By using a Data Lake, you no longer need to worry about structuring or transforming data before storing it. A Data Lake on AWS enables your organization to more rapidly analyze data, helping you quickly discover new business insights. Join us for our webinar to learn about the benefits of building a Data Lake on AWS and how your organization can begin reaping their rewards. In this webinar, select APN Partners will share their specific methodology for implementing a Data Lake on AWS and best practices for getting the most from your Data Lake.
Metadata is hotter than ever, according to a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information.
While metadata has been managed for decades, new strategies & approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies & technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption & use.
How to build a business glossary linked with data dictionaryPiotr Kononow
This document discusses how to build a business glossary linked with a data dictionary. It defines a business glossary as focusing on business concepts and terms, while a data dictionary lists tables and columns to understand data assets. Building these together brings benefits like easier data discovery, a business layer for technical data, and improved business-IT communication. The document demonstrates how the Dataedo tool can be used to create an integrated business glossary and data dictionary, including defining terms, mapping relationships, and linking data elements to the glossary.
Using a Semantic and Graph-based Data Catalog in a Modern Data FabricCambridge Semantics
Watch this webinar to learn about the benefits of using semantic and graph database technology to create a Data Catalog of all of an enterprise's data, regardless of source or format, as part of a modern IT or data management stack and an important step toward building an Enterprise Data Fabric.
• History of Data Management
• Business Drivers for implementation of data governance • Building Data Strategy & Governance Framework
• Data Management Maturity Models
• Data Quality Management
• Metadata and Governance
• Metadata Management
• Data Governance Stakeholder Communication Strategy
The Importance of MDM - Eternal Management of the Data MindDATAVERSITY
Despite its immaterial nature, data has a tendency to pile up as time goes on, and can quickly be rendered unusable or obsolete without careful maintenance and streamlining of processes for its management. This presentation will provide you with an understanding of reference and master data management (MDM), one such method for keeping mass amounts of business data organized and functional towards achieving business goals.
MDM’s guiding principles include the establishment and implementation of authoritative data sources and effective means of delivering data to various business processes, as well as increases to the quality of information used in organizational analytical functions (such as BI).
To that end, attendees of this webinar will learn how to:
- Structure their data management processes around these principles
- Incorporate data quality engineering into the planning of reference and MDM
- Understand why MDM is so critical to their organization’s overall data strategy
How to Make a Data Governance Program that LastsDATAVERSITY
Traditional data governance initiatives fail by focusing too heavily on policies, compliance, and enforcement, which quickly lose business interest and support. This leaves data management and governance leaders having to continually make the case for data governance to secure business adoption. Join Cameron, VP, Product Management, Precisely, as he shares a lean, business-first data governance approach that connects key initiatives to governance capabilities and quickly delivers business value for the long-term. He will give examples of organizations worldwide who have successfully implemented a data governance program by engaging with key stakeholders using innovative techniques such as gamification and data catalog scavenger hunts.
This talk is quick reference of all the different queerability options that MongoDB offers to developers that want to build mobile and geospatial referenced applications. We reviewed the basic functionality but also recent improvements in the query and indexation engine of MongoDB geospatial features
By using a Data Lake, you no longer need to worry about structuring or transforming data before storing it. A Data Lake on AWS enables your organization to more rapidly analyze data, helping you quickly discover new business insights. Join us for our webinar to learn about the benefits of building a Data Lake on AWS and how your organization can begin reaping their rewards. In this webinar, select APN Partners will share their specific methodology for implementing a Data Lake on AWS and best practices for getting the most from your Data Lake.
Metadata is hotter than ever, according to a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information.
While metadata has been managed for decades, new strategies & approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies & technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption & use.
How to build a business glossary linked with data dictionaryPiotr Kononow
This document discusses how to build a business glossary linked with a data dictionary. It defines a business glossary as focusing on business concepts and terms, while a data dictionary lists tables and columns to understand data assets. Building these together brings benefits like easier data discovery, a business layer for technical data, and improved business-IT communication. The document demonstrates how the Dataedo tool can be used to create an integrated business glossary and data dictionary, including defining terms, mapping relationships, and linking data elements to the glossary.
Using a Semantic and Graph-based Data Catalog in a Modern Data FabricCambridge Semantics
Watch this webinar to learn about the benefits of using semantic and graph database technology to create a Data Catalog of all of an enterprise's data, regardless of source or format, as part of a modern IT or data management stack and an important step toward building an Enterprise Data Fabric.
• History of Data Management
• Business Drivers for implementation of data governance • Building Data Strategy & Governance Framework
• Data Management Maturity Models
• Data Quality Management
• Metadata and Governance
• Metadata Management
• Data Governance Stakeholder Communication Strategy
The Importance of MDM - Eternal Management of the Data MindDATAVERSITY
Despite its immaterial nature, data has a tendency to pile up as time goes on, and can quickly be rendered unusable or obsolete without careful maintenance and streamlining of processes for its management. This presentation will provide you with an understanding of reference and master data management (MDM), one such method for keeping mass amounts of business data organized and functional towards achieving business goals.
MDM’s guiding principles include the establishment and implementation of authoritative data sources and effective means of delivering data to various business processes, as well as increases to the quality of information used in organizational analytical functions (such as BI).
To that end, attendees of this webinar will learn how to:
- Structure their data management processes around these principles
- Incorporate data quality engineering into the planning of reference and MDM
- Understand why MDM is so critical to their organization’s overall data strategy
How to Make a Data Governance Program that LastsDATAVERSITY
Traditional data governance initiatives fail by focusing too heavily on policies, compliance, and enforcement, which quickly lose business interest and support. This leaves data management and governance leaders having to continually make the case for data governance to secure business adoption. Join Cameron, VP, Product Management, Precisely, as he shares a lean, business-first data governance approach that connects key initiatives to governance capabilities and quickly delivers business value for the long-term. He will give examples of organizations worldwide who have successfully implemented a data governance program by engaging with key stakeholders using innovative techniques such as gamification and data catalog scavenger hunts.
This talk is quick reference of all the different queerability options that MongoDB offers to developers that want to build mobile and geospatial referenced applications. We reviewed the basic functionality but also recent improvements in the query and indexation engine of MongoDB geospatial features
An introductory session to DAX and common analytic patterns that we've built and used in enterprise environments. This session was originally presented at SQL Saturday Silicon Valley 2016.
Modern Cloud Data Warehousing ft. Equinox Fitness Clubs: Optimize Analytics P...Amazon Web Services
Most companies are overrun with data, yet they lack critical insights to make timely and accurate business decisions. They are missing the opportunity to combine large amounts of new, unstructured big data that resides outside their data warehouse with trusted, structured data inside their data warehouse. In this session, we discuss the most common use cases with Amazon Redshift, and we take an in-depth look at how modern data warehousing blends and analyzes all your data to give you deeper insights to run your business. Equinox Fitness Clubs joins us to share their journey from static reports, redundant data, and inefficient data intergration to a modern and flexible data lake and data warehouse architecture that delivers dynamic reports based on trusted data.
Customer-Centric Data Management for Better Customer ExperiencesInformatica
With consumer and business buyer expectations growing exponentially, more businesses are competing on the basis of customer experience. But executing preferred customer experiences requires data about who your customers are today and what will they likely need in the future. Every business can benefit from an AI-powered master data management platform to supply this information to line-of-business owners so they can execute great experiences at scale. This same need is true from an internal business process perspective as well. For example, many businesses require better data management practices to deliver preferred employee experiences. Informatica provides an MDM platform to solve for these examples and more.
This document provides an overview of the conceptual data flow and architecture for a Customer 360 solution. Key components include extracting data from various admin systems, transforming and loading it into a data quality repository, matching and merging records in MDM, propagating updates to downstream systems like Salesforce, and enabling data steward review of matches and merges. The data flows both systematically and in response to user changes in various applications and portals.
From Data Lakes to the Data Fabric: Our Vision for Digital StrategyCambridge Semantics
In this presentation for Strata NY 2018, we share our vision for digital innovation as a shift to something powerful, expedient and future-proof. This is accomplished through the use of a 'Data Fabric'. Utilizing graph technology, this Data Fabric connects enterprise data in an overlay fashion that does not disrupt current investments for unprecedented access to data. This interconnected and reliable data can then be used to automate scalable AI and ML efforts to improve business outcomes.
Using Knowledge Graphs to Predict Customer Needs and Improve QualityNeo4j
The document discusses using knowledge graphs to gain insights from data by revealing patterns and relationships. It describes current approaches like data warehouses and search engines that treat data as isolated and don't capture connections. The key advantage of a knowledge graph is that entities are naturally connected, allowing for multiple access patterns and enabling artificial intelligence. Building a knowledge graph involves extracting structure from various sources and providing tools for analysis and visualization.
Geek Sync | Data Architecture and Data Governance: A Powerful Data Management...IDERA Software
You can watch the replay for this Geek Sync webcast, Data Architecture and Data Governance: A Powerful Data Management Duo, on the IDERA Resource Center, http://ow.ly/95yL50A4rZg.
Batman and Robin. Han Solo and Chewbacca. Mario and Luigi. Just like these famous pairings, so it is for data architecture and data governance — they’re aligned to support each other in a variety of ways. Like data governance, data architecture as a practice can be leveraged to identify and enforce standards within the systems landscape to support business objectives. And data architecture certainly benefits from sound business oversight and stakeholder influences inherent in a successful data governance program.
Join Kelle O’Neal to learn about the critical aspects of aligning data architecture and data governance, with a specific focus on:
-Why aligning data architecture and data governance is important
-The key intersections of people, processes and technology between data architecture and data governance
-How data architecture and data governance work together to enforce standards
-The capabilities that data governance can apply to data architecture without interfering
-How your project and development methodologies can help drive alignment
Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But RDBMS cannot model or store data and its relationships without complexity, which means performance degrades with the increasing number and levels of data relationships and data size. Additionally, new types of data and data relationships require schema redesign that increases time to market.
A native graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL.
This presentation explains what data engineering is and describes the data lifecycles phases briefly. I used this presentation during my work as an on-demand instructor at Nooreed.com
Improving Data Literacy Around Data ArchitectureDATAVERSITY
Data Literacy is an increasing concern, as organizations look to become more data-driven. As the rise of the citizen data scientist and self-service data analytics becomes increasingly common, the need for business users to understand core Data Management fundamentals is more important than ever. At the same time, technical roles need a strong foundation in Data Architecture principles and best practices. Join this webinar to understand the key components of Data Literacy, and practical ways to implement a Data Literacy program in your organization.
This introduction to data governance presentation covers the inter-related DM foundational disciplines (Data Integration / DWH, Business Intelligence and Data Governance). Some of the pitfalls and success factors for data governance.
• IM Foundational Disciplines
• Cross-functional Workflow Exchange
• Key Objectives of the Data Governance Framework
• Components of a Data Governance Framework
• Key Roles in Data Governance
• Data Governance Committee (DGC)
• 4 Data Governance Policy Areas
• 3 Challenges to Implementing Data Governance
• Data Governance Success Factors
The what, why, and how of master data managementMohammad Yousri
This presentation explains what MDM is, why it is important, and how to manage it, while identifying some of the key MDM patterns and best practices that are emerging. This presentation is a high-level treatment of the problem space.
The presentation is summarizing the article of Microsoft in a simple way.
https://msdn.microsoft.com/en-us/library/bb190163.aspx
Dublin Core, the DCMI Abstract Model & DC Application ProfilesEduserv Foundation
Describes the 2007-06-04 version of the DCMI Abstract Model and introduces current work within DCMI on formalising the notion of the "DC application profile" in the context of the DCAM, particularly the draft specification on Description Set Profiles and the "Singapore Framework".
AstraZeneca - Re-imagining the Data Landscape in Compound Synthesis & ManagementNeo4j
1) The document discusses reimagining the data landscape for compound synthesis and management by building a graph database using Neo4j.
2) Key data from various sources such as orders, samples, compounds would be imported as nodes and relationships in the graph.
3) The graph database can then be used to power applications and dashboards, enable complex queries across multiple data sources previously difficult, and allow for graph analysis and machine learning.
This document discusses big data challenges at NASA and describes the Apache OODT framework. It provides examples of large data projects at NASA including climate modeling, remote sensing, and planetary science datasets. The author is a senior computer scientist at NASA and professor who is involved with several Apache projects including OODT. OODT is an open source data platform originally developed at NASA to help build large-scale data systems and is now used by NASA Earth science missions, other government agencies, and universities for their big data needs.
Big data architectures and the data lakeJames Serra
The document provides an overview of big data architectures and the data lake concept. It discusses why organizations are adopting data lakes to handle increasing data volumes and varieties. The key aspects covered include:
- Defining top-down and bottom-up approaches to data management
- Explaining what a data lake is and how Hadoop can function as the data lake
- Describing how a modern data warehouse combines features of a traditional data warehouse and data lake
- Discussing how federated querying allows data to be accessed across multiple sources
- Highlighting benefits of implementing big data solutions in the cloud
- Comparing shared-nothing, massively parallel processing (MPP) architectures to symmetric multi-processing (
The document discusses using MongoDB as a tick store for financial data. It provides an overview of MongoDB and its benefits for handling tick data, including its flexible data model, rich querying capabilities, native aggregation framework, ability to do pre-aggregation for continuous data snapshots, language drivers and Hadoop connector. It also presents a case study of AHL, a quantitative hedge fund, using MongoDB and Python as their market data platform to easily onboard large volumes of financial data in different formats and provide low-latency access for backtesting and research applications.
The document outlines a proposed smart data strategy for the UAE government. It begins by establishing a vision of "data driven Smart Government" and a mission of "Utilising Smart Data for Smart Services". Three strategic priorities are identified: Efficiency, Effectiveness, and Engagement. For each priority, objectives and key performance indicators are defined. Projects are then prioritized based on their impact on the strategic priorities. The strategy is designed to strengthen the government's data value chain and foster outcomes like improved rankings, productivity, and citizen happiness.
Build a simple data lake on AWS using a combination of services, including AWS Glue Data Catalog, AWS Glue Crawlers, AWS Glue Jobs, AWS Glue Studio, Amazon Athena, Amazon Relational Database Service (Amazon RDS), and Amazon S3.
Link to the blog post and video: https://garystafford.medium.com/building-a-simple-data-lake-on-aws-df21ca092e32
An introductory session to DAX and common analytic patterns that we've built and used in enterprise environments. This session was originally presented at SQL Saturday Silicon Valley 2016.
Modern Cloud Data Warehousing ft. Equinox Fitness Clubs: Optimize Analytics P...Amazon Web Services
Most companies are overrun with data, yet they lack critical insights to make timely and accurate business decisions. They are missing the opportunity to combine large amounts of new, unstructured big data that resides outside their data warehouse with trusted, structured data inside their data warehouse. In this session, we discuss the most common use cases with Amazon Redshift, and we take an in-depth look at how modern data warehousing blends and analyzes all your data to give you deeper insights to run your business. Equinox Fitness Clubs joins us to share their journey from static reports, redundant data, and inefficient data intergration to a modern and flexible data lake and data warehouse architecture that delivers dynamic reports based on trusted data.
Customer-Centric Data Management for Better Customer ExperiencesInformatica
With consumer and business buyer expectations growing exponentially, more businesses are competing on the basis of customer experience. But executing preferred customer experiences requires data about who your customers are today and what will they likely need in the future. Every business can benefit from an AI-powered master data management platform to supply this information to line-of-business owners so they can execute great experiences at scale. This same need is true from an internal business process perspective as well. For example, many businesses require better data management practices to deliver preferred employee experiences. Informatica provides an MDM platform to solve for these examples and more.
This document provides an overview of the conceptual data flow and architecture for a Customer 360 solution. Key components include extracting data from various admin systems, transforming and loading it into a data quality repository, matching and merging records in MDM, propagating updates to downstream systems like Salesforce, and enabling data steward review of matches and merges. The data flows both systematically and in response to user changes in various applications and portals.
From Data Lakes to the Data Fabric: Our Vision for Digital StrategyCambridge Semantics
In this presentation for Strata NY 2018, we share our vision for digital innovation as a shift to something powerful, expedient and future-proof. This is accomplished through the use of a 'Data Fabric'. Utilizing graph technology, this Data Fabric connects enterprise data in an overlay fashion that does not disrupt current investments for unprecedented access to data. This interconnected and reliable data can then be used to automate scalable AI and ML efforts to improve business outcomes.
Using Knowledge Graphs to Predict Customer Needs and Improve QualityNeo4j
The document discusses using knowledge graphs to gain insights from data by revealing patterns and relationships. It describes current approaches like data warehouses and search engines that treat data as isolated and don't capture connections. The key advantage of a knowledge graph is that entities are naturally connected, allowing for multiple access patterns and enabling artificial intelligence. Building a knowledge graph involves extracting structure from various sources and providing tools for analysis and visualization.
Geek Sync | Data Architecture and Data Governance: A Powerful Data Management...IDERA Software
You can watch the replay for this Geek Sync webcast, Data Architecture and Data Governance: A Powerful Data Management Duo, on the IDERA Resource Center, http://ow.ly/95yL50A4rZg.
Batman and Robin. Han Solo and Chewbacca. Mario and Luigi. Just like these famous pairings, so it is for data architecture and data governance — they’re aligned to support each other in a variety of ways. Like data governance, data architecture as a practice can be leveraged to identify and enforce standards within the systems landscape to support business objectives. And data architecture certainly benefits from sound business oversight and stakeholder influences inherent in a successful data governance program.
Join Kelle O’Neal to learn about the critical aspects of aligning data architecture and data governance, with a specific focus on:
-Why aligning data architecture and data governance is important
-The key intersections of people, processes and technology between data architecture and data governance
-How data architecture and data governance work together to enforce standards
-The capabilities that data governance can apply to data architecture without interfering
-How your project and development methodologies can help drive alignment
Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But RDBMS cannot model or store data and its relationships without complexity, which means performance degrades with the increasing number and levels of data relationships and data size. Additionally, new types of data and data relationships require schema redesign that increases time to market.
A native graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL.
This presentation explains what data engineering is and describes the data lifecycles phases briefly. I used this presentation during my work as an on-demand instructor at Nooreed.com
Improving Data Literacy Around Data ArchitectureDATAVERSITY
Data Literacy is an increasing concern, as organizations look to become more data-driven. As the rise of the citizen data scientist and self-service data analytics becomes increasingly common, the need for business users to understand core Data Management fundamentals is more important than ever. At the same time, technical roles need a strong foundation in Data Architecture principles and best practices. Join this webinar to understand the key components of Data Literacy, and practical ways to implement a Data Literacy program in your organization.
This introduction to data governance presentation covers the inter-related DM foundational disciplines (Data Integration / DWH, Business Intelligence and Data Governance). Some of the pitfalls and success factors for data governance.
• IM Foundational Disciplines
• Cross-functional Workflow Exchange
• Key Objectives of the Data Governance Framework
• Components of a Data Governance Framework
• Key Roles in Data Governance
• Data Governance Committee (DGC)
• 4 Data Governance Policy Areas
• 3 Challenges to Implementing Data Governance
• Data Governance Success Factors
The what, why, and how of master data managementMohammad Yousri
This presentation explains what MDM is, why it is important, and how to manage it, while identifying some of the key MDM patterns and best practices that are emerging. This presentation is a high-level treatment of the problem space.
The presentation is summarizing the article of Microsoft in a simple way.
https://msdn.microsoft.com/en-us/library/bb190163.aspx
Dublin Core, the DCMI Abstract Model & DC Application ProfilesEduserv Foundation
Describes the 2007-06-04 version of the DCMI Abstract Model and introduces current work within DCMI on formalising the notion of the "DC application profile" in the context of the DCAM, particularly the draft specification on Description Set Profiles and the "Singapore Framework".
AstraZeneca - Re-imagining the Data Landscape in Compound Synthesis & ManagementNeo4j
1) The document discusses reimagining the data landscape for compound synthesis and management by building a graph database using Neo4j.
2) Key data from various sources such as orders, samples, compounds would be imported as nodes and relationships in the graph.
3) The graph database can then be used to power applications and dashboards, enable complex queries across multiple data sources previously difficult, and allow for graph analysis and machine learning.
This document discusses big data challenges at NASA and describes the Apache OODT framework. It provides examples of large data projects at NASA including climate modeling, remote sensing, and planetary science datasets. The author is a senior computer scientist at NASA and professor who is involved with several Apache projects including OODT. OODT is an open source data platform originally developed at NASA to help build large-scale data systems and is now used by NASA Earth science missions, other government agencies, and universities for their big data needs.
Big data architectures and the data lakeJames Serra
The document provides an overview of big data architectures and the data lake concept. It discusses why organizations are adopting data lakes to handle increasing data volumes and varieties. The key aspects covered include:
- Defining top-down and bottom-up approaches to data management
- Explaining what a data lake is and how Hadoop can function as the data lake
- Describing how a modern data warehouse combines features of a traditional data warehouse and data lake
- Discussing how federated querying allows data to be accessed across multiple sources
- Highlighting benefits of implementing big data solutions in the cloud
- Comparing shared-nothing, massively parallel processing (MPP) architectures to symmetric multi-processing (
The document discusses using MongoDB as a tick store for financial data. It provides an overview of MongoDB and its benefits for handling tick data, including its flexible data model, rich querying capabilities, native aggregation framework, ability to do pre-aggregation for continuous data snapshots, language drivers and Hadoop connector. It also presents a case study of AHL, a quantitative hedge fund, using MongoDB and Python as their market data platform to easily onboard large volumes of financial data in different formats and provide low-latency access for backtesting and research applications.
The document outlines a proposed smart data strategy for the UAE government. It begins by establishing a vision of "data driven Smart Government" and a mission of "Utilising Smart Data for Smart Services". Three strategic priorities are identified: Efficiency, Effectiveness, and Engagement. For each priority, objectives and key performance indicators are defined. Projects are then prioritized based on their impact on the strategic priorities. The strategy is designed to strengthen the government's data value chain and foster outcomes like improved rankings, productivity, and citizen happiness.
Build a simple data lake on AWS using a combination of services, including AWS Glue Data Catalog, AWS Glue Crawlers, AWS Glue Jobs, AWS Glue Studio, Amazon Athena, Amazon Relational Database Service (Amazon RDS), and Amazon S3.
Link to the blog post and video: https://garystafford.medium.com/building-a-simple-data-lake-on-aws-df21ca092e32
مختصر من كتاب د. رجاء محمد أبو علام
بعنوان: مناهج البحث في العلوم النفسية والتربوية
الفصل الأول: اسس البحث العلمي
الفصل الثاني: المفاهيم والمتغيرات
الفصل الثالث: اختيار المشكلة وإعداد خطة البحث
الفصل الثاني عشر: البحوث الكيفية
يساهم البحث العلمي بشكل كبير في حل المشكلات التي تعاني منها المجتمعات بشتى أنواعها الاقتصادية والاجتماعية وغيرها وذلك بما يوفره لها من حقائق وابتكارات، وبما يساعد في تحسين نوعية الحياة، حتى أصبح البحث العلمي له تأثير أساسي في رُقِيّ المجتمعات ونهضتها، من خلال الإبداع ووضع الحلول للمشكلات والآفات الاجتماعية والصحية والبيئية, وتحسين الموارد الطبيعية المتاحة والنهوض بالقدرات العلمية والبشرية والمادية وتحسين كفاءة استخدامها.
تتعاظم أهمية المعلومات يومًا بعد يوم، لذا؛ فإن توفير مصادر المعلومات الحديثة يُعد أساسًا للبحث العلمي الحديث، ومهما حاولت المكتبات من تحديث مقتنياتها الرقمية لا يمكنها الإحاطة بالإنتاج الفكري الضخم في زمن ثورة المعلومات والاتصالات الذي يتزايد فيه الإنتاج المعرفي تزايدًا مضطربًا، غير أنه وإلى الآن لا يزال البحث عن المعلومة، لاسيما
من خلال قواعد البيانات الإلكترونية يحتاج إلى دقة في البحث.
وتعد الشبكة العنكبوتية أكبر مصدر للمعلومات في العصر الحالي. ومن ثم؛ فالباحث اليوم في مواجهة مجموعة من التحديات تتعلق في مجملها بعملية البحث عن المعلومات، وتساهم في توجيه اتجاهاته في رحلة التنقيب عن المعلومة من مختلف مصادرها، خاصةً منها مصادر المعلومات الإلكترونية التي فرضت نفسها على المكتبات ومراكز المعلومات في شكلٍ مادي جديد، يرتدي ثوبًا تكنولوجيًا يناسب تطورات التكنولوجية للمعلومات، فأصبحت مصادر المعلومات الإلكترونية تشكل جزءًا مهمًا في كيان مقتنيات مراكز المعلومات، من أوعية المعلومات بمختلف أنواعها.
ولقد أدى تطور ونمو الشبكة العنكبوتية (WWW or The Web) إلى حدوث تغيير كبير في أساليب البحث عن المعلومات وسبل الإفادة من المصادر المتاحة من خلال شبكة الإنترنت ويرجع ذلك بشكلٍ كبير إلى النمو السريع والهائل في عدد وأشكال وأنواع مصادر المعلومات المتاحة من خلال الشبكة العنكبوتية، بالإضافة إلى تنوع تلك المصادر، وسهولة الوصول إليها، هذا إلى جانب طبيعة تلك المصادر والتكنولوجيات المستخدمة في إتاحتها.
لذا على الباحث الذي يريد الوصول للمعلومات التي يحتاج إليها في بحثه؛ أن يمتلك بعض مهارات البحث في مصادر المعلومات الإلكترونية.
1. الطبي باألدب نبحث كيف
How to search medical
literature?
د.حمزة عمار
Ammar Hamzeh
PYG 3 Almoasat University Hospital
Ophtalmology Department
03/08/2017