Object multifunctional indexing with an open API
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NBITSearch is a search engine with an open API for local stations, LAN and Internet. Advantages over counterparts: 1. Object indexing. It allows to index objects S of any types T. 2. Multifunctional indexing. It allows to index objects simultaneously by a set of any functions F (S). 3. Very fast search. It allows to save time and money.
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Elasticsearch is an open source search engine based on Lucene. It allows for distributed, highly available, and real-time search and analytics of documents. Documents are indexed and stored across multiple nodes in a cluster, with the ability to scale horizontally by adding more nodes. Elasticsearch uses an inverted index to allow fast full-text searches of documents.
Oz search
The document discusses the limitations of traditional keyword search engines for patent search and introduces a new patent search engine called Crony. Crony uses text block search technology rather than keyword search to find relevant patents based on both the content and location of text within documents. This allows for accurate, easy and economic patent searches that produce results within seconds without requiring users to create complex search formulas or legal expertise. Crony also saves resources through its lightweight indexing database and real-time updates.
Elasticsearch
This slide deck talks about Elasticsearch and its features.
When you talk about ELK stack it just means you are talking
about Elasticsearch, Logstash, and Kibana. But when you talk
about Elastic stack, other components such as Beats, X-Pack
are also included with it.
what is the ELK Stack?
ELK vs Elastic stack
What is Elasticsearch used for?
How does Elasticsearch work?
What is an Elasticsearch index?
Shards
Replicas
Nodes
Clusters
What programming languages does Elasticsearch support?
Amazon Elasticsearch, its use cases and benefits
Michigan Information Retrieval Enthusiasts Group Meetup - August 19, 2010
Two presentation from the Michigan Information Retrieval Enthusiasts Group Meetup on August 19 by Cengage Learning search platform development team.
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Relevance Tuning Using TREC Dataset by Rohit Laungani and Ivan Provalov describes the TREC dataset used by the team to improve the relevance of the Lucene-based search platform. Goes over IBM paper and describe the approaches tried: Lexical Affinities, Stemming, Pivot Length Normalization, Sweet Spot Similarity, Term Frequency Average Normalization. Talks about Pseudo Relevance Feedback.
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The document summarizes a presentation about eScience and its implications. It discusses how eScience is driven by massive amounts of sensor data and requires analysis of large datasets. It also describes how technologies like cloud computing, databases, data mining and machine learning enable eScience. Finally, it argues that eScience capabilities will be essential for any organization to remain competitive in the future.
E Science As A Lens On The World Lazowska
The document summarizes a presentation about eScience and its implications. It discusses how eScience is driven by massive amounts of sensor data and requires analysis of large datasets using technologies like databases, data mining, machine learning and data visualization on cluster computing systems at enormous scales. It states that eScience capabilities will be required for organizations to remain competitive in the future. It also discusses how technologies like Amazon EC2 enable scalable computing resources for any organization and how broadband access and networks like Internet2 played an important role in enabling eScience.
large_scale_search.pdf
This document discusses techniques for large scale image search. It begins by outlining the problem of searching large image databases or the internet to find similar images. It then categorizes existing algorithms for large scale image search into tree-based structures, hashing techniques, and binary small codes. The document goes into detail about locality sensitive hashing (LSH), which hashes similar images together to enable sub-linear search times, and binary small codes, which represent each image with a compact binary code to allow for very fast search speeds. It describes methods for learning the binary codes, including spectral hashing which learns codes by solving an eigenproblem on a graph representing image similarities. Experimental results on large datasets demonstrate that binary small codes can retrieve similar
How a search engine works slide
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Roaring with elastic search sangam2018
Concepts of Elastic search and ELK stack. Also listed some of the usecase with Oracle and web application
UNIT V.pdf
This document provides an overview of peer-to-peer computing and distributed shared memory. It discusses characteristics of peer-to-peer networks like decentralized control and anonymity. Structured and unstructured overlays are described, with Chord given as an example of a structured overlay using distributed hash tables. Search techniques for unstructured overlays include flooding and random walks. Distributed shared memory provides abstraction through memory consistency models for shared access across distributed nodes.
OSDC 2016 - Chronix - A fast and efficient time series storage based on Apach...
How to store billions of time series points and access them within a few milliseconds? Chronix!
Chronix is a young but mature open source project that allows one for example to store about 15 GB (csv) of time series in 238 MB with average query times of 21 ms. Chronix is built on top of Apache Solr a bulletproof distributed NoSQL database with impressive search capabilities. In this code-intense session we show how Chronix achieves its efficiency in both respects by means of an ideal chunking, by selecting the best compression technique, by enhancing the stored data with (pre-computed) attributes, and by specialized query functions.
A Fast and Efficient Time Series Storage Based on Apache Solr
OSDC 2016, Berlin: Talk by Florian Lautenschlager (@flolaut, Senior Software Engineer at QAware)
Abstract: How to store billions of time series points and access them within a few milliseconds? Chronix! Chronix is a young but mature open source project that allows one for example to store about 15 GB (csv) of time series in 238 MB with average query times of 21 ms. Chronix is built on top of Apache Solr a bulletproof distributed NoSQL database with impressive search capabilities. In this code-intense session we show how Chronix achieves its efficiency in both respects by means of an ideal chunking, by selecting the best compression technique, by enhancing the stored data with (pre-computed) attributes, and by specialized query functions.
Chronix: A fast and efficient time series storage based on Apache Solr
Chronix is a fast and efficient time series storage system based on Apache Solr. It can store large amounts of time-correlated data objects, like 68 billion data objects from sensor data collected over a year, using only 32GB of disk space and retrieving data within milliseconds. It achieves this through compressing time series data into chunks and storing the compressed chunks and associated attributes in records within Apache Solr. Chronix provides specialized time series aggregations and analyses through its query language to enable common time series operations like aggregations, trend analysis, and outlier detection.
The new time series kid on the block
Chronix is a time series database that can efficiently store billions of time series data points in a small amount of disk space and retrieve data within milliseconds. It works by splitting time series into fixed-size chunks, compressing the chunks, and storing the compressed chunks and associated metadata in Solr/Lucene records. Chronix provides common time series aggregations, transformations, and analyses through its API. The developers tuned Chronix's performance by evaluating different compression techniques and chunk sizes on real-world datasets. Chronix outperformed other time series databases in storage needs, query speed, and memory usage in their tests.
Chronix Time Series Database - The New Time Series Kid on the Block
Chronix is a time series database that can efficiently store billions of time series data points in a small amount of disk space and retrieve data within milliseconds. It works by splitting time series into fixed-size chunks, compressing the chunks, and storing the compressed chunks and associated metadata in Solr/Lucene records. Chronix provides common time series aggregations, transformations, and analyses through its API. The developers tuned Chronix's performance by evaluating different compression techniques and chunk sizes on real-world time series data. Chronix outperformed other time series databases in storage needs and query speeds in their tests.
Евгений Бобров "Powered by OSS. Масштабируемая потоковая обработка и анализ б...
Технологии с открытым исходным кодом, такие как Microsoft Orleans и ElasticSearch, - ключевые элементы архитектуры YouScan. О том, как они помогают справляться с постоянно растущими объемами данных из социальных сетей, об эволюции архитектуры YouScan, я расскажу в данном докладе.
Lucece Indexing
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.
I explore
I Explore is a multilingual enterprise search engine built using C++ and PHP. It consists of 6 main components: preprocessing, creating multilingual indexes, a query processor, query optimization, results optimization, and a user interface. The document describes each component in detail, including preprocessing steps like parsing, stemming, and removing stopwords. It also discusses the benefits of using a three-tier architecture and describes techniques like query expansion and the Okapi++ retrieval model.
Intro to elasticsearch
1) The document discusses information retrieval and search engines. It describes how search engines work by indexing documents, building inverted indexes, and allowing users to search indexed terms.
2) It then focuses on Elasticsearch, describing it as a distributed, open source search and analytics engine that allows for real-time search, analytics, and storage of schema-free JSON documents.
3) The key concepts of Elasticsearch include clusters, nodes, indexes, types, shards, and documents. Clusters hold the data and provide search capabilities across nodes.
Redis Modules - Redis India Tour - 2017
The document provides an overview of Redis Modules, which allow Redis to be extended through dynamically loaded libraries written in C. Some key modules discussed include ReJSON for storing and querying JSON documents natively in Redis, RediSearch for full-text search capabilities, and ReBloom for implementing scalable Bloom filters. Redis Modules can be used to add new data types, commands, and capabilities to Redis in order to adapt it to specific use cases and data models. Performance benchmarks show modules like ReJSON providing significant performance advantages over alternatives that rely on Redis' core data structures and Lua scripting.
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Michigan Information Retrieval Enthusiasts Group Meetup - August 19, 2010
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OSDC 2016 - Chronix - A fast and efficient time series storage based on Apach...
OSDC 2016 - Chronix - A fast and efficient time series storage based on Apach...
A Fast and Efficient Time Series Storage Based on Apache Solr
A Fast and Efficient Time Series Storage Based on Apache Solr
Chronix: A fast and efficient time series storage based on Apache Solr
Chronix: A fast and efficient time series storage based on Apache Solr
Chronix Time Series Database - The New Time Series Kid on the Block
Chronix Time Series Database - The New Time Series Kid on the Block
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Object multifunctional indexing with an open API
- 1. Index ing and Fast Search engine NBITSearch parameters www.nbitsearch.com Novosib-BIT LLC version 1.03.3
- 2. NBITSearch System NBITSearch is a search engine with an open API . --------------------------- NBITSearch is a programme kernel for ― Database Management Systems , - ― Warehouses of Large Data, - ― Search Systems applied to any Objects . .
- 4. Exact and Fuzzy Search Interval queries provide fuzzy ( inexact ) search . Precise ( exact ) search is a particular case of fuzzy search .
- 5. Indexable Objects Objects S of any types T
- 6. The system indexes objects S of any types T simultaneously by a set any functions F (S) . Multifunctionality
- 7. Sizes of Indexable Arrays The most tangible effect in the speed of search is shown for such arrays of objects , which support ≈ 50 ÷ 100 million and more objects for one index. A size of arrays of indexable objects can be 1 0 ÷ 100 terabyte and larger .
- 8. Indexing Limitations One index supports ≈ 2 billion of its objects . Limitations of number of indexes are artificial .
- 9. What is a Billion? 1 billion seconds is ≈ 32 years . 1 billion pages for a laser printer is a pile with a height of ≈ 100 km .
- 10. Indexing Speed Estimator : T ~ ( N ) * LOG (N) T – time of forming one index , N – number of indexable objects .
- 11. Compactness of Indexes A size of one index can vary within the range of 0 . 1 % ÷ 5 . 0 % of the size of indexable objects .
- 12. Search Speed Time estimation of defining the address of the first potential block of data : T ~ LOG (N) T – time of “logic probing” , N – number of indexed objects .
- 13. Search Speed A speed of fetching the result of interval queries from a hard disk can be 10 ÷ 100 times higher than (for the large data array) , the speed of similar operation in a standard relational DBMS .
- 14. Search Speed A speed of fetching the result of interval queries from a hard disk can be 1000 times ( and more ) higher than (for the large data array) , the speed of similar operation when solving the problems with the use of brute force method .
- 15. Search Speed A time of fetching the result of interval queries from a hard disk depends linearly on objects number in result set .
- 16. Search Memory Due to compactness of indexes the system loads each of them in RAM entirely before queries are made .
- 17. Search Memory A size of memory buffers to fetch the data depends on user’s needs . This size is often infinitesimal (~10 megabyte) .
- 18. Reading of Result Set Reading the result set from a hard disk to RAM is optimum : magnetic head does not oscillate .
- 19. THANK YOU ! www.nbitsearch.com Technology developed with support from FASIE formed by the Government of Russian Federation Novosib-BIT LLC © 2004 - 2010 Patented