This document discusses external sorting algorithms and the polyphase merge sorting algorithm. It begins by explaining that external sorting is needed when data is too large to fit in main memory, and involves initially sorting runs that fit in memory and then merging the runs. The document then provides details on the balanced two-way merge algorithm and multi-way merge algorithm. It proceeds to describe the polyphase merge sorting algorithm, which decreases the number of runs at each iteration by merging runs into larger runs. The document provides pseudocode and an example of the polyphase merge sorting process. It concludes by analyzing the number of comparisons needed for run construction and merging in polyphase merge sorting.
• Priority Scheduling merupakan algoritma penjadwalan yang mendahulukan proses yang memiliki prioritas tertinggi. Setiap proses memiliki prioritasnya masing-masing. Prioritas suatu proses dapat ditentukan melalui beberapa karakteristik antara lain:
This document discusses graph mining, including its motivation, applications, and algorithms. Graph mining aims to discover repetitive subgraphs in graph datasets. It has many applications including analyzing chemical compounds, biological networks, program flows, and social networks. The document outlines several graph mining algorithms, including the Apriori-based FSG algorithm, the DFS-based gSpan algorithm, and the greedy Subdue algorithm. It also distinguishes between the transaction setting and single graph setting for graph mining problems.
Program ini membahas tentang array of record, yang merupakan array dimana elemennya adalah record. Program ini menjelaskan konsep array, record, dan array of record beserta contoh kode programnya. Program ini juga memberikan latihan dan tugas untuk mempraktikkan penggunaan array of record dalam mengolah data mahasiswa dan pegawai.
Frequent Pattern Growth Algorithm is a tree model to find frequent patterns without candidate generation. FP method works faster than the Apriori algorithm.
The document discusses the different types of files in Linux, including regular files, directories, links, special files, sockets, and pipes. Regular files contain text, data, and executable programs. Directories track and locate other files. Links provide shortcuts to files. Special files represent devices for input/output. Sockets and pipes allow communication between processes, with sockets enabling communication over networks.
4.1 Operasi Dasar Singly Linked List 1 (primitive list)Kelinci Coklat
Dokumen tersebut membahas operasi dasar pada singly linked list meliputi penyisipan (insert), penghapusan (delete), penelusuran (traversal), dan pencarian (searching) elemen. Terdapat penjelasan algoritma dan fungsi untuk operasi insert first, insert last, delete first, delete last, serta traversal untuk menelusuri seluruh elemen list.
This document provides an introduction to data structures. It discusses key concepts like abstract data types, different types of data structures including primitive and non-primitive, and common operations on data structures like traversing, searching, inserting, deleting, sorting and merging. It also covers algorithm analysis including time and space complexity and asymptotic notations. Specific data structures like arrays, linked lists, stacks, queues, trees and graphs are mentioned. The document concludes with discussions on pointers and structures in C/C++.
This document provides an overview of the Natural Language Toolkit (NLTK), a Python library for natural language processing. It discusses NLTK's modules for common NLP tasks like tokenization, part-of-speech tagging, parsing, and classification. It also describes how NLTK can be used to analyze text corpora, frequency distributions, collocations and concordances. Key functions of NLTK include tokenizing text, accessing annotated corpora, analyzing word frequencies, part-of-speech tagging, and shallow parsing.
Given a list of n integer, find the first and last occurrences of the largest integer in the list. Given an ordered list of n distinct integers, determine the position of an integer in the list using a binary search. Given a list of n integers, sort then using an insertion sort. Given the starting and editing times of n talks, use the appropriate greedy algorithm to schedule the most talks possible in a single lecture hall. Given a list of n integers, determine the number of comparisons used by the bubble sort and by the insertion sort to sort this list
Dokumen tersebut membahas tentang algoritma Naive Bayes Classifier untuk klasifikasi dan prediksi data. Algoritma ini bekerja dengan menghitung probabilitas kelas berdasarkan pengalaman sebelumnya dengan asumsi independensi antar variabel. Diberikan contoh perhitungan Naive Bayes untuk memprediksi kemungkinan seseorang membeli komputer dan status kelulusannya berdasarkan atribut-atribut tertentu.
Complexity analysis - The Big O NotationJawad Khan
Complexity analysis determines how resource requirements like time and memory scale with problem size. Computation time depends on hardware, while complexity analyzes algorithm scaling. Big O notation describes asymptotic function growth. Common complexities are O(1) constant, O(log n) logarithmic, O(n) linear, O(n^2) quadratic. Statements are O(1), if/else max branch, loops run n times, nested loops run n*m times, functions match calling structure, and when statements have undefined time.
Este documento proporciona una lista de comandos y sus modificadores y teclas de acceso para Microsoft Word. Ofrece más de 100 comandos para realizar tareas como abrir documentos, copiar y pegar texto, formatear fuentes y párrafos, e insertar campos e hipervínculos.
This document provides an introduction and overview of NumPy, a Python library used for numerical computing. It discusses NumPy's origins and capabilities, how to install NumPy on Linux, key NumPy concepts like the ndarray object, and how NumPy can be used with Matplotlib for plotting. Examples are given of common NumPy operations and functions for arrays, as well as plotting simple graphs with Matplotlib.
•Common Problems Needs Computers
•The Search Problem
•Basic Search Algorithms
–Algorithms used for searching the contents of an array
•Linear or Sequential Search
•Binary Search
•Comparison Between Linear and Binary Search
•Algorithms for solving shortest path problems
–Sequential Search Algorithms
•Depth-First Search
•Breadth First Search
–Parallel or distributed Search Algorithms
•Parallel Depth-First Search
•Parallel Breadth First Search
Dokumen tersebut membahas tentang konsep antrian (queue) dan implementasinya menggunakan array dan linked list. Terdapat penjelasan mengenai operasi insert queue dan delete queue serta ilustrasinya. Juga dibahas mengenai queue circular untuk menghindari ketidakefisienan saat delete. Di akhir ada latihan untuk membuat prosedur insert pada priority queue.
Manajemen file penting untuk mengatur penyimpanan dan organisasi file serta folder agar terhindar dari kehilangan data. File disimpan dalam folder dan masing-masing memiliki format serta ekstensi berbeda. Windows Explorer digunakan untuk mengakses, memindahkan, menyalin, atau menghapus file dan folder secara teratur.
4.1 Operasi Dasar Singly Linked List 1 (primitive list)Kelinci Coklat
Dokumen tersebut membahas operasi dasar pada singly linked list meliputi penyisipan (insert), penghapusan (delete), penelusuran (traversal), dan pencarian (searching) elemen. Terdapat penjelasan algoritma dan fungsi untuk operasi insert first, insert last, delete first, delete last, serta traversal untuk menelusuri seluruh elemen list.
This document provides an introduction to data structures. It discusses key concepts like abstract data types, different types of data structures including primitive and non-primitive, and common operations on data structures like traversing, searching, inserting, deleting, sorting and merging. It also covers algorithm analysis including time and space complexity and asymptotic notations. Specific data structures like arrays, linked lists, stacks, queues, trees and graphs are mentioned. The document concludes with discussions on pointers and structures in C/C++.
This document provides an overview of the Natural Language Toolkit (NLTK), a Python library for natural language processing. It discusses NLTK's modules for common NLP tasks like tokenization, part-of-speech tagging, parsing, and classification. It also describes how NLTK can be used to analyze text corpora, frequency distributions, collocations and concordances. Key functions of NLTK include tokenizing text, accessing annotated corpora, analyzing word frequencies, part-of-speech tagging, and shallow parsing.
Given a list of n integer, find the first and last occurrences of the largest integer in the list. Given an ordered list of n distinct integers, determine the position of an integer in the list using a binary search. Given a list of n integers, sort then using an insertion sort. Given the starting and editing times of n talks, use the appropriate greedy algorithm to schedule the most talks possible in a single lecture hall. Given a list of n integers, determine the number of comparisons used by the bubble sort and by the insertion sort to sort this list
Dokumen tersebut membahas tentang algoritma Naive Bayes Classifier untuk klasifikasi dan prediksi data. Algoritma ini bekerja dengan menghitung probabilitas kelas berdasarkan pengalaman sebelumnya dengan asumsi independensi antar variabel. Diberikan contoh perhitungan Naive Bayes untuk memprediksi kemungkinan seseorang membeli komputer dan status kelulusannya berdasarkan atribut-atribut tertentu.
Complexity analysis - The Big O NotationJawad Khan
Complexity analysis determines how resource requirements like time and memory scale with problem size. Computation time depends on hardware, while complexity analyzes algorithm scaling. Big O notation describes asymptotic function growth. Common complexities are O(1) constant, O(log n) logarithmic, O(n) linear, O(n^2) quadratic. Statements are O(1), if/else max branch, loops run n times, nested loops run n*m times, functions match calling structure, and when statements have undefined time.
Este documento proporciona una lista de comandos y sus modificadores y teclas de acceso para Microsoft Word. Ofrece más de 100 comandos para realizar tareas como abrir documentos, copiar y pegar texto, formatear fuentes y párrafos, e insertar campos e hipervínculos.
This document provides an introduction and overview of NumPy, a Python library used for numerical computing. It discusses NumPy's origins and capabilities, how to install NumPy on Linux, key NumPy concepts like the ndarray object, and how NumPy can be used with Matplotlib for plotting. Examples are given of common NumPy operations and functions for arrays, as well as plotting simple graphs with Matplotlib.
•Common Problems Needs Computers
•The Search Problem
•Basic Search Algorithms
–Algorithms used for searching the contents of an array
•Linear or Sequential Search
•Binary Search
•Comparison Between Linear and Binary Search
•Algorithms for solving shortest path problems
–Sequential Search Algorithms
•Depth-First Search
•Breadth First Search
–Parallel or distributed Search Algorithms
•Parallel Depth-First Search
•Parallel Breadth First Search
Dokumen tersebut membahas tentang konsep antrian (queue) dan implementasinya menggunakan array dan linked list. Terdapat penjelasan mengenai operasi insert queue dan delete queue serta ilustrasinya. Juga dibahas mengenai queue circular untuk menghindari ketidakefisienan saat delete. Di akhir ada latihan untuk membuat prosedur insert pada priority queue.
Manajemen file penting untuk mengatur penyimpanan dan organisasi file serta folder agar terhindar dari kehilangan data. File disimpan dalam folder dan masing-masing memiliki format serta ekstensi berbeda. Windows Explorer digunakan untuk mengakses, memindahkan, menyalin, atau menghapus file dan folder secara teratur.
Dokumen tersebut memberikan penjelasan mengenai konsep dasar data mining klasifikasi, proses klasifikasi menggunakan algoritma Naive Bayes, serta contoh kasus klasifikasi menggunakan atribut usia, pendapatan, pekerjaan, dan punya deposito atau tidak.
The document provides an overview of quantum computing concepts and the IBM Quantum Experience platform. It begins with a short history of quantum computing developments from the 1930s to present. It then explains basic quantum concepts like qubits, superposition, entanglement, and quantum gates. The document outlines requirements for building a quantum computer, including well-defined qubits, initialization, gates, coherence times, and measurement. It describes the IBM Quantum Experience as a platform that provides access to an actual quantum processor via the cloud, along with simulation and tutorial capabilities. Users can design circuits using a graphical Quantum Composer interface and run algorithms on real quantum hardware or simulation.
This document discusses looping structures in algorithms and programming. It defines looping as repeating statements to fulfill a looping condition. The main types of looping structures are for, while, and repeat loops. Examples are given in pseudocode and Pascal to illustrate for loops that count ascending and descending, while loops, and repeat loops. Exercises are provided to practice different types of loops.
This short document provides instructions for reducing unwanted LinkedIn update emails that fill up users' inboxes. It outlines a 3 step process for adjusting LinkedIn settings to only receive emails the user wants by going to the settings page and adjusting notification preferences.
Ryanair has utilized several key strategies to become Europe's largest low-cost carrier. These include maintaining a laser focus on cost containment, operating frequent point-to-point flights on short-haul routes, and maximizing ancillary revenue sources. Ryanair keeps costs low by operating efficiently from secondary airports and maintaining a young, fuel-efficient fleet. The strategy has proven highly sustainable due to Ryanair's continued growth and profitability, though recommendations include increasing flight frequencies and opening new routes to drive further expansion.
Algorithm and Programming (Introduction of dev pascal, data type, value, and ...Adam Mukharil Bachtiar
This file contains explanation about introduction of dev pascal, data type, value, and identifier. This file was used in my Algorithm and Programming Class.
Introduction to Quantum Computing & Quantum Information TheoryRahul Mee
This document provides an introduction to quantum computing and quantum information theory. It discusses how technological limitations of conventional computing motivate the development of quantum computing. The key laws of quantum mechanics that enable quantum computing are introduced, including superposition, entanglement, and the Heisenberg uncertainty principle. The document explains how quantum bits (qubits) can represent more than the two states of classical bits, and how quantum gates operate on qubits. It provides examples of one-qubit gates like the Hadamard gate. The potential for quantum computers to massively scale parallelism through quantum effects like entanglement is also summarized.
Migrating IBM Cloud Orchestrator environment from v2.4.0.2 to v2.5.0.1Paulraj Pappaiah
The document outlines the steps to migrate an IBM Cloud Orchestrator environment from version 2.4.0.2 to 2.5.0.1. The key steps include: 1) checking prerequisites and discovering the topology, 2) migrating images between the systems, 3) exporting OpenStack data from the original system, 4) importing the data into the new IBM Cloud Manager, and 5) verifying the migrated resources on both systems and ensuring resources are no longer available on the original system.
Chapter 4: basic search algorithms data structureMahmoud Alfarra
1) The document discusses two common search algorithms: sequential search and binary search. Sequential search looks at each item in a list sequentially until the target is found. Binary search works on a sorted list and divides the search space in half at each step.
2) It provides pseudocode examples of how each algorithm works step-by-step to find a target value in a list or array.
3) Binary search is more efficient than sequential search when the list is sorted, as it can significantly reduce the number of comparisons needed to find the target. Sequential search is used when the list is unsorted.
The document discusses different types of searching algorithms. It describes sequential search which searches an unsorted list sequentially until the target item is found or the entire list is searched. The average runtime is O(n) as the item could be anywhere in the list. Binary search is described as more efficient for sorted lists, repeatedly dividing the search space in half and comparing the target to the middle element. Indexes are also summarized, including clustered vs unclustered indexes and different approaches to storing data entries.
This document discusses various searching and sorting algorithms. It begins by defining searching as finding an element in a given list. Linear search and binary search are described as two common searching algorithms. Linear search has O(n) time complexity while binary search has O(log n) time complexity but requires a sorted list. The document also discusses different sorting algorithms like bubble sort, insertion sort, and merge sort, and defines key concepts related to sorting like stability, efficiency, and passes.
• Read and understand Java-based software code of medium-to-high complexity.
• Use standard and third party Java's API’s when writing applications.
• Understand the basic principles of creating Java applications with graphical user interface (GUI).
• Understand the features of Java supporting object oriented programming
• Understand the relative merits of Java as an object oriented programming language
• Understand how to produce object-oriented software using Java
• Understand how to apply the major object-oriented concepts such as encapsulation, inheritance and polymorphism to implement object oriented programs in Java.
• Understand advanced features of Java specifically stream I/O, Files etc.
• Apply the abovementioned points to design, implement, appropriately document and test a Java application of medium complexity, consisting of multiple classes.
• Read and understand Java-based software code of medium-to-high complexity.
• Use standard and third party Java's API’s when writing applications.
• Understand the basic principles of creating Java applications with graphical user interface (GUI).
• Understand the features of Java supporting object oriented programming
• Understand the relative merits of Java as an object oriented programming language
• Understand how to produce object-oriented software using Java
• Understand how to apply the major object-oriented concepts such as encapsulation, inheritance and polymorphism to implement object oriented programs in Java.
• Understand advanced features of Java specifically stream I/O, Files etc.
• Apply the abovementioned points to design, implement, appropriately document and test a Java application of medium complexity, consisting of multiple classes.
• Read and understand Java-based software code of medium-to-high complexity.
• Use standard and third party Java's API’s when writing applications.
• Understand the basic principles of creating Java applications with graphical user interface (GUI).
• Understand the features of Java supporting object oriented programming
• Understand the relative merits of Java as an object oriented programming language
• Understand how to produce object-oriented software using Java
• Understand how to apply the major object-oriented concepts such as encapsulation, inheritance and polymorphism to implement object oriented programs in Java.
• Understand advanced features of Java specifically stream I/O, Files etc.
• Apply the abovementioned points to design, implement, appropriately document and test a Java application of medium complexity, consisting of multiple classes.
The document discusses binary search and its advantages over sequential search. It explains that binary search improves search efficiency by dividing the search space in half at each step, allowing it to find an item in a sorted list in O(log n) time compared to O(n) for sequential search. The key steps of binary search are outlined, including comparing the search key to the middle element, and recursively searching either the left or right half depending on whether the key is smaller or larger than the middle element. Examples are provided to illustrate how binary search works on a sorted array to find a target value.
The document discusses different searching algorithms. It describes sequential search which compares the search key to each element in the list sequentially until a match is found. The best case is 1 comparison, average is N/2 comparisons, and worst case is N comparisons. It also describes binary search which divides the sorted list in half at each step, requiring log(N) comparisons in the average and worst cases. The document also covers indexing which structures data for efficient retrieval based on key values and includes clustered vs unclustered indexes.
The document discusses two searching algorithms: linear search and binary search. Linear search sequentially compares an element to each element in an unsorted array, making it slower than binary search. Binary search works by dividing the search space in half at each step based on comparing the target element to the middle element. It is faster than linear search but requires the array to be sorted first. Key differences are that linear search works on unsorted data while binary search requires sorted data, and binary search has better time complexity of O(log n) while linear search is O(n).
The document discusses two searching algorithms - linear search and binary search. Linear search sequentially compares the target element to each element in the array, while binary search uses a divide and conquer approach to quickly hone in on the target element in a sorted array. Both algorithms are demonstrated with pseudocode and examples.
This document discusses linear search and binary search algorithms. Linear search sequentially checks each element of an unsorted array to find a target value, resulting in O(n) time complexity. Binary search works on a sorted array, comparing the target to the middle element and recursively searching half the array, requiring O(log n) time. The document provides pseudocode for both algorithms and compares their performance on different sized inputs. It also discusses properties of greedy algorithms and provides an example of when a greedy solution fails to find the optimal result.
The document discusses binary search, an efficient algorithm for finding a target value within a sorted collection. It works by repeatedly dividing the search space in half and focusing on only one subdivision, based on whether the target value is less than or greater than the middle element. This reduces the number of iterations needed. The key steps are to calculate the middle element, compare it to the target, and either search the left or right half accordingly. Binary search provides significant performance gains over linear search for large sorted datasets.
This document discusses various sorting and searching algorithms. It begins by listing sorting algorithms like selection sort, insertion sort, bubble sort, merge sort, and radix sort. It then discusses searching algorithms like linear/sequential search and binary search. It provides details on the implementation and time complexity of linear search, binary search, bubble sort, insertion sort, selection sort, and merge sort. Key points covered include the divide and conquer approach of merge sort and how binary search recursively halves the search space.
Searching algorithms are used to find elements within datasets. Sequential search linearly checks each element until a match is found, taking O(n) time on average. Interval search algorithms like binary search target the center of a sorted structure and divide the search space in half at each step, taking O(log n) time on average. Jump search checks fewer elements than linear search by jumping ahead by a fixed number of steps or block size, typically the square root of the list length. Interpolation search may check non-middle indexes based on the searched value, working best for uniformly distributed sorted data.
Algorithm and Data Structure - Linear SearchAndiNurkholis1
This material aims to enable students to:
1) Understanding searching algorithm concept
2) Understanding characteristic of linear search
3) Understanding steps of linear search
4) Knowing of advantage and disadvantage of linear search
The document discusses various searching algorithms. It introduces linear search and binary search. Linear search sequentially checks each element until the target is found or the entire array is searched. Binary search works on a sorted array by repeatedly dividing the search space in half and focusing on only one half. The best case for both is O(1) while the worst case for linear search is O(N) and for binary search is O(LogN).
1. Linear search sequentially checks each element of an array to find a target item. It adds the item to the end of the array and uses a counter to check each element until it finds a match.
2. Binary search works on a sorted array. It checks the middle element first, then searches either the left or right half depending on if the target is smaller or larger than the middle element.
3. The example demonstrates linear search finding the letter 'G' in an array and binary search locating the number 44 through a series of steps that narrow the search space.
Dokumen tersebut memberikan tips untuk membuat formatting kode program yang baik agar mudah dibaca dan dipahami. Terdapat dua jenis formatting, yaitu vertical dan horizontal formatting. Secara vertical, kode perlu diatur dengan memperhatikan konsep-konsep, jarak antar konsep, kerapatan kode yang berkaitan, dan letak deklarasi dan pemanggilan fungsi. Secara horizontal, perlu memperhatikan pemberian jarak, penyamaan baris, dan pengindentasian untuk membedakan struktur program.
Slide ini menjelaskan perihal penggunaan komentar yang baik dan buruk pada suatu kode program. Slide ini merupakan bahan ajar untuk mata kuliah Clean Code dan Design Pattern.
Dokumen tersebut memberikan tips-tips untuk membuat nama variabel, fungsi, kelas, dan paket yang baik dalam pembuatan kode program. Beberapa tips utama adalah menggunakan nama yang jelas maksudnya, hindari penggunaan encoding, gunakan kata benda untuk nama kelas dan verba untuk nama metode, serta tambahkan konteks yang bermakna.
Dokumen tersebut membahas tentang pengujian perangkat lunak, termasuk definisi pengujian perangkat lunak, tujuan pengujian, jenis pengujian seperti manual testing, automated testing, unit testing, integration testing, serta metode pengujian seperti white box testing dan black box testing.
Slide ini berisi penjelasan tentang Data Mining Klasifikasi. Di dalamnya ada tiga algoritma yang dibahas, yaitu: Naive Bayes, kNN, dan ID3 (Decision Tree).
Dokumen tersebut membahas algoritma program dinamis untuk menentukan lintasan terpendek antara dua simpul dalam sebuah graf. Metode yang digunakan adalah program dinamis mundur dimana permasalahan dibagi menjadi beberapa tahap dan dihitung secara mundur untuk menentukan nilai optimal pada setiap tahap. Hasil akhir adalah terdapat tiga lintasan terpendek dengan panjang 11 antara simpul 1 dan 10.
Teks tersebut membahas strategi algoritma Divide and Conquer untuk memecahkan masalah. Strategi ini membagi masalah menjadi submasalah kecil, memecahkan submasalah tersebut secara rekursif, lalu menggabungkan hasilnya untuk mendapatkan solusi masalah awal. Dua contoh masalah yang dijelaskan adalah mencari nilai maksimum dan minimum dalam tabel, serta mencari pasangan titik terdekat dalam himpunan titik.
Slide ini berisi penjelasan tentang teorema-teorema yang berlaku untuk notasi asimptotik beserta cara perhitungannya untuk kebutuhan waktu suatu algoritma.
The Future of Open Source Reporting Best Alternatives to Jaspersoft.pdfVarsha Nayak
In recent years, organizations have increasingly sought robust open source alternative to Jasper Reports as the landscape of open-source reporting tools rapidly evolves. While Jaspersoft has been a longstanding choice for generating complex business intelligence and analytics reports, factors such as licensing changes and growing demands for flexibility have prompted many businesses to explore other options. Among the most notable alternatives to Jaspersoft, Helical Insight stands out for its powerful open-source architecture, intuitive analytics, and dynamic dashboard capabilities. Designed to be both flexible and budget-friendly, Helical Insight empowers users with advanced features—such as in-memory reporting, extensive data source integration, and customizable visualizations—making it an ideal solution for organizations seeking a modern, scalable reporting platform. This article explores the future of open-source reporting and highlights why Helical Insight and other emerging tools are redefining the standards for business intelligence solutions.
In this session we cover the benefits of a migration to Cosmos DB, migration paths, common pain points and best practices. We share our firsthand experiences and customer stories. Adiom is the trusted partner for migration solutions that enable seamless online database migrations from MongoDB to Cosmos DB vCore, and DynamoDB to Cosmos DB for NoSQL.
Invited Talk at RAISE 2025: Requirements engineering for AI-powered SoftwarE Workshop co-located with ICSE, the IEEE/ACM International Conference on Software Engineering.
Abstract: Foundation Models (FMs) have shown remarkable capabilities in various natural language tasks. However, their ability to accurately capture stakeholder requirements remains a significant challenge for using FMs for software development. This paper introduces a novel approach that leverages an FM-powered multi-agent system called AlignMind to address this issue. By having a cognitive architecture that enhances FMs with Theory-of-Mind capabilities, our approach considers the mental states and perspectives of software makers. This allows our solution to iteratively clarify the beliefs, desires, and intentions of stakeholders, translating these into a set of refined requirements and a corresponding actionable natural language workflow in the often-overlooked requirements refinement phase of software engineering, which is crucial after initial elicitation. Through a multifaceted evaluation covering 150 diverse use cases, we demonstrate that our approach can accurately capture the intents and requirements of stakeholders, articulating them as both specifications and a step-by-step plan of action. Our findings suggest that the potential for significant improvements in the software development process justifies these investments. Our work lays the groundwork for future innovation in building intent-first development environments, where software makers can seamlessly collaborate with AIs to create software that truly meets their needs.
FME as an Orchestration Tool - Peak of Data & AI 2025Safe Software
Processing huge amounts of data through FME can have performance consequences, but as an orchestration tool, FME is brilliant! We'll take a look at the principles of data gravity, best practices, pros, cons, tips and tricks. And of course all spiced up with relevant examples!
Async-ronizing Success at Wix - Patterns for Seamless Microservices - Devoxx ...Natan Silnitsky
In a world where speed, resilience, and fault tolerance define success, Wix leverages Kafka to power asynchronous programming across 4,000 microservices. This talk explores four key patterns that boost developer velocity while solving common challenges with scalable, efficient, and reliable solutions:
1. Integration Events: Shift from synchronous calls to pre-fetching to reduce query latency and improve user experience.
2. Task Queue: Offload non-critical tasks like notifications to streamline request flows.
3. Task Scheduler: Enable precise, fault-tolerant delayed or recurring workflows with robust scheduling.
4. Iterator for Long-running Jobs: Process extensive workloads via chunked execution, optimizing scalability and resilience.
For each pattern, we’ll discuss benefits, challenges, and how we mitigate drawbacks to create practical solutions
This session offers actionable insights for developers and architects tackling distributed systems, helping refine microservices and adopting Kafka-driven async excellence.
Automating Map Production With FME and PythonSafe Software
People still love a good paper map, but every time a request lands on a GIS team’s desk, it takes time to create that perfect, individual map—even when you're ready and have projects prepped. Then come the inevitable changes and iterations that add even more time to the process. This presentation explores a solution for automating map production using FME and Python. FME handles the setup of variables, leveraging GIS reference layers and parameters to manage details like map orientation, label sizes, and layout elements. Python takes over to export PDF maps for each location and template size, uploading them monthly to ArcGIS Online. The result? Fresh, regularly updated maps, ready for anyone to grab anytime—saving you time, effort, and endless revisions while keeping users happy with up-to-date, accessible maps.
Join the Denver Marketo User Group, Captello and Integrate as we dive into the best practices, tools, and strategies for maintaining robust, high-performing databases. From managing vendors and automating orchestrations to enriching data for better insights, this session will unpack the key elements that keep your data ecosystem running smoothly—and smartly.
We will hear from Steve Armenti, Twelfth, and Aaron Karpaty, Captello, and Frannie Danzinger, Integrate.
In today's world, artificial intelligence (AI) is transforming the way we learn.
This talk will explore how we can use AI tools to enhance our learning experiences, by looking at some (recent) research that has been done on the matter.
But as we embrace these new technologies, we must also ask ourselves:
Are we becoming less capable of thinking for ourselves?
Do these tools make us smarter, or do they risk dulling our critical thinking skills?
This talk will encourage us to think critically about the role of AI in our education. Together, we will discover how to use AI to support our learning journey while still developing our ability to think critically.
Insurance policy management software transforms complex, manual insurance operations into streamlined, efficient digital workflows, enhancing productivity, accuracy, customer service, and profitability for insurers. Visit https://www.damcogroup.com/insurance/policy-management-software for more details!
Maximizing Business Value with AWS Consulting Services.pdfElena Mia
An overview of how AWS consulting services empower organizations to optimize cloud adoption, enhance security, and drive innovation. Read More: https://www.damcogroup.com/aws-cloud-services/aws-consulting.
Revolutionize Your Insurance Workflow with Claims Management SoftwareInsurance Tech Services
Claims management software enhances efficiency, accuracy, and satisfaction by automating processes, reducing errors, and speeding up transparent claims handling—building trust and cutting costs. Explore More - https://www.damcogroup.com/insurance/claims-management-software
Who will create the languages of the future?Jordi Cabot
Will future languages be created by language engineers?
Can you "vibe" a DSL?
In this talk, we will explore the changing landscape of language engineering and discuss how Artificial Intelligence and low-code/no-code techniques can play a role in this future by helping in the definition, use, execution, and testing of new languages. Even empowering non-tech users to create their own language infrastructure. Maybe without them even realizing.
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7. WhatisSequentialSearch
• Trace group of data one by one.
• Start the process from the first data.
• If the data was found in group then stop
the searching but if not, search until the
last data in grup.
9. Ilustration of Seq. Search Without Sentinel
Given an array to be processed:
Number
Data that want to be sought : 9
• Number[1] = 9?
• Number[2] = 9?
• Number[3] = 9?
Result: 9 is found in number[3]
5 1 9 4 2
[1] [2] [3] [4] [5]
i i + 1
i i + 1
i (STOP SEARCH)
10. Sequential Search Without Sentinel
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Procedure SeqSearchTanpaSentinel (Input nama_array:tipe_array)
{I.S. : elemen array [1..maks_array] sudah terdefinisi}
{F.S. : menampilkan hasil pencarian (ditemukan/tidak)}
Kamus:
i : integer
data_cari : tipedata
Algoritma:
input(data_cari)
i 1
while(nama_array [i] ≠ data_cari) and (i < maks_array) do
i i + 1
endwhile
if (nama_array[i] = data_cari)
then
output(data_cari,’ ditemukan pada indeks ke-’,i)
else
output(data_cari,’ tidak ditemukan’)
endif
EndProcedure
11. SequentialSearchUseSentinel
• Place the data that want to be sought in
sentinel.
• Sentinel is additional index that was placed in
max array + 1.
• If the data is found in sentinel that means the
result is data is not found and vice versa.
12. Ilustration of Seq. Search Use Sentinel
Data that was sought: 9
Number
Result: Data was found in Number[3]
Data that was sought: 10
Number
Result: Data was not found
5 1 9 4 2 9
[1] [2] [3] [4] [5] [6]
sentinel
5 1 9 4 2 10
[1] [2] [3] [4] [5] [6]
sentinel
13. Sequential Search Use Sentinel
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Procedure SeqSearchSentinel (Input nama_array:tipe_array)
{I.S. : elemen array [1..maks_array] sudah terdefinisi}
{F.S. : menampilkan hasil pencarian (ditemukan/tidak)}
Kamus:
i : integer
data_cari : tipedata
Algoritma:
input(data_cari)
i 1
nama_array(maks_array + 1) data_cari
while (nama_array [i] ≠ data_cari) do
i i + 1
endwhile
if (i < maks_array+1)
then
output(data_cari,’ ditemukan pada indeks ke-’,i)
else
output(data_cari,’ tidak ditemukan’)
endif
EndProcedure
15. Ilustration of Seq. Search Use Boolean
Given an array to be processed:
Number
Data that want to be sought : 9
• Number[1] = 9?
• Number[2] = 9?
• Number[3] = 9?
Result: 9 is found in number[3]
5 1 9 4 2
[1] [2] [3] [4] [5]
FOUND FALSE
FOUND FALSE
FOUND TRUE (STOP SEARCH)
16. Sequential Search Use Sentinel
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Procedure SeqSearchBoolean (Input nama_array:tipe_array)
{I.S. : elemen array [1..maks_array] sudah terdefinisi}
{F.S. : menampilkan data yg dicari ditemukan atau tidak ditemukan}
Kamus:
i : integer
ketemu : boolean
data_cari : tipedata
Algoritma:
input(data_cari)
i 1
ketemu false
while (not ketemu) and (i ≤ maks_array) do
if(nama_var_array(i) = data_cari)
then
ketemu true
else
i i + 1
endif
endwhile
if (ketemu)
then
output(data_cari,’ ditemukan pada indeks ke-’,i)
else
output(data_cari,’ tidak ditemukan’)
endif
EndProcedure
18. WhatisBinarySearch
• Searching algorithm that divide group of data into
two parts (left and right).
• First, check data in the middle. If same with the
data that was sought then data is found. If not then
continue searching process to left or right (based
on condition).
• Group of data must be sorted before the searching
process.
19. Data that was sought: 7
Number
Result: ?
CaseExampleofBinarySearch
3 7 12 15 29
[1] [2] [3] [4] [5]
20. Case Example of Binary Search
Data that was sought: 7
Number
Result: ?
3 7 12 15 29
[1] [2] [3] [4] [5]
21. Case Example of Binary Search
Step 1 : Divide array into 2 parts. Count the middle
position (k) of array to start searching
k = (Ia + Ib) div 2
= (1 + 5) div 2
= 3
la : lower bound (for index)
lb : upper bound (for index)
3 7 12 15 29
[1] [2] [3] [4] [5]
Ia k Ib
Left Side Right Side
22. Case Example of Binary Search
Step 2 :
• check data in k. If it’s same with data that was sought then
stop search and data is found.
• If it’s not then check whether data was bigger or smaller than
data in k.
• If it’s bigger one then continue searching to right side and la
= k+1. if it’s smaller one then continue searching to the left
side and lb = k-1 (data wa sorted in ascending way).
3 7
1 2
Ia Ib
23. Case Example of Binary Search
Step 3 : repeat step 1 until step 2 until data is found or until
la>lb then stop searching.
Result : 7 is found in Number[2] and in the third looping.
3 7
1 2
Ia Ib
k
Left Side Right Side
24. Binary Search
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Procedure BinarySearch (Input nama_array : tipe_array)
{I.S. : elemen array yang terurut secara ascending sudah terdefinisi}
{F.S. : menampilkan data yg dicari ditemukan atau tidak ditemukan}
Kamus:
Ia, Ib, k : integer
ketemu : boolean
data_cari : tipedata
Algoritma:
input(data_cari)
Ia 1
Ib maks_array
ketemu false
while (not ketemu) and (Ia ≤ Ib) do
k (Ia + Ib) div 2
if (nama_var_array[k] = data_cari)
then
ketemu true
else
if (nama_var_array[k] < data_cari)
then
Ia k + 1
else
Ib k – 1
endif
endif
endwhile