- The document provides an overview of the Lemur Toolkit, which is software for building search applications using language modeling techniques.
- It describes how to install Lemur, index documents, run queries, evaluate results, and use the Indri query language. Parameters for indexing, retrieval, and formatting results are configured through XML parameter files.
- Key functions covered include building indexes, running queries, evaluating results, and using the Indri query language to combine terms and restrict searches to fields. Smoothing and other language modeling techniques are also summarized.
The document provides an overview and agenda for an Apache Solr crash course. It discusses topics such as information retrieval, inverted indexes, metrics for evaluating IR systems, Apache Lucene, the Lucene and Solr APIs, indexing, searching, querying, filtering, faceting, highlighting, spellchecking, geospatial search, and Solr architectures including single core, multi-core, replication, and sharding. It also provides tips on performance tuning, using plugins, and developing a Solr-based search engine.
Webinar slides: Interoperability between resources involved in TDM at the lev...openminted_eu
The OpenMinted project aims to develop an open e-infrastructure for text and data mining (TDM) of scientific literature, enabling researchers to collaboratively create, share, and reuse knowledge from diverse text-based sources. It focuses on achieving interoperability among various resources and metadata schemas, facilitating the automatic discovery of compatible content and software components. Key elements include a registry service for resource search, workflow development for TDM service providers, and guidelines to standardize metadata for better documentation and resources discoverability.
Document indexing is the process of tagging information to files for easy search and retrieval, often involving metadata. Indexing is crucial for business workflows, and various technologies like OCR and barcodes facilitate efficient indexing with minimal user intervention. This document outlines methods for capturing index data and integrating it into document management systems.
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.
This document summarizes a Solr Recipes Workshop presented by Erik Hatcher of Lucid Imagination. It introduces Lucene and Solr, describes how to index different content sources into Solr including CSV, XML, rich documents, and databases, and provides an overview of using the DataImportHandler to index from a relational database.
The document discusses various technologies for metasearching or cross-searching multiple databases at once, including Z39.50 for real-time searching, SRU/SRW web services, and OAI-PMH for metadata harvesting. It explains concepts like XML, web services, SOAP, and WSDL, and provides examples of how technologies like Z39.50, SRU, and OAI-PMH enable searching across different data sources.
The document provides an overview of application data storage locations across various operating systems, including Windows, OS X, and Linux, along with methods of investigating this data for forensic purposes. It details popular application types such as internet browsers and email clients, and discusses various package managers and their roles in tracking installed applications. Additionally, it covers specific analysis techniques for web browsers like Google Chrome and email clients like Microsoft Outlook, emphasizing the importance of understanding data formats and tools used for digital forensics.
This document describes the basic architecture of a search engine, including its two main processes: indexing and query. The indexing process involves acquiring text from sources, transforming it by parsing, stemming, etc., and creating indexes for fast searching. The query process allows users to input queries, transform queries, rank and retrieve relevant documents from the indexes, and output search results. Key components described are crawlers, parsers, stemmers, inverted indexes, ranking algorithms, and query logs for evaluation.
La big datacamp-2014-aws-dynamodb-overview-michael_limcacoData Con LA
This document discusses Amazon DynamoDB, a fully managed NoSQL database service from AWS. It provides three key points:
1. DynamoDB offers fast and predictable performance with single-digit millisecond latency, automatic scaling of storage and throughput capacity, and built-in security, backup and disaster recovery capabilities.
2. DynamoDB uses a flexible data model with key-value and document data structures, and supports both document and relational data structures. It also includes rich query capabilities and SDKs/APIs for developers.
3. The document provides an example of modeling user data and files for a media catalog application using DynamoDB tables and secondary indexes to support various access patterns like searching by
The document discusses best practices for data archiving and sharing, emphasizing the importance of preserving data using appropriate file formats, creating metadata, and adhering to FAIR principles. It includes exercises to identify bad data management decisions and encourages researchers to utilize standards and repositories for better data accessibility and usability. Overall, it highlights the need for proactive data management throughout the research cycle.
Elasticsearch is a search engine based on Apache Lucene that provides distributed, full-text search capabilities. It allows users to store and search documents of any structure in near real-time. Documents are organized into indexes, shards, and clusters to provide scalability and fault tolerance. Elasticsearch uses analysis and mapping to index documents for full-text search. Queries can be built using the Elasticsearch DSL for complex searches. While Elasticsearch provides fast search, it has disadvantages for transactional operations or large document churn. Elastic HQ is a web plugin that provides monitoring and management of Elasticsearch clusters through a browser-based interface.
Building Search & Recommendation EnginesTrey Grainger
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- Semantic publishing enhances the meaning and automated discovery of published articles by enriching them with metadata. This allows articles to be more easily linked, integrated, and analyzed.
- SPAR and EARMARK are ontologies that can be used to semantically annotate TEI documents. SPAR defines terms for scholarly objects, and EARMARK defines how to annotate text ranges without modifying the source document.
- An example is provided of how SPAR and EARMARK could be used to annotate a TEI version of Shakespeare's The Tempest, linking text excerpts to definitions in DBPedia and recording different annotators' interpretations.
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The document discusses the AudioMD metadata scheme created by the Library of Congress to describe technical qualities of digital audio objects. It defines AudioMD, provides examples of its use, and describes its importance in understanding audio files. The scheme captures administrative, technical, and preservation metadata in a structured XML format. It has evolved through versions 1.0 and 2.0. Additionally, the document outlines the BIBFRAME initiative led by the Library of Congress to transform bibliographic standards to a linked data model and make library catalog records more accessible online.
1. The document summarizes new features in Oracle Text 11g and the roadmap for Oracle's search products, including Oracle Text and Secure Enterprise Search.
2. Key new features in Oracle Text 11g include composite domain indexes, automatic language recognition with context-sensitive stemming, and offline index creation. Oracle Text 11.2.0.2 introduces entity extraction, name search, and a result set interface that returns XML results.
3. The roadmap discusses merging Oracle Text and Secure Enterprise Search and bringing additional natural language processing, partitioning, faceted navigation, and performance improvements to Oracle's search products.
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Page 18Goal Implement a complete search engine. Milestones.docxsmile790243
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This document provides an introduction and overview of an IS220 Database Systems course. It outlines that the course will cover topics like database design, file organization, indexing and hashing, query processing and optimization, transactions, object-oriented and XML databases. It notes that the class will be 70% theory and 30% hands-on assignments completed in pairs. Assessment will include group work, tests, and a final exam. Class rules require punctuality, use of English, dressing professionally, and minimum 80% attendance.
This document discusses the implementation of a Data Management Plan (DMP) for effective metadata management and data storage at INRAE. It emphasizes the importance of creating data repositories that employ JSON formatted metadata files, which are accessible via a web interface, to enhance organization and searchability of research data. The approach aims to be flexible, allowing diverse domains to utilize controlled vocabularies while standardizing best practices for metadata entry and storage.
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This document describes a research project that developed a client-side search module for a native XML website using XML technologies like XSLT, XPath, and DOM as well as JavaScript. The researcher created both a basic search and an advanced search utility. The basic search allowed searching across all text fields in a table using one text box, while the advanced search provided more options to search specific columns and refine searches. The project showed that an XML website can be effectively searched using a combination of XML technologies and JavaScript. The researcher plans to expand the search capabilities with more advanced regular expressions and server-side searching in the future.
The document outlines a project aimed at harvesting metadata from the Theseus online repository, which provides access to theses from Finnish universities. The project involves utilizing the OAI-PMH protocol for data extraction, storing this metadata in a MySQL database, and creating a web portal for analysis and visualization of thesis data. Key outcomes include statistical insights into thesis production by universities and departments, along with keyword analysis.
This document provides a summary of the Solr search platform. It begins with introductions from the presenter and about Lucid Imagination. It then discusses what Solr is, how it works, who uses it, and its main features. The rest of the document dives deeper into topics like how Solr is configured, how to index and search data, and how to debug and customize Solr implementations. It promotes downloading and experimenting with Solr to learn more.
Crossref Content Registration - LIVE MumbaiCrossref
This document provides information about registering metadata with Crossref, including:
1. Crossref assigns members a prefix and login to begin the registration process. Members can register metadata for various types of scholarly content.
2. Metadata should include important details about publications like author names, article titles, publication dates, and assigned DOIs to improve discovery of content.
3. Members can deposit metadata by uploading an XML file, using a web form, or integrating with their publishing systems. High quality, accurate metadata benefits discovery for both members and the public.
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A Robust Keywords Based Document Retrieval by Utilizing Advanced Encryption S...IRJET Journal
This document presents a methodology for a robust keyword-based document retrieval system utilizing advanced encryption. Key aspects of the methodology include:
1) Performing stop word removal and term frequency analysis to create feature vectors for documents.
2) Assigning unique numbers to terms to create a dictionary and document codes for identification and comparison.
3) Using advanced encryption techniques like substitution and mixing on the document codes.
4) Comparing the codes of user queries to document codes to find and rank the most relevant documents.
The methodology is tested on real and artificial datasets, showing improved accuracy, precision, and recall over previous methods according to experimental results.
OpenACC and Open Hackathons Monthly Highlights June 2025OpenACC
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- Semantic publishing enhances the meaning and automated discovery of published articles by enriching them with metadata. This allows articles to be more easily linked, integrated, and analyzed.
- SPAR and EARMARK are ontologies that can be used to semantically annotate TEI documents. SPAR defines terms for scholarly objects, and EARMARK defines how to annotate text ranges without modifying the source document.
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The document outlines the Accelrys Catalog and Protocol Validation, detailing its purpose, functionality, and the challenges of managing numerous protocols within an enterprise platform. It provides insights into the indexing and search capabilities through a Google-like interface, targeting productivity for various user types, including administrators and clients. Additionally, it discusses troubleshooting methods and enhancements for effective protocol validation, emphasizing the importance of these tools in enterprise deployments.
The document discusses the AudioMD metadata scheme created by the Library of Congress to describe technical qualities of digital audio objects. It defines AudioMD, provides examples of its use, and describes its importance in understanding audio files. The scheme captures administrative, technical, and preservation metadata in a structured XML format. It has evolved through versions 1.0 and 2.0. Additionally, the document outlines the BIBFRAME initiative led by the Library of Congress to transform bibliographic standards to a linked data model and make library catalog records more accessible online.
1. The document summarizes new features in Oracle Text 11g and the roadmap for Oracle's search products, including Oracle Text and Secure Enterprise Search.
2. Key new features in Oracle Text 11g include composite domain indexes, automatic language recognition with context-sensitive stemming, and offline index creation. Oracle Text 11.2.0.2 introduces entity extraction, name search, and a result set interface that returns XML results.
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This document provides an introduction and overview of an IS220 Database Systems course. It outlines that the course will cover topics like database design, file organization, indexing and hashing, query processing and optimization, transactions, object-oriented and XML databases. It notes that the class will be 70% theory and 30% hands-on assignments completed in pairs. Assessment will include group work, tests, and a final exam. Class rules require punctuality, use of English, dressing professionally, and minimum 80% attendance.
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2. Metadata should include important details about publications like author names, article titles, publication dates, and assigned DOIs to improve discovery of content.
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A Robust Keywords Based Document Retrieval by Utilizing Advanced Encryption S...IRJET Journal
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Curietech AI in action - Accelerate MuleSoft developmentshyamraj55
CurieTech AI in Action – Accelerate MuleSoft Development
Overview:
This presentation demonstrates how CurieTech AI’s purpose-built agents empower MuleSoft developers to create integration workflows faster, more accurately, and with less manual effort
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Dedicated AI agents for every stage: Coding, Testing (MUnit), Documentation, Code Review, and Migration
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DataWeave automation: Generate mappings from tables or samples—95%+ complete within minutes
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Integration flow generation: Auto-create Mule flows based on specifications—speeds up boilerplate development
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Efficient code reviews: Gain intelligent feedback on flows, patterns, and error handling
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Test & documentation automation: Auto-generate MUnit test cases, sample data, and detailed docs from code
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Achieve 10× productivity gains, slashing development time from hours to minutes
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2. About The Lemur Project
• The Lemur Project was started in 2000 by the Center for
Intelligent Information Retrieval (CIIR) at the University of
Massachusetts, Amherst, and the Language Technologies
Institute (LTI) at Carnegie Mellon University. Over the years, a
large number of UMass and CMU students and staff have
contributed to the project.
• The project's first product was the Lemur Toolkit, a collection
of software tools and search engines designed to support
research on using statistical language models for information
retrieval tasks. Later the project added the Indri search engine
for large-scale search, the Lemur Query Log Toolbar for
capture of user interaction data, and the ClueWeb09 dataset
for research on web search.
4. Installation
• http://www.lemurproject.org
• JAVA Runtime(JDK 6) need for evaluation tool.
• Linux, OS/X:
– Extract lemur-4.12.tar.gz
– ./configure --prefix=/install/path
– ./make
– ./make install
– Modify Environment Variable: ~/.bash_profile
• Windows
– Run lemur-4.12-install.exe
– Documentation in windoc/index.html
– Modify Environment Variable
5. How to use Lemur Project
• Indexing
• Document Preparation
• Indexing Parameters
• Retrieval
• Parameters
6. How to use Lemur Project
• Indexing
• Document Preparation
• Indexing Parameters
• Retrieval
• Parameters
7. Two Index Formats
• KeyFile
• Term Positions
• Metadata
• Offline Incremental
• InQuery Query
Language
• Indri
• Term Positions
• Metadata
• Fields / Annotations
• Online Incremental
• InQuery and Indri
Query Languages
8. Indexing: Document Preparation
• Lemur
• TREC Text
• TREC Web
• HTML
• Indri
• TREC Text
• TREC Web
• Plain Text
• DOC
• PPT
• HTML
• XML
• PDF
• Mbox
Document Formats
The Lemur Toolkit can inherently deal with several different
document format types without any modification:
9. Indexing: Document Preparation
If your documents are not in a format that the Lemur Toolkit can
inherently process:
• If necessary, extract the text from the document.
• Wrap the plaintext in TREC-style wrappers:
<DOC>
<DOCNO>document_id</DOCNO>
<TEXT>
Index this document text.
</TEXT>
</DOC>
• – or –
For more advanced users, write your own parser to extend the Lemur
Toolkit.
10. How to use Lemur Project
• Indexing
• Document Preparation
• Indexing Parameters
• Retrieval
• Parameters
11. Indexing: Parameters
• Basic usage to build index:
– IndriBuildIndex <parameter_file>
• Parameter file includes options for
• Where to find your data files
• Where to place the index
• How much memory to use
• Stopword, stemming, fields
• Many other parameters.
12. Indexing: Parameters
• Standard parameter file specification an XML
document:
<parameters>
<option></option>
<option></option>
…
<option></option>
</parameters>
13. Indexing: Parameters
• <corpus>: where to find your source files and what type
to expect
• <path>: (required) the path to the source files (absolute or relative)
• <class>: (optional) the document type to expect. If omitted,
IndriBuildIndex will attempt to guess at the filetype based on the
file’s extension.
<parameters>
<corpus>
<path>/path/to/source/files</path>
<class>trectext</class>
</corpus>
<index>/path/to/the/index</index>
<memory>256M</memory>
<stopper>
<word>first_word</word>
<word>next_word</word>
…
<word>final_word</word>
</stopper>
</parameters>
14. Indexing: Parameters
• The <index> parameter tells IndriBuildIndex where to
create or incrementally add to the index
• If index does not exist, it will create a new one
• If index already exists, it will append new
documents into the index.
<parameters>
<corpus>
<path>/path/to/source/files</path>
<class>trectext</class>
</corpus>
<index>/path/to/the/index</index>
<memory>256M</memory>
<stopper>
<word>first_word</word>
<word>next_word</word>
…
<word>final_word</word>
</stopper>
</parameters>
15. Indexing: Parameters
• <memory>: used to define a “soft-limit” of the
amount of memory the indexer should use before
flushing its buffers to disk.
• Use K for kilobytes, M for megabytes, and G for gigabytes.
<parameters>
<corpus>
<path>/path/to/source/files</path>
<class>trectext</class>
</corpus>
<index>/path/to/the/index</index>
<memory>256M</memory>
<stopper>
<word>first_word</word>
<word>next_word</word>
…
<word>final_word</word>
</stopper>
</parameters>
16. Indexing: Parameters
• Stopwords can be defined within a <stopper> block
with individual stopwords within enclosed in <word>
tags.
<parameters>
<corpus>
<path>/path/to/source/files</path>
<class>trectext</class>
</corpus>
<index>/path/to/the/index</index>
<memory>256M</memory>
<stopper>
<word>first_word</word>
<word>next_word</word>
…
<word>final_word</word>
</stopper>
</parameters>
17. Indexing: Parameters
• Term stemming can be used while indexing as
well via the <stemmer> tag.
– Specify the stemmer type via the <name> tag within.
– Stemmers included with the Lemur Toolkit include the
Krovetz Stemmer and the Porter Stemmer.
<parameters>
<corpus>
<path>/path/to/source/files</path>
<class>trectext</class>
</corpus>
<index>/path/to/the/index</index>
<memory>256M</memory>
<stemmer>
<name>krovetz</name>
</stemmer>
</parameters>
20. How to use Lemur Project
• Indexing
• Document Preparation
• Indexing Parameters
• Retrieval
• Parameters
21. Retrieval: Parameters
• Basic usage for retrieval:
• IndriRunQuery/RetEval <parameter_file>
• Parameter file includes options for
• Where to find the index
• The query or queries
• How much memory to use
• Formatting options
• Many other parameters.
22. Retrieval: Parameters
• Just as with indexing:
• A well-formed XML document with
options, wrapped by <parameters> tags:
<parameters>
<options></options>
<options></options>
…
<options></options>
</parameters>
23. Retrieval: Parameters
• The <index> parameter
tells
IndriRunQuery/RetEval
where to find the
repository.
<parameters>
<index>/path/to/the/index</index>
<query>
<number>1</number>
<text>this is the first query</text>
</query>
<memory>256M</memory>
<stopper>
<word>first_word</word>
…
</stopper>
<count>50</count>
<runID>runName</runID>
<trecFormat>true</trecFormat>
</parameters>
24. Retrieval: Parameters
• The <query>
parameter specifies a
query
• plain text or using the
Indri query language
<parameters>
<index>/path/to/the/index</index>
<query>
<number>1</number>
<text>this is the first query</text>
</query>
<memory>256M</memory>
<stopper>
<word>first_word</word>
…
</stopper>
<count>50</count>
<runID>runName</runID>
<trecFormat>true</trecFormat>
</parameters>
25. Retrieval: Parameters
• To specify a maximum
number of results to
return, use the
<count> tag
<parameters>
<index>/path/to/the/index</index>
<query>
<number>1</number>
<text>this is the first query</text>
</query>
<memory>256M</memory>
<stopper>
<word>first_word</word>
…
</stopper>
<count>50</count>
<runID>runName</runID>
<trecFormat>true</trecFormat>
</parameters>
26. Retrieval: Parameters
• TREC – Formatting
directives:
• <runID>: a string
specifying the id for
a query run, used in
TREC scorable
output.
• <trecFormat>: true
to produce TREC
scorable output,
otherwise use false
(default).
<parameters>
<index>/path/to/the/index</index>
<query>
<number>1</number>
<text>this is the first query</text>
</query>
<memory>256M</memory>
<stopper>
<word>first_word</word>
…
</stopper>
<count>50</count>
<runID>runName</runID>
<trecFormat>true</trecFormat>
</parameters>
29. Introducing the API
• Lemur “Classic” API
– Many objects, highly customizable
– May want to use this when you want to change how
the system works
– Support for clustering, distributed IR, summarization
• Indri API
– Two main objects
– Best for integrating search into larger applications
– Supports Indri query language, XML retrieval, “live”
incremental indexing, and parallel retrieval
30. Lemur Index Browsing
• The Lemur API gives access to the index data (e.g.
inverted lists, collection statistics)
• IndexManager::openIndex
– Returns a pointer to an index object
– Detects what kind of index you wish to open, and returns
the appropriate kind of index class
31. Lemur Index Browsing
Index::term
term( char* s ) : convert term string to a number
term( int id ) : convert term number to a string
Index::document
document( char* s ) : convert doc string to a number
document( int id ) : convert doc number to a string
Index::termCount
termCount() : Total number of terms indexed
termCount( int id ) : Total number of occurrences of term number id.
Index::documentCount
docCount() : Number of documents indexed
docCount( int id ) : Number of documents that contain term number id.
32. Lemur Index Browsing
Index::docLength( int docID )
The length, in number of terms, of document number docID.
Index::docLengthAvg
Average indexed document length
Index::termCountUnique
Size of the index vocabulary
33. Lemur Index Browsing
Index::docInfoList( int termID )
Returns an iterator to the inverted list for termID.
The list contains all documents that contain
termID, including the positions where termID
occurs.
Index::termInfoList( int docID )
Returns an iterator to the direct list for docID.
The list contains term numbers for every term contained in
document docID, and the number of times each word occurs.
(use termInfoListSeq to get word positions)