Database systems that were based on the object data model were known originally as object-oriented databases (OODBs).These are mainly used for complex objects
Advance Database Management Systems -Object Oriented Principles In DatabaseSonali Parab
An OODBMS is the result of combining object oriented programming principles with database management principles. Object oriented programming concepts such as encapsulation, polymorphism and inheritance are enforced as well as database management concepts such as the ACID properties (Atomicity, Consistency, Isolation and Durability) which lead to system integrity, support for an ad hoc query language and secondary storage management systems which allow for managing very large amounts of data. The Object Oriented Database Manifesto specifically lists the following features as mandatory for a system to support before it can be called an OODBMS; Complex objects, Object identity, Encapsulation , Types and Classes , Class or Type Hierarchies, Overriding, overloading and late binding, Computational completeness , Extensibility,Persistence , Secondary storage management, Concurrency, Recovery and an Ad Hoc Query Facility.
Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations.
PHP provides access to a great number of different database systems, many of which are relational in nature and can be interrogated using Structured Query Language (SQL).
Database systems that were based on the object data model were known originally as object-oriented databases (OODBs).These are mainly used for complex objects
Advance Database Management Systems -Object Oriented Principles In DatabaseSonali Parab
An OODBMS is the result of combining object oriented programming principles with database management principles. Object oriented programming concepts such as encapsulation, polymorphism and inheritance are enforced as well as database management concepts such as the ACID properties (Atomicity, Consistency, Isolation and Durability) which lead to system integrity, support for an ad hoc query language and secondary storage management systems which allow for managing very large amounts of data. The Object Oriented Database Manifesto specifically lists the following features as mandatory for a system to support before it can be called an OODBMS; Complex objects, Object identity, Encapsulation , Types and Classes , Class or Type Hierarchies, Overriding, overloading and late binding, Computational completeness , Extensibility,Persistence , Secondary storage management, Concurrency, Recovery and an Ad Hoc Query Facility.
Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations.
PHP provides access to a great number of different database systems, many of which are relational in nature and can be interrogated using Structured Query Language (SQL).
Type constructor concepts where the examples are clearly described and points are easy to understand. Every point is clearly connected once we go through ppts.
Type constructor concepts where the examples are clearly described and points are easy to understand. Every point is clearly connected once we go through ppts.
Decoding the Role of a Data Engineer.pdfDatavalley.ai
A data engineer is a crucial player in the field of big data. They are responsible for designing, building, and maintaining the systems that manage and process vast amounts of data. This requires a unique combination of technical skills, including programming, database management, and data warehousing. The goal of a data engineer is to turn raw data into valuable insights and information that can be used to support decision-making and drive business outcomes.
Power Management in Micro grid Using Hybrid Energy Storage Systemijcnes
This paper proposed for power management in micro grid using a hybrid distributed generator based on photovoltaic, wind-driven PMDC and energy storage system is proposed. In this generator, the sources are together connected to the grid with the help of interleaved boost converter followed by an inverter. Thus, compared to earlier schemes, the proposed scheme has fewer power converters. FUZZY based MPPT controllers are also proposed for the new hybrid scheme to separately trigger the interleaved DC-DC converter and the inverter for tracking the maximum power from both the sources. The integrated operations of both the proposed controllers for different conditions are demonstrated through simulation with the help of MATLAB software
UNIT - 1 Part 2: Data Warehousing and Data MiningNandakumar P
DBMS Schemas for Decision Support , Star Schema, Snowflake Schema, Fact Constellation Schema, Schema Definition, Data extraction, clean up and transformation tools.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
2. Volume 2, Issue 6, November-December-2017 | www.ijsrcseit.com | UGC Approved Journal [ Journal No : 64718 ] 892
Here are some most popular Database Systems:
1. Oracle RDBMS
https://www.oracle.com/database/index.html
2. Microsoft SQL Server
https://www.microsoft.com/en-us/sql-server/
3. IBM DB2
https://www.ibm.com/analytics/us/en/technology/db2
4. Teradata
http://www.teradata.com/
5. MySQL
https://www.mysql.com/
II. QUERY PROCESSING AND OPTIMIZATION
Query processing is a set of activities involving in
getting the result of a query expressed in high-level
language. The basic steps are:
1. Parsing and translation: Translate the query into its
internal form. This is then translated into relational
algebra. Parser checks syntax, verifies relation.
2. Optimization: SQL is a very high level language:
The users specify what to search for- not how the
search is actually done The algorithms are chosen
automatically by the DBMS. For a given SQL query
there may be many possible execution plans. Amongst
all equivalent plans choose the one with lowest cost.
Cost is estimated using statistical information from the
database catalog.
3. Evaluation: The query evaluation engine takes a
query evaluation plan, executes that plan and returns
the answer to that query.
2.1 EXAMPLE
SELECT Ename FROM Employee WHERE
Salary > 5000;
Translated into Relational Algebra Expression
σ Salary > 5000 (π Ename (Employee))
OR
π Ename (σ Salary > 5000 (Employee))
Figure . Query Execution Plan
III.DATA WAREHOUSING
A data warehouse is a relational database that is
designed for query and analysis rather than for
transaction processing. It usually contains historical
data derived from transaction data, but it can include
data from other sources.
There are two approaches to data warehousing, top
down and bottom up. The top down approach spins
off data marts for specific groups of users after the
complete data warehouse has been created. The
bottom up approach builds the data marts first and
then combines them into a single, all-encompassing
data warehouse.These technologies help executives to
use the warehouse quickly and effectively. They can
gather data, analyze it, and take decisions based on
the information present in the warehouse. The
information gathered in a warehouse can be used in
any of the following domains –
1. Tuning Production Strategies: The product
strategies can be well tuned by repositioning the
products and managing the product portfolios by
comparing the sales quarterly or yearly.
2. Customer Analysis: Customer analysis is done by
analyzing the customer's buying preferences, buying
time, budget cycles, etc.
3. Operations Analysis: Data warehousing also helps
in customer relationship management, and making
3. Volume 2, Issue 6, November-December-2017 | www.ijsrcseit.com | UGC Approved Journal [ Journal No : 64718 ] 893
environmental corrections. The information also allows
us to analyze business operations.
3.1 Application of Data Warehouse
1. Information Processing: Quering, basic statistical
analysis and reporting using crosstabs, table, cahrts or
graphs. A current trend in data warehouse
information processing is to construct low cost web
based accessing tools that are then integrated with
web browsers.
2. Analytical Processing: A data warehouse is often
used as the basis for a decision support system. Data
can be analyzed by means of basic OLAP operations,
including slice and dice, drill-down. It generally
operates on historical data in both summarized and
detailed forms.
3. Data Mining: It is also called knowledge
discovery. There are two main kinds of models is data
mining. One is predictive models and another one is
descriptive models.
3.2 Online Analytical Processing
OLAP is computer processing that enables a user to
easily and selectively extract and view data from
different point of view. OLAP is best known
technology that allows a user to slice and dice data or
drill-down into data. OLAP is technology that uses a
multidimensional view of aggregate data to offer fast
access to strategic for the purpose of advanced
analysis, deeper understanding.
3.3 Role of the OLAP Server in the Data
Warehouse
OLAP servers deliver warehouse applications such as
performance reporting, sales forecasting product line
and customer profitability, sales analysis, marketing
analysis, what-if analysis and manufacturing mix
analysis — applications that require historical,
projected and derived data. With OLAP servers’ robust
calculation engines, historical data is made vastly more
useful by transforming it into derived and projected
data. Users gain broader insights by combining
standard access tools with a powerful analytic engine.
3.4 Data Marts
A data mart is a simple form of data warehouse that is
focused on a single subject like sales, Finance,
Marketing. Data marts are often built and controlled by
a single department with in an organization. Data marts
usually takes data from only a few sources because of
single subject. The sources could be internal
operational systems a central data warehouse or
external data. Data marts improve end-user response
time ny allowing user to have access to the specific
type of data.
3.5 Difference between Data mining & OLAP
Data Mining OLAP
Used to predict the
future.
Prepare data, launch
mining tool and sit back
It has large number of
dimensions.
It deals with summary of
data.
Used to analyze the past.
Multidimensional, drill-
down and slice and dice.
It has limited number of
dimensions.
It deals with detailed
transaction level data.
IV. CONCLUSIONS
The advanced database system is nothing but are some new
features in the database system. As we discussed about the
query processing and optimization and data ware house,
these are the new feature of database system and are in trends.
One of the most critical functional requirements of a DBMS
is its ability to process queries in a timely manner, query
process and optimization are used to process query in timely.
And we also discuss the three phases of its through which a
query is process and optimized where data ware house is
subject oriented, integrated, time –variant and non-volatile
collection of data in support of management’s decision
making process.
4. Volume 2, Issue 6, November-December-2017 | www.ijsrcseit.com | UGC Approved Journal [ Journal No : 64718 ] 894
V. REFERENCES
[1]. J. C., Freytag D, and Maier G (eds.) Vossen
Query Processing for Advanced Database
Systems.Morgan Kaufmann, 1994
[2]. "Data, data everywhere".The Economist.25
February 2010.Retrieved 9 December 2012.
[3]. "Community cleverness required".Nature 455
(7209): 1.
[4]. 4 September 2008.doi:10.1038/455001a.
[5]. "Sandia sees data management challenges
spiral".HPC Projects.4 August 2009.