Data cube computation involves precomputing aggregations to enable fast query performance. There are different materialization strategies like full cubes, iceberg cubes, and shell cubes. Full cubes precompute all aggregations but require significant storage, while iceberg cubes only store aggregations that meet a threshold. Computation strategies include sorting and grouping to aggregate similar values, caching intermediate results, and aggregating from smallest child cuboids first. The Apriori pruning method can efficiently compute iceberg cubes by avoiding computing descendants of cells that do not meet the minimum support threshold.
This Presentation is about NoSQL which means Not Only SQL. This presentation covers the aspects of using NoSQL for Big Data and the differences from RDBMS.
Density-Based Clustering refers to one of the most popular unsupervised learning methodologies used in model building and machine learning algorithms .
This Presentation is about NoSQL which means Not Only SQL. This presentation covers the aspects of using NoSQL for Big Data and the differences from RDBMS.
Density-Based Clustering refers to one of the most popular unsupervised learning methodologies used in model building and machine learning algorithms .
k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
Classification of common clustering algorithm and techniques, e.g., hierarchical clustering, distance measures, K-means, Squared error, SOFM, Clustering large databases.
This presentation discusses the follow topics
What is Hadoop?
Need for Hadoop
History of Hadoop
Hadoop Overview
Advantages and Disadvantages of Hadoop
Hadoop Distributed File System
Comparing: RDBMS vs. Hadoop
Advantages and Disadvantages of HDFS
Hadoop frameworks
Modules of Hadoop frameworks
Features of 'Hadoop‘
Hadoop Analytics Tools
k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
Classification of common clustering algorithm and techniques, e.g., hierarchical clustering, distance measures, K-means, Squared error, SOFM, Clustering large databases.
This presentation discusses the follow topics
What is Hadoop?
Need for Hadoop
History of Hadoop
Hadoop Overview
Advantages and Disadvantages of Hadoop
Hadoop Distributed File System
Comparing: RDBMS vs. Hadoop
Advantages and Disadvantages of HDFS
Hadoop frameworks
Modules of Hadoop frameworks
Features of 'Hadoop‘
Hadoop Analytics Tools
ODAM is an Experiment Data Table Management System (EDTMS) that gives you an open access to your data and make them ready to be mined - A data explorer as bonus
This slide deck gives a general overview of Data Visualization, with inspiring examples, the strength and weaknesses of the human visual system, a few technical frameworks that may be used for creating your own visualizations and some design concepts from the data visualization field.
In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output, making it a non-probabilistic binary linear classifier.
2. "Design Patterns: Elements of Reusable Object-Oriented Software" by Erich Gamma, Richard Helm, Ralph Johnson, and John Vlissides:
Understanding design patterns is crucial for building scalable and maintainable software. This book introduces 23 classic design patterns that solve recurring design problems. It's an excellent resource for software architects and developers looking to enhance their object-oriented design skills.
3. "The Pragmatic Programmer: Your Journey to Mastery" by Dave Thomas and Andy Hunt:
This book provides pragmatic advice for programmers at all levels. It covers a wide range of topics, including code organization, debugging, testing, and automation. The authors share valuable insights and best practices that can significantly impact your efficiency and effectiveness as a developer.
4. "Introduction to Algorithms" by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein:
For a deep dive into algorithms and data structures, this book is a comprehensive resource. It's widely used in computer science courses and covers essential algorithms, their analysis, and their application in solving real-world problems. The book's clarity and rigor make it suitable for both beginners and experienced developers.
5. "Code Complete: A Practical Handbook of Software Construction" by Steve McConnell:
"Code Complete" is a comprehensive guide to software construction, covering a wide array of topics related to writing high-quality code. It's suitable for developers at various experience levels and provides practical advice, examples, and case studies to help you improve your coding skills.
6. "The Mythical Man-Month: Essays on Software Engineering" by Frederick P. Brooks Jr.:
This classic book offers valuable insights into software engineering and project management. Frederick Brooks discusses the challenges of software development, including the famous concept of "The Mythical Man-Month," which explores the complexities of managing large software projects. It remains relevant and thought-provoking decades after its initial publication.
7. "Refactoring: Improving the Design of Existing Code" by Martin Fowler:
In the real world, developers often work with existing codebases. This book provides practical strategies for improving the design of existing code through refactoring. Martin Fowler introduces numerous refactorings and explains the principles behind them, making it an invaluable resource for enhancing code maintainability.
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
Data Cube in the contest of data warehousing and OLAP is core operator. The data cube was proposed to pre compute the aggregation for all possible combination of dimension to answer analytical queries efficiently. It is a generalization of the group-by operator over all possible combination of dimension with various granularity aggregates. Efficient and Compressed computation of data cube are Fundamental issues. Data Warehouses tend to be order of magnitude larger than operational database in size. So by studying and comparing all these methods we can find out that which methods are applicable and suitable for which kind of Data. Here I have compared range cube with Bit cube .Each problem is of particular interest in the field of data analysis and query answering. So by comparing various methods, we can verify the trades of between time and space as per the requirements and type of problem. Different Types of measures are available for aggregating the datasets. Such as Major-Minor, Count, Sum etc. So comparative study shows that we can find which measures can be compute data cube incrementally.
Students will be able to learn the concepts of advanced trees like Splay Tree, B Tree, Red Black Tree, Priority Queue or Heap. In Heap Data Structure the following methods are covered: Binary Heap, d-heap, Leftist Heap and Skew Heap.
Analysis of Allocation Algorithms in Memory Managementijtsrd
Memory management is the process of controlling and coordinating computer memory, assigning portions called blocks to various running programs to optimize overall system performance and also known as memory allocation. Placement algorithms are implemented to determine the slot that can be allocated process amongst the available ones in the partitioned memory. Memory slots allocated to processes might be too big when using the existing placement algorithms hence losing a lot of space due to internal fragmentation. In dynamic partitioning, external fragmentation occurs when there is a sufficient amount of space in the memory to satisfy the memory request of a process but the process's memory request cannot be satisfied as the memory available is in a non contiguous manner. This paper describes how to resolve external fragmentation using three allocation algorithms. These algorithms are First fit, Best fit and Worst fit. We will present the implementation of three algorithms and compare their performance on generated virtual trace. Lae Wah Htun | Moh Moh Myint Kay | Aye Aye Cho "Analysis of Allocation Algorithms in Memory Management" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26731.pdfPaper URL: https://www.ijtsrd.com/computer-science/operating-system/26731/analysis-of-allocation-algorithms-in-memory-management/lae-wah-htun
Accumulo Summit 2016: GeoMesa: Using Accumulo for Optimized Spatio-Temporal P...Accumulo Summit
LocationTech GeoMesa is a project that builds on open-source, distributed databases like Accumulo, HBase, and Cassandra to scale up indexing, querying, and analyzing billions of spatio-temporal data points. GeoMesa uses space-filling curves to index multi-dimensional data in Accumulo, and we'll discuss recent improvements for non-point geometries. Over the two and a half years GeoMesa has been an open-source project, GeoMesa's Accumulo schemas have evolved and our team has had a chance to work through creating and optimizing custom Accumulo iterators. These custom iterators allow for better query performance and interesting aggregations. GeoMesa provides support for distributed processing in Spark via MapReduce input and output formats that extend their Accumulo counterparts. We will discuss the performance benefit gained by reducing the number of default map/Spark tasks created for complex query patterns. The talk will conclude with updates about GeoMesa's integration with Jupyter notebook and improvements to GeoMesa's Spark integration.
– Speaker –
Dr. James Hughes
Mathematician, Commonwealth Computer Research, Inc (CCRi)
Dr. James Hughes is a mathematician at Commonwealth Computer Research, Inc. in Charlottesville, Virginia. He is a core committer for GeoMesa which leverages Accumulo and other distributed database systems to provide distributed computation and query engines. He is a LocationTech committer for GeoMesa, SFCurve, and GeoBench. He serves on the LocationTech Project Management Committee and Steering Committee. Through work with LocationTech and OSGeo projects like GeoTools and GeoServer, he works to build end-to-end solutions for big spatio-temporal problems. He holds a PhD in algebraic topology from the University of Virginia.
— More Information —
For more information see http://www.accumulosummit.com/
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.
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
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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.
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
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
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.
2. Why data cube computation is needed?
• To retrieve the information from the data cube in the most efficient way
possible.
• Queries run on the cube will be fast.
4. The Full cube
• The multi way array aggregation method computes full data cube by using
a multidimensional array as its basic data structure
1. Partition array into the chunks
2. Compute aggregate by visiting (i.e. accessing the values at) cube cells
Advantage
the queries run on the cube will be very fast.
Disadvantage
pre-computed cube requires a lot of memory.
5. An Iceberg-Cube
• contains only those cells of the data cube that meet an aggregate
condition.
• It is called an Iceberg-Cube because it contains only some of the cells of
the full cube, like the tip of an iceberg.
• The purpose of the Iceberg-Cube is to identify and compute only those
values that will most likely be required for decision support queries.
• The aggregate condition specifies which cube values are more
meaningful and should therefore be stored.
• This is one solution to the problem of computing versus storing data
cubes.
Advantage:
pre-compute only those cells in the cube which will most likely be used for
decision support queries.
6. A Closed Cube
A closed cube is a data cube consisting of only closed cells
Shell Cube
we can choose to precompute only portions or fragments of the
cube shell, based on cuboids of interest.
7. General strategies for data cube
computation
1. Sorting hashing and grouping
2. Simultaneous aggregation and caching intermediate
results
3. Aggregation from smallest child when there exist
multiple child cuboid
4. The Apriori pruning method can be explored to compute
iceberg cube efficiently
8. 1. Sorting, hashing and grouping.
These operations facilitate aggregation, i.e. computation of the cells that share
the same set of dimension values.
These techniques can also perform:
o shared-sorts: sharing sorting costs across multiple cuboids
o share-partitions: sharing partitioning costs across multiple cuboids
Example:
To compute total sales by branch, day, and item, it is more efficient to sort tuples
or cells by branch, and then by day, and then group them according to the item
name.
9. 2. Simultaneous aggregation and caching intermediate
results.
Reduce expensive disk I/O operations by computing higher-level
group-bys from computed lower-level group-bys.
These techniques can also perform:
o Amortized-scans: computing as many cuboids as possible at the
same time to reduce disk reads
Example:
To compute sales by branch, we can use the intermediate results derived from the
computation of a lower-level cuboid, such as sales by branch and day.
10. 3. Aggregation from the smallest child.
If a parent ‘cuboid’ has more than one child, it is efficient to compute it
from the smallest previously computed child ‘cuboid’.
Example:
To compute a sales cuboid, Cbranch, when there exist two previously computed
cuboids, C{branch,year} and C{branch,tem}, it is obviously more efficient to compute
Cbranch from the former than from the latter if there are many more distinct items
than distinct years.
11. 4. The Apriori pruning method can be explored to compute
iceberg cube efficiently
The Apriori property, in the context of data cubes, states as follow:
If given cell does not satisfy minimum support, then no descendant (i.e. more
specialized or detailed version ) of the cell will satisfy minimum support either.
This property can be used to substantially reduce the computation of iceberg
cubes.