This is topic is describe by Rahul Gupta, Alon Halevy, Xuezhi Wang, Steven Whang, Fei Wu. This is only i read report and make a presentation to explain the paper what is actually author want to say.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This paper proposes three algorithms (ScanCount, MergeSkip, and DivideSkip) to efficiently search for approximate string matches from a collection of strings. The algorithms utilize inverted indexes of substrings (grams) to find candidate strings. The paper also studies how to integrate filtering techniques with the merging algorithms to further improve performance. Experiments show the proposed techniques significantly outperform existing algorithms and that filters need to be judiciously combined with merging to achieve the best results.
This document provides an introduction to various data structures, including linear data structures (arrays, stacks, queues, linked lists), non-linear data structures (trees, graphs), and how they organize and store data. It discusses common terms related to data structures and includes examples of different array and linked list operations and programs. The document aims to explain fundamental concepts for understanding and implementing various data structures through programming languages like C/C++.
The document discusses arrays in Java. It defines arrays as ordered lists that store multiple values of the same type. Arrays allow accessing elements using indexes, and declaring arrays involves specifying the type and size. The document covers key array concepts like initialization, bounds checking, passing arrays as parameters, multidimensional arrays, and sorting and searching arrays.
This document provides an overview of key concepts related to writing classes in Java, including:
- Defining classes to create custom objects with state (data) and behaviors (methods)
- Encapsulation and using access modifiers like public and private to control visibility
- Declaring methods, parameters, and return types
- Overloading methods by having multiple methods with the same name but different parameters
- Constructors for initializing new objects
Abstract data types are data structures defined by their behavior (semantics) rather than their implementation. They export a type and a set of operations on that type. Operations are the only way to interact with the data structure. Characteristics include exporting a type, set of operations, and operations being the only access to the type's data.
This document discusses binary search, an algorithm that searches a sorted list by dividing the list in half at each step. It works by comparing the search key to the middle element of the list and eliminating half of the elements from further consideration based on whether the key is smaller or larger than the middle element. The algorithm has a runtime complexity of O(log n), making it very efficient for large lists. An example implementation is provided that starts by comparing the search key to the middle element, and recursively searches either the left or right half of the list depending on the comparison.
The document provides an overview of common data structures including lists, stacks, queues, trees, and hash tables. It describes each data structure, how it can be implemented both statically and dynamically, and how to use the core Java classes like ArrayList, Stack, LinkedList, and HashMap that implement these structures. Key points covered include common operations for each structure, examples of using the Java classes, and applications like finding prime numbers in a range or matching brackets in an expression.
Binary search is a search algorithm that finds the position of a target value within a sorted array. It works by comparing the target value to the middle element of the array each time, eliminating half of the remaining elements from consideration based on whether the target is less than or greater than the middle element. This process continues, halving the search space each time, until the target is found or the search space is empty, indicating the target is not in the array. Binary search runs in logarithmic time, making it faster than the linear time complexity of linear search.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This paper proposes three algorithms (ScanCount, MergeSkip, and DivideSkip) to efficiently search for approximate string matches from a collection of strings. The algorithms utilize inverted indexes of substrings (grams) to find candidate strings. The paper also studies how to integrate filtering techniques with the merging algorithms to further improve performance. Experiments show the proposed techniques significantly outperform existing algorithms and that filters need to be judiciously combined with merging to achieve the best results.
This document provides an introduction to various data structures, including linear data structures (arrays, stacks, queues, linked lists), non-linear data structures (trees, graphs), and how they organize and store data. It discusses common terms related to data structures and includes examples of different array and linked list operations and programs. The document aims to explain fundamental concepts for understanding and implementing various data structures through programming languages like C/C++.
The document discusses arrays in Java. It defines arrays as ordered lists that store multiple values of the same type. Arrays allow accessing elements using indexes, and declaring arrays involves specifying the type and size. The document covers key array concepts like initialization, bounds checking, passing arrays as parameters, multidimensional arrays, and sorting and searching arrays.
This document provides an overview of key concepts related to writing classes in Java, including:
- Defining classes to create custom objects with state (data) and behaviors (methods)
- Encapsulation and using access modifiers like public and private to control visibility
- Declaring methods, parameters, and return types
- Overloading methods by having multiple methods with the same name but different parameters
- Constructors for initializing new objects
Abstract data types are data structures defined by their behavior (semantics) rather than their implementation. They export a type and a set of operations on that type. Operations are the only way to interact with the data structure. Characteristics include exporting a type, set of operations, and operations being the only access to the type's data.
This document discusses binary search, an algorithm that searches a sorted list by dividing the list in half at each step. It works by comparing the search key to the middle element of the list and eliminating half of the elements from further consideration based on whether the key is smaller or larger than the middle element. The algorithm has a runtime complexity of O(log n), making it very efficient for large lists. An example implementation is provided that starts by comparing the search key to the middle element, and recursively searches either the left or right half of the list depending on the comparison.
The document provides an overview of common data structures including lists, stacks, queues, trees, and hash tables. It describes each data structure, how it can be implemented both statically and dynamically, and how to use the core Java classes like ArrayList, Stack, LinkedList, and HashMap that implement these structures. Key points covered include common operations for each structure, examples of using the Java classes, and applications like finding prime numbers in a range or matching brackets in an expression.
Binary search is a search algorithm that finds the position of a target value within a sorted array. It works by comparing the target value to the middle element of the array each time, eliminating half of the remaining elements from consideration based on whether the target is less than or greater than the middle element. This process continues, halving the search space each time, until the target is found or the search space is empty, indicating the target is not in the array. Binary search runs in logarithmic time, making it faster than the linear time complexity of linear search.
Chapter 5 discusses enhancing classes through object references, static modifiers, exceptions, interfaces, nested classes, and graphical user interfaces. Key topics include aliases of object references, passing objects as parameters, using the static modifier for methods and variables, exception handling, implementing interfaces, nested and inner classes, creating dialog boxes, and building GUIs with components, events, and listeners.
The document discusses C programming concepts like strcpy() function implementation, data types, operators, functions, pointers, arrays, strings and more. It provides code snippets to demonstrate various C programming techniques like implementing string copy functions, converting numbers to different bases, evaluating polynomials, swapping variables, reversing strings, matrix multiplication and more. It also answers questions about common C programming topics to test understanding.
This document discusses data structures and their types. It defines data structures as the logical or mathematical organization of data in computer memory or on disk. The main types are linear data structures like arrays, stacks, queues, and linked lists, and non-linear structures like trees and graphs. Common operations on data structures include traversing, searching, inserting, deleting, sorting, and merging. Algorithms manipulate data in these structures to solve problems.
This document provides an introduction to Entity-Relationship (ER) data modeling. It describes the basic concepts of entities, attributes, relationships, and keys. It explains how ER diagrams can be used to graphically represent these concepts and the structure of a database. The document also covers entity types, relationship types, participation constraints, mapping cardinalities, weak entities, and how to represent these concepts in an ER diagram.
The document discusses stacks and queues as data structures. It defines stacks as first-in last-out (LIFO) structures where elements are added and removed from one end of the list. Queues are defined as first-in first-out (FIFO) structures where elements are added to one end and removed from the other. The document provides examples of using stacks and queues in programming and describes the common operations that can be performed on each type of data structure.
This document discusses data types in C programming. It describes primitive data types like integers, floats, characters and their syntax. It also covers non-primitive data types like arrays, structures, unions, and linked lists. Arrays store a collection of similar data types, structures group different data types, and unions store different types in the same memory location. Linked lists are dynamic data structures using pointers. The document also provides overviews of stacks and queues, describing their LIFO and FIFO properties respectively.
This document provides an outline of topics for learning the R programming language, including R basics, vectors and factors, arrays, matrices, lists, data frames, if/else statements, for loops, user defined functions, objects and classes, reading data files, string operations, and regular expressions. Key concepts covered are defining vectors and factors, performing operations on vectors, summarizing data, accessing and manipulating arrays and matrices, the structure and operations of data frames, using if/else statements and for/while loops, defining user functions, detecting object classes and converting between types, reading different file types into R, and using string and regular expression functions.
This chapter discusses searching and sorting algorithms. It covers sequential search, binary search, selection sort and insertion sort. Sequential search has linear time complexity while binary search has logarithmic time complexity, making it more efficient for large lists. Selection sort works by finding the minimum element and swapping it into place, taking on average n(n-1)/2 comparisons to sort a list of length n. Insertion sort iterates through the list and inserts each element into its sorted position, taking on average (n^2 + 3n - 4)/4 comparisons.
Ranking Objects by Exploiting Relationships: Computing Top-K over AggregationJason Yang
The document proposes an approach to efficiently find the top K target objects that are related to and best match a set of keywords, by exploiting relationships between documents and objects. It presents a system that indexes documents and relationships, defines scoring functions to aggregate relevance scores from related documents, and employs an early termination strategy using upper and lower bounds to avoid computing scores for all objects.
Triple-Triple RDF Store with Greedy Graph based GroupingVinoth Chandar
This document proposes improvements to the query performance of triple stores that store RDF data in a relational database. It explores storing RDF triples in different orders in three tables and developing a query rewriting scheme. It also looks at optimizing the physical schema through graph clustering techniques that aim to reduce the number of disk accesses by grouping related triples closer together. The paper presents an implementation of these techniques over a million triples and shows they can yield significant performance benefits on complex queries.
A data structure is a way of storing data in computer memory so that it can be retrieved and manipulated efficiently. There are two main categories of data structures: linear and non-linear. Linear data structures include arrays, stacks, and queues where elements are stored in a linear order. Non-linear structures include trees and graphs where elements are not necessarily in a linear order. Common operations on data structures include traversing, searching, insertion, deletion, sorting, and merging. Algorithms use data structures to process and solve problems in an efficient manner.
An Efficient Annotation of Search Results Based on Feature Ranking Approach f...Computer Science Journals
With the increased number of web databases, major part of deep web is one of the bases of database. In several search engines, encoded data in the returned resultant pages from the web often comes from structured databases which are referred as Web databases (WDB).
The binary search is faster than the sequential search. The complexity of binary search is O(log n) whereas the complexity of a sequential search is O(n). Stacks are used to evaluate algebraic or arithmetic expressions using prefix or postfix notations. Heap sort involves creating a max heap from the array and then replacing the root with the last element and rebuilding the heap for the remaining elements, repeating this process to sort the entire array.
Chapter 3 introduces several Java program statements:
- Conditional statements like if-else allow programs to make decisions based on boolean expressions.
- Loops like while and for allow code to repeat based on conditions.
- Logical operators like && and || combine boolean expressions.
- Proper program design is important, involving requirements, design, implementation, and testing stages.
YouTube Link: https://youtu.be/QswQA1lRIQY
** Python Certification Training: https://www.edureka.co/python **
This Edureka PPT on 'Collections In Python' will cover the concepts of Collection data type in python along with the collections module and specialized collection data structures like counter, chainmap, deque etc. Following are the topics discussed:
What Are Collections In Python?
What Is A Collection Module In Python?
Specialized Collection Data Structures
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Data structure is an arrangement of data in computer's memory. It makes the data quickly available to the processor for required operations.It is a software artifact which allows data to be stored, organized and accessed.
Java arrays allow storing multiple values of the same data type. There are one-dimensional arrays, which store elements in a list, and multi-dimensional arrays, which can be thought of as tables with rows and columns. Methods are blocks of code that perform operations, and can take parameters and return values. Parameters can be passed by value, where the method gets a copy of the argument, or by reference, where changes to the parameter affect the original argument.
The document discusses various algorithms like priority queues, heaps, heap sort, merge sort, quick sort, binary search, and algorithms for finding the maximum and minimum elements in an array. It provides definitions and explanations of these algorithms along with code snippets and examples. Key steps of algorithms like heap sort, merge sort, and quick sort are outlined. Methods for implementing priority queues, binary search and finding max/min in optimal comparisons are also presented.
This document discusses using machine learning techniques to automatically generate semantic metadata for web services. It explores supervised learning approaches for classifying domains, datatypes and categories of web services. It also examines unsupervised clustering algorithms for grouping web services into coherent categories. The techniques are evaluated on a collection of web services and forms, with results showing the machine learning approaches outperform simple baselines.
This document provides an overview and summary of a project report on text clustering. The report describes a system that takes in a collection of documents as input, clusters the documents into groups based on similarity, and allows the user to iteratively explore and refine the clusters to find relevant documents. The system represents documents as vectors, uses cosine similarity to initially cluster documents, and applies Bayesian machine learning to further refine the clusters. It aims to allow users to efficiently browse and retrieve relevant documents without viewing the entire collection.
Chapter 5 discusses enhancing classes through object references, static modifiers, exceptions, interfaces, nested classes, and graphical user interfaces. Key topics include aliases of object references, passing objects as parameters, using the static modifier for methods and variables, exception handling, implementing interfaces, nested and inner classes, creating dialog boxes, and building GUIs with components, events, and listeners.
The document discusses C programming concepts like strcpy() function implementation, data types, operators, functions, pointers, arrays, strings and more. It provides code snippets to demonstrate various C programming techniques like implementing string copy functions, converting numbers to different bases, evaluating polynomials, swapping variables, reversing strings, matrix multiplication and more. It also answers questions about common C programming topics to test understanding.
This document discusses data structures and their types. It defines data structures as the logical or mathematical organization of data in computer memory or on disk. The main types are linear data structures like arrays, stacks, queues, and linked lists, and non-linear structures like trees and graphs. Common operations on data structures include traversing, searching, inserting, deleting, sorting, and merging. Algorithms manipulate data in these structures to solve problems.
This document provides an introduction to Entity-Relationship (ER) data modeling. It describes the basic concepts of entities, attributes, relationships, and keys. It explains how ER diagrams can be used to graphically represent these concepts and the structure of a database. The document also covers entity types, relationship types, participation constraints, mapping cardinalities, weak entities, and how to represent these concepts in an ER diagram.
The document discusses stacks and queues as data structures. It defines stacks as first-in last-out (LIFO) structures where elements are added and removed from one end of the list. Queues are defined as first-in first-out (FIFO) structures where elements are added to one end and removed from the other. The document provides examples of using stacks and queues in programming and describes the common operations that can be performed on each type of data structure.
This document discusses data types in C programming. It describes primitive data types like integers, floats, characters and their syntax. It also covers non-primitive data types like arrays, structures, unions, and linked lists. Arrays store a collection of similar data types, structures group different data types, and unions store different types in the same memory location. Linked lists are dynamic data structures using pointers. The document also provides overviews of stacks and queues, describing their LIFO and FIFO properties respectively.
This document provides an outline of topics for learning the R programming language, including R basics, vectors and factors, arrays, matrices, lists, data frames, if/else statements, for loops, user defined functions, objects and classes, reading data files, string operations, and regular expressions. Key concepts covered are defining vectors and factors, performing operations on vectors, summarizing data, accessing and manipulating arrays and matrices, the structure and operations of data frames, using if/else statements and for/while loops, defining user functions, detecting object classes and converting between types, reading different file types into R, and using string and regular expression functions.
This chapter discusses searching and sorting algorithms. It covers sequential search, binary search, selection sort and insertion sort. Sequential search has linear time complexity while binary search has logarithmic time complexity, making it more efficient for large lists. Selection sort works by finding the minimum element and swapping it into place, taking on average n(n-1)/2 comparisons to sort a list of length n. Insertion sort iterates through the list and inserts each element into its sorted position, taking on average (n^2 + 3n - 4)/4 comparisons.
Ranking Objects by Exploiting Relationships: Computing Top-K over AggregationJason Yang
The document proposes an approach to efficiently find the top K target objects that are related to and best match a set of keywords, by exploiting relationships between documents and objects. It presents a system that indexes documents and relationships, defines scoring functions to aggregate relevance scores from related documents, and employs an early termination strategy using upper and lower bounds to avoid computing scores for all objects.
Triple-Triple RDF Store with Greedy Graph based GroupingVinoth Chandar
This document proposes improvements to the query performance of triple stores that store RDF data in a relational database. It explores storing RDF triples in different orders in three tables and developing a query rewriting scheme. It also looks at optimizing the physical schema through graph clustering techniques that aim to reduce the number of disk accesses by grouping related triples closer together. The paper presents an implementation of these techniques over a million triples and shows they can yield significant performance benefits on complex queries.
A data structure is a way of storing data in computer memory so that it can be retrieved and manipulated efficiently. There are two main categories of data structures: linear and non-linear. Linear data structures include arrays, stacks, and queues where elements are stored in a linear order. Non-linear structures include trees and graphs where elements are not necessarily in a linear order. Common operations on data structures include traversing, searching, insertion, deletion, sorting, and merging. Algorithms use data structures to process and solve problems in an efficient manner.
An Efficient Annotation of Search Results Based on Feature Ranking Approach f...Computer Science Journals
With the increased number of web databases, major part of deep web is one of the bases of database. In several search engines, encoded data in the returned resultant pages from the web often comes from structured databases which are referred as Web databases (WDB).
The binary search is faster than the sequential search. The complexity of binary search is O(log n) whereas the complexity of a sequential search is O(n). Stacks are used to evaluate algebraic or arithmetic expressions using prefix or postfix notations. Heap sort involves creating a max heap from the array and then replacing the root with the last element and rebuilding the heap for the remaining elements, repeating this process to sort the entire array.
Chapter 3 introduces several Java program statements:
- Conditional statements like if-else allow programs to make decisions based on boolean expressions.
- Loops like while and for allow code to repeat based on conditions.
- Logical operators like && and || combine boolean expressions.
- Proper program design is important, involving requirements, design, implementation, and testing stages.
YouTube Link: https://youtu.be/QswQA1lRIQY
** Python Certification Training: https://www.edureka.co/python **
This Edureka PPT on 'Collections In Python' will cover the concepts of Collection data type in python along with the collections module and specialized collection data structures like counter, chainmap, deque etc. Following are the topics discussed:
What Are Collections In Python?
What Is A Collection Module In Python?
Specialized Collection Data Structures
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Data structure is an arrangement of data in computer's memory. It makes the data quickly available to the processor for required operations.It is a software artifact which allows data to be stored, organized and accessed.
Java arrays allow storing multiple values of the same data type. There are one-dimensional arrays, which store elements in a list, and multi-dimensional arrays, which can be thought of as tables with rows and columns. Methods are blocks of code that perform operations, and can take parameters and return values. Parameters can be passed by value, where the method gets a copy of the argument, or by reference, where changes to the parameter affect the original argument.
The document discusses various algorithms like priority queues, heaps, heap sort, merge sort, quick sort, binary search, and algorithms for finding the maximum and minimum elements in an array. It provides definitions and explanations of these algorithms along with code snippets and examples. Key steps of algorithms like heap sort, merge sort, and quick sort are outlined. Methods for implementing priority queues, binary search and finding max/min in optimal comparisons are also presented.
This document discusses using machine learning techniques to automatically generate semantic metadata for web services. It explores supervised learning approaches for classifying domains, datatypes and categories of web services. It also examines unsupervised clustering algorithms for grouping web services into coherent categories. The techniques are evaluated on a collection of web services and forms, with results showing the machine learning approaches outperform simple baselines.
This document provides an overview and summary of a project report on text clustering. The report describes a system that takes in a collection of documents as input, clusters the documents into groups based on similarity, and allows the user to iteratively explore and refine the clusters to find relevant documents. The system represents documents as vectors, uses cosine similarity to initially cluster documents, and applies Bayesian machine learning to further refine the clusters. It aims to allow users to efficiently browse and retrieve relevant documents without viewing the entire collection.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
This document proposes an approach for automatic programming using deep learning. It describes a hybrid method using generative recurrent neural networks trained on source code to generate predictions, which are then used to build abstract syntax trees (ASTs) representing potential code structures. The ASTs are combined and mutated using techniques from genetic programming and random forests. Experimental results found the method was able to generate functions like computing the square root using an iterative method, demonstrating it can generalize logical algorithms from short descriptions. The document outlines the scope of the problem and approach, and describes using a GitHub scraper to collect a dataset of relevant Python source code files to train and evaluate the models.
Mining Code Examples with Descriptive Text from Software ArtifactsPreetha Chatterjee
The document describes an exploratory study conducted to understand the types of information provided about code snippets embedded in different software-related documents. The study analyzed 60 documents across 12 categories and identified 17 labels and sub-labels for annotating the information about code snippets. Research papers were found to contain the most code snippets on average (8.6 per paper) with the longest descriptions (439 lines of text on average). The study aims to help develop techniques for mining relevant information from various document types to assist with software engineering tasks.
[Research] protocols and structures for inference a res tful api for machine...PAPIs.io
Diversity in machine learning APIs works against realising machine learning's full potential by making it difficult to compose multiple algorithms. This paper introduces the Protocols and Structures for Inference (PSI) service architecture and specification for presenting learning algorithms and data as RESTful web resources that are accessible via a common but flexible and extensible interface. This is joint work with Dr. Mark Reid of the Australian National University and NICTA and Dr. Barry Drake of Canon Information Systems Research Australia.
This document discusses a system for extracting data using comparable entity mining. It begins with an introduction to information extraction and comparative sentences. It then describes the system architecture and algorithms used, including pattern generation, bootstrapping, and mutual bootstrapping. Experimental results show the system can identify comparative questions and extract comparator pairs while reducing time and cost compared to previous methods. The system allows data to be accessed both online and offline.
Extraction of Data Using Comparable Entity Miningiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This document presents a taxonomy of approaches for automatic schema matching. It discusses different types of matchers based on whether they operate on schemas or instances, elements or structures, languages or constraints. It also covers match cardinality and use of auxiliary information. Example approaches are described like LSD, SKAT, TransScm and ARTEMIS which combine different matching techniques. The taxonomy aims to characterize and compare previous matching implementations to aid in developing new matching algorithms.
This document defines key concepts in database management systems including:
1. A DBMS is a collection of interrelated data and programs to access the data. It is used in applications like banking, airlines, universities, etc.
2. Data is abstracted and stored at different levels (physical, logical, view). Schemas define the overall database design and instances represent the data stored at a moment in time.
3. Relationships associate entities and are modeled in ER diagrams using lines and diamonds. Keys uniquely identify entities and relationships.
The document describes the logical structure and functional requirements of an article manager database. It translates the natural language requirements into mathematical statements using propositional and predicate calculus. It notes that the translation process from natural to formal languages removes ambiguity and redundancy present in natural languages to create unambiguous, literal mathematical statements. Key functions of the system include searching articles by author or category, communicating with users, adding or updating author and reviewer information.
This document discusses generics and collections in .NET. It introduces generics as a way to write reusable code for different data types. Generic methods and classes are covered, allowing a single definition to work with multiple types. The document also discusses collection classes like ArrayList, HashTable, Stack and Queue, which provide common data structures. It notes the benefits of generic collections over non-generic ones in terms of type safety and efficiency.
Describes the 2007-04-02 version of the DCMI Abstract Model and presents some thoughts on the way the emergence of the DCAM has the potential to change perceptions of "Dublin Core".
Object Oriented Programming, Networking, and Linux/Unix commands were discussed. Key points included: defining classes and instantiating objects in Python, class variables and methods, inheritance and method overriding, networking terminology like DNS and VPN, common Linux shell commands like ls, cd, grep, and piping commands together. Networking concepts like LAN, MAN, WAN and the basic communication flow of requests and responses were also covered.
Bca3020– data base management system(dbms)smumbahelp
This document provides information about getting solved assignments by email or phone. It includes contact details for an assignment help service and then provides sample questions and answers related to a database management systems course. The questions cover topics like entities, attributes, relationships, database manager responsibilities, file organization, the LIKE predicate, relational algebra operations, and object-oriented programming features.
This document provides an overview of the relational data model and relational database concepts. It defines what a relational database is and how data is organized into tables with rows and columns. It describes key components like schemas and relational database management systems. The document also covers relational algebra operations like select, project, join, and set operations. Finally, it provides a basic introduction to the structured query language SQL, including some common SQL commands and how it is used to perform queries, updates, and other data operations on relational databases.
A Recommender System for Refining Ekeko/X TransformationCoen De Roover
This document discusses an automated recommender system for refining Ekeko/X transformations. It begins by introducing logic meta-programming and how it allows querying a "database" of program information using logic relations. Templates with meta-variables and directives are used to specify transformations, and formal operators define ways to mutate templates. A genetic search evaluates templates based on precision, recall, partial matches, and directive usage to recommend refinements for better specifying transformations.
The document discusses algorithms and data structures. It begins by introducing common data structures like arrays, stacks, queues, trees, and hash tables. It then explains that data structures allow for organizing data in a way that can be efficiently processed and accessed. The document concludes by stating that the choice of data structure depends on effectively representing real-world relationships while allowing simple processing of the data.
The document discusses SQL database concepts including:
- The SQL data definition language allows specification of schemas, integrity constraints, and other metadata.
- Relations are defined using CREATE TABLE statements which specify attributes and their data types.
- Basic queries use SELECT, FROM, and WHERE clauses to retrieve and filter tuples from one or more relations.
- Integrity constraints like PRIMARY KEY and NOT NULL can be defined to enforce data validity.
- SQL supports operations like JOIN, aggregation, sorting, and more.
Similar to Biperpedia: An ontology of Search Application (20)
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Infrastructure Challenges in Scaling RAG with Custom AI modelsZilliz
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
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Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
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• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
3. Introduction
Search engines make significant efforts to recognize queries that
can be answered by structured data and invest heavily in creating
and maintaining high-precision databases.
While these databases have a relatively wide coverage of entities,
the number of attributes they model (e.g., GDP, CAPITAL, ANTHEM)
is relatively small.
3
4. Introduction (Cont.)
We describe Biperpedia, an ontology with 1.6M (class, attribute)
pairs and 67K distinct attribute names.
Biperpedia extracts attributes from the query stream, and then uses
the best extractions to seed attribute extraction from text.
For every attribute Biperpedia saves a set of synonyms and text
patterns in which it appears, thereby enabling it to recognize the
attribute in more contexts.
In addition to a detailed analysis of the quality of Biperpedia, we
show that it can increase the number of Web tables whose
semantics we can recover by more than a factor of 4 compared
with Freebase(FREEBASE.COM).
4
6. Introduction (Cont.)
We describe Biperpedia, an ontology of binary attributes that
contains up to two orders of magnitude more attributes than
Freebase.
An attribute in Biperpedia (see Figure 1) is a relationship between a
pair of entities (e.g., CAPITAL of countries), between an entity and a
value (e.g., COFFEE PRODUCTION), or between an entity and a
narrative (e.g., CULTURE).
Biperpedia is concerned with attributes at the schema level.
Extracting actual values for these attributes is a subject of a future
effort.
Biperpedia is a best-effort ontology in the sense that not all the
attributes it contains are meaningful.
6
7. Introduction (Cont.)
Biperpedia includes a set of constructs that facilitates query and
text understanding.
In particular, Biperpedia attaches to every attribute a set of
common misspellings of the attribute, its synonyms (some which may
be approximate), other related attributes (even if the specific
relationship is not known), and common text phrases that mention
the attribute.
7
8. Agenda
Section 2 : defines our problem Setting
Section 3 : describes the architecture of Biperpedia.
Section 4 : describes how we extract attributes from the query Stream
Section 5 : describes how we extract additional attributes from text.
Section 6 : describes how we merge the attribute extractions and enhance the
ontology with synonyms.
Section 7 : evaluates the attribute quality.
Section 8 : describes an algorithm for placing attributes in the hierarchy.
Section 9 : describes how we use Biperpedia to improve our interpretation of
Web tables.
Section 10 : describes related work
Section 11 : concludes.
8
9. Problem Definition
The goal of Biperpedia is to find schema-level attributes that can be
associated with classes of entities.
For example, we want to discover CAPITAL, GDP(Gross domestic
product), LANGUAGES SPOKEN, and HISTORY as attributes of
COUNTRIES.
Biperpedia is not concerned with the values of the attributes. That is,
we are not trying to find the specific GDP of a given country.
9
10. It Solve The Problem In Following
Steps:
Name, domain class, and range:
Synonyms and misspellings:
Related attributes and mentions:
Provenance:
Differences from a traditional ontology:
Evaluation:
10
11. The Biperpedia System
The Biperpedia extraction pipeline is shown in Figure 2. At a high
level, the pipeline has two phases.
In the first phase, we extract attribute candidates from multiple data
sources, and in the second phase we merge the extractions and
enhance the ontology by finding synonyms, related attributes, and
the best classes for attributes.
The pipeline is implemented as a FlumeJava pipeline .(FlumeJava is
one type of java library)
11
14. Extraction From Web Text
Noun and Verb (Concept)
Extraction via distant supervision
Attribute classification
14
15. Extraction Via Distant Supervision
Figure shows the yield of the top induced extraction patterns.
Although we induce more than 2500 patterns, we see that the top-
200 patterns account for more than 99% of the extractions.
15
18. Synonym Detection
For spell correction, we rely on the search engine. Given an
attribute A of a class C, we examine the spell corrections that the
search engine would propose for the query “C A”.
If one of the corrections is an attribute A’ of C, then we deem A to
be a misspelling of A’.
For example, given the attribute WRITTER of class BOOKS, the search
engine will propose that books writer is a spell correction of books
writter.
18
19. Attribute Quality
DBPedia
DBpedia is a crowd-sourced community effort to extract structured
information from Wikipedia and make this information available on the
Web.
Experimental setting
Overall quality
19
21. Overall Quality
3 evaluators to determine whether an attribute is good or bad for
this class.
1. Rank by Query
2. Rank by Text
3. Precision (specifies the fraction of attributes that were labelled as good)
21
22. Finding The Best Class
Biperpedia attaches attribute to every class in hierarchy.
For more modular ontology or attribute that can contribute to
freebase, best class need to be found.
22
24. Placement Algorithm
How can we decide which can be best class for the attribute?
The algorithm traverses, in a bottom up fashion, each tree of classes
for which A has been marked as relevant
Equation:-
Squery(C, A) = InstanceCount(C, A)
Max A*{InstanceCount(C, A*)}
Support(S) is the ratio between the number of instances of C that
have A and the maximal number of instances for any attribute of C.
24
25. (contd..)
Which one to choose When there are several siblings with sufficient
support.
Diversity Measure for the sibling.
𝐷(𝐶1, … , 𝐶 𝑛, 𝐴) =
1
𝑛−1 𝑖=1
𝑛
max
𝑗=1,..,𝑛
𝑆 𝐶 𝑗 ,𝐴 −𝑆(𝐶 𝑗,𝐴)
max
𝑗=1,..,𝑛
𝑆 𝐶 𝑗 ,𝐴
0 𝑓𝑜𝑟 𝑛 = 1
n>1,
25
27. Evaluation
We can Check whether the assignment of the attribute is exact or
not.
Precision Measures:
Mexact: ratio of number of exact assignments to all assignments.
Mapprox: ratio of number of approximate assignments to all
assignments. Note that an approximate assignment is still valuable
because a human curator would only have to consider a small
neighbourhood of classes to find the exact match.
27
29. Interpreting WEB TABLES
Biperpedia is useful if it can improve search applications.
There are millions of high-quality HTML tables on the Web with very
diverse content.
One of the major challenges with Web tables is to understand the
attributes that are represented in the tables.
29
31. Interpretation Quality
The Representative column shows the number of tables for which at
least one correct representative attribute was found.
The Overall (P/R) column shows the average precision/recall over all
mappings.
The Avg. P/R per table columns compute the precision/recall per
table and then averages over all the tables.
31
32. Comparison with Freebase
The first set of columns shows the number of mappings to Biperpedia
attributes, the number that were mapped to Freebase attributes,
and the ratio between them.
The second set of columns show these numbers for mappings to
representative attributes.
32
33. Error Analysis
Noisy token in the surrounding text and page title
Incorrect string matching against column headers
Table is too specific.
Not enough information.
Evaluator Disagreement.
Biperpedia too small.
33
35. Conclusion
Biperpedia, an ontology search application that extends Freebase
from query stream and Web text. It enables interpreting over a factor
of 4 more Web tables than is possible with Freebase. This algorithm can
be applied to any query stream with possibly different results.
35
36. References
M. D. Adelfio and H. Samet. Schema extraction for tabular data on
the web. PVLDB, 2013.
S. Auer, C. Bizer, G. Kobilarov, J. Lehmann, R. Cyganiak, and Z. G.
Ives. Dbpedia: A nucleus for a web of open data.
M. J. Cafarella, A. Y. Halevy, D. Z. Wang, E. Wu, and Y. Zhang.
Webtables: exploring the power of tables on the web.
A. Carlson, J. Betteridge, B. Kisiel, B. Settles, E. R. Hruschka, and T. M.
Mitchell. Toward an architecture for never-ending language
learning.
A. Doan, A. Y. Halevy, and Z. G. Ives. Principles of Data Integration.
Morgan Kaufmann, 2012.
36