Apache Tika is an open source toolkit for detecting and extracting metadata and structured text content from various file types. It provides a common API for integrating multiple parsing libraries and can automatically detect file types. The project is incubating under the Apache Lucene PMC and aims to support parsing of formats like PDF, Microsoft Office files, HTML, XML and more to extract metadata and content that can be indexed by search engines like Lucene.
The document discusses content extraction with Apache Tika. It introduces Tika and describes how it can be used to extract full text and metadata from various file formats. It also discusses using Tika with Solr and Lucene, including feeding parsed content directly into a Lucene index and using the ExtractingRequestHandler with Solr. Special considerations for large documents and link extraction are also covered.
A presentation from ApacheCon Europe 2015 / Apache Big Data Europe 2015
Apache Tika detects and extracts metadata and text from a huge range of file formats and types. From Search to Big Data, single file to internet scale, if you've got files, Tika can help you get out useful information!
Apache Tika has been around for nearly 10 years now, and in that time, a lot has changed. Not only has the number of formats supported gone up and up, but the ways of using Tika have expanded, and some of the philosophies on the best way to handle things have altered with experience. Tika has gained support for a wide range of programming languages to, and more recently, Big-Data scale support, and ways to automatically compare effects of changes to the library.
Whether you're an old-hand with Tika looking to know what's hot or different, or someone new looking to learn more about the power of Tika, this talk will have something in it for you!
From the Fast Feather Track at ApacheCon NA 2010 in Atlanta
This quick talk provides an overview of Apache Tika, looks at a new features and supported file formats. It then shows how to create a new parser, and finishes with using Tika from your own application.
Tika is a toolkit for detecting and extracting metadata and structured text content from various documents such as PDFs, Word, and HTML. It allows parsing of document files into XHTML output and metadata. Tika uses a ContentHandler interface to parse document streams into SAX events and extract metadata using a Parser interface. It supports many file formats through built-in parsers and uses Apache Lucene for type detection.
The document discusses Apache Tika, an open source content analysis and detection toolkit. It provides an overview of Tika's history and capabilities, including MIME type detection, language identification, and metadata extraction. It also describes how NASA uses Tika within its Earth science data systems to process large volumes of scientific data files in formats like HDF and netCDF.
Text and metadata extraction with Apache TikaJukka Zitting
The document provides an overview of Apache Tika, an open-source content analysis toolkit. It discusses Tika's history and basics, including its architecture, parsers, metadata extraction, and common use cases. The document also outlines how Tika can be integrated into applications and addresses some frequently asked questions.
Tika is a toolkit for extracting metadata and text from various document formats. It allows developers to parse documents and extract metadata and text content in 3 main steps. Tika shields systems like Alfresco from needing to integrate many individual parsing components. Alfresco uses Tika to index content from various formats by passing file streams through Tika's parsers rather than using multiple custom transformers.
Apache Tika is an open source toolkit for detecting and extracting metadata and structured text content from various file types. It provides a common API for integrating multiple parsing libraries and can automatically detect file types. The project is incubating under the Apache Lucene PMC and aims to support parsing of formats like PDF, Microsoft Office files, HTML, XML and more to extract metadata and content that can be indexed by search engines like Lucene.
The document discusses content extraction with Apache Tika. It introduces Tika and describes how it can be used to extract full text and metadata from various file formats. It also discusses using Tika with Solr and Lucene, including feeding parsed content directly into a Lucene index and using the ExtractingRequestHandler with Solr. Special considerations for large documents and link extraction are also covered.
A presentation from ApacheCon Europe 2015 / Apache Big Data Europe 2015
Apache Tika detects and extracts metadata and text from a huge range of file formats and types. From Search to Big Data, single file to internet scale, if you've got files, Tika can help you get out useful information!
Apache Tika has been around for nearly 10 years now, and in that time, a lot has changed. Not only has the number of formats supported gone up and up, but the ways of using Tika have expanded, and some of the philosophies on the best way to handle things have altered with experience. Tika has gained support for a wide range of programming languages to, and more recently, Big-Data scale support, and ways to automatically compare effects of changes to the library.
Whether you're an old-hand with Tika looking to know what's hot or different, or someone new looking to learn more about the power of Tika, this talk will have something in it for you!
From the Fast Feather Track at ApacheCon NA 2010 in Atlanta
This quick talk provides an overview of Apache Tika, looks at a new features and supported file formats. It then shows how to create a new parser, and finishes with using Tika from your own application.
Tika is a toolkit for detecting and extracting metadata and structured text content from various documents such as PDFs, Word, and HTML. It allows parsing of document files into XHTML output and metadata. Tika uses a ContentHandler interface to parse document streams into SAX events and extract metadata using a Parser interface. It supports many file formats through built-in parsers and uses Apache Lucene for type detection.
The document discusses Apache Tika, an open source content analysis and detection toolkit. It provides an overview of Tika's history and capabilities, including MIME type detection, language identification, and metadata extraction. It also describes how NASA uses Tika within its Earth science data systems to process large volumes of scientific data files in formats like HDF and netCDF.
Text and metadata extraction with Apache TikaJukka Zitting
The document provides an overview of Apache Tika, an open-source content analysis toolkit. It discusses Tika's history and basics, including its architecture, parsers, metadata extraction, and common use cases. The document also outlines how Tika can be integrated into applications and addresses some frequently asked questions.
Tika is a toolkit for extracting metadata and text from various document formats. It allows developers to parse documents and extract metadata and text content in 3 main steps. Tika shields systems like Alfresco from needing to integrate many individual parsing components. Alfresco uses Tika to index content from various formats by passing file streams through Tika's parsers rather than using multiple custom transformers.
What's with the 1s and 0s? Making sense of binary data at scale with Tika and...gagravarr
If you have one or two files, you can take the time to manually work out what they are, what they contain, and how to get the useful bits out (probably....). However, this approach really doesn't scale, mechanical turks or no! Luckily, there are Apache projects out there which can help!
In this talk, we'll first look at how we can work out what a given blob of 1s and 0s actually is, be it textual or binary. We'll then see how to extract common metadata from it, along with text, embedded resources, images, and maybe even the kitchen sink! We'll see how to do all of this with Apache Tika, and how to dive down to the underlying libraries (including its Apache friends like POI and PDFBox) for specialist cases. Finally, we'll look a little bit about how to roll this all out on a Big Data or Large-Search case.
Scientific data curation and processing with Apache TikaChris Mattmann
This document summarizes a talk about Apache Tika, a content analysis and detection toolkit. It discusses why content type detection is important, provides an overview of what Tika is and its history/community. It demonstrates how to use Tika's APIs for MIME detection, parsing, and metadata extraction. Finally, it discusses how NASA uses Tika within its Earth science data systems to process scientific file formats and extract metadata at large scales.
The document provides an overview of how search engines and the Lucene library work. It explains that search engines use web crawlers to index documents, which are then stored and searched. Lucene is an open source library for indexing and searching documents. It works by analyzing documents to extract terms, indexing the terms, and allowing searches to match indexed terms. The document details Lucene's indexing and searching process including analyzing text, creating an inverted index, different query types, and using the Luke tool.
Lucene is an open-source search engine library written in Java. It provides functionality for indexing, searching, and ranking documents. Key Lucene concepts include Documents, Fields, Analyzers, IndexWriters, IndexSearchers, and Queries. Documents contain Fields, which represent sections of text to index. Analyzers prepare text for indexing by performing operations like tokenization. IndexWriters create and maintain indexes, while IndexSearchers search through indexes using Query objects.
The document provides an overview of full text search and different approaches to implementing it including wild card database queries, using database-specific full text search functionality, leveraging third party search engines, and using text indexing libraries. It focuses on using Lucene, describing how to index and search text data with Lucene including the key classes, steps, and options involved. It also demonstrates Lucene functionality through code examples and mentions other search technologies that can be used beyond Lucene like Solr, Compass and ElasticSearch.
The document discusses Lucene indexing which is used to build search indexes. It describes the key components of a Lucene index including documents, fields, terms, and inverted indexes. It explains the indexing and search algorithms used by Lucene to add and retrieve documents from the index in an efficient manner through the use of techniques like segmenting, merging, skipping, and compression.
Presented by Adrien Grand, Software Engineer, Elasticsearch
Although people usually come to Lucene and related solutions in order to make data searchable, they often realize that it can do much more for them. Indeed, its ability to handle high loads of complex queries make Lucene a perfect fit for analytics applications and, for some use-cases, even a credible replacement for a primary data-store. It is important to understand the design decisions behind Lucene in order to better understand the problems it can solve and the problems it cannot solve. This talk will explain the design decisions behind Lucene, give insights into how Lucene stores data on disk and how it differs from traditional databases. Finally, there will be highlights of recent and future changes in Lucene index file formats.
This document discusses using Lucene to index both static and dynamic web pages. It describes parsing Apache web server logs to extract parameters for dynamic pages and generate URLs. These URLs are then used to fetch results pages, which are analyzed and indexed. The indexing process is implemented in a Java program that reads logs, generates URLs, and uses Lucene to extract text and build an index from the dynamic content. A demo shows searching the index from both a command prompt and web interface. Lucene provides powerful yet easy to use search capabilities and can index dynamic pages not normally accessible to search engines.
Arakno is a machine learning tool that analyzes PDF files to detect and correlate new malicious PDFs in an automated fashion. It extracts features from PDFs and runs them through machine learning algorithms and YARA rule scans to determine if they are malicious or clean. The tool provides a web interface and API to allow researchers to search files, view similar files and decompressed object streams, and identify malware and attacker patterns from the correlations. Its goal is to proactively identify new malicious PDFs and provide threat intelligence on cybercriminal groups and packers.
May 2012 JaxDUG presentation by Zachary Gramana on using the Lucene.NET library to add search functionality to .NET applications. Contains an overview of search/information retrieval concepts and highlights some common use-cases.
Lucene is an open-source information retrieval library written in Java. It was created in 1999 and is now developed by the Apache Software Foundation. Lucene provides full-text search, structured search, highlighting, faceting, and suggestions capabilities. It embeds an inverted index for efficient query execution, a document store to retrieve original data, and a column store for sorting and analytics. Lucene indexes are divided into immutable segments that are periodically merged to reclaim space and improve performance.
This presentation gives an overview of: 1) Fedora Commons, 2) it's current use by CLARIN B centres, and 3) the new TLA/FLAT setup that meets the CLARIN B centre requirements using the Fedora Commons/Islandora stack.
This document provides an overview of Lucene and how it can be used with MySQL. It discusses:
- What Lucene is and its origins as an open source information retrieval library.
- How Lucene works as a toolkit for building search applications rather than a turnkey search engine.
- Core Lucene classes like IndexWriter, Directory, Analyzer, and Document that are used for indexing data.
- Classes like IndexSearcher and Query that support basic search operations through queries and hits.
- Examples of loading data from a MySQL database into a Lucene index and performing searches on that indexed data.
Introduction to Lucene & Solr and UsecasesRahul Jain
Rahul Jain gave a presentation on Lucene and Solr. He began with an overview of information retrieval and the inverted index. He then discussed Lucene, describing it as an open source information retrieval library for indexing and searching. He discussed Solr, describing it as an enterprise search platform built on Lucene that provides distributed indexing, replication, and load balancing. He provided examples of how Solr is used for search, analytics, auto-suggest, and more by companies like eBay, Netflix, and Twitter.
This document provides an overview of LDAP (Lightweight Directory Access Protocol):
- LDAP is a protocol for querying and modifying directory services running over TCP/IP networks. It allows clients to retrieve and store information about users, computers, applications and other network resources from a central directory server.
- A directory in LDAP refers to a specialized database that stores information in an organized manner to be easily shared among applications. The directory structure follows a tree hierarchy defined by distinguished names.
- Common LDAP operations include binding, searching, comparing, adding, deleting and modifying directory entries. Microsoft Active Directory is a widely used LDAP-compliant directory service that centralizes user authentication and authorization.
- LDAP is commonly used to
This document provides an overview of the Lightweight Directory Access Protocol (LDAP). It describes LDAP as an open standard for accessing distributed directory services that is optimized for read performance. The document outlines LDAP's information model, naming model, directory structure, supported operations, and security features. It also provides information on configuring an LDAP server and the software available to implement LDAP directories and clients.
Introduction to Linked Data Platform (LDP)Hector Correa
The Linked Data Platform (LDP) defines rules for HTTP operations on web resources to provide an architecture for read-write Linked Data on the web. Key concepts include resources, RDF sources, non-RDF sources, and containers. LDP uses HTTP requests and responses to create, retrieve, update, and delete resources. Resources can be contained within different types of containers, including basic, direct, and indirect containers. LDP provides a standard way to manage Linked Data using HTTP.
Latent semantic analysis (LSA) is a technique used in natural language processing to analyze relationships between documents and terms by producing concepts related to them. LSA assumes words with similar meanings will occur in similar texts, and uses a documents-terms matrix and singular value decomposition to discover hidden concepts and represent words and documents as vectors in a semantic vector space. Apache OpenNLP is a machine learning toolkit that can be used for various natural language processing tasks like part-of-speech tagging and parsing, and LSA can be seen as part of natural language processing.
Active Directory is a common interface for organizing and maintaining information related to resources connected to a variety of network directories.
Lightweight Directory Access Protocol (LDAP) is an Internet protocol used to access information directories.
A directory service is a distributed database application designed to manage the entries and attributes in a directory.
The document describes how to model an address book application using the Linked Data Platform (LDP) and Hydra Core Vocabularies. It provides examples of modeling an address book container and contacts as LDP resources, supporting common operations like GET, POST and PATCH. It also shows how to describe the application's API using the Hydra Core Vocabulary, including supported classes, operations and documentation. Potential conflicts between LDP and Hydra concepts like containers vs collections and paging are discussed.
Actors are a model of concurrent computation that treats isolated "actors" as the basic unit. Actors communicate asynchronously by message passing and avoid shared state. This model addresses issues in Java's thread/lock model like deadlocks. In Gpars, actors include stateless actors like DynamicDispatchActor and stateful actors like DefaultActor. The demo shows examples of stateless and stateful actors in Gpars.
Spring Web Flow allows implementing "flows" in a web application that encapsulate a sequence of steps guiding a user through a business task across multiple HTTP requests. It handles state, transactions, and reusable navigation for tasks like checkout flows. It addresses disadvantages of traditional MVC like complex navigation rules and lack of state control. Key components include flows, states, transitions, and flow data. States include view, action, decision, subflow, and end states. The Grails Web Flow plugin integrates Spring Web Flow into Grails applications and provides features like different scopes and exception handling.
What's with the 1s and 0s? Making sense of binary data at scale with Tika and...gagravarr
If you have one or two files, you can take the time to manually work out what they are, what they contain, and how to get the useful bits out (probably....). However, this approach really doesn't scale, mechanical turks or no! Luckily, there are Apache projects out there which can help!
In this talk, we'll first look at how we can work out what a given blob of 1s and 0s actually is, be it textual or binary. We'll then see how to extract common metadata from it, along with text, embedded resources, images, and maybe even the kitchen sink! We'll see how to do all of this with Apache Tika, and how to dive down to the underlying libraries (including its Apache friends like POI and PDFBox) for specialist cases. Finally, we'll look a little bit about how to roll this all out on a Big Data or Large-Search case.
Scientific data curation and processing with Apache TikaChris Mattmann
This document summarizes a talk about Apache Tika, a content analysis and detection toolkit. It discusses why content type detection is important, provides an overview of what Tika is and its history/community. It demonstrates how to use Tika's APIs for MIME detection, parsing, and metadata extraction. Finally, it discusses how NASA uses Tika within its Earth science data systems to process scientific file formats and extract metadata at large scales.
The document provides an overview of how search engines and the Lucene library work. It explains that search engines use web crawlers to index documents, which are then stored and searched. Lucene is an open source library for indexing and searching documents. It works by analyzing documents to extract terms, indexing the terms, and allowing searches to match indexed terms. The document details Lucene's indexing and searching process including analyzing text, creating an inverted index, different query types, and using the Luke tool.
Lucene is an open-source search engine library written in Java. It provides functionality for indexing, searching, and ranking documents. Key Lucene concepts include Documents, Fields, Analyzers, IndexWriters, IndexSearchers, and Queries. Documents contain Fields, which represent sections of text to index. Analyzers prepare text for indexing by performing operations like tokenization. IndexWriters create and maintain indexes, while IndexSearchers search through indexes using Query objects.
The document provides an overview of full text search and different approaches to implementing it including wild card database queries, using database-specific full text search functionality, leveraging third party search engines, and using text indexing libraries. It focuses on using Lucene, describing how to index and search text data with Lucene including the key classes, steps, and options involved. It also demonstrates Lucene functionality through code examples and mentions other search technologies that can be used beyond Lucene like Solr, Compass and ElasticSearch.
The document discusses Lucene indexing which is used to build search indexes. It describes the key components of a Lucene index including documents, fields, terms, and inverted indexes. It explains the indexing and search algorithms used by Lucene to add and retrieve documents from the index in an efficient manner through the use of techniques like segmenting, merging, skipping, and compression.
Presented by Adrien Grand, Software Engineer, Elasticsearch
Although people usually come to Lucene and related solutions in order to make data searchable, they often realize that it can do much more for them. Indeed, its ability to handle high loads of complex queries make Lucene a perfect fit for analytics applications and, for some use-cases, even a credible replacement for a primary data-store. It is important to understand the design decisions behind Lucene in order to better understand the problems it can solve and the problems it cannot solve. This talk will explain the design decisions behind Lucene, give insights into how Lucene stores data on disk and how it differs from traditional databases. Finally, there will be highlights of recent and future changes in Lucene index file formats.
This document discusses using Lucene to index both static and dynamic web pages. It describes parsing Apache web server logs to extract parameters for dynamic pages and generate URLs. These URLs are then used to fetch results pages, which are analyzed and indexed. The indexing process is implemented in a Java program that reads logs, generates URLs, and uses Lucene to extract text and build an index from the dynamic content. A demo shows searching the index from both a command prompt and web interface. Lucene provides powerful yet easy to use search capabilities and can index dynamic pages not normally accessible to search engines.
Arakno is a machine learning tool that analyzes PDF files to detect and correlate new malicious PDFs in an automated fashion. It extracts features from PDFs and runs them through machine learning algorithms and YARA rule scans to determine if they are malicious or clean. The tool provides a web interface and API to allow researchers to search files, view similar files and decompressed object streams, and identify malware and attacker patterns from the correlations. Its goal is to proactively identify new malicious PDFs and provide threat intelligence on cybercriminal groups and packers.
May 2012 JaxDUG presentation by Zachary Gramana on using the Lucene.NET library to add search functionality to .NET applications. Contains an overview of search/information retrieval concepts and highlights some common use-cases.
Lucene is an open-source information retrieval library written in Java. It was created in 1999 and is now developed by the Apache Software Foundation. Lucene provides full-text search, structured search, highlighting, faceting, and suggestions capabilities. It embeds an inverted index for efficient query execution, a document store to retrieve original data, and a column store for sorting and analytics. Lucene indexes are divided into immutable segments that are periodically merged to reclaim space and improve performance.
This presentation gives an overview of: 1) Fedora Commons, 2) it's current use by CLARIN B centres, and 3) the new TLA/FLAT setup that meets the CLARIN B centre requirements using the Fedora Commons/Islandora stack.
This document provides an overview of Lucene and how it can be used with MySQL. It discusses:
- What Lucene is and its origins as an open source information retrieval library.
- How Lucene works as a toolkit for building search applications rather than a turnkey search engine.
- Core Lucene classes like IndexWriter, Directory, Analyzer, and Document that are used for indexing data.
- Classes like IndexSearcher and Query that support basic search operations through queries and hits.
- Examples of loading data from a MySQL database into a Lucene index and performing searches on that indexed data.
Introduction to Lucene & Solr and UsecasesRahul Jain
Rahul Jain gave a presentation on Lucene and Solr. He began with an overview of information retrieval and the inverted index. He then discussed Lucene, describing it as an open source information retrieval library for indexing and searching. He discussed Solr, describing it as an enterprise search platform built on Lucene that provides distributed indexing, replication, and load balancing. He provided examples of how Solr is used for search, analytics, auto-suggest, and more by companies like eBay, Netflix, and Twitter.
This document provides an overview of LDAP (Lightweight Directory Access Protocol):
- LDAP is a protocol for querying and modifying directory services running over TCP/IP networks. It allows clients to retrieve and store information about users, computers, applications and other network resources from a central directory server.
- A directory in LDAP refers to a specialized database that stores information in an organized manner to be easily shared among applications. The directory structure follows a tree hierarchy defined by distinguished names.
- Common LDAP operations include binding, searching, comparing, adding, deleting and modifying directory entries. Microsoft Active Directory is a widely used LDAP-compliant directory service that centralizes user authentication and authorization.
- LDAP is commonly used to
This document provides an overview of the Lightweight Directory Access Protocol (LDAP). It describes LDAP as an open standard for accessing distributed directory services that is optimized for read performance. The document outlines LDAP's information model, naming model, directory structure, supported operations, and security features. It also provides information on configuring an LDAP server and the software available to implement LDAP directories and clients.
Introduction to Linked Data Platform (LDP)Hector Correa
The Linked Data Platform (LDP) defines rules for HTTP operations on web resources to provide an architecture for read-write Linked Data on the web. Key concepts include resources, RDF sources, non-RDF sources, and containers. LDP uses HTTP requests and responses to create, retrieve, update, and delete resources. Resources can be contained within different types of containers, including basic, direct, and indirect containers. LDP provides a standard way to manage Linked Data using HTTP.
Latent semantic analysis (LSA) is a technique used in natural language processing to analyze relationships between documents and terms by producing concepts related to them. LSA assumes words with similar meanings will occur in similar texts, and uses a documents-terms matrix and singular value decomposition to discover hidden concepts and represent words and documents as vectors in a semantic vector space. Apache OpenNLP is a machine learning toolkit that can be used for various natural language processing tasks like part-of-speech tagging and parsing, and LSA can be seen as part of natural language processing.
Active Directory is a common interface for organizing and maintaining information related to resources connected to a variety of network directories.
Lightweight Directory Access Protocol (LDAP) is an Internet protocol used to access information directories.
A directory service is a distributed database application designed to manage the entries and attributes in a directory.
The document describes how to model an address book application using the Linked Data Platform (LDP) and Hydra Core Vocabularies. It provides examples of modeling an address book container and contacts as LDP resources, supporting common operations like GET, POST and PATCH. It also shows how to describe the application's API using the Hydra Core Vocabulary, including supported classes, operations and documentation. Potential conflicts between LDP and Hydra concepts like containers vs collections and paging are discussed.
Actors are a model of concurrent computation that treats isolated "actors" as the basic unit. Actors communicate asynchronously by message passing and avoid shared state. This model addresses issues in Java's thread/lock model like deadlocks. In Gpars, actors include stateless actors like DynamicDispatchActor and stateful actors like DefaultActor. The demo shows examples of stateless and stateful actors in Gpars.
Spring Web Flow allows implementing "flows" in a web application that encapsulate a sequence of steps guiding a user through a business task across multiple HTTP requests. It handles state, transactions, and reusable navigation for tasks like checkout flows. It addresses disadvantages of traditional MVC like complex navigation rules and lack of state control. Key components include flows, states, transitions, and flow data. States include view, action, decision, subflow, and end states. The Grails Web Flow plugin integrates Spring Web Flow into Grails applications and provides features like different scopes and exception handling.
JfreeChart is an open source library developed in Java, which can be used within Java based applications to create a wide range of charts.
By using JFreeChart, we can create all the major type of 2D and 3D charts such as pie chart, bar chart, line chart, XY chart and 3D charts and more.
RESTEasy is a JBoss project that helps build RESTful Java applications by implementing the JAX-RS 2.0 specification. It provides a portable implementation that can run in any servlet container. RESTEasy supports content negotiation and includes annotations like @Path, @GET and @POST to define RESTful resources. It also features rich providers for XML, JSON and other data formats and integrates with frameworks like EJB, Seam and Spring.
The document discusses benchmarking in Java using JMH (Java Microbenchmark Harness). It covers background on benchmarking, types of benchmarks (macro and micro), factors to consider in benchmarking, issues with hand-written benchmarks, and how to get started with JMH. Key points include that JMH helps minimize JVM optimizations to get accurate measurements, and that benchmarks should include a warmup phase to initialize the environment before recording results.
Progressive Web App (PWA) is a term used to denote web apps that use the latest web technologies. Progressive web apps are technically regular web pages (or websites) but can appear to the user like traditional applications or (native) mobile applications. This new application type attempts to combine features offered by most modern browsers with the benefits of mobile experience.
Gradle is an open source build automation system that builds upon the concepts of Apache Ant and Apache Maven and introduces a Groovy-based domain-specific language (DSL) instead of the XML form used by Apache Maven for declaring the project configuration.
This document discusses Hamcrest, a framework for writing matcher objects that allow match rules to be defined declaratively. It can be used with JUnit and TestNG for writing flexible tests. The document outlines how to use Hamcrest with assertThat instead of assertEquals for more readable and flexible testing. It also provides an overview of the different types of matchers in Hamcrest, including object, collection, core, number, and text matchers. Finally, it discusses how to create custom matchers using FeatureMatcher or TypeSafeMatcher.
ECMAScript is the name of the international standard that defines JavaScript. ES6 → ECMAScript 2015. Latest ECMAScript version is ES7 which is ECMAScript 2016.
Basically it is a superset of es5
The document introduces reactive programming and RxJava. It defines reactive programming as working with asynchronous data streams using operators to combine, filter, and transform streams. It then discusses RxJava, a library that enables reactive programming in Java. It covers key reactive components like Observables, Operators, Schedulers, and Subjects. It provides examples of how to use various operators to transform, filter, combine streams and handle errors and backpressure. In conclusion, it discusses pros and cons of using reactive programming with RxJava.
Jsoup is a Java library that allows users to parse HTML and extract and manipulate data from documents. It can be used to scrape and parse HTML from URLs, files, or strings. Jsoup provides methods to navigate documents using DOM traversal or CSS selectors, modify HTML elements and attributes, clean user-submitted content to prevent XSS attacks, and output tidy HTML. Documents can be parsed from URLs, strings, or files and then data can be extracted and elements can be modified using DOM methods or CSS selectors.
Java is Object Oriented Programming. Java 8 is the latest version of the Java which is used by many companies for the development in many areas. Mobile, Web, Standalone applications.
Unit testing allows testing individual units of code in isolation using Spock, a testing framework for Java and Groovy. Spock specifications extend Specification and contain fixture methods like setup() and feature methods to define test cases and expected behavior. Feature methods use blocks like when, then, and expect to define stimuli and verify outputs. Spock supports data-driven testing using a data table and mocking dependencies using Mock() to focus testing on the unit. Basic Spock commands include running tests with grails test-app and viewing test reports.
- Cosmos DB is Microsoft's globally distributed database service that can scale worldwide to manage large amounts of data. It supports multiple APIs and data models including document, graph, table, and MongoDB APIs.
- Key advantages include global distribution of data across regions, automatic scaling of storage and throughput, low latency and high availability, flexible schemas, and low cost of ownership.
- Cosmos DB supports document, table, graph and MongoDB APIs. MongoDB and DocumentDB are both document oriented and store data as JSON, while table API uses a key-value model and graph API models relationships.
- The document compares Cosmos DB to other database options like MySQL and MongoDB, covering their data models, usage, and how to set
Vert.x is a toolkit or platform for implementing reactive applications on the JVM.
Vert.x is an open-source project at the Eclipse Foundation. Vert.x was initiated in 2012 by Tim Fox.
General Purpose Application Framework, Polyglot (Java, Groovy, Scala, Kotlin, JavaScript, Ruby and Ceylon), Event Driven, non-blocking, Lightweight & fast, Reusable modules.
This document introduces Apache Tika, an open source toolkit for detecting and extracting metadata and structured text content from various documents. It discusses Apache Tika's parser interface, which allows client applications to extract content and metadata from different file formats through a single method. The document provides examples of using Apache Tika to extract metadata from a PDF file and to determine which document was last modified from a set of URLs. It also lists some of the file formats that Apache Tika can parse and extract text from.
This document introduces Apache Tika, an open source toolkit for detecting and extracting metadata and structured text content from various documents. It discusses Apache Tika's parser interface, which allows client applications to extract content and metadata from different file formats through a single method. It also provides examples of using Apache Tika to extract metadata from a PDF file and to determine which document was last modified from a set of URLs. The document aims to help readers understand and get started with using Apache Tika.
In this session, we will look first at the rich metadata that documents in your repository have, how to control the mapping of this on to your content model, and some of the interesting things this can deliver. We'll then move on to the content transformation and rendition services, and see how you can easily and powerfully generate a wide range of media from the content you already have.
Applying ocr to extract information : Text miningSaurabh Singh
Text Analysis (TA) is a process which takes unseen texts as input and produces fixed-format, unambiguous data as output.
This data may be used directly for display to users, or may be stored in a database or spreadsheet for later analysis, or may be used for indexing purposes in Information Retrieval (IR) applications.
In this session, we will look first at the rich metadata that documents in your repository have, how to control the mapping of this on to your content model, and some of the interesting things this can deliver. We’ll then move on to the content transformation and rendition services, and see how you can easily and powerfully generate a wide range of media from the content you already have. Finally, we’ll look at how to extend these services to support additional formats.
Tika is an open source project that provides a generic API for extracting metadata and structured text content from various document formats. It uses automatic content type detection to parse documents without needing to know the file type in advance. The project aims to pool efforts across various Apache projects like Apache POI and Apache PDFBox to provide a common solution for parsing different file types.
This document provides an overview of file handling in C++. It discusses the need for data files and the two main types: text files and binary files. Text files store readable character data separated by newline characters, while binary files store data in the same format as memory. The key classes for file input/output in C++ are ifstream, ofstream, and fstream. Functions like open(), read(), write(), get(), put(), and close() are used to work with files. Files can be opened in different modes like append, read, or write and it is important to check if they open successfully.
The document outlines file handling in C++, including the need for data files, types of files (text and binary), basic file operations for each type, and the components used in C++ for file handling like header files, classes, and functions. It discusses opening, reading, writing, and closing files, as well as file pointers and random vs sequential access.
The document discusses file handling in C++. It explains that files are used to store data permanently on storage devices like hard disks, while variables are stored temporarily in memory. It then describes C++ streams and classes used for file input and output like ifstream, ofstream, and fstream. It also covers opening, closing, reading from and writing to files, as well as checking file pointers and seeking to different positions in a file.
This document discusses file handling in C++. It begins by explaining the differences between main memory and secondary memory (files on storage devices). It then discusses C++ streams and the classes used for file input/output (ifstream, ofstream, fstream). The rest of the document covers various file operations like opening, closing, reading from and writing to files. It also discusses text files versus binary files and sequential versus random file access. File pointers and associated functions like seekg(), tellg(), seekp() and tellp() are explained for navigating within files. An example program demonstrates reading from one file and writing to another.
The document discusses a project called CollOnBus that aims to extract knowledge from social tagging data on the web. It presents an approach called "metadata first, ontologies second" which involves mapping tags to Dublin Core metadata structures before converting them to ontologies. The document also describes tools developed as part of CollOnBus called folk2onto and Tag Distiller that are used to filter tags, map them to senses, and generate XML files representing the mapped tags and their relationships.
The data science process document outlines the typical steps involved in a data science project including: 1) setting research goals, 2) retrieving data from internal or external sources, 3) preparing data through cleansing and transformation, 4) performing exploratory data analysis, 5) building models using techniques like machine learning or statistics, and 6) presenting and automating results. It also discusses challenges in working with different file formats and the importance of understanding various formats as a data scientist.
File handling in Python allows programs to work with files stored on disk by performing operations like opening, reading, writing, and modifying files. The open() function is used to open a file and return a file object, which can then be used to read or write to the file. There are different file access modes like 'r' for read-only, 'w' for write-only, and 'a' for append. Common methods for reading files include read() to read characters, readline() to read one line, and readlines() to read all lines into a list. Files can be written to using write() and writelines() methods and deleted using functions in the os, shutil, or pathlib modules.
This document discusses files and streams in .NET framework 4.5. It covers navigating the file system using classes like FileInfo, DirectoryInfo, and DriveInfo. It also discusses reading and writing files using streams, including FileStream for binary data and StreamReader/StreamWriter for text. Key points covered include getting information on files and directories, creating/deleting files and folders, and reading/writing files using streams in a simple way compared to FileStream.
Dataset description: DCAT and other vocabulariesValeria Pesce
This document discusses metadata needed to describe datasets for applications to find and understand them when stored in data catalogs or repositories. It examines existing dataset description vocabularies like DCAT and their limitations in fully capturing necessary metadata.
Key points made:
- Machine-readable metadata is important for datasets to be discoverable and usable by applications when stored across repositories.
- Metadata should describe the dataset, distributions, dimensions, semantics, protocols/APIs, subsets etc.
- Vocabularies like DCAT provide some metadata but don't fully cover dimensions, semantics, protocols/APIs or subsets.
- No single vocabulary or data catalog solution currently provides all necessary metadata for full semantic interoperability.
The document provides an overview of file handling in C++. It discusses key concepts such as streams, file types (text and binary), opening and closing files, file modes, input/output operations, and file pointers. Functions for reading and writing to text files include put(), get(), and getline(). Binary files use write() and read() functions. File pointers can be manipulated using seekg(), seekp(), tellg(), and tellp() to move through files.
This document discusses key concepts related to files in R including file names, formats, paths, encodings, and types. It describes text files as human-readable files organized in lines with different extensions for different programs. Binary files contain machine-readable 1s and 0s. Paths locate files in a directory hierarchy using components like parent directories denoted by "..". Common encodings include ASCII for English and UTF-8 for multiple languages. R supports text, binary, and delimited files like CSVs that separate values with commas.
This document discusses different techniques for reading files in Python. It begins by explaining what files are and the different types, with a focus on text files. It then demonstrates opening a file and reading the entire contents in one string. Next, it shows how to read each line of a file as a separate string using readlines(). Finally, it provides an example of printing the lines of a file in reverse order to illustrate reading files in different ways. The key techniques covered are reading the entire file, reading a specified number of characters, reading each line as a separate string, and iterating through the lines in reverse order.
CSCI6505 Project:Construct search engine using ML approachbutest
This document summarizes a student project report on developing a topic-based search engine for a website using machine learning. The project uses an instance-based learning algorithm (k-nearest neighbors) to classify HTML files into topics like artificial intelligence, programming languages, etc. It includes modules for training a classifier, crawling a website to index files into topics, and a search interface for users. The report describes implementing classes for preprocessing HTML, indexing, classification, and search functionality. Sample results show a keyword-based and topic-based search interface that returns relevant files.
Alexa is Amazon’s cloud-based voice service.
It is a way to communicate the system using our voice.
Alexa provides a set of built-in capabilities, referred to as skills.
GraalVM is an ecosystem and runtime that provides performance advantages to JVM languages like Java, Scala, Groovy, and Kotlin as well as other languages. It includes a just-in-time compiler called Graal that improves efficiency, polyglot APIs for combining languages, and SDK for embedding languages and creating native images. Installation can be done with the JDK which includes Graal starting with JDK 9, or by directly downloading GraalVM from Oracle's website.
This document provides an overview of Docker and Kubernetes (K8S). It defines Docker as an open platform for developing, shipping and running containerized applications. Key Docker features include isolation, low overhead and cross-cloud support. Kubernetes is introduced as an open-source tool for automating deployment, scaling, and management of containerized applications. It operates at the container level. The document then covers K8S architecture, including components like Pods, Deployments, Services and Nodes, and how K8S orchestrates containers across clusters.
Apache Commons is an Apache project focused on all aspects of reusable Java components.
It is divided into three components: Commons Proper, Commons Sandbox, Commons Dormant.
This document provides an overview of HazelCast IMDG (In-Memory Data Grid), which is middleware software that manages objects across distributed servers in RAM, enabling scaling and fault tolerance. It discusses cache access patterns, cache types, use cases for HazelCast including scaling applications and sharing data across clusters, features like dynamic clustering and distributed data structures, data partitioning, and configurations. It also covers advanced techniques, alternatives to HazelCast like Redis, and performance comparisons.
Mysql PRO provides an overview of MySQL basics, architecture, transactions, triggers, PL/SQL, and engines. The document discusses SELECT statements, joins, INSERT, UPDATE, DELETE, and transactions. It explains MySQL architecture including optimization, execution, and concurrency control using table locks and row locks. Transactions ensure atomicity and consistency by allowing statements to be treated as single units that either all succeed or fail as a whole.
The document discusses microservice architecture using Spring Boot with React and Redux. It defines a microservice as a software development technique where an application is composed of loosely coupled services. It outlines characteristics of microservice architecture such as independent, loosely coupled services that communicate via APIs and can be deployed independently. The document provides an example portal application architecture broken into microservices and discusses components like API gateways, service discovery, configuration services, and client libraries.
Swagger is an open source software framework backed by
a large ecosystem of tools that helps developers
design, build, document and consume RESTful Web
services.
The theory of SOLID principles was
introduced by Robert C. Martin in his 2000
paper “Design Principles and Design
Patterns”.
SOLID => Single Responsibility, Open/Closed, Liskov Substitution, Interface Segregation, Dependency Inversion.
ArangoDB is a native multi-model database system developed by triAGENS GmbH. The database system supports three important data models (key/value, documents, graphs) with one database core and a unified query language AQL (ArangoDB Query Language). ArangoDB is a NoSQL database system but AQL is similar in many ways to SQL
TypeScript is a superset of JavaScript that adds optional static typing and class-based object-oriented programming. It adds additional features like interfaces and modules to JavaScript to allow code to scale. The document provides an introduction to TypeScript, explaining what it is, why to use it, its basic types, annotations, functions, interfaces, classes, generics, modules, and compiling. It also provides references for further reading.
The document contains code for 6 sample smart contracts:
1) An Adder contract that allows adding two integers and setting/getting a name string
2) A Greeter contract that allows setting/getting a greeting string
3) An AuditLog contract that logs a uid, audit details, and date
4) A Voting contract that allows voting for candidates and getting vote counts
5) A FeverContract that tracks temperature, allows increasing/decreasing it, and checks for fever
6) Each contract code includes functions for setting/getting values and other relevant logic
The document describes the steps to create a private Ethereum network with 4 nodes using the same genesis block. It details how to initialize and start each node with different ports, check connectivity between nodes, create and transfer accounts, and begin mining to generate blocks across the network. The genesis code provided specifies the initial empty state of the private network before any transactions occur.
Geth is widely used to interact with Ethereum networks. Ethereum software enables a user to set up a
“private” or “testnet” Ethereum chain. This chain will be totally different from main chain.
Component that tell geth that we want to use/create a private Ethereum Chain:
1. Custom Genesis file
2. Custom Data Directory
3. Custom Network Id
4. Disable Node Discovery
Ethereum is an open software platform based on blockchain technology that enables developers to
build and deploy decentralized applications.
Ethereum is a distributed public blockchain network.
While the Bitcoin blockchain is used to track ownership of digital currency (bitcoins), the Ethereum
blockchain focuses on running the programming code of any decentralized application.
Ether is a cryptocurrency whose blockchain is generated by the Ethereum platform. Ether can be
transferred between accounts and used to compensate participant mining nodes for computations
performed.
The document discusses microservices architecture and how to implement it using Spring Boot and Spring Cloud. It describes how microservices address challenges with monolithic architectures like scalability and innovation. It then covers how to create a microservices-based application using Spring Boot, register services with Eureka, communicate between services using RestTemplate and Feign, and load balance with Ribbon.
This document provides an introduction to Redux, including what it is, its core principles and building blocks. Redux is a predictable state container for JavaScript apps that can be used with frameworks like React, Angular and Vue. It follows the Flux architecture pattern and is based on three principles - state is immutable, state can only be changed through actions, and changes are made with pure functions called reducers. The main building blocks are actions, reducers and the store.
Google Authenticator is a software token that implements two-step verification services using the Time-based One-time Password Algorithm (TOTP) and HMAC-based One-time Password Algorithm (HOTP), for authenticating users of mobile applications by Google. The service implements algorithms specified in RFC 6238 and RFC 4226, respectively.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
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.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
OpenID AuthZEN Interop Read Out - AuthorizationDavid Brossard
During Identiverse 2024 and EIC 2024, members of the OpenID AuthZEN WG got together and demoed their authorization endpoints conforming to the AuthZEN API
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
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
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
2. What Is Apache Tika?
Apache Tika is a library that is used for document type detection and content extraction from various file formats.
Internally, Tika uses existing various document parsers and document type detection techniques to detect and
extract data.
Tika provides a single generic API for parsing different file formats. It uses 83 existing specialized parser ibraries
for each document type. Like Apache PdfBox and POI.
All these parser libraries are encapsulated under a single interface called the Parser interface.
3. WHy Tika?
According to filext.com, there are about 15k to 51k content types, and this number is growing day by day.
Data is being stored in various formats such as text documents, excel spreadsheet, PDFs, images, and multimedia
files.
Therefore, applications such as search engines and content management systems need additional support for easy
extraction of data from these document types.
Apache Tika serves this purpose by providing a generic API to locate and extract data from multiple file formats.
4. Apache Tika Applications
Search Engines
Tika is widely used while developing search engines to index the text contents of digital documents.
Digital Asset Management
Some organizations manage their digital assets such as photographs, e-books, drawings, music and video
using a special application known as digital asset management (DAM).
Such applications take the help of document type detectors and metadata extractor to classify the various
documents.
Content Analysis
Websites like Amazon recommend newly released contents of their website to individual users according to
their interests.
To do so, these websites take the help of social media websites like Facebook to extract required information
such as likes and interests of the users. This gathered information will be in the form of html tags or other
formats that require further content type detection and extraction.
5. Features Of Tika
Unified parser Interface : Tika encapsulates all the third party parser libraries within a single parser interface.
Due to this feature, the user escapes from the burden of selecting the suitable parser library and use it
according to the file type encountered.
Fast processing : Quick content detection and extraction from applications can be expected.
Parser integration : Tika can use various parser libraries available for each document type in a single application.
MIME type detection : Tika can detect and extract content from all the media types included in the MIME
standards.
Language detection : Tika includes language identification feature, therefore can be used in documents based on
language type in a multi lingual websites.
6. Functionalities Of Tika
Tika supports various functionalities:
Document type detection
Content extraction
Metadata extraction
Language detection
7. Tika Facade Class?
Users can embed Tika in their applications using the Tika facade class. It has methods to explore all the
functionalities of Tika.
Since it is a facade class, Tika abstracts the complexity behind its functions.
It abstracts all the internal implementations and provides simple methods to access the Tika functionalities.
8. Some Methods Of tika facade class
String parseToString (File file) :- This method and all its variants parses the file passed as parameter and returns the
extracted text content in the String format. By default, the length of this string parameter is limited.
int getMaxStringLength () :- Returns the maximum length of strings returned by the parseToString methods.
void setMaxStringLength (int maxStringLength) :- Sets the maximum length of strings returned by the parseToString methods.
Reader parse (File file) :- This method and all its variants parses the file passed as parameter and returns the extracted text content in
the form of java.io.reader object.
String detect (InputStream stream, Metadata metadata) :- This method and all its variants accepts an InputStream object and a
Metadata object as parameters, detects the type of the given document, and returns the document type name as String object. This
method abstracts the detection mechanisms used by Tika.
9. Parser interface
This is the interface that is implemented by all the parser classes of Tika package.
The following is the important method of Tika Parser interface:
❖ parse (InputStream stream, ContentHandler handler, Metadata metadata, ParseContext context)
This method parses the given document into a sequence of XHTML and SAX events. After parsing, it places the extracted
document content in the object of the ContentHandler class and the metadata in the object of the Metadata class.
10. Metadata class
Following are the methods of this class
1. add (Property property, String value)
2. add (String name, String value)
3. String get (Property property)
4. String get (String name)
5. Date getDate (Property property)
6. String[] getValues (Property property)
7. String[] getValues (String name)
8. String[] names()
9. set (Property property, Date date)
10. set(Property property, String[] values)
11. Language Identifier class
This class identifies the language of the given content.
LanguageIdentifier (String content) : This constructor can instantiate a language identifier by passing on a String from text content
String getLanguage () : Returns the language given to the current LanguageIdentifier object.
12. Type detection in tika
Tika supports all the Internet media document types provided in MIME. Whenever a file is passed through Tika, it detects the file and
its document type.
Type detection using Facade class
The detect() method of facade class is used to detect the document type. This method accepts a file as input.
Eg:
File file = new File("example.mp3");
Tika tika = new Tika();
String filetype = tika.detect(file);
13. Content extraction using tika
For parsing documents, the parseToString() method of Tika facade class is generally used.
Shown below are the steps involved in the parsing process and these are abstracted by the Tika ParsertoString()
method.
14. Content extraction using parser InterfaCE
The parser package of Tika provides several interfaces and classes using which we can parse a text document.
15. Content extraction using parser InterfaCE
parse() method
Along with parseToString(), you can also use the parse() method of the parser Interface. The prototype of this
method is shown below.
Given below is an example that shows how the parse() method is used.
1. Instantiate any of the classes providing the implementation for this interface.
Parser parser = new AutoDetectParser();
(or)
Parser parser = new CompositeParser();
(or)
object of any individual parsers given in Tika Library
1. Create a handler class object.
BodyContentHandler handler = new BodyContentHandler( );
16. Content extraction using parser InterfaCE
3. Create the Metadata object as shown below:
Metadata metadata = new Metadata();
4. Create any of the input stream objects, and pass your file that should be extracted to it.
File file=new File(filepath)
FileInputStream inputstream=new FileInputStream(file);
(or)
InputStream stream = TikaInputStream.get(new File(filename));
5. Create a parse context object as shown below:
ParseContext context =new ParseContext();
6. Instantiate the parser object, invoke the parse method, and pass all the objects required.
parser.parse(inputstream, handler, metadata, context);
17. METADATA extraction using parser InterfaCE
Besides content, Tika also extracts the metadata from a file. Metadata is nothing but the additional information supplied with a file.
If we consider an audio file, the artist name, album name, title comes under metadata.
Whenever we parse a file using parse(), we pass an empty metadata object as one of the parameters.
This method extracts the metadata of the given file (if that file contains any), and places them in the metadata object.
Therefore, after parsing the file using parse(), we can extract the metadata from that object.
18. Adding new and setting values in existing metadata
Adding New
We can add new metadata values using the add() method of the metadata class
metadata.add(“author”,”Tutorials point”);
The Metadata class has some predefined properties.
metadata.add(Metadata.SOFTWARE,"ms paint");
Edit Existing
You can set values to the existing metadata elements using the set() method.
metadata.set(Metadata.DATE, new Date());
19. Language detection in tika
Among all the 184 standard languages standardized by ISO 639-1, Tika can detect 18 languages.
Language detection in Tika is done using the getLanguage() method of the LanguageIdentifier class.
This method returns the code name of the language in String format.
Given below is the list of the 18 language-code pairs detected by Tika:
20. Language detection in tika
While instantiating the LanguageIdentifier class, you should pass the String format of the content to be extracted.
Eg:
LanguageIdentifier object=new LanguageIdentifier(“this is english”);
Language Detection of a Document
To detect the language of a given document, you have to parse it using the parse() method.
The parse() method parses the content and stores it in the handler object, which was passed to it as one of the arguments.
Eg:
parser.parse(inputstream, handler, metadata, context);
LanguageIdentifier object = new LanguageIdentifier(handler.toString());