How DataCite and Crossref Support Research Data Sharing - Crossref LIVE HannoverCrossref
Britta Dreyer from DataCite presents on how DataCite and Crossref collaboratively support research data sharing. Presented at Crossref LIVE Hannover, June 27th 2018.
Introduction to Elasticsearch for Business Intelligence and Application InsightsData Works MD
Video of the presentation is available here: https://youtu.be/L6EMnvALYtU
Talk: Elasticsearch for Business Intelligence and Application Insights
Speaker: Sean Donnelly
Elasticsearch is a distributed, RESTful search and analytics engine capable of solving a growing number of use cases. In this talk, I’ll discuss the fundamentals of storage and retrieval in Elasticsearch, why we decided to use it for search in our applications, and how you can also leverage it for both business intelligence and application insights.
This book explains the Linked Data domain by adopting a bottom-up approach: it introduces the fundamental Semantic Web technologies and building blocks, which are then combined into methodologies and end-to-end examples for publishing datasets as Linked Data, and use cases that harness scholarly information and sensor data. It presents how Linked Data is used for web-scale data integration, information management and search. Special emphasis is given to the publication of Linked Data from relational databases as well as from real-time sensor data streams. The authors also trace the transformation from the document-based World Wide Web into a Web of Data. Materializing the Web of Linked Data is addressed to researchers and professionals studying software technologies, tools and approaches that drive the Linked Data ecosystem, and the Web in general.
Review the steps involved in the research process (identifying the research problem, reviewing the literature, planning/design, collecting, analyzing storing & sharing data, quality control).
Identify the latest technology tools and apps (mobile, cloud-based, web-based) available for Lecturers and Librarians to utilize at each stage of the research process.
Introduce a range of emerging technology tools to enable researchers to conceptualize, conduct and complete research projects.
this slide is for brief introduction to the big data with little bit of fun through memes.
it is prepared with the articles from different websites about big data and some of my own words so it would be great if you like it
How DataCite and Crossref Support Research Data Sharing - Crossref LIVE HannoverCrossref
Britta Dreyer from DataCite presents on how DataCite and Crossref collaboratively support research data sharing. Presented at Crossref LIVE Hannover, June 27th 2018.
Introduction to Elasticsearch for Business Intelligence and Application InsightsData Works MD
Video of the presentation is available here: https://youtu.be/L6EMnvALYtU
Talk: Elasticsearch for Business Intelligence and Application Insights
Speaker: Sean Donnelly
Elasticsearch is a distributed, RESTful search and analytics engine capable of solving a growing number of use cases. In this talk, I’ll discuss the fundamentals of storage and retrieval in Elasticsearch, why we decided to use it for search in our applications, and how you can also leverage it for both business intelligence and application insights.
This book explains the Linked Data domain by adopting a bottom-up approach: it introduces the fundamental Semantic Web technologies and building blocks, which are then combined into methodologies and end-to-end examples for publishing datasets as Linked Data, and use cases that harness scholarly information and sensor data. It presents how Linked Data is used for web-scale data integration, information management and search. Special emphasis is given to the publication of Linked Data from relational databases as well as from real-time sensor data streams. The authors also trace the transformation from the document-based World Wide Web into a Web of Data. Materializing the Web of Linked Data is addressed to researchers and professionals studying software technologies, tools and approaches that drive the Linked Data ecosystem, and the Web in general.
Review the steps involved in the research process (identifying the research problem, reviewing the literature, planning/design, collecting, analyzing storing & sharing data, quality control).
Identify the latest technology tools and apps (mobile, cloud-based, web-based) available for Lecturers and Librarians to utilize at each stage of the research process.
Introduce a range of emerging technology tools to enable researchers to conceptualize, conduct and complete research projects.
this slide is for brief introduction to the big data with little bit of fun through memes.
it is prepared with the articles from different websites about big data and some of my own words so it would be great if you like it
Search Solutions 2011: Successful Enterprise Search By DesignMarianne Sweeny
When your colleagues say they want Google, they don’t mean the Google Search Appliance. They mean the Google Search user experience: pervasive, expedient and delivering the information that they need. Successful enterprise search does not start with the application features, is not part of the information architecture, does not come from a controlled vocabulary and does not emerge on its own from the developers. It requires enterprise-specific data mining, enterprise-specific user-centered design and fine tuning to turn “search sucks” into search success within the firewall. This presentation looks at action items, tools and deliverables for Discovery, Planning, Design and Post Launch phases of an enterprise search deployment.
If you think you need a search application, there are some useful first steps to take:
* validating that full-text search is the right technology
* producing sets of ideal results you'd like to return for a range of queries
* considering the value of supplementing a basic search result list with document clustering
* producing more specific requirements and investigating technology options
In today’s day and age, where almost every work is possible through online applications and software, it is evident that people are going to turn to online methods to solve a problem. To help with accurate data collection you can choose a field data collection tool out of numerous tools available.
Field data collection software help you to collect surveys and research data in a systematized, easily presentable, and accurate manner. Using the right platform is very crucial for this. There are some data collection platforms mentioned here that make your work easier :
In today’s day and age, where almost every work is possible through online applications and software, it is evident that people are going to turn to online methods to solve a problem. To help with accurate data collection you can choose a field data collection tool out of numerous tools available.
Field data collection software help you to collect surveys and research data in a systematized, easily presentable, and accurate manner. Using the right platform is very crucial for this. There are some data collection platforms mentioned here that make your work easier :
In today’s day and age, where almost every work is possible through online applications and software, it is evident that people are going to turn to online methods to solve a problem. To help with accurate data collection you can choose a field data collection tool out of numerous tools available.
In today’s day and age, where almost every work is possible through online applications and software, it is evident that people are going to turn to online methods to solve a problem. To help with accurate data collection you can choose a field data collection tool out of numerous tools available.
Field data collection software help you to collect surveys and research data in a systematized, easily presentable, and accurate manner. Using the right platform is very crucial for this. There are some data collection platforms mentioned here that make your work easier :
professional fuzzy type-ahead rummage around in xml type-ahead search techni...Kumar Goud
Abstract – It is a research venture on the new information-access standard called type-ahead search, in which systems discover responds to a keyword query on-the-fly as users type in the uncertainty. In this paper we learn how to support fuzzy type-ahead search in XML. Underneath fuzzy search is important when users have limited knowledge about the exact representation of the entities they are looking for, such as people records in an online directory. We have developed and deployed several such systems, some of which have been used by many people on a daily basis. The systems received overwhelmingly positive feedbacks from users due to their friendly interfaces with the fuzzy-search feature. We describe the design and implementation of the systems, and demonstrate several such systems. We show that our efficient techniques can indeed allow this search paradigm to scale on large amounts of data.
Index Terms - type-ahead, large data set, server side, online directory, search technique.
I have collected information for the beginners to provide an overview of big data and hadoop which will help them to understand the basics and give them a Start-Up.
An information storage and retrieval system (ISRS) is a network with a built-in user interface that facilitates the creation, searching, and modification of stored data.
SharePoint vs OneDrive – Differences and SimilaritiesDynamics Square
The primary difference between SharePoint and oneDrive is that SharePoint exists within the Microsoft application ecosystem. It is just one tool in a much larger toolbox that helps drive organizational collaboration and productivity. Users are able manage content and documents using the Microsoft 365 platform. Read More here: https://www.dynamicssquare.com.au/blog/sharepoint-vs-onedrive/
Performance Of The Google Desktop, Arabic Google Desktop and Peer to Peer App...ijseajournal
The Arabic language is a complex language; it is different from Western languages especially at the
morphological and spelling variations. Indeed, the performance of information retrieval systems in the
Arabic language is still a problem. For this reason, we are interested in studying the performance of the
most famous search engine, which is a Google Desktop, while searching in Arabic language documents.
Then, we propose an update to the Google Desktop to take into consideration in search the Arabic words
that have the same root. After that, we evaluate the performance of the Google Desktop in this context.
Also, we are interested in evaluation the performance of peer-to-peer application in two ways. The first one
uses a simple indexation that indexes Arabic documents without taking in consideration the root of words.
The second way takes in consideration the roots in the indexation of Arabic documents. This evaluation is
done by using a corpus of ten thousand documents and one hundred different queries.
Weaviate and Pinecone are both search engines that allow developers to build powerful search and discovery applications. Weaviate is designed specifically for natural language or numerical data and is based on contextualized embeddings, while Pinecone is a more general-purpose vector search engine that can be used for a wide range of data types, including images, audio, and sensor data.
Both Weaviate and Pinecone use similar approaches to document loading and vectorization, but differ in their focus and capabilities. Weaviate provides REST and GraphQL APIs that allow developers to easily interact with the search engine using Lua or other programming languages, and supports features such as natural language processing and knowledge graph creation. Pinecone, on the other hand, provides built-in similarity search functionality and is optimized for large-scale, high-throughput search applications.
When choosing between Weaviate and Pinecone, it's important to consider factors such as your specific use case, performance requirements, flexibility, data sources, and cost. Weaviate may be a better fit if your use case involves natural language processing or you need to integrate with Lua-based tools such as OpenResty or Tarantool. Pinecone may be a better fit if you need to handle large-scale, high-throughput search applications or work with a wide range of data types.
Ultimately, the choice between Weaviate and Pinecone will depend on the specific requirements of your project and the features and capabilities that are most important to you.
Noogle reflects the basic elements of the knowledge system, as well as the features of their mutual transformation and the possibilities of legal ownership.
Noggle is dedicated to providing its customers with innovative PC search technology to optimize the use of their computer by increasing their productivity.
Search Solutions 2011: Successful Enterprise Search By DesignMarianne Sweeny
When your colleagues say they want Google, they don’t mean the Google Search Appliance. They mean the Google Search user experience: pervasive, expedient and delivering the information that they need. Successful enterprise search does not start with the application features, is not part of the information architecture, does not come from a controlled vocabulary and does not emerge on its own from the developers. It requires enterprise-specific data mining, enterprise-specific user-centered design and fine tuning to turn “search sucks” into search success within the firewall. This presentation looks at action items, tools and deliverables for Discovery, Planning, Design and Post Launch phases of an enterprise search deployment.
If you think you need a search application, there are some useful first steps to take:
* validating that full-text search is the right technology
* producing sets of ideal results you'd like to return for a range of queries
* considering the value of supplementing a basic search result list with document clustering
* producing more specific requirements and investigating technology options
In today’s day and age, where almost every work is possible through online applications and software, it is evident that people are going to turn to online methods to solve a problem. To help with accurate data collection you can choose a field data collection tool out of numerous tools available.
Field data collection software help you to collect surveys and research data in a systematized, easily presentable, and accurate manner. Using the right platform is very crucial for this. There are some data collection platforms mentioned here that make your work easier :
In today’s day and age, where almost every work is possible through online applications and software, it is evident that people are going to turn to online methods to solve a problem. To help with accurate data collection you can choose a field data collection tool out of numerous tools available.
Field data collection software help you to collect surveys and research data in a systematized, easily presentable, and accurate manner. Using the right platform is very crucial for this. There are some data collection platforms mentioned here that make your work easier :
In today’s day and age, where almost every work is possible through online applications and software, it is evident that people are going to turn to online methods to solve a problem. To help with accurate data collection you can choose a field data collection tool out of numerous tools available.
In today’s day and age, where almost every work is possible through online applications and software, it is evident that people are going to turn to online methods to solve a problem. To help with accurate data collection you can choose a field data collection tool out of numerous tools available.
Field data collection software help you to collect surveys and research data in a systematized, easily presentable, and accurate manner. Using the right platform is very crucial for this. There are some data collection platforms mentioned here that make your work easier :
professional fuzzy type-ahead rummage around in xml type-ahead search techni...Kumar Goud
Abstract – It is a research venture on the new information-access standard called type-ahead search, in which systems discover responds to a keyword query on-the-fly as users type in the uncertainty. In this paper we learn how to support fuzzy type-ahead search in XML. Underneath fuzzy search is important when users have limited knowledge about the exact representation of the entities they are looking for, such as people records in an online directory. We have developed and deployed several such systems, some of which have been used by many people on a daily basis. The systems received overwhelmingly positive feedbacks from users due to their friendly interfaces with the fuzzy-search feature. We describe the design and implementation of the systems, and demonstrate several such systems. We show that our efficient techniques can indeed allow this search paradigm to scale on large amounts of data.
Index Terms - type-ahead, large data set, server side, online directory, search technique.
I have collected information for the beginners to provide an overview of big data and hadoop which will help them to understand the basics and give them a Start-Up.
An information storage and retrieval system (ISRS) is a network with a built-in user interface that facilitates the creation, searching, and modification of stored data.
SharePoint vs OneDrive – Differences and SimilaritiesDynamics Square
The primary difference between SharePoint and oneDrive is that SharePoint exists within the Microsoft application ecosystem. It is just one tool in a much larger toolbox that helps drive organizational collaboration and productivity. Users are able manage content and documents using the Microsoft 365 platform. Read More here: https://www.dynamicssquare.com.au/blog/sharepoint-vs-onedrive/
Performance Of The Google Desktop, Arabic Google Desktop and Peer to Peer App...ijseajournal
The Arabic language is a complex language; it is different from Western languages especially at the
morphological and spelling variations. Indeed, the performance of information retrieval systems in the
Arabic language is still a problem. For this reason, we are interested in studying the performance of the
most famous search engine, which is a Google Desktop, while searching in Arabic language documents.
Then, we propose an update to the Google Desktop to take into consideration in search the Arabic words
that have the same root. After that, we evaluate the performance of the Google Desktop in this context.
Also, we are interested in evaluation the performance of peer-to-peer application in two ways. The first one
uses a simple indexation that indexes Arabic documents without taking in consideration the root of words.
The second way takes in consideration the roots in the indexation of Arabic documents. This evaluation is
done by using a corpus of ten thousand documents and one hundred different queries.
Weaviate and Pinecone are both search engines that allow developers to build powerful search and discovery applications. Weaviate is designed specifically for natural language or numerical data and is based on contextualized embeddings, while Pinecone is a more general-purpose vector search engine that can be used for a wide range of data types, including images, audio, and sensor data.
Both Weaviate and Pinecone use similar approaches to document loading and vectorization, but differ in their focus and capabilities. Weaviate provides REST and GraphQL APIs that allow developers to easily interact with the search engine using Lua or other programming languages, and supports features such as natural language processing and knowledge graph creation. Pinecone, on the other hand, provides built-in similarity search functionality and is optimized for large-scale, high-throughput search applications.
When choosing between Weaviate and Pinecone, it's important to consider factors such as your specific use case, performance requirements, flexibility, data sources, and cost. Weaviate may be a better fit if your use case involves natural language processing or you need to integrate with Lua-based tools such as OpenResty or Tarantool. Pinecone may be a better fit if you need to handle large-scale, high-throughput search applications or work with a wide range of data types.
Ultimately, the choice between Weaviate and Pinecone will depend on the specific requirements of your project and the features and capabilities that are most important to you.
Noogle reflects the basic elements of the knowledge system, as well as the features of their mutual transformation and the possibilities of legal ownership.
Noggle is dedicated to providing its customers with innovative PC search technology to optimize the use of their computer by increasing their productivity.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Ch03-Managing the Object-Oriented Information Systems Project a.pdf
Document search
1. Get The Free Drive Space That You Had Always Wanted For Your Device
Ever thought about how you are besieged with steady notice and data over-burden. With the expansion in
the openness, you are associated with the web more often than not. Alongside that every one of the
applications and the sites send you steady updates about the happenings. Such huge numbers of records
top off your distributed storage and hard drive making them excess, looking for the applicable archive a
testing assignment.
Noggle application gives you a one-stop answer for every one of your quests. Document Search, image
search, record search, and media search and so on in the hard drive or over the cloud, noggle gives you a
chance to look through your vital archive
effectively. This application utilizes
subjective pursuit as the fundamental
standard and streamlines the whole inquiry
activity. Like virtual help, this application
creeps through the whole space and grabs
expressions and keywords significant to
the pursuit. It orchestrates the information
into classifications dependent on the set
parameters and calls the information when
inquired.
The Knowledge Sharing application
makes classifications which are then loaded up with a comparative kind of information that together
structures a totally masterminded cluster of data. Its help highlight gives you a chance to call for activity
of the majority of the data that is pre-arranged by the application. Intelligent duplicate detection causes
you to wipe out copy and excess records that are a weight on the capacity and furthermore decreases the
framework speed. So don't hang tight for the minute when the framework crashes, simply give it the hunt
support it needs.