calculate the address of a given element, and b
4.4 external hashing
This document discusses extendible hashing, which is a hashing technique for dynamic files that allows efficient insertion and deletion of records. It works by using a directory to map hash values to buckets, and dynamically expanding the directory size and number of buckets as needed to accommodate new records. When a bucket overflows, it is split into two buckets, and the directory is expanded to distinguish them. The directory size can also be contracted when buckets can be combined due to deletions. Alternative approaches like dynamic hashing and linear hashing that address the same problem of dynamic files are also overviewed.
Introduction to R r.nabati - iausdj.ac.ir
R is an object-oriented programming language commonly used for statistical analysis and visualization. The document provides an introduction to installing and using R, including how to install R and RStudio, load packages, and work with different data types like vectors, matrices, and data frames. It also covers basic functions, control structures like if/else statements and for loops, plotting graphs, and resources for learning more about R.
Hashing notes data structures (HASHING AND HASH FUNCTIONS)
A Hash table is a data structure used for storing and retrieving data very quickly. Insertion of data in the hash table is based on the key value. Hence every entry in the hash table is associated with some key.
HASHING AND HASH FUNCTIONS, HASH TABLE REPRESENTATION, HASH FUNCTION, TYPES OF HASH FUNCTIONS, COLLISION, COLLISION RESOLUTION, CHAINING, OPEN ADDRESSING – LINEAR PROBING, QUADRATIC PROBING, DOUBLE HASHING
Data structure using c bcse 3102 pcs 1002
This document contains a data structures exam from 2008 with 8 pages. It includes 10 multiple choice questions covering topics like data structures, binary trees, queues, stacks, graphs, and sorting algorithms. It also contains 8 additional problems requiring algorithms and programs related to linked lists, binary trees, heaps, graphs, searching, and polynomial representations. Students are instructed to answer question 1 and 5 of the additional problems.
Hashing In Data Structure
This document provides an introduction to hashing and hash tables. It defines hashing as a data structure that uses a hash function to map values to keys for fast retrieval. It gives an example of mapping list values to array indices using modulo. The document discusses hash tables and their operations of search, insert and delete in O(1) time. It describes collisions that occur during hash function mapping and resolution techniques like separate chaining and linear probing.
R language introduction
- R is a free software environment for statistical computing and graphics. It has an active user community and supports graphical capabilities.
- R can import and export data, perform data manipulation and summaries. It provides various plotting functions and control structures to control program flow.
- Debugging tools in R include traceback, debug, browser and trace which help identify and fix issues in functions.
HCR's Series (Expansion of factorial of any natural number)
This is a finite series which is the sum of first „n‟ natural numbers multiplied by their own respective
factorials. The series has been derived from HCR‟s Rank formula which was proposed by the author. It is
extremely useful in case studies & computations. Although HCR‟s Series is different from the Arithmetic,
Geometric, Harmonic & Taylor‟s Series of simple functions, it is the expansion of factorial of any natural
number in form of discrete summation thus it is also named as HCR‟s divergence series.
Data structure lecture 2
This document discusses algorithms and their complexity. It provides an example of a linear search algorithm to find a target value in an array. The complexity of this algorithm is analyzed for the worst and average cases. In the worst case, the target is the last element and all n elements must be checked, resulting in O(n) time complexity. On average, about half the elements (n+1)/2 need to be checked, resulting in average time complexity of O(n).
Bca ii dfs u-1 introduction to data structure
This document provides an introduction to data structures. It defines data structures as a way of organizing and storing data in a computer so it can be used efficiently. There are two main types: primitive data structures like integers and characters that are directly operated on by the CPU, and non-primitive structures like arrays and linked lists that are more complex. Key aspects of data structures covered include operations, properties, performance analysis using time and space complexity, and examples of linear structures like arrays and non-linear structures like trees. Common algorithms are analyzed based on their asymptotic worst-case running times.
Hashing Techniques in Data Structures Part2
The document discusses different approaches to handling collisions in hash tables: chaining and open addressing such as linear probing. Chaining involves storing collided keys in linked lists at each array index, while linear probing resolves collisions by probing subsequent indices in the array. The example demonstrates linear probing by inserting several keys into a hash table and showing the array indices where each key is stored.
DBMS 9 | Extendible Hashing
Contents:
1. Direct Address Table
2. Hashing
3. Characteristics of a good hash function
4. Collision Resolution using Chaining and Probing
5. Static vs Dynamic Hashing
6. Extendible Hashing
7. B+ tree vs Hashing
Data structure
This document provides summaries of different data structures: arrays, stacks, queues, and linked lists. Arrays allow storing multiple values in a single variable using indexes. Stacks follow LIFO order using push and pop operations. Queues follow FIFO order using enqueue and dequeue operations. Linked lists contain nodes with a data and pointer to the next node, allowing dynamic size lists.
4.4 hashing
This document discusses hashing and hash tables. It begins by introducing hash tables and describing how hashing works by mapping keys to array indices using a hash function to allow for fast insertion, deletion and search operations in O(1) average time. However, hash tables do not support ordering of elements efficiently. The document then discusses issues with hash functions such as collisions when different keys map to the same index. It describes techniques for collision resolution including separate chaining, where each index points to a linked list, and open addressing techniques like linear probing, quadratic probing and double hashing that resolve collisions by probing alternate indices in the array.
Data structure ppt
The document provides an introduction to data structures. It defines data structures as representations of logical relationships between data elements that consider both the elements and their relationships. It classifies data structures as either primitive or non-primitive. Primitive structures are directly operated on by machine instructions while non-primitive structures are built from primitive ones. Common non-primitive structures include stacks, queues, linked lists, trees and graphs. The document then discusses arrays as a data structure and operations on arrays like traversal, insertion, deletion, searching and sorting.
2nd puc computer science chapter 3 data structures 1
This document provides an introduction and overview of data structures. It defines data structures as specialized formats for organizing and storing data to improve efficiency of storage and processing. It classifies data structures as primitive, non-primitive, linear, and non-linear. Primitive structures include basic types like integers while non-primitive structures are derived groups like arrays and lists. Common operations on data structures like traversal, insertion, and deletion are also discussed.
358 33 powerpoint-slides_15-hashing-collision_chapter-15
This document discusses hashing and collision handling in data structures. It introduces hash tables as a data structure where keys are mapped to array positions using a hash function. This allows constant-time search by generating an address from the key. The document describes different hash functions including division, multiplication, mid-square, and folding methods. It provides examples of how keys are hashed to array positions using these functions.
Application of hashing in better alg design tanmay
The document discusses hashing and its components. [1] It describes how hashing works by mapping keys to locations in a hash table using a hash function. [2] Common collision resolution techniques are chaining, linear probing, quadratic probing, and double hashing. [3] Hashing provides fast average-case performance of O(1) by storing data in blocks based on the hash value and load factor.
Stack and Hash Table
This document proposes a new approach to speed up combinatorial search strategies using stack and hash table data structures. The method uses a temporary array to help generate combinations in each iteration. A stack is created to push the first parameter, and the algorithm iterates popping values from the stack until it is empty. Indexes of a combination array are set to the stack length and popped values. Hashing provides a more reliable and flexible method of data retrieval than other structures, and is faster than searching arrays or lists. This approach could speed up generation and search processes for combinatorial approaches.
Day 5 u8f13
Composite functions refer to combining two functions, where the output of one function is used as the input of the other. The notation used is (f ◦ g)(x) = f(g(x)), where the inner function g(x) is evaluated first, and then substituted into the outer function f(x). Some examples are given of evaluating composite functions using function tables or graphs.
5.4 randomized datastructures
This document discusses randomized data structures and algorithms. It begins by motivating randomized data structures by noting that some data structures like binary search trees have average case performance but worst case inputs. Randomizing the data structure removes dependency on inputs and provides expected case performance. The document then discusses treaps and randomized skip lists as examples of randomized data structures that provide efficient expected case performance for operations like insertion, deletion, and search. It also covers topics like randomized number generation, primality testing, and how randomization can transform average case runtimes into expected case runtimes.
Set data structure
Sets are collections of unique elements that do not allow repetition. Elements must satisfy membership rules to be included in a set. Common set operations include union, intersection, difference and subset testing. Sets can be mutable, allowing addition and removal of elements, or immutable. Hash functions are used to map elements to locations in hash tables, enabling fast set operations on large collections. Spelling checkers use hash tables to implement sets and check dictionary words against input words.
Data structure and its types
Data structures allow for the organization and storage of data. There are linear and non-linear data structures. Linear structures include arrays, stacks, queues, and linked lists. Arrays store elements in contiguous memory locations. Stacks and queues follow first-in last-out and first-in first-out rules respectively. Linked lists connect nodes using pointers. Non-linear structures include trees and graphs which emulate hierarchical and network-like connections. Common operations on data structures include traversing, searching, insertion, and deletion.
Hashing
This document summarizes a lecture on hashing techniques. It discusses using a hash function to map keys to table slots, addressing collisions through chaining or open addressing. Chaining stores colliding keys in linked lists, while open addressing resolves collisions by probing for empty slots using techniques like linear or quadratic probing. The performance of search, insertion and deletion is analyzed in terms of load factor and different hashing methods. Universal hashing and perfect hashing are also introduced to improve performance.
Row major and column major in 2 d
This document discusses row-major and column-major ordering for representing 2D arrays in memory. In row-major order, elements are arranged sequentially row by row, with the first row occupying the first memory locations. Column-major order arranges elements column by column. Formulas are provided to calculate the memory location of a given element based on its indices, the array base address, element size, number of rows, and whether the array uses row-major or column-major ordering. An example calculates the location of element A[3,2] for both a row-major and column-major 3x4 integer array.
Hashing
Hashing is a technique that maps large amounts of data to smaller data structures using a hashing function. A hash function takes inputs of any size and maps them to a fixed-size table called a hash table. To handle collisions where two keys map to the same slot, separate chaining uses linked lists attached to each slot while open addressing resolves collisions by probing to the next slot using techniques like linear probing, quadratic probing, or double hashing. As the hash table fills up, rehashing may be needed to recalculate hashcodes and move entries to a larger table.
Introduction of data structure
This document introduces key concepts in data structures and algorithms. It defines an algorithm as a set of instructions to accomplish a task, and discusses how algorithms are evaluated based on their time and space complexity. It then defines data structures as organized data with relationships and permitted operations, and lists common examples like arrays, stacks and queues. The document also introduces abstract data types as mathematical models with defined operations.
Lecture 2a arrays
The document discusses arrays, which are a fundamental data structure that store a collection of elements of the same type in contiguous memory locations that can be individually accessed via an index. It describes one-dimensional arrays as lists and two-dimensional arrays as matrices. Key aspects covered include the components of an array like elements, data type, subscript, and brackets. Operations on and applications of arrays are also summarized.
R programming intro with examples
This document provides an overview of topics that will be covered in a two-day statistical programming course in R, including:
1. Vector and matrix operations, file input/output, and probability density functions.
2. Distributions like binomial, Poisson, normal and uniform as well as hypothesis testing using t, z, F, and chi-square.
3. Linear and multiple regression techniques, including prediction, residual analysis and modeling.
Case studies and examples are provided for many of these statistical techniques in R, such as linear regression, hypothesis testing, and probability distributions.
R Language Introduction
The document outlines various statistical and data analysis techniques that can be performed in R including importing data, data visualization, correlation and regression, and provides code examples for functions to conduct t-tests, ANOVA, PCA, clustering, time series analysis, and producing publication-quality output. It also reviews basic R syntax and functions for computing summary statistics, transforming data, and performing vector and matrix operations.
More Related Content
What's hot
Data structure lecture 2
This document discusses algorithms and their complexity. It provides an example of a linear search algorithm to find a target value in an array. The complexity of this algorithm is analyzed for the worst and average cases. In the worst case, the target is the last element and all n elements must be checked, resulting in O(n) time complexity. On average, about half the elements (n+1)/2 need to be checked, resulting in average time complexity of O(n).
Bca ii dfs u-1 introduction to data structure
This document provides an introduction to data structures. It defines data structures as a way of organizing and storing data in a computer so it can be used efficiently. There are two main types: primitive data structures like integers and characters that are directly operated on by the CPU, and non-primitive structures like arrays and linked lists that are more complex. Key aspects of data structures covered include operations, properties, performance analysis using time and space complexity, and examples of linear structures like arrays and non-linear structures like trees. Common algorithms are analyzed based on their asymptotic worst-case running times.
Hashing Techniques in Data Structures Part2
The document discusses different approaches to handling collisions in hash tables: chaining and open addressing such as linear probing. Chaining involves storing collided keys in linked lists at each array index, while linear probing resolves collisions by probing subsequent indices in the array. The example demonstrates linear probing by inserting several keys into a hash table and showing the array indices where each key is stored.
DBMS 9 | Extendible Hashing
Contents:
1. Direct Address Table
2. Hashing
3. Characteristics of a good hash function
4. Collision Resolution using Chaining and Probing
5. Static vs Dynamic Hashing
6. Extendible Hashing
7. B+ tree vs Hashing
Data structure
This document provides summaries of different data structures: arrays, stacks, queues, and linked lists. Arrays allow storing multiple values in a single variable using indexes. Stacks follow LIFO order using push and pop operations. Queues follow FIFO order using enqueue and dequeue operations. Linked lists contain nodes with a data and pointer to the next node, allowing dynamic size lists.
4.4 hashing
This document discusses hashing and hash tables. It begins by introducing hash tables and describing how hashing works by mapping keys to array indices using a hash function to allow for fast insertion, deletion and search operations in O(1) average time. However, hash tables do not support ordering of elements efficiently. The document then discusses issues with hash functions such as collisions when different keys map to the same index. It describes techniques for collision resolution including separate chaining, where each index points to a linked list, and open addressing techniques like linear probing, quadratic probing and double hashing that resolve collisions by probing alternate indices in the array.
Data structure ppt
The document provides an introduction to data structures. It defines data structures as representations of logical relationships between data elements that consider both the elements and their relationships. It classifies data structures as either primitive or non-primitive. Primitive structures are directly operated on by machine instructions while non-primitive structures are built from primitive ones. Common non-primitive structures include stacks, queues, linked lists, trees and graphs. The document then discusses arrays as a data structure and operations on arrays like traversal, insertion, deletion, searching and sorting.
2nd puc computer science chapter 3 data structures 1
This document provides an introduction and overview of data structures. It defines data structures as specialized formats for organizing and storing data to improve efficiency of storage and processing. It classifies data structures as primitive, non-primitive, linear, and non-linear. Primitive structures include basic types like integers while non-primitive structures are derived groups like arrays and lists. Common operations on data structures like traversal, insertion, and deletion are also discussed.
358 33 powerpoint-slides_15-hashing-collision_chapter-15
This document discusses hashing and collision handling in data structures. It introduces hash tables as a data structure where keys are mapped to array positions using a hash function. This allows constant-time search by generating an address from the key. The document describes different hash functions including division, multiplication, mid-square, and folding methods. It provides examples of how keys are hashed to array positions using these functions.
Application of hashing in better alg design tanmay
The document discusses hashing and its components. [1] It describes how hashing works by mapping keys to locations in a hash table using a hash function. [2] Common collision resolution techniques are chaining, linear probing, quadratic probing, and double hashing. [3] Hashing provides fast average-case performance of O(1) by storing data in blocks based on the hash value and load factor.
Stack and Hash Table
This document proposes a new approach to speed up combinatorial search strategies using stack and hash table data structures. The method uses a temporary array to help generate combinations in each iteration. A stack is created to push the first parameter, and the algorithm iterates popping values from the stack until it is empty. Indexes of a combination array are set to the stack length and popped values. Hashing provides a more reliable and flexible method of data retrieval than other structures, and is faster than searching arrays or lists. This approach could speed up generation and search processes for combinatorial approaches.
Day 5 u8f13
Composite functions refer to combining two functions, where the output of one function is used as the input of the other. The notation used is (f ◦ g)(x) = f(g(x)), where the inner function g(x) is evaluated first, and then substituted into the outer function f(x). Some examples are given of evaluating composite functions using function tables or graphs.
5.4 randomized datastructures
This document discusses randomized data structures and algorithms. It begins by motivating randomized data structures by noting that some data structures like binary search trees have average case performance but worst case inputs. Randomizing the data structure removes dependency on inputs and provides expected case performance. The document then discusses treaps and randomized skip lists as examples of randomized data structures that provide efficient expected case performance for operations like insertion, deletion, and search. It also covers topics like randomized number generation, primality testing, and how randomization can transform average case runtimes into expected case runtimes.
Set data structure
Sets are collections of unique elements that do not allow repetition. Elements must satisfy membership rules to be included in a set. Common set operations include union, intersection, difference and subset testing. Sets can be mutable, allowing addition and removal of elements, or immutable. Hash functions are used to map elements to locations in hash tables, enabling fast set operations on large collections. Spelling checkers use hash tables to implement sets and check dictionary words against input words.
Data structure and its types
Data structures allow for the organization and storage of data. There are linear and non-linear data structures. Linear structures include arrays, stacks, queues, and linked lists. Arrays store elements in contiguous memory locations. Stacks and queues follow first-in last-out and first-in first-out rules respectively. Linked lists connect nodes using pointers. Non-linear structures include trees and graphs which emulate hierarchical and network-like connections. Common operations on data structures include traversing, searching, insertion, and deletion.
Hashing
This document summarizes a lecture on hashing techniques. It discusses using a hash function to map keys to table slots, addressing collisions through chaining or open addressing. Chaining stores colliding keys in linked lists, while open addressing resolves collisions by probing for empty slots using techniques like linear or quadratic probing. The performance of search, insertion and deletion is analyzed in terms of load factor and different hashing methods. Universal hashing and perfect hashing are also introduced to improve performance.
Row major and column major in 2 d
This document discusses row-major and column-major ordering for representing 2D arrays in memory. In row-major order, elements are arranged sequentially row by row, with the first row occupying the first memory locations. Column-major order arranges elements column by column. Formulas are provided to calculate the memory location of a given element based on its indices, the array base address, element size, number of rows, and whether the array uses row-major or column-major ordering. An example calculates the location of element A[3,2] for both a row-major and column-major 3x4 integer array.
Hashing
Hashing is a technique that maps large amounts of data to smaller data structures using a hashing function. A hash function takes inputs of any size and maps them to a fixed-size table called a hash table. To handle collisions where two keys map to the same slot, separate chaining uses linked lists attached to each slot while open addressing resolves collisions by probing to the next slot using techniques like linear probing, quadratic probing, or double hashing. As the hash table fills up, rehashing may be needed to recalculate hashcodes and move entries to a larger table.
Introduction of data structure
This document introduces key concepts in data structures and algorithms. It defines an algorithm as a set of instructions to accomplish a task, and discusses how algorithms are evaluated based on their time and space complexity. It then defines data structures as organized data with relationships and permitted operations, and lists common examples like arrays, stacks and queues. The document also introduces abstract data types as mathematical models with defined operations.
Lecture 2a arrays
The document discusses arrays, which are a fundamental data structure that store a collection of elements of the same type in contiguous memory locations that can be individually accessed via an index. It describes one-dimensional arrays as lists and two-dimensional arrays as matrices. Key aspects covered include the components of an array like elements, data type, subscript, and brackets. Operations on and applications of arrays are also summarized.
What's hot (20)
2nd puc computer science chapter 3 data structures 1
2nd puc computer science chapter 3 data structures 1
358 33 powerpoint-slides_15-hashing-collision_chapter-15
358 33 powerpoint-slides_15-hashing-collision_chapter-15
Application of hashing in better alg design tanmay
Application of hashing in better alg design tanmay
Similar to Three way join in one round on hadoop
R programming intro with examples
This document provides an overview of topics that will be covered in a two-day statistical programming course in R, including:
1. Vector and matrix operations, file input/output, and probability density functions.
2. Distributions like binomial, Poisson, normal and uniform as well as hypothesis testing using t, z, F, and chi-square.
3. Linear and multiple regression techniques, including prediction, residual analysis and modeling.
Case studies and examples are provided for many of these statistical techniques in R, such as linear regression, hypothesis testing, and probability distributions.
R Language Introduction
The document outlines various statistical and data analysis techniques that can be performed in R including importing data, data visualization, correlation and regression, and provides code examples for functions to conduct t-tests, ANOVA, PCA, clustering, time series analysis, and producing publication-quality output. It also reviews basic R syntax and functions for computing summary statistics, transforming data, and performing vector and matrix operations.
2 data structure in R
This presentation provides a brief introduction to data types and objects in R. I've not covered 'array' in the presentation, which is a multi-dimensional object [More general than matrix].
R for Statistical Computing
This document provides an overview of statistical concepts and analysis techniques in R, including measures of central tendency, data variability, correlation, regression, and time series analysis. Key points covered include mean, median, mode, variance, standard deviation, z-scores, quartiles, standard deviation vs variance, correlation, ANOVA, and importing/working with different data structures in R like vectors, lists, matrices, and data frames.
R Cheat Sheet for Data Analysts and Statisticians.pdf
This document provides a cheat sheet for common commands and functions in R for data manipulation, statistical analysis, and graphics. It summarizes key topics such as accessing and manipulating data, conducting statistical tests, fitting linear and generalized linear models, performing clustering and multivariate analyses, and creating basic plots and graphics. The cheat sheet is organized into sections covering basics, vectors and data types, data frames, input/output, indexing, missing values, numerical and tabulation functions, programming, operators, graphics, and statistical models and distributions.
Lecture 01 reals number system
The document discusses sets and operations involving sets of real numbers. It defines key sets such as the real numbers, rational numbers, integers, natural numbers, whole numbers, and irrational numbers. It also covers properties of real numbers like closure, commutativity, associativity, distributivity, identity, and inverse properties as they relate to addition and multiplication. Examples are provided to illustrate set notation and properties. The document contains exercises asking to identify which properties justify certain statements involving real number operations.
TENSOR DECOMPOSITION WITH PYTHON
This document discusses tensor decomposition with Python. It begins by explaining what tensor decomposition and factorization are, and how they can be used to represent multi-dimensional datasets and perform dimensionality reduction. It then discusses matrix and tensor factorization methods like NMF, topic modeling, and CP/PARAFAC decomposition. The remainder of the document provides examples of tensor decomposition using Python tools and libraries, and discusses applications to analyzing temporal network and sensor data.
lecture 15
The document discusses two algorithms for matrix multiplication and finding the median of an unsorted list:
1) Strassen's algorithm improves on the traditional O(n^3) matrix multiplication algorithm by using divide and conquer to achieve O(n^lg7) time complexity.
2) Finding the median can be done in expected O(n) time using quickselect, or deterministically in O(n) time by choosing the median of medians as the pivot.
DATA STRUCTURES unit 1.pptx
This document provides an introduction to data structures and algorithms. It discusses arrays, stacks, queues and their applications. It also covers time and space complexity analysis of algorithms. Arrays are introduced as a linear data structure for storing similar data elements. Array elements can be accessed using an index or subscript. Multi-dimensional arrays can also be implemented for storing data in rows and columns.
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...
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.
Introduction to R
R can be used to analyze data and perform statistical analysis. Functions like help(), ? and help.start() provide information about other functions. Objects created in R sessions are stored by name and can be removed with rm(). Vectors like x=c(1,2,3,4,5) can be created and their length checked with length(x). Subsets of vectors can be selected using logical or integer indexes inside square brackets. Matrices are multi-dimensional generalizations of vectors that can be manipulated using operators like * and %. Data can be read into R from external files using functions like read.table() and read.delim(). Common statistical distributions like normal, uniform and exponential are available as functions in R for
Principal Components Analysis, Calculation and Visualization
The article explains dimension reduction principles, PCA algorithm and mathematics behind. The PCA calculation and data projection is demonstrated in R, Python and Apache Spark. Finally the results are visualized with D3.js.
Basic R Data Manipulation
This document provides an overview of essential data wrangling tasks in R, including importing, exploring, indexing/subsetting, reshaping, merging, aggregating, and repeating/looping data. It discusses functions for reading different file types like CSV, Excel, and plain text. It also covers exploring data structure and summary statistics, subsetting vectors, data frames and matrices, reshaping between wide and long format, performing different types of joins to merge data, and using loops and sequences to repeat operations.
Survey Demo
This document demonstrates analyzing responses to Likert-scale survey questions using R. It generates dummy survey data with 1000 responses across 5 questions. It then uses dplyr and ggplot2 to: 1) Summarize responses to a single question as a percentage bar graph. 2) Plot responses to two related questions side-by-side using gridExtra. 3) Compare responses to one question between two demographic groups in another side-by-side graph. The goal is to showcase analyzing survey data with R packages like dplyr and ggplot2.
Statistics lab 1
R can be used to analyze data and perform statistical analysis. Key functions include help() and ? to get information on functions, and objects() to view stored objects. Vectors can be created with c() and manipulated using arithmetic operators. Matrices are two-dimensional arrays that can be operated on using *, /, and t(). Larger datasets are typically read from external files using read.table() or read.delim(). Common distributions can be explored using functions like dnorm(), pnorm(), and rnorm(). Statistical analysis includes commands like cov() and cor() to measure covariance and correlation between variables.
Ch2
This document summarizes key concepts from Chapter 2 on the relational database model. It introduces relations as tables with rows and columns, and discusses attribute types, relation schemas, and relation instances. It covers the concepts of keys and foreign keys. It also summarizes the core relational algebra operations of selection, projection, union, intersection, difference, Cartesian product, natural join, and renaming. Finally, it notes that relational algebra is not Turing complete and cannot perform aggregation operations like SUM and AVG.
R gráfico
RStudio is a trademark of RStudio, PBC. This document provides a cheat sheet on tidying data with the tidyr package in R. It defines tidy data as having each variable in its own column and each observation in its own row. It discusses tibbles as an enhanced data frame format and provides functions for constructing, subsetting, and printing tibbles. It also covers reshaping data through pivoting, handling missing values, expanding and completing data, and working with nested data through nesting, unnesting, and applying functions to list columns.
Advanced algebra
The document discusses various topics in advanced algebra including inequalities, arithmetic progressions, geometric progressions, harmonic progressions, permutations, combinations, matrices, determinants, and solving systems of linear equations using matrices. Key properties and formulas are provided for each topic. Examples are included to demonstrate solving problems related to each concept.
Big Data Mining in Indian Economic Survey 2017
Presentation to Indian Economic Service probationers on Data Mining methods using R and QGIS by G Gayathri, Parth Khare
R tutorial for a windows environment
R is a software package for data analysis and graphical representation. It provides functions, results of analysis as objects, and a flexible environment for model building. This document provides tutorials on basic R operations including computation, vectors, matrices, and graphics. Key functions introduced are cbind(), rbind(), seq(), rep(), and matrix() for creating and manipulating objects, and plot() for data visualization.
Similar to Three way join in one round on hadoop (20)
R Cheat Sheet for Data Analysts and Statisticians.pdf
R Cheat Sheet for Data Analysts and Statisticians.pdf
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...
Principal Components Analysis, Calculation and Visualization
Principal Components Analysis, Calculation and Visualization
Recently uploaded
一比一原版(Harvard毕业证书)哈佛大学毕业证如何办理
毕业原版【微信:41543339】【(Harvard毕业证书)哈佛大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Analysis insight about a Flyball dog competition team's performance
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
My burning issue is homelessness K.C.M.O.
My burning issue is homelessness in Kansas City, MO
To: Tom Tresser
From: Roger Warren
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
毕业原版【微信:176555708】【(GWU,GW毕业证书)乔治·华盛顿大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Learn SQL from basic queries to Advance queries
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
在线办理(英国UCA毕业证书)创意艺术大学毕业证在读证明一模一样
学校原件一模一样【微信：741003700 】《(英国UCA毕业证书)创意艺术大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
一比一原版(Glasgow毕业证书)格拉斯哥大学毕业证如何办理
毕业原版【微信:41543339】【(Glasgow毕业证书)格拉斯哥大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
一比一原版(UO毕业证)渥太华大学毕业证如何办理
UO毕业证录取书【微信95270640】购买（渥太华大学毕业证成绩单硕士学历）Q微信95270640代办UO学历认证留信网伪造渥太华大学学位证书精仿渥太华大学本科/硕士文凭证书补办渥太华大学 diplomaoffer,Transcript购买渥太华大学毕业证成绩单购买UO假毕业证学位证书购买伪造渥太华大学文凭证书学位证书,专业办理雅思、托福成绩单，学生ID卡，在读证明，海外各大学offer录取通知书，毕业证书，成绩单，文凭等材料:1:1完美还原毕业证、offer录取通知书、学生卡等各种在读或毕业材料的防伪工艺（包括 烫金、烫银、钢印、底纹、凹凸版、水印、防伪光标、热敏防伪、文字图案浮雕，激光镭射，紫外荧光，温感光标）学校原版上有的工艺我们一样不会少，不论是老版本还是最新版本，都能保证最高程度还原，力争完美以求让所有同学都能享受到完美的品质服务。
文凭办理流程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：微信95270640我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
7完成交易删除客户资料
高精端提供以下服务：
一：渥太华大学渥太华大学毕业证文凭证书全套材料从防伪到印刷水印底纹到钢印烫金
二：真实使馆认证（留学人员回国证明）使馆存档
三：真实教育部认证教育部存档教育部留服网站可查
四：留信认证留学生信息网站可查
五：与学校颁发的相关证件1:1纸质尺寸制定（定期向各大院校毕业生购买最新版本毕,业证成绩单保证您拿到的是鲁昂大学内部最新版本毕业证成绩单微信95270640）
A.为什么留学生需要操作留信认证?
留信认证全称全国留学生信息服务网认证,隶属于北京中科院。①留信认证门槛条件更低,费用更美丽,并且包过,完单周期短,效率高②留信认证虽然不能去国企,但是一般的公司都没有问题,因为国内很多公司连基本的留学生学历认证都不了解。这对于留学生来说,这就比自己光拿一个证书更有说服力,因为留学学历可以在留信网站上进行查询!
B.为什么我们提供的毕业证成绩单具有使用价值？
查询留服认证是国内鉴别留学生海外学历的唯一途径但认证只是个体行为不是所有留学生都操作所以没有办理认证的留学生的学历在国内也是查询不到的他们也仅仅只有一张文凭。所以这时候我们提供的和学校颁发的一模一样的毕业证成绩单就有了使用价值。只硕大的蛇皮袋手里拎着长铁钩正站在门口朝黑色的屋内张望不好坏人小偷山娃一怔却也灵机一动立马仰起头双手拢在嘴边朝楼上大喊：“爸爸爸——有人找——那人一听朝山娃尴尬地笑笑悻悻地走了山娃立马“嘭的一声将铁门锁死心却咚咚地乱跳当山娃跟父亲说起这事时父亲很吃惊抚摸着山娃的头说还好醒得及时要不家早被人掏空了到时连电视也没得看啰不过父亲还是夸山娃能临危不乱随机应变有胆有谋山娃笑笑说那都是书上学的看童话和小说时多
Experts live - Improving user adoption with AI
Bekijk de slides van onze sessie Enhancing Modern Workplace Efficiency op Experts Live 2024.
Population Growth in Bataan: The effects of population growth around rural pl...
A population analysis specific to Bataan.
Intelligence supported media monitoring in veterinary medicine
Media monitoring in veterinary medicien
DSSML24_tspann_CodelessGenerativeAIPipelines
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
Recently uploaded (20)
A presentation that explain the Power BI Licensing
A presentation that explain the Power BI Licensing
Analysis insight about a Flyball dog competition team's performance
Analysis insight about a Flyball dog competition team's performance
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Influence of Marketing Strategy and Market Competition on Business Plan
Influence of Marketing Strategy and Market Competition on Business Plan
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
Udemy_2024_Global_Learning_Skills_Trends_Report (1).pdf
Udemy_2024_Global_Learning_Skills_Trends_Report (1).pdf
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
Population Growth in Bataan: The effects of population growth around rural pl...
Population Growth in Bataan: The effects of population growth around rural pl...
Intelligence supported media monitoring in veterinary medicine
Intelligence supported media monitoring in veterinary medicine
Three way join in one round on hadoop
- 1. Three-way join in one round on Hadoop COMP 6231 GROUP 7 IRAJ HEDAYATISOMARIN, ZAKARIA NASERELDINE, J INYANG DU
- 2. Problem statement 푅 ⋈ 푆 ⋈ 푇 In this section of second project we aimed to calculate three-way join in one round of Map-Reduce algorithm. S R T R join S join T
- 3. Algorithm Overview First relation: R a, b Second relation: S b, c Third relation: T c, d 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Mapper h(b)=x h(c)=y R,(a,b) S,(b,c) T,(c,d) x y <KEY, VALUE>=<(X,Y), (relation_name, tuple)> In memory join Coordinate of a reducer in imagined matrix of reducers
- 4. Mapping and Hashing <KEY, VALUE>=<(X,Y), (relation_name, tuple)> Exactly same as input Fetch from file name Input tuple First relation: R (h(b),1) (h(b),2) Second relation: S (h(b),h(c)) Third relation: T … (h(b),11) (1,h(c)) (1,h(c)) … (11,h(c)) 푅푒푑푢푐푒푟 # = (푥 − 1) × # 표푓 푟푒푑푢푐푒푟푠 + 푦 h(b)=x h(c)=y
- 5. In-memory join algorithm NESTED LOOP JOIN For each tuple in R For each tuple in S If R.b==S.b then For each tuple in T If S.c==T.c then Print (R.a, S.b, S.c, T.d) SORT-BASED JOIN ALGORITHM 1. divide input list in three sorted lists using Binary Search 푂(푛 algorithm log 푛) 2. Execute in-memory join algorithm •UNTIL R and S are not empty DO • IF the first items in both list are equal THEN • make sure all the tuples with the same value have been joined together and remove them from the list • ELSE • Choose the smallest one and remove items until reach an item equal or greater than the front item in the another list 푂(푛3) 1.Divide list: 푂(푛 log 푛) 2.In-memory join: 1.푅 ⋈ 푆 = 푂 푛 2.푅푆 ⋈ 푇 = 푂 푛
- 6. Number of reducers We decide to use a square matrix. This choice would be a constraint on number of reducers. For example in this case, we had 128 reducers available but actually we just use 121 of them On the other hand selecting different number of reducers in each dimension, we will have data replication and inefficiency.
- 7. Number of reducers (example 1, replication problem) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 2 3 4 # of reducers=128 Assumption: R>>T Both of them have uniform distribution T(R) = 1,000,000 T(T) = 1,000 For square matrix: Replicated data=1,000,000*11+1,000*11=11,011,000 For above matrix: Replicated data=1,000,000*16+1,000*16=16,016,000
- 8. Number of reducers (example 1, inefficiency problem) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 2 3 4 IDLE FULL IDLE FULL # of reducers=128 Assumption: T>>R T is not uniformly distributed T(R) = 1,000 T(T) = 1,000,000 When the range is reduced, it’s more likely two value hash in to the same location.
- 9. Experimental results 37 seconds
- 10. Any Question?