NBITSearch. Features.
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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 set any functions F (S). 3. Very fast search. It allows to save time and money.
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Similar to NBITSearch. Features.
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When you talk about ELK stack it just means you are talking
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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. 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
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.
How a search engine works slide
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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.
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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.
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.
A tour of Amazon Redshift
A quick tour in 16 slides of Amazon's Redshift clustered, massively parallel database.
Find out what differentiates it from the other database products Amazon has, including SimpleDB, DynamoDB and RDS (MySQL, SQL Server and Oracle).
Learn how it stores data on disk in a columnar format and how this relates to performance and interesting compression techniques.
Contrast the difference between Redshift and a MySQL instance and discover how the clustered architecture may help to dramatically reduce query time.
Making Machine Learning Scale: Single Machine and Distributed
This document summarizes machine learning scalability from single machine to distributed systems. It discusses how true scalability is about how long it takes to reach a target accuracy level using any available hardware resources. It introduces GraphLab Create and SFrame/SGraph for scalable machine learning and graph processing. Key points include distributed optimization techniques, graph partitioning strategies, and benchmarks showing GraphLab Create can solve problems faster than other systems by using fewer machines.
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.
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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...
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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
Евгений Бобров "Powered by OSS. Масштабируемая потоковая обработка и анализ б...
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Making Machine Learning Scale: Single Machine and Distributed
Making Machine Learning Scale: Single Machine and Distributed
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HCL Notes and Domino License Cost Reduction in the World of DLAU
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
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Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
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Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
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Main news related to the CCS TSI 2023 (2023/1695)
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
The Microsoft 365 Migration Tutorial For Beginner.pptx
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
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Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
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5th LF Energy Power Grid Model Meet-up Slides
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Artificial Intelligence and Electronic Warfare
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This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
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Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
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NBITSearch. Features.
- 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 - 201 1 Patented