Data generalization abstracts data from a low conceptual level to higher levels. Different cube materialization methods include full, iceberg, closed, and shell cubes. The Apriori property states that if a cell does not meet minimum support, neither will its descendants, and can reduce iceberg cube computation. BUC constructs cubes from the apex downward, allowing pruning using Apriori and sharing partitioning costs. Discovery-driven exploration assists users in intelligently exploring aggregated data cubes. Constrained gradient analysis incorporates significance, probe, and gradient constraints to reduce the search space. Attribute-oriented induction generalizes based on attribute values to characterize data. Attribute generalization is controlled through thresholds and relations.
Distribution transparency and Distributed transactionshraddha mane
Distribution transparency and Distributed transaction.deadlock detection .Distributed transaction and their types and threads and processes and their difference.
Distribution transparency and Distributed transactionshraddha mane
Distribution transparency and Distributed transaction.deadlock detection .Distributed transaction and their types and threads and processes and their difference.
Query Processing : Query Processing Problem, Layers of Query Processing Query Processing in Centralized Systems – Parsing & Translation, Optimization, Code generation, Example Query Processing in Distributed Systems – Mapping global query to local, Optimization,
This presentation discusses the following topics:
Introduction to Query Processing
Need for Query processing
Architecture of Query Processing
Query Processing Steps
Phases in a typical query processing
Represented in relational structures
Translating SQL Queries into Relational Algebra
Query Optimization
Importance of Query Optimization
Actions of Query Optimization
Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.
Query Processing : Query Processing Problem, Layers of Query Processing Query Processing in Centralized Systems – Parsing & Translation, Optimization, Code generation, Example Query Processing in Distributed Systems – Mapping global query to local, Optimization,
This presentation discusses the following topics:
Introduction to Query Processing
Need for Query processing
Architecture of Query Processing
Query Processing Steps
Phases in a typical query processing
Represented in relational structures
Translating SQL Queries into Relational Algebra
Query Optimization
Importance of Query Optimization
Actions of Query Optimization
Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.
Databases are the prime technique used to develop any information system used in modern business. There are many different types of database available used for different purposes.
Substitution of single letters separately—simple substitution—can be demonstrated by writing out the alphabet in some order to represent the substitution. This is termed a substitution alphabet. The cipher alphabet may be shifted or reversed (creating the Caesar and Atbash ciphers, respectively)
The role of materialized views is becoming vital in today’s distributed Data warehouses. Materialization is
where parts of the data cube are pre-computed. Some of the real time distributed architectures are
maintaining materialization transparencies in the sense the users are not known with the materialization at
a node. Usually what all followed by them is a cache maintenance mechanism where the query results are
cached. When a query requesting materialization arrives at a distributed node it checks in its cache and if
the materialization is available answers the query. What if materialization is not available- the node
communicates the query in the network until a node answering the requested materialization is available.
This type of network communication increases the number of query forwarding’s between nodes. The aim
of this paper is to reduce the multiple redirects. In this paper we propose a new CB-pattern tree indexing to
identify the exact distributed node where the needed materialization is available.
Hortizontal Aggregation in SQL for Data Mining Analysis to Prepare Data SetsIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
Recent Trends in Incremental Clustering: A ReviewIOSRjournaljce
This paper presents a review on recent trends in incremental clustering algorithms. It tries to focus on both clustering based on similarity measure and clustering not based on similarity measure. In this context, the paper is devoted to various typical incremental clustering algorithms. Mainly optimization, genetic and fuzzy approaches of these algorithms is covered in the paper. The paper is original with respect to one aspect that is, it provides a complete overview that is fully devoted to evolutionary algorithms for incremental clustering. A number of references are provided that describe applications of evolutionary algorithms for incremental clustering in different domains, such as human activity detection, online fault detection, information security, track an object consistently throughout the network solving boundary problem etc.
It is a data mining technique used to place the data elements into their related groups. Clustering is the process of partitioning the data (or objects) into the same class, The data in one class is more similar to each other than to those in other cluster.
UNIT - 4: Data Warehousing and Data MiningNandakumar P
UNIT-IV
Cluster Analysis: Types of Data in Cluster Analysis – A Categorization of Major Clustering Methods – Partitioning Methods – Hierarchical methods – Density, Based Methods – Grid, Based Methods – Model, Based Clustering Methods – Clustering High, Dimensional Data – Constraint, Based Cluster Analysis – Outlier Analysis.
CONTENT BASED VIDEO CATEGORIZATION USING RELATIONAL CLUSTERING WITH LOCAL SCA...ijcsit
This paper introduces a novel approach for efficient video categorization. It relies on two main
components. The first one is a new relational clustering technique that identifies video key frames by
learning cluster dependent Gaussian kernels. The proposed algorithm, called clustering and Local Scale
Learning algorithm (LSL) learns the underlying cluster dependent dissimilarity measure while finding
compact clusters in the given dataset. The learned measure is a Gaussian dissimilarity function defined
with respect to each cluster. We minimize one objective function to optimize the optimal partition and the
cluster dependent parameter. This optimization is done iteratively by dynamically updating the partition
and the local measure. The kernel learning task exploits the unlabeled data and reciprocally, the
categorization task takes advantages of the local learned kernel. The second component of the proposed
video categorization system consists in discovering the video categories in an unsupervised manner using
the proposed LSL. We illustrate the clustering performance of LSL on synthetic 2D datasets and on high
dimensional real data. Also, we assess the proposed video categorization system using a real video
collection and LSL algorithm.
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.
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
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
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
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
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
2. What is Data generalization? Data generalization is a process that abstracts a large set of task-relevant data in a database from a relatively low conceptual level to higher conceptual levels.
3. What are efficient methods for Data Cube Computation? Different Data cube materialization include Full Cube Iceberg Cube Closed Cube Shell Cube
4. General Strategies for Cube Computation 1: Sorting, hashing, and grouping.2: Simultaneous aggregation and caching intermediate results.3: Aggregation from the smallest child, when there exist multiple child cuboids.4: The Apriori pruning method can be explored to compute iceberg cubes efficiently
5. What is Apriori Property? The Apriori property, in the context of data cubes, states as follows: If a 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.
6. The Full Cube The Multi way Array Aggregation (or simply Multi Way) method computes a full data cube by using a multidimensional array as its basic data structure Partition the array into chunks Compute aggregates by visiting (i.e., accessing the values at) cube cells
7. BUC: Computing Iceberg Cubes from the Apex Cuboid’s Downward BUC stands for “Bottom-Up Construction" , BUC is an algorithm for the computation of sparse and iceberg cubes. Unlike Multi Way, BUC constructs the cube from the apex cuboids' toward the base cuboids'. This allows BUC to share data partitioning costs. This order of processing also allows BUC to prune during construction, using the Apriori property. (for algorithm refer wiki)
8. Development of Data Cube and OLAP Technology Discovery-Driven Exploration of Data Cubes Tools need to be developed to assist users in intelligently exploring the huge aggregated space of a data cube. Discovery-driven exploration is such a cube exploration approach. Complex Aggregation at Multiple Granularity: Multi feature Cubes Data cubes facilitate the answering of data mining queries as they allow the computation of aggregate data at multiple levels of granularity
9. Constrained Gradient Analysis in Data Cubes Constrained multidimensional gradient analysis reduces the search space and derives interesting results. It incorporates the following types of constraints: Significance constraint Probe constraint Gradient constraint
10. Alternative Method for Data Generalization Attribute-Oriented Induction for Data CharacterizationThe attribute-oriented induction approach is basically a query-oriented, generalization-based, on-line data analysis technique The general idea of attribute-oriented induction is to first collect the task-relevant data using a database query and then perform generalization based on the examination of the number of distinct values of each attribute in the relevant set of data
11. Cont.. Attribute generalization is based on the following rule: If there is a large set of distinct values for an attribute in the initial working relation, and there exists a set of generalization operators on the attribute, then a generalization operator should be selected and applied to the attribute.
12. Different ways to control a generalization process The control of how high an attribute should be generalized is typically quite subjective. The control of this process is called attribute generalization control. Attribute generalization threshold control Generalized relation threshold control
13. Mining Classes Data collection Dimension relevance analysis Synchronous generalization Presentation of the derived comparison
14. Visit more self help tutorials Pick a tutorial of your choice and browse through it at your own pace. The tutorials section is free, self-guiding and will not involve any additional support. Visit us at www.dataminingtools.net